Z-score Method Research Articles

Nutritional Status and Anemia among Scheduled Caste Adolescent Girls of District Yamunanagar, Haryana, India

Background: During adolescence, nutrition is crucial for physical and cognitive development, yet socio-economic disparities and nutritional deficiencies, especially in girls, lead to health vulnerabilities, including high anemia rates. Despite recent data from the National Family Health Survey, there is limited information on adolescents aged 11-16 years, requiring targeted research. This cross-sectional study aims to assess the nutritional status, determine the prevalence of anemia, and explore associated factors among adolescent girls of the scheduled caste in District Yamunanagar, Haryana, India. Methods: The study sampled 450 scheduled caste girls aged 11-16 years from schools in District Yamunanagar, Haryana. Height and weight were measured to calculate Body Mass Index (BMI). Data on micro-environmental factors, socio-economic and demographic variables, and dietary habits were collected through interview-based schedule. Hemoglobin levels were measured using Sahli’s Acid Haematin technique, and nutritional status was assessed using the World Health Organization (WHO) standards (Z-score method). Results: The study found increases in height, weight, and BMI with age, with notable spurts at 12-13 years for height and 11-12 years for weight and BMI. Significant differences in height, weight, and BMI were observed between age groups. Moreover, 92.2% girls were found to be anemic, with most of them suffering from moderate anemia. Additionally, the prevalence of stunting was 67.33% and wasting was 64.90% among the girls. Household size, number of family members and menarcheal status of the girls showed a significant association with the nutritional status and anemic of the girls. Conclusion: The study emphasizes the need of regular screening and timely interventions to improve the nutritional and anemic status of adolescent girls.

Financial Distress Analysis of Companies Carrying Out Mass Layoffs Throughout 2023 using the Altman Z-Score Method, Springate Method, and Zmijewski Method

This research aims to estimate the financial condition of four companies that will carry out mass layoffs throughout 2023 using three financial distress prediction methods, namely the Z-Score Altman method, the Springate method and the Zmijewski method. Secondary data was used sourced from the financial reports of each company from 2022 to 2023. The research results showed that financial performance was analyzed using the Z-Score Altman method, the Springate method and the Zmijewski method in four companies, namely PT Net Visi Media Tbk for the period 2022-2023 classified as having the potential to experience bankruptcy, PT GoTo Gojek Tokopedia Tbk for the 2022-2023 period is classified as having a high potential for bankruptcy, PT Bukalapak.com Tbk for the 2022-2023 period is classified as being in good health, and JD.com, Inc. (JD) for the 2022-2023 period is classified as having absolutely no potential for bankruptcy.

Machine learning-based outlier detection for pipeline in-line inspection data

Pipeline companies are facing challenges in maintaining the integrity and reliability of their pipelines. They are working towards predictive maintenance using machine learning-based approaches to predicting anomalies. Training machine learning models requires sufficient data. Data quality is therefore becoming important because inaccurate data will lead to an inaccurate or wrong decision on pipeline condition assessment and the following management. This research paper intends to address the data quality issues of pipeline inspection data such as in-line inspection (ILI) data using machine learning models. Different machine learning models developed by random forest regression, linear regression, and nearest neighbors’ methods were tested to detect outliers in the ILI data. In this paper, the ILI data collected from an oil pipeline over a period of 22 years was applied to testing and analysis. To verify the outlier detection results of machine learning models, we used statistical analysis including Z-score method to check and find if there are any gaps in the analysis. It verifies that all these methods show almost the same or very similar results for the detection of the outliers. Hence, this study presents a robust method for the field applications in the pipeline industry.

The Turbofan Engine Remaining Useful Life Prediction Using 1-Dimentional Convolutional Neural Network

Turbofan engines have been the dominant type of engine in aircraft for the last forty years. Ensuring the quality of these engines is crucial for flight safety, particularly for long-distance flights. However, their performance degrades over time, impacting flight safety. To address this issue, it is essential to predict potential engine failures by estimating the Remaining Useful Life (RUL) of the engines Deep learning, especially Convolutional Neural Networks (CNNs), has demonstrated exceptional proficiency in handling intricate, non-linear data, leading to improved RUL predictionsdue to their ability to process complex and non-linear data. In this project, a 1-D CNN is used to predict RUL using the NASA C-MAPSS FD001 dataset, which consists of 3 settings and 21 sensors, though sensors with stagnant readings are excluded. The dataset is normalized using min-max and z-score methods, and then segmented into sequences for input into the 1-D CNN model. Various training scenarios were evaluated, with the best RMSE of 3.26 achieved using 10 epochs, a learning rate of 0.0001, and z-score normalization. The results indicate that feature selection can produce a lower RMSE compared to scenarios without feature selection.

