Statistical Techniques Research Articles

A tandem approach for precipitant-free highly selective recovery of valuable metals from end-of-life lithium-ion batteries using a green deep eutectic solvent

Recently, recycling lithium-ion batteries (LIBs) waste and the efficient recovery of valuable metals has become crucial for the circular economy. Deep eutectic solvents (DESs) have shown immense potential for metal extraction from end-of-life (EoL) LIBs. However, a complicated subsequent separation process is required due to the non-selectivity of the DES components for metal. Herein, we propose a tandem precipitant-free hydrometallurgical process for the selective recovery of Li and other valuable metals (Ni, Co, Mn) from EoL LIBs using a green DES. The process parameters, viz., reaction temperature (T), time (t), pulp density (S/L), and molar ratio (MR) of ethylene glycol to tartaric acid, were optimized via the statistical optimization technique of response surface methodology. The results revealed maximum extraction efficiencies of 99.2±0.5%, 96.1±1.4%, 95.2±1.2%, and 97.8±1.5% for Li, Ni, Co, and Mn, respectively, under the optimized process conditions of T = 118 °C, t = 17 min, S/L = 42 g/L, and MR = 3:1. The kinetic study showed that interfacial reaction governed the extraction process. A plausible mechanism depicting the role of DES constituents in synergistically facilitating the extraction reaction was elucidated. Moreover, Li and other metals (Ni, Co, and Mn) were selectively separated and recovered with an overall efficiency of ∼99%. The reusability of the recovered metal products and the DES were also assessed for a closed-loop recycling process. At the outset, the proposed process paves the way for a greener alternative for recycling valuable metals from EoL LIBs rather than conventional acid-based leaching processes.

Investigation on Interfacial Bond Strength Characteristics of Concrete Filled in Galvanized Steel Tubes Utilizing Statistical Analysis and Advanced Prediction Techniques

Investigation on Interfacial Bond Strength Characteristics of Concrete Filled in Galvanized Steel Tubes Utilizing Statistical Analysis and Advanced Prediction Techniques

Explaining vulnerabilities of heart rate biometric models securing IoT wearables

In the field of health informatics, extensive research has been conducted to predict diseases and extract valuable insights from patient data. However, a significant gap exists in addressing privacy concerns associated with data collection. Therefore, there is an urgent need to develop a machine-learning authentication model to secure the patients’ data seamlessly and continuously, as well as to find potential explanations when the model may fail. To address this challenge, we propose a unique approach to secure patients’ data using novel eigenheart features calculated from coarse-grained heart rate data Various statistical and visualization techniques are utilized to explain the potential vulnerabilities of the model. Though it is feasible to develop continuous user authentication models from readily available heart rate data with reasonable performance, they are affected by factors such as age and Body Mass Index (BMI). These factors will be crucial for developing a more robust authentication model in the future.

Portable spectroscopy, digital imaging colorimetry and multivariate statistical tools in contaminant identification: A case study of mint (Mentha) and basil (Ocimum basilicum)

The advent of portable Fourier-Transform Infrared (FTIR) and Raman spectrometers has revolutionized analysis capabilities, presenting the possibility of on-site contaminant identification without the need for specialized laboratory settings. Compared to laboratory instrumentation, portable spectroscopy is more prone to noise, and appropriate spectral processing procedures need to be established. This paper introduces a comprehensive methodology that integrates acquisition techniques, spectral analysis, and mathematical tools necessary for utilizing handheld spectrometers to diagnose plant contamination. It focuses on determining the efficacy of handheld FTIR, Raman spectroscopy, and digital imaging for detecting contaminants in two food plants, Basil (Ocimum basilicum) and Mint (Mentha). The study examines the impact of three pollutants: iron (II) sulphate (FeSO4), zinc (II) sulphate (ZnSO4), and copper (II) sulphate (CuSO4), on these plants, but also the necessary amount of measurements to spot the pollutants' effects. Measurements were conducted at the start, after 24 hours, and after 48 hours of exposure, on both fresh and dried plant leaves, as well as in solution. Spectral effects of each of the pollutants were identified with the use of multivariate statistical process control techniques. With the help of the developed methodologies, researchers can identify in-situ contaminant effects, exposure times and run diagnostics directly on the leaf both in alive and dried plants.

