Statistical Approach Research Articles

Determination of Nurses' Knowledge toward Medication used in Critical Care Units

Background: The critical care unit is one of the important units in the hospital that receives critical and dangerous cases. There are medications used in these units, including shock medications. The nurse must have enough knowledge about these medications to reduce the rate of medication error and prevent harm that occurs to patients. Aims: to determine nurses' knowledge of drugs used in critical care units and to find the relationship between nurses' knowledge and their demographic data. Methods: A quantitative descriptive study was carried out in Critical Care Units affiliated with Al-Furat Al-Awsat Hospital, Al-Hakim General Hospital, and Al-Saddr Teaching Hospital in Al-Najaf City/Al-Najaf Al-Ashraf Health Directorate. A non-probability (convenience) sample of 77 nurses was obtained from the above settings, regardless of their characteristics. The study began from December 10th, 2022 to April 1st, 2023. Data were collected through a special questionnaire prepared to collect the data which was divided into two parts. The validity of the questionnaire was acquired by examination of (13) experts in different fields, and correlation was utilized as a statistical approach to evaluate the reliability of the study questionnaire. Data were analyzed using descriptive and inferential statistics. Results: The general assessment of the nurses' knowledge regarding drug in intensive care units was fail with an average score of less than (1.50), as the scores were unsatisfactory concerning medication administration. Conclusion: A study concluded that a nurse's knowledge about the medications used in intensive care units was Low, less than (1.50) overall.

Environmental disclosure practices dimensions and total value-added growth of the Nigerian listed manufacturing companies

Purpose: This research explored the interaction of environmental disclosure practices dimensions (environmental financial information, environmental non-financial information, and environmental performance information) on total value-added growth of the Nigerian listed manufacturing companies.Design/methodology/approach: Using a secondary data set culled from 39 Nigerian manufacturing businesses traded on the Stock Exchange between 2010-2019, this investigation utilised an ex post facto research strategy. The multiple regression statistical approach was utilised to analyse the data.Findings: The findings indicated that environmental disclosure practices dimensions (environmental, financial information, environmental non-financial information, and environmental performance information) significantly and positively affect the total value-added growth of Nigerian listed manufacturing companies.Research limitation/implication: This study focused on Nigerian-listed manufacturing companies. Future studies could include non-listed manufacturing companies. Also, other industries could be considered in future studies.Originality/value: As far as the authors are aware, this is the first research to establish a connection between the environmental disclosure procedures of traded manufacturing businesses in Nigeria and the overall value-added increase. In addition, the findings showed the interaction between environmental disclosure practices factors on total value-added growth in listed manufacturing businesses in Nigeria are novel and significant.

Regionalized multiple-point statistical simulation for calibrating process-based geologic models to seismic data

Calibrating process-based geologic models to seismic data is critical and has been challenging for decades. The traditional approach to data calibration involves tuning the model input parameters by trial-and-error or through an automated inverse procedure. This can improve the model calibration to data but can hardly reach a fully satisfactory result. We adopt a multiple-point statistics (MPS) approach where a process-based geologic model is used as a training image for statistical pattern recognition. First, we define a rock-physics model from the process-based geologic model and derive its seismic attributes through seismic forward modeling. Then, we use the process-based model and its seismic attributes as coupled training images for geologic pattern recognition and regeneration under seismic data constraints. The method differs from the conventional MPS method in several ways: (1) the training image is a process-based geologic model of the reservoir of interest, thus defined on the same grid of the reservoir model; (2) the training image is generally nonstationary but there is no need to partition the nonstationary training image into pseudostationary ones; (3) the geologic facies and seismic constraint are related through seismic forward modeling instead of statistical inference, thus there is no need to convert seismic data to facies proportion or probability; (4) multiple-point statistics are based on Bayes’ law and Gaussian kernel approximation of conditional probability instead of a somehow arbitrary probability combination scheme or a heuristic rule; and (5) the method does not involve an iterative optimization procedure. Therefore, it also differs from the neural-network-based machine-learning approach, where the data conditioning is achieved through an iterative optimization procedure. These differences make our method advantageous for calibrating process-based geologic models. The two examples with synthetic data illustrate the effectiveness of the method. The first one is derived from a satellite image of a river delta, and the second one from the Stanford VI reservoir model.