Bankruptcy Analysis Using the Altman Z-Score Model (Study on Retail Companies Listed on the Indonesia Stock Exchange)

Bankruptcy is a legal or economic event that often results in the complete cessation of all business activities and operations. This bankruptcy usually starts with liquidity problems, but is closely linked to situations where a company's debt exceeds its total assets. The objective of this study is to evaluate the effectiveness of the Altman Z-score model as a tool to predict bankruptcy. The main focus of this study is retail companies listed on the Indonesia Stock Exchange in 2021-2023. 31 companies participated in the study, supported by 93 financial statements. The researchers used purposive sampling method for the sampling process. The study uses the modified Altman Z-score method (1995) to analyze the obtained data. The analysis revealed that 17 companies (54.8%) could be classified as healthy. 3 companies (9.7%) were classified as weak, and 11 companies (36.7%) were expected to go bankrupt. These results provide insight into the financial health of the retail companies surveyed and highlight the importance of using predictive models to determine bankruptcy risk.

The Role of the Altman Z-Score Method in Predicting Corporate Bankruptcy

The Role of the Altman Z-Score Method in Predicting Corporate Bankruptcy

Comparison of energy consumption prediction models for air conditioning at different time scales for large public buildings

Large public buildings have complex functions and high air conditioning energy consumption, and the prediction of energy consumption for air conditioning in different time scales can realise different energy saving management needs. Taking a large public building in Guangzhou as a research case, the long and short-term memory neural network (LSTM) is used as the basis to establish the machine learning model for energy consumption prediction for air conditioning at different time scales in terms of 10 min, hourly and daily, and a comparative analysis is carried out. The original data were analysed and processed by Z-Score and Local Outlier Factor (LOF) outlier detection methods. The relationship between meteorological parameters for Typical Meteorological Year (TMY) and the actual local meteorological parameters and energy consumption for air conditioning was analysed using linear regression, Pearson and Spearman correlation coefficients, taking meteorological parameters as input variables. The results show that comparing energy consumption for air conditioning with the meteorological parameters in TMY and actual local meteorological parameters, the actual meteorological parameters had a stronger correlation energy consumption for air conditioning. When the actual local meteorological parameters are taken as input variables, the mean absolute percentage error (MAPE) is reduced by about 0.85 %, and the coefficient of determination (R2) is increased by about 0.01. At the same time, the MAPE and R2 of the 10-min energy consumption prediction model are about 6.43 % and 0.9738, respectively, the MAPE and R2 of the hourly prediction model are about 9.29 % and 0.9568, respectively, while the MAPE and R2 of the daily prediction model are about 25.76 % and 0.7998. Meanwhile, an improved energy consumption prediction model is proposed to reduce the daily energy consumption prediction error to 6.36 % for MAPE, and the R2 of this model is 0.9927.

Pengaruh Operating Capacity dan Working Capital Turnover Terhadap Financial Distress

Financial distress is a decline in a company's financial condition that occurs before bankruptcy or liquidation, which begins with the company's inability to fulfill its obligations. Based on data on the Indonesian Stock Exchange, several companies have experienced bankruptcy for two consecutive years in mining sector companies in 2019-2022. The aim of this research is to determine the effect of operating capacity and working capital turnover on financial distress. This type of research is quantitative with documentation methods. The total population is 86 companies with a sample of 39 companies. The sample was selected using a purposive sampling method, the data used were the annual financial reports of various mining sectors for the 2019-2022 period. Data analysis uses classical assumptions, T test and F test and multiple linear regression analysis using the Altman Z-Score method. The results of this research show that operating capacity has a positive and significant effect on financial distress and working capital turnover has no significant effect on financial distress.