Characteristics and perceptions of project management in the productive sectors towards a sustainable future

Integrating sustainable approaches is imperative to align project management with the current demands and growing emphasis on environmental, social, and economic considerations. Despite that, the application of Sustainability faces challenges from two key aspects: the diversity of sustainability concepts, which adapt to various contexts, and the limited availability of methodologies to guide its adoption, monitoring, and implementation. This study aims to uncover perceptions related to project management and Sustainability by exploring the experiences, practices, and perceptions of expert professor-researchers of the Universidad Pedagógica y Tecnológica de Colombia. The focus is on productive sectors that significantly contribute to the gross domestic product. The methodological design involves developing and implementing a survey-type data collection instrument administered to 55 individuals. Internal consistency is assessed using Cronbach's alpha coefficient, and the results are analyzed using Principal Component Analysis, a multivariate statistical technique. The study's findings reveal critical impacts and existing connections, including the sustainability indicator and elements to consider for more suitable implementations in the short term within project management. Additionally, the complex relationships within sustainable considerations in project management are evident, theoretically supported but not consistently applied in the productive sectors. These contributions pave the way for future research, allowing the proposed instrument's replication in other groups and sectors interested in characterizing participants' conceptual performance regarding these elements.

Enhancing Moroccan legal cases analysis through ontology-based information extraction

The efficient organization of diverse disorder cases within a unified memory necessitates an adaptable representation. This study introduces an ontology-based approach for extracting facts from Moroccan legal cases. Leveraging ontological frameworks, a comprehensive case architecture is established, enabling advanced information extraction. Utilizing rules, patterns, and knowledge modeling harmonizes cases and identifies pervasive legal concepts. Statistical techniques unveil latent entities within complex legal textual discourse. Empirical validation demonstrates proficiency, extracting up to 25 regular entities. The rule-based mechanism achieves an F1-score of 99.5%, highlighting precision, while the statistical extractor achieves an 88.3% F1-score, revealing concealed entities. This work presents an innovative ontology-based paradigm for legal information extraction, contributing to advanced knowledge management in the legal domain.

The Latrobe Smoking Support Service: A quantitative study of participants in a regional area.

More than 70% of current smokers in Australia have a definite plan to stop smoking and around half of them try to quit every year. Latrobe Community Health Service (LCHS) was commissioned by Gippsland Primary Health Network to establish Latrobe Smoking Support Service (LSSS) to break down barriers to accessing services and increase support for smoking cessation. This research aims to assess the feasibility of an ongoing smoking cessation support service and determine the effect the LSSS has on client smoking behavior. Quantitative data were collected for the LSSS situated at LCHS during the period from September 2021 to March 2022. A new client survey, a returning client survey, and a 6-week follow-up survey were conducted by Clinic staff. The consent forms were obtained from the clients. A total of 117 clients attended the LSSS at least once, and a further 315 returning client sessions were conducted. The data analysis was undertaken by means of various descriptive and inferential statistical techniques, such as multiple linear regression analysis. The research findings demonstrate the strong positive effect of the LSSS in helping clients to change their smoking behavior. Results of multiple regression analysis highlight the significant role of behavioral intervention strategies in the LSSS's success. A combination of both nicotine replacement therapy (NRT) and counseling was a key contributor to the project's success. This research proposed and tested the model of a smoking cessation support service that combines a comprehensive mix of services for smokers including free NRT, free counseling, and ongoing support of counselors or/and nurse practitioners.