Superpixel-ComBat modeling: A joint approach for harmonization and characterization of inter-scanner variability in T1-weighted images

Abstract T1-weighted imaging holds wide applications in clinical and research settings; however, the challenge of inter-scanner variability arises when combining data across scanners, which impedes multi-site research. To address this, post-acquisition harmonization methods such as statistical or deep learning approaches have been proposed to unify cross-scanner images. Nevertheless, how inter-scanner variability manifests in images and derived measures, and how to harmonize it in an interpretable manner, remains underexplored. To broaden our knowledge of inter-scanner variability and leverage it to develop a new harmonization strategy, we devised a pipeline to assess the interpretable inter-scanner variability in matched T1-weighted images across four 3T MRI scanners. The pipeline incorporates ComBat modeling with 3D superpixel parcellation algorithm (namely SP-ComBat), which estimates location and scale effects to quantify the shift and spread in relative signal distributions, respectively, concerning brain tissues in the image domain. The estimated parametric maps revealed significant contrast deviations compared to the joint signal distribution across scanners (p < 0.001), and the identified deviations in signal intensities may relate to differences in the inversion time acquisition parameter. To reduce the inter-scanner variability, we implemented a harmonization strategy involving proper image preprocessing and site effect removal by ComBat-derived parameters, achieving substantial improvement in image quality and significant reduction in variation of volumetric measures of brain tissues (p < 0.001). We also applied SP-ComBat to evaluate and characterize the performance of various image harmonization techniques, demonstrating a new way to assess image harmonization. In addition, we reported various metrics of T1-weighted images to quantify the impact of inter-scanner variation, including signal-to-noise ratio, contrast-to-noise ratio, signal inhomogeneity index, and structural similarity index. This study demonstrates a pipeline that extends the implementation of statistical ComBat method to the image domain in a practical manner for characterizing and harmonizing the inter-scanner variability in T1-weighted images, providing further insight for the studies focusing on the development of image harmonization methodologies and their applications.

Composite dyadic models for spatio-temporal data.

Mechanistic statistical models are commonly used to study the flow of biological processes. For example, in landscape genetics, the aim is to infer spatial mechanisms that govern gene flow in populations. Existing statistical approaches in landscape genetics do not account for temporal dependence in the data and may be computationally prohibitive. We infer mechanisms with a Bayesian hierarchical dyadic model that scales well with large data sets and that accounts for spatial and temporal dependence. We construct a fully connected network comprising spatio-temporal data for the dyadic model and use normalized composite likelihoods to account for the dependence structure in space and time. We develop a dyadic model to account for physical mechanisms commonly found in physical-statistical models and apply our methods to ancient human DNA data to infer the mechanisms that affected human movement in Bronze Age Europe.

Tradeoffs between economy wide future net zero and net negative economy systems: The case of Chile

Given the possible economic consequences, poorer countries have more challenges in delivering their Nationally Determined Contributions. As a developing country, Chile has pledged to attain carbon neutrality by 2050. While Chile has implemented several mitigation measures, it still relies heavily on carbon sequestration, intending to sequester around 65 MtCO2e by 2050. However, heavy reliance on sequestration poses several risks as the literature shows that natural sinks, particularly forest and land, are exposed to severe impacts from global warming and climate change. Fortunately, Chile has significant renewable energy potential, which, if fully utilized, may move the country towards a net negative emissions context. To assess if such a net-negative system is feasible in the context of Chile, a new regional version of the Global Change Analysis Model for Chile is developed. The model is used to investigate the effects and required levels of investment in renewable energy and decarbonization of end-use sectors to achieve economy-wide net negative emissions scenarios. The design of net negative pathways follows a statistical approach based on the expected sequestration capacity in 2050 and its corresponding confidence interval. The results are compared to scenarios that are aligned with the objective of carbon neutrality by 2050. The findings show that obtaining net-zero emissions by 2050 is possible, however achieving net negative systems will be dependent on existing sequestration capacity and the application of economic incentives to boost green energy deployment in Chile as well as to push such green energy, in the form of electricity or e-fuels, into hard to decarbonize final demand sectors, such as transport, mining, and industry demand sectors. The results also indicate that after significantly reducing CO2 emissions from the energy sector (primarily the power sector), the agricultural sector and other urban and industrial sectors still contribute to non-significant levels of CH4 and N2O emissions.