Temporal Forecast of Maize Production in Tanzania: An Autoregressive Integrated Moving Average Approach

This paper aims to model and forecast maize production in Tanzania, emphasizing its crucial contribution to the agricultural sector. Utilizing secondary data on annual maize production in Tanzania from 1961 to 2021, measured in tonnes and sourced from the FAO database, the study employs statistical techniques such as unit root tests, the ARIMA model, and the Ljung-Box test for a comprehensive analysis. The optimal model for forecasting maize production in Tanzania is identified as ARIMA (5,1,5), and the adequacy assessment confirms its effectiveness in predicting maize production values. The ten-year forecast reveals an intermittent pattern, offering valuable insights into anticipated trends. While not explicitly developing an existing theory, this research enhances the understanding of maize production dynamics in Tanzania through the application of advanced statistical methods. Policymakers, agricultural stakeholders, and researchers can leverage these findings to enhance agricultural productivity and food security in Tanzania.

Quantifying pesticide residues in food matrices using statistical methods

Traditional techniques for pesticide residues detection and quantification in food using mass spectrometry often require the analysis of standards for each compound, leading to time-consuming and laborious procedures, especially considering that these methods usually involve hundreds of pesticides to be targeted. This paper presents a novel approach to compound quantitation, where multiple target compounds can be accurately quantified using few predictor compounds, significantly reducing the experimental time and minimizing resource requirements. For this purpose, data on detector response which encompassed calibration slopes of a total of 96 pesticide standards on GC-MS/MS and 66 standards on LC-MS/MS, over a period of 4 years, were collected. Two fundamental statistical techniques, Pearson correlation and linear regression, were used to create a predictive model, which accuracy was further evaluated using R-square, adjusted R-square, and Root Mean Square Error (RMSE). Four predicted compounds for the LC dataset and seven predicted compounds for GC dataset were identified, and various predictor combinations were considered. A linear regression model was developed using predictor combinations to estimate the calibration slope of each of the target compound. This model was applied for the quantitation of several pesticide residues in food samples. The results validated the model’s high accuracy.

INNOVATIVE TECHNOLOGICAL SOLUTIONS FOR REMOVAL OF PHARMACEUTICAL CONTAMINANTS FROM WATER SOURCES

The waste derived from pharmaceutical products represent a huge worldwide problem. The aim of this paper is to describe, analyze and compare some depollution procedures for such kind of contaminants and to highlight their advantages and disadvantages from various points of view in order to propose concrete cleaning solutions. Classical methods used for contaminant removal from wastewaters in wastewater treatment plants (WTP) and from drinkable water in treatment plants (TP) do not satisfactorily reduce the content of pharmaceutical pollutants (PhP). After an extended analysis of many factors which can be involved in the treatment efficiency, this paper proposes some rehabilitation solutions for existent TP and WTP by adding to the conventional technologies, some operations of advanced treatment. These advanced operations are filtration through semipermeable membranes, i.e., microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), advanced oxidation processes (AOPs), i.e., ozonation, H2O2, adsorption on activated carbon (AC). Statistical procedures are applied to compare these removal methods for such type of pollutants and to prove their efficacy. In this context, it is demonstrated that multivariate statistical techniques, combined with water quality indices represent a viable combination to perform accurate monitoring and spatiotemporal analysis of water quality assessment. This work presents concrete solutions for TP rehabilitation in order to comply with regulations in force related to PhP.

Using Principal Component Analysis and Logistics Regression to Model Major Types of Cancer among Youth

Cancer poses a significant health challenge among young Nigerians due to high incidence rates and limited research on risk factors and outcomes. This study examines the utility of logistic regression and principal component analysis (PCA) in understanding cancer patterns, identifying risk factors, and predicting outcomes among Nigerian youth. Logistic regression assesses various factors such as genetics, environment, and lifestyle to predict cancer occurrence, enabling early detection and tailored interventions. PCA aids in uncovering complex data patterns by reducing dimensionality while preserving data variability. Nigeria experiences over 100,000 new cancer cases annually and a rising burden, effective statistical modelling is vital for guiding prevention and intervention strategies. The study compares different cancer types, develops risk assessment models, and forecasts cancer probabilities using logistic regression and PCA. These methodologies hold promise in enhancing cancer comprehension, diagnosis, and management among youth. Patient cancer data from the National Hospital in Abuja and the Aminu Kano Teaching Hospital in Kano were utilized, and selected through convenient sampling for accessibility and convenience. Direct observation served as the primary data collection method. This research underscores the importance of employing statistical modelling techniques, particularly logistic regression and PCA, to address the multifaceted challenges of cancer among youth. Leveraging analytical frameworks can advance early detection initiatives, tailored prevention strategies, and timely interventions, aligned with the region's unique socio-economic and cultural dynamics. Recommendations include prioritizing model validation, fostering collaborative efforts, increasing investment in cancer research infrastructure, and leveraging statistical modelling techniques to enhance cancer epidemiology understanding and healthcare outcomes among youth.

Perceived stigma and help seeking behavior among Indian Men

This study delves into the intricate relationship between Perceived Stigma and Help Seeking Behavior among Indian Men. The study also explores the difference among Indian Men pursuing Under graduation and Post graduation with regard to Perceived Stigma and Help Seeking Behavior. A sample of 200 Indian men, encompassing individuals pursuing undergraduate and postgraduate courses was part of the study. Statistical techniques like Correlation analysis and Independent Samples t Test were employed for data analysis. The findings revealed a significant positive correlation between Perceived Stigma and Help Seeking Behavior. Moreover, the study compared the differences in Perceived Stigma and Help Seeking Behavior between undergraduate and postgraduate education levels. Results indicated no significant difference between the two education levels with regard to Perceived Stigma or Help Seeking Behavior.

Validation of the applicability of the ideal gas equation of state and its correction based on molecular simulation and statistical methods

The ideal gas equation of state is a theoretical model devised to simplify the behaviour of real gases, although its usefulness is limited in numerous realistic scenarios. The experimental subject of this study is the Ar atomic gas, and data is gathered using molecular simulation techniques to assess the suitability and scope of the ideal gas equation of state. Simultaneously, statistical techniques such as linear regression and polynomial regression are employed to construct a novel model. Additionally, the ideal gas equation of state is adapted under specific circumstances, leading to the proposition of a fresh empirical gas equation of state. The study determined that the ideal gas equation of state can be applied to Ar atomic gases within the temperature range of 300-500 K and gas densities ranging from 0.1-0.6 g/cm^3. However, when examining higher gas densities, specifically at temperatures of 300 K and densities exceeding 0.6 g/cm^3, a new empirical gas equation was derived. This equation demonstrates that the pressure of Ar atomic gas is influenced by the 1st and 6th power terms of its density.

Improving short-term sea ice concentration forecasts using deep learning

Abstract. Reliable short-term sea ice forecasts are needed to support maritime operations in polar regions. While sea ice forecasts produced by physically based models still have limited accuracy, statistical post-processing techniques can be applied to reduce forecast errors. In this study, post-processing methods based on supervised machine learning have been developed for improving the skill of sea ice concentration forecasts from the TOPAZ4 prediction system for lead times from 1 to 10 d. The deep learning models use predictors from TOPAZ4 sea ice forecasts, weather forecasts, and sea ice concentration observations. Predicting the sea ice concentration for the next 10 d takes about 4 min (including data preparation), which is reasonable in an operational context. On average, the forecasts from the deep learning models have a root mean square error 41 % lower than TOPAZ4 forecasts and 29 % lower than forecasts based on persistence of sea ice concentration observations. They also significantly improve the forecasts for the location of the ice edges, with similar improvements as for the root mean square error. Furthermore, the impact of different types of predictors (observations, sea ice, and weather forecasts) on the predictions has been evaluated. Sea ice observations are the most important type of predictors, and the weather forecasts have a much stronger impact on the predictions than sea ice forecasts.