Sample size for assessing a diagnostic accuracy of AI-based software in radiology

Introduction. Determining the minimum sample size for solving various tasks is an extremely important and at the same time unexplored problem. There are many methods, but most of them are not applicable for AI-based software validation.Aim: To consider a methodology for determining a balance of classes “norm”/ “abnormality” and propose a statistical approach to determine the data amount necessary for testing AI-based software (validation).Material and Methods. The results of AI-based software were analyzed using dataset of mammograms. Mammograms were classified by the presence of breast cancer (“abnormality”) and the absence of breast cancer (“norm”). The general set contains 123,301 unique studies. The original balance of classes in the study was “norm” 89.3%/“abnormality” 10.7%. As the results of AI-based software (ML-algorithm), a probability of the presence of pathology in the entire study was taken. The following values were used as empirical data (GT): 0 – in case of Bi-RADS classes 1 or 2 diagnosed by a doctor, and 1 – in case of Bi-RADS classes 3, 4, 5. Each data sample is transferred to AI-based software for processing. Quality metrics are calculated based on its results: AUC ROC. All the described actions were repeated 10,000 times for all the studied balances of “norm”/”abnormality”. Based on the results of AUC ROC calculations, mean values were calculated for different random data series with the same balances. Mean AUC ROC values were subjected to analysis.Results. A maximum value of the coefficient of variation of AUC ROC values for 10% “abnormality” share is achieved at the number of studies equal to 190; for the 20% share, it is 80 studies; for the 30% share – 120 studies, for the 40% share – 110 studies, and for the 50% share – 70 studies.Conclusion. Summarizing the conducted study results, it can be concluded that when testing AI-based software, it is necessary to consider that the number of studies reflecting the greatest heterogeneity of AUC ROC values (the largest deviation from the mean value) is different for various class balances. If the purpose of validation is to establish the worst-case behavior of AUC ROC values, then for the studied AI-based software, the “abnormality” share should be 10%, and the number of studies 190. If the validation is carried out under conditions of a limited amount of data, then the “abnormality” share should be 50% and the number of studies equal to 70.

A Strategy for Modeling Nonstatistical Reactivity Effects: Combining Chemical Activation Estimates with a Vibrational Relaxation Model.

The kinetics of many chemical reactions can be readily explained with a statistical approach, for example, using a form of transition state theory and comparing calculated Gibbs energies along the reaction coordinate(s). However, there are cases where this approach fails, notably when the vibrational relaxation of the molecule to its statistical equilibrium occurs on the same time scale as the reaction dynamics, whether it is caused by slow relaxation, a fast reaction, or both. These nonstatistical phenomena are then often explored computationally using (quasi)classical ab initio molecular dynamics by calculating a large number of trajectories while being prone to issues such as zero-point energy leakage. On the other side of the field, we see resource-intensive quantum dynamics simulations, which significantly limit the size of explorable systems. We find that using a Fermi's golden rule type of model for vibrational relaxation, based on anharmonic coupling constants, we can extract the same qualitative information while giving insights into how to enhance (or destroy) the bottlenecks causing the phenomena. We present this model as a middle ground for exploring complex nonstatistical behavior, capable of treating medium-sized organic molecules or biologically relevant fragments. We also cover the challenges involved, in particular quantifying the excess energy in terms of vibrational modes. Relying on readily available electronic structure methods and providing results in a simple master equation form, this model shows promise as a screening tool for opportunities in mode-selective chemistry without external control.