Rainfall Patterns, Cropping Systems, and Groundwater Dynamics in Maharashtra: A Comprehensive Analysis

Maharashtra is one of the largest agricultural states in India, with agriculture being the primary source of livelihood for a majority of the rural population. However, the state has been facing several challenges, such as water scarcity, depletion of groundwater resources, and climate change, which have a significant effect on the agricultural production and socio-economic conditions of farmers. The present study collected secondary data from various sources, which was analyzed using statistical tools and techniques to determine the correlation between rainfall and cropping patterns, the impact of cropping patterns on groundwater resources, and the socio-economic conditions of farmers in the selected districts. The findings of the study suggest that there is a direct correlation between rainfall and cropping patterns, and the impact of cropping patterns on groundwater resources and socio-economic conditions of farmers varies in the selected districts. The result highlights the need for sustainable agricultural practices that are suitable for the local agro-climatic conditions and can help improve the socio-economic conditions of the farmers.

Climate change as main driver of centennial decline in river sediment transport across the Mediterranean region

The analysis of suspended sediment transport and of its variations over time is crucial for understanding environmental evolution and it is the key to future challenges caused by current global warming. The Mediterranean area is a hot spot for global changes, and the variation of precipitation amount and intensity will modify the environment of this region.In this work, we analyse the time series of suspended sediment transport of two rivers located in central Italy by using statistical and artificial intelligence techniques. Our study aims to re-analyze time series of suspended sediment transport, in order to demonstrate that climate change is responsible for the substantial decrease in the amount of sediment over the past century, in relation to the atmospheric teleconnections of the North Atlantic. Anthropic pressures like reforestation, land use change, and dam building have influenced the sediment transport capacity of rivers, causing a reduction of sediment concentration in water.These results are key factors to determine the future management of the Mediterranean areas, where the future scenarios predict a greater drop in yearly precipitation, and therefore a possible further decrease in the sediment transport capacity of the rivers, with major consequences for coastal and fluvial environments.

Utilizing statistical and MCDM techniques in indexing morphometric parameters towards improved watershed management in the Nandhour-Kalish drainage system

Abstract Morphometric analysis plays a crucial role in the effective management of the surface as well as subsurface water resources. In the present study, ASTER DEM and Survey of India toposheets were utilized to characterize various morphological aspects of the Nandhour-Kalish watershed in Uttarakhand, India. In total, 12 sub-watersheds were determined using the SWAT 2012 in ArcGIS v10.4.1 and were coded as SW1–SW12. For each sub-watershed, 17 morphometric indices were estimated using Principal Component Analysis to determine the most influential indices or criteria of morphometric parameters in the context of the hydro-sedimentological response. The relative influence of identified criteria parameters was assessed using the Criteria Importance for Intercriteria Correlation (CRITIC) method. The developed sub-watersheds were then prioritized towards the prompt implementation of soil and water conservation measures. The results indicate that three sub-watersheds (SW2, SW3, and SW4) were categorized under a very high class of priority, while three (SW1, SW5, and SW6) were indexed under a high priority class. The study is an attempt to ease out the morphometry-based prioritization, which otherwise is relatively complex due to the availability of a large number of input parameters and ambiguity concerning parameter selection for the process of prioritization.