Improving analysis of cognitive outcomes in cardiovascular trials using different statistical approaches

BackgroundThe Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) questionnaires are commonly used to measure global cognition in clinical trials. Because these scales are discrete and bounded with ceiling and floor effects and highly skewed, their analysis as continuous outcomes presents challenges. Normality assumptions of linear regression models are usually violated, which may result in failure to detect associations with variables of interest.MethodsAlternative approaches to analyzing the results of these cognitive batteries include transformations (standardization, square root, or log transformation) of the scores in the multivariate linear regression (MLR) model, the use of nonlinear beta-binomial regression (which is not dependent on the assumption of normality), or Tobit regression, which adds a latent variable to account for bounded data. We aim to empirically compare the model performance of all proposed approaches using four large randomized controlled trials (ORIGIN, TRANSCEND, COMPASS, and NAVIGATE-ESUS), and using as metrics the Akaike information criterion (AIC). We also compared the treatment effects for the methods that have the same unit of measure (i.e., untransformed MLR, beta-binomial, and Tobit).ResultsThe beta-binomial consistently demonstrated superior model performance, with the lowest AIC values among nearly all the approaches considered, followed by the MLR with square root and log transformations across all four studies. Notably, in ORIGIN, a substantial AIC reduction was observed when comparing the untransformed MLR to the beta-binomial, whereas other studies had relatively small AIC reductions. The beta-binomial model also resulted in a significant treatment effect in ORIGIN, while the untransformed MLR and Tobit regression showed no significance. The other three studies had similar and insignificant treatment effects among the three approaches.ConclusionWhen analyzing discrete and bounded outcomes, such as cognitive scores, as continuous variables, a beta-binomial regression model improves model performance, avoids spurious significance, and allows for a direct interpretation of the actual cognitive measure.Trials registrationORIGIN (NCT00069784). Registered on October 1, 2003; TRANSCEND (NCT00153101). Registered on September 9, 2005; COMPASS (NCT01776424). Registered on January 24, 2013; NAVIGATE-ESUS (NCT02313909). Registered on December 8, 2014.

B-170 Age-Specific reference intervals for ethanolamine plasmalogen species in red blood cells using liquid chromatography tandem mass spectrometry

Abstract Background Plasmalogens are critical membrane structural components that are mainly generated by de novo synthesis starting in peroxisomes. Hence, patients with defects in peroxisome biogenesis (PBD) exhibit markedly reduced plasmalogen levels. Plasmalogen ratios are traditionally measured by gas chromatography-mass spectrometry (GC-MS); however, this method entails a lengthy sample extraction and derivatization and does not report concentrations of individual plasmalogen species. We have developed a robust and easy-to-implement liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to quantify the 18 most abundant ethanolamine plasmalogen (PlsEtn) species in packed red blood cells (RBCs). However, no reference intervals have been published for individual PlsEtn. Here, we describe the establishment of age-specific reference intervals for individual PlsEtn species and their total values (16:0, 18:0, 18:1 species, and total plasmalogens). Methods Plasmalogens were extracted using methanol containing two labeled internal standards, shaking for one hour at room temperature. Chromatographic separation was performed using an Acquity Premier BEH C18 UPLC column with a binary gradient of 5 mM ammonium acetate in water:methanol (15:85) and 5 mM ammonium acetate in methanol. Analysis was performed using a XEVO TQ-XS Mass Spectrometer with Ultra-High Performance Liquid Chromatography (Waters) in multiple reaction monitoring mode. Eighteen PlsEtn species were quantified using four commercially available standards; additionally, totals were calculated for 16:0, 18:0 or 18:1 species, and for total plasmalogens. Reference intervals were established using 376 RBCs from self-reported healthy volunteers and de-identified clinical samples referred for unrelated testing (182 females and 194 males; range 0 to 88 years). Data was analyzed using the R programming language. The study was approved by the Institutional Review Board of the University of Utah. Results Initial age groups were identified using a model-based clustering algorithm followed by iterative Harris-Boyd analysis. Finally, the adjacent groups were merged if their means differed by less than 10%. Once the final age groups were partitioned, data in each individual age group were analyzed using parametric or non-parametric statistics to determine reference intervals (95%, with 90%confidence intervals). PlsEtn species displayed the lowest concentration in the first few months of life, which increased in childhood until adolescence or adulthood (depending on PlsEtn). For most species, the concentrations increased over time reaching a plateau between 18 and 48 years of age, and then starting to decrease. The total values followed the same trend, with neonates showing significantly lower values compared to other age groups. Conclusions We applied a novel statistical approach to identify age groups and determine age-specific reference intervals for 18 individual PlsEtn in RBC and their totals. Lacking previously published data, this study is critical for supporting test implementation in clinical laboratories.

Monitoring Kiloelectron-Volt Emission Variability in Partially Observed Nuclear Events through Distance Weighted Censored Control Charts

Abstract The variation in the lifespan of particles is a crucial parameter in nuclear physics and is essential for identification purposes. Modern particle detectors excel at recognizing individual radioactive nuclei arrivals and their subsequent decay events. However, challenges arise when matching arrivals with departures, especially when departures are only partially observed. One inefficient approach involves conducting experiments with very low arrival rates to facilitate matching. The kiloelectron-volt E(keV) emission is obtained during this radio active process. This study focuses on the meticulous surveillance of the standard deviation in keV emission from partially observed events within the domain of nuclear physics.The utilization of censored control charts allows for the effective management of incomplete data, enabling researchers to make informed decisions despite potential limitations in observation. To accomplish this, the methodology employs the statistical approach known as Distance Weighted Mean based Standard Deviation (DWMS) integrated with the application of censored control charts. We propose a DWMS based exponentially weighted moving (DWMS-E) control chart for monitoring kiloelectron-volt E(keV) data. The proposed charts is developed for Weibull lifetimes with type-I censored data. The goal is to find changes in the mean of Weibull lifetimes with known and estimated parameter conditions. The performance of the proposed DWMS-E chart is evaluated by the average run length (ARL). Besides a simulation study, a real-life data set on E(keV) related to the alpha decays of 177 Lutetium isotope is also discussed.

Classifying compounds as prebiotics - scientific perspectives and recommendations.

Microbiomes provide key contributions to health and potentially important therapeutic targets. Conceived nearly 30 years ago, the prebiotic concept posits that targeted modulation of host microbial communities through the provision of selectively utilized growth substrates provides an effective approach to improving health. Although the basic tenets of this concept remain the same, it is timely to address certain challenges pertaining to prebiotics, including establishing that prebiotic-induced microbiota modulation causes the health outcome, determining which members within a complex microbial community directly utilize specific substrates in vivo and when those microbial effects sufficiently satisfy selectivity requirements, and clarification of the scientific principles on which the term 'prebiotic' is predicated to inspire proper use. In this Expert Recommendation, we provide a framework for the classification of compounds as prebiotics. We discuss ecological principles by which substrates modulate microbiomes and methodologies useful for characterizing such changes. We then propose statistical approaches that can be used to establish causal links between selective effects on the microbiome and health effects on the host, which can help address existing challenges. We use this information to provide the minimum criteria needed to classify compounds as prebiotics. Furthermore, communications to consumers and regulatory approaches to prebiotics worldwide are discussed.

Investigation of Tribological Behavior of PTFE Composites Reinforced with Bronze Particles by Taguchi Method

Reinforced PTFE materials can be designed to show high mechanical stability against harder materials under sliding wear conditions. Especially bearing metal-reinforced PTFE is of high practical interest. In this class of materials, bronze-filled PTFE was reported to obtain high wear resistance, a low coefficient of friction (COF), and excellent self-lubrication properties in sliding conditions. In the statistical approach of this work, PTFE composites reinforced with 25 vol%, 40 vol%, and 60 vol% bronze particles were evaluated against pure PTFE regarding wear behavior under varied wear test parameters, i.e., material, normal load, and sliding speed. Wear tests were planned to use a standard orthogonal array based on the Taguchi design method. An analysis of variance test was utilized to quantify the effects of test parameters on the wear behavior of the bronze/PTFE composites and pure PTFE. According to the variance analysis, the material type has the largest influence on the COF and the specific wear rate (SWR) under test conditions of this work. Both COF and SWR were found to be influenced by the material type (29.83% and 96.16%), the normal load (33.34% and 0.95%), and sliding speed (9.14% and 1.28%). The lowest SWR and COF values were achieved at the optimum wear test conditions where the wear test parameters were 1 m/s sliding speed (A4B2C2) at PTFE + 60 vol.% bronze reinforced composite 50 N application load and 0.32 m/s sliding speed (A4B3C1) at PTFE + 60 vol.% bronze reinforced composite 100 N application load, respectively.