Role of Market Segmentation and the Impact on Alibaba Company Online Business

This study investigates the influence of market segmentation on Chinese internet companies, focusing on measurability, market demand, and entry barriers. It aims to fill a gap in the existing literature by analyzing how these factors affect segmentation methods, particularly in enterprises like Alibaba. Employing a quantitative approach, the research used surveys distributed to employees and senior managers from twenty randomly selected Chinese enterprises. The survey, developed with input from literature review and expert consultation, covered various aspects including measurement methodologies, market demand, entry barriers, and market segmentation. Data analysis involved statistical techniques such as Cronbach alpha, factor loading analysis, Pearson correlation analysis, and multiple linear regression analysis. Results indicated that market demand significantly influences segmentation strategies and decision-making, emphasizing the importance of understanding consumer preferences. Entry barriers were found to shape market segments and strategies, underscoring the need for firms to align their strategies with market demands to deliver tailored products. The study sheds light on the complexities of China's market dynamics and entry barriers, emphasizing the strategic importance of addressing these challenges and the impact of market demand on segmentation. These findings offer practical insights for navigating the Chinese market and contribute to academic understanding of the relationship between market segmentation, consumer behavior, and business strategy in Chinese online companies.

EFFECT OF GREEN ACCOUNTING, LEVERAGE, FIRM SIZE ON FIRM VALUE WITH PROFITABILITY AS INTERVENING VARIABLE

Purpose: This study investigates how green accounting practices, leverage (debt levels), and company size influence profitability and ultimately firm value. The research focuses on companies listed in the Jakarta Islamic Index (JII). Previous research on these relationships has yielded mixed and inconsistent results. They point out that few studies have examined these variables together in a single model. This study aims to address this gap by creating a new model that explores the combined effects of green accounting, leverage, and size on profitability, and ultimately, firm value within the JII context. Theoretical Framework: This research is grounded in two key theories: stakeholder theory and signaling theory. While past empirical studies haven't yielded definitive results, this research offers a fresh perspective. The novel model developed in this study builds upon existing knowledge by examining how several previously linked variables interact and influence firm performance. Design/Methodology/Approach: This research employed a descriptive approach, analyzing financial data from company annual reports. To understand the relationships between the variables, a statistical technique called Structural Equation Modeling (SEM) was used. The study focused on companies listed on the Jakarta Islamic Index (JII) of the Indonesian Stock Exchange between 2019 and 2022. A 120-sample size from 30 companies was chosen strategically using a purposive sampling method from the JII. Result and Discussion: This study found that leverage (debt) and company size have a positive and statistically significant impact on profitability. Interestingly, green accounting practices did not have a clear influence on firm value. There was a negative effect, but it wasn't statistically significant. Firm size itself also did not directly affect firm value in a statistically significant way. However, profitability itself has a strong and positive impact on firm value. Additionally, profitability can act as an indirect factor, mediating the relationship between leverage and firm value. Based on these results, the study recommends that companies listed on the Jakarta Islamic Index (JII) consider strategic use of debt financing. This can be a way to improve profitability and ultimately attract investors, leading to a higher firm value. Originality/Value: This research introduces a novel model that investigates the interconnected effects of several variables with proven relationships.

Unravelling the complexity of environmental exposures and health: A novel exposome-centered framework for occupational and environmental epidemiology

The exposome-centered approach marks a significant shift in occupational and environmental epidemiology, prioritizing a holistic evaluation of environmental exposures throughout an individual's life to elucidate their effects on health. Unlike traditional methods that single out environmental factors for their direct health impacts, this approach seeks an integrated analysis of all non-genetic influences, encompassing specific and general external environments and internal responses. While acknowledging environmental factors' pivotal role in chronic diseases and global mortality, existing methods struggle with precisely measuring and analyzing the broad spectrum of exposures and their combined effects. The exposome framework addresses these gaps through advanced omics technologies, refined exposure assessment models, and comprehensive data analytics. Yet, its application faces hurdles like the requirement for substantial infrastructure, cross-disciplinary training, and new statistical techniques for high-dimensional data analysis. Recent endeavors in applying the exposome concept show its promise in improving our understanding of environmental and occupational health, underlining its importance in advancing epidemiological research and public health. This abstract condenses the rationale, challenges, and ongoing efforts to incorporate the exposome into epidemiol-ogical studies, emphasizing its potential in enhancing the field.