Technology Focus: Data Analytics (October 2024)

In recent years, advancements in artificial intelligence, particularly the revolutionary applications of deep-learning-based computer vision and large language models, have significantly affected the oil and gas industry. These technological developments have underscored the growing importance and diversity of data analytics across various aspects of the industry’s daily operations. Maintaining this momentum, data analytics practitioners in the industry have reexamined existing workflows and realized the substantial benefits that artificial intelligence brings, including increased efficiency and expedited turnaround times. Organizations have allocated resources to develop a big-data-driven, multidisciplinary platform that leverages artificial intelligence and machine learning to enhance rapid and robust decision-making in asset management and risk mitigation. Integrated data-management platforms use both proprietary data and public domain data to streamline and automate processes that otherwise could be tedious and time-consuming. Successful case studies have been shared in the literature on topics such as candidate screening and production forecasting. A prominent area of focus garnering extensive attention is the integration of artificial intelligence and domain knowledge, exemplified by physics-informed machine learning. Several innovative workflows using proxy or surrogate models in conjunction with machine learning and deep learning have been published. These workflows demonstrate promising test cases that extract valuable insights from physics-based modeling results and provide production forecasts through a statistical approach. Undoubtedly, we can anticipate a surge in research and publications on this topic in the coming years, which will contribute significantly to reshaping our industry. As advancements in generative artificial intelligence and big data continue to accelerate, swiftly adapting and pivoting toward digital transformation will be essential in the emerging digital era. Recommended additional reading at OnePetro: www.onepetro.org. SPE 217665 Well Construction in the Era of Big Data: It’s Not Data Analytics, It’s Engineering With Data by G.S. Payette, ExxonMobil, et al. IPTC 24126 Deep-Learning-Based Long Short-Term Memory for Production Data Analysis of Hydraulically Fractured Wells by Fengyuan Zhang, China University of Petroleum-Beijing, et al. URTeC 3857064 Application of Data Analytics to Chemometric Analysis of Conventionally Produced Oil Samples From the Delaware Basin by Timothy Carpp, Texas A&M University, et al.

Ageing durability of SiR under prolonged voltage stress, contaminant flow and contaminant concentration: a statistical approach

Ageing durability of SiR under prolonged voltage stress, contaminant flow and contaminant concentration: a statistical approach

Dissecting the genetic architecture of sunflower disc diameter using genome-wide association study.

Sunflower (Helianthus annuus L.) plays an essential role in meeting the demand for edible oil worldwide. The yield of sunflower seeds encompasses several component traits, including the disc diameter. Over three consecutive years, 2019, 2020, and 2022, we assessed phenotypic variation in disc diameter across a diverse set of sunflower accessions (N = 342) in replicated field trials. Upon aggregating the phenotypic data from multiple years, we estimated the broad sense heritability (H 2) of the disc diameter trait to be 0.88. A subset of N = 274 accessions was genotyped by using the tunable genotyping-by-sequencing (tGBS) method, resulting in 226,779 high-quality SNPs. Using these SNPs and the disc diameter phenotype, we conducted a genome-wide association study (GWAS) employing two statistical approaches: the mixed linear model (MLM) and the fixed and random model circulating probability unification (farmCPU). The MLM and farmCPU GWAS approaches identified 106 and 8 significant SNPs located close to 53 and 21 genes, respectively. The MLM analysis identified two significant peaks: a prominent signal on chromosome 10 and a relatively weaker signal on chromosome 16, both of which were also detected by farmCPU. The genetic loci associated with disc diameter, as well as the related candidate genes, present promising avenues for further functional validation and serve as a basis for sunflower oil yield improvement.

Seeding young entrepreneurs: The role of business incubators

This study focusses on young entrepreneurs and considers the extent of business incubators’ impact on the development and visibility of projects headed by young people. To do so, we analyse incubators’ role as ecosystems for entrepreneurship in Spain based on the 2023 Funcas survey. First, we categorised incubators according to entrepreneurs’ age, defining young incubators as those in which most of the businesses are run by individuals under 35, excluding those over that age. We analyse variables that include the services offered and the frequency of events or training within the incubators to determine which approaches benefit or harm young entrepreneurs. This study uses a statistical approach, with quantitative data from the questionnaire as a starting point to characterise and identify young entrepreneurial profiles in business incubators. We develop and test two hypotheses using the chi-squared test, which is a non-parametric (distribution free) technique. Finally, we include a brief analysis of young entrepreneurs’ personality traits to shed light on some defining characteristics, concluding that personal initiative is a key feature amongst entrepreneurs under the age of 35. We conclude that the incubator system in Spain harms young entrepreneurs and propose some relevant recommendations to reverse this trend.

Optimum and Green Fabrication of MIL-100(Fe) for Crystal Violet Dye Removal from Aqueous Solution

MIL-100(Fe) was prepared and subsequently used to remove crystal violet dye from aqueous solutions simulating dye-containing wastewater in the environment. In the future, it is aimed that MIL-100(Fe) can be used in managing dye-containing wastewater in the environment and reducing the negative impacts it can cause. Here, MIL-100(Fe) fabrication needs to be optimized to obtain optimum process conditions, which are environmentally friendly and can produce MIL-100(Fe) with the best characteristics. This study focused on optimizing the fabrication of MIL-100(Fe), which is a type of MOF with good chemical stability, thermal stability, and flexible structure. In this study, the room-temperature fabrication of MIL-100(Fe) was established using a ligand-to-metal molar ratio of 0.95 and an acetic acid concentration of 5.1 vol% for 6.2 h. The optimum MIL-100(Fe) was tested for crystal violet removal and provided an optimum removal capacity of 182.66 ± 3.81 mg/g. Statistical approaches are used to investigate the independent parameters and their interactions contributing to MIL-100(Fe) formation.

Effects of land use types on the soil physicochemical characteristics of Inyi community, Enugu State, Nigeria: Application of multivariate statistical, and eco-toxicological and human risk model approaches.

The constant and increased use of arable lands for cultivation has led to its degradation; hence the impacts of different land-use patterns on the soil physical and chemical properties were investigated. Soil samples (n = 36) were collected from the oil palm plantation (OPP), cashew plantation (CP) and untampered forest (UF), in Inyi community, Enugu State, Nigeria. They were air-dried, sieved, and analyzed for their physicochemical properties. The results revealed that the average soil pH ranged from 5.9 to 8.2. Moisture and carbon contents ranged from 6.3 to 35.4, and 3.5 to 6.7 % respectively. Low CEC values were recorded for all the soil samples. The total mean soil textural fractions decreased as follows: sand > clay > silt. Also, the total mean concentrations of N, P, K, and Mg in the study area decreased in the order: OPP>UF>CP, OPP>UF>CP, UF>OPP>CP, and OPP>UF>CP respectively while the S, NO3-, NO2-, and HCO3- followed the order; OPP>UF>CP, UF>OPP>CP, OPP>CP>UF, and CP>OPP>UF respectively. The soil ecological risk index was < 40 in all sample locations, indicating slight contamination of the study area. Therefore, regular monitoring and soil nutrient management practices should be maintained and encouraged.

TC‐GEN: Data‐Driven Tropical Cyclone Downscaling Using Machine Learning‐Based High‐Resolution Weather Model

AbstractSynthetic downscaling of tropical cyclones (TCs) is critically important to estimate the long‐term hazard of rare high‐impact storm events. Existing downscaling approaches rely on statistical or statistical‐deterministic models that are capable of generating large samples of synthetic storms with characteristics similar to observed storms. However, these models do not capture the complex two‐way interactions between a storm and its environment. In addition, these approaches either necessitate a separate TC size model to simulate storm size or involve post‐processing to capture the asymmetries in the simulated surface wind. In this study, we present an innovative data‐driven approach for TC synthetic downscaling. Using a machine learning‐based high‐resolution global weather model (ML‐GWM), our approach can simulate the full life cycle of a storm with asymmetric surface wind that accounts for the two‐way interactions between the storm and its environment. This approach consists of multiple components: a data‐driven model for generating synthetic TC seeds, a blending method that seamlessly integrates storm seeds into the surrounding while maintaining the seed structure, and a model based on a recurrent neural network to correct for biases in storm intensity. Compared to observations and synthetic storms simulated using existing statistical‐deterministic and statistical downscaling approaches, our method shows the ability to effectively capture many aspects of TC statistics, including track density, landfall frequency, landfall intensity, and outermost wind extent. Leveraging the computational efficiency of ML‐GWM, our approach shows substantial potential for TC regional hazard and risk assessment.