Classification of Crude Palm Oil Quality Using Artificial Neural Networks Based on Chemical Components

Palm oil, known as Crude Palm Oil (CPO), is a flagship product in the palm oil industry, playing a crucial role in the economies of various tropical countries such as Indonesia. This study aims to classify the quality of CPO based on chemical components using the Artificial Neural Networks (ANN) method with a backpropagation algorithm. The research data was obtained from PT. Perkebunan Lembah Bhakti (PLB) 2 from January to October 2023, consisting of 225 entries with five main chemical variables: impurity level, moisture content, free fatty acid (FFA) level, Deterioration of Bleachability Index (DOBI), and carotenoids. The data preprocessing stage involved transforming and normalizing the data using the Z-score method. The ANN model used has a 5-5-2 architecture with ReLU activation functions for the input and hidden layers and a Softmax function for the output layer. Model evaluation was conducted using accuracy metrics, which showed that the ANN model could classify CPO quality with an accuracy of 97.78% on the test data. The research results show that ANN can classify CPO quality with a high level of accuracy. This indicates that this method has great potential for use in the industry to improve CPO quality assessment. The improvement in accuracy and validity of the ANN classification results has significant implications for the industry. With high accuracy, ANN can reduce human errors in quality assessment, speed up the process, and increase the consistency of assessment results. This is crucial because consistent quality assessment can enhance operational efficiency and reduce production costs.

A momentum quantification method for tennis matches based on t-SNE and k means clustering

Momentum is a crucial factor influencing the outcomes of sports competitions. This study proposes a method for visualising and quantifying momentum using various modern analytical techniques. These techniques include feature engineering, dimensionality reduction with the t-SNE algorithm, and k-means clustering. By analysing data from the past five years of Wimbledon, the study extracted various performance features such as serve success rate and scoring rate. By calculating the similarities between features and conducting cluster analysis, the study reveals changes in momentum during matches. This research employs data visualisation and clustering results to not only display the conditions of matches under different momentum states but also quantifies momentum changes by calculating the Euclidean distance between data points and cluster centroids and standardising these distances using the Z-score method. This approach provides a visual perspective that reveals the dynamic changes that occur within matches and quantifies momentum. It demonstrates that these changes in momentum can indeed reflect the progression and outcomes of matches.

Dynamic flood mapping by a normalized probabilistic classification method using satellite radar amplitude time series

ABSTRACT Owing to the vast development of Synthetic Aperture Radar (SAR), especially the improvement of spatio-temporal resolution, observing and quantifying the complex and dynamic flood process becomes increasingly feasible. Utilizing the Sentinel-1 Ground Range Detected (GRD) dataset, we proposed an improved probabilistic flood mapping method combining image Pareto Scaling (PS) normalization and Bayesian probability estimation. We validated our method during a flood event in Xinjiang County, Shaanxi Province of China in October 2021 using a high spatial resolution (0.1 m) Unmanned Aerial Vehicle (UAV) image. The overall reliability of the new method agrees 95% to the UAV measurements and achieves the highest accuracy (85.2%) when compared to the Sentinel-1 dual-polarized water index (SDWI) threshold method and the Z-score method. Our results distinguished four flood stages: flood emergence, peak, receding, and disappearance, which provide valuable insights into the dynamic change process of floods. Notably, we observed that pixels with different flood probabilities exhibited distinct temporal characteristics. The extremely high probability pixel experienced rapid fluctuations, while the medium probability pixel showed more gradual changes over time. We believe our proposed method can enhance our understanding of flood-prone areas and their dynamics so that decision-makers can develop targeted mitigation measures and response plans.

Research on sales strategies for target customers of electric vehicles based on gradient boosting ensemble trees

This paper focuses on the automotive industry, especially the new energy vehicle sector, which is vital for the economy and needs careful market sales decisions. It analyzes data from 1,964 potential customers, including their satisfaction and personal information. The study uses machine learning to create a math model and give sales advice. The data is first cleaned without changing its original structure, and then normalized using the Z-Score method. For any outlier values in the data, the mean of similar items is used to fill in gaps. Specifically, for 500 missing entries about the number of children (B7), the paper groups customers by age, calculates the frequency of children in each group, and uses these frequencies to fill in missing values.Using the cleaned data, the XGBoost algorithm ranks the factors influencing customer decisions. For Brand 1, the top factors are economy (a3), work experience (B10), and safety performance (a4). For Brand 2, comfort (a2), disposable income (B15), and mortgage expenses (B16) are key. For Brand 3, car loan expenses (B17), battery technology (a1), and again mortgage expenses (B16) are most important.The paper also addresses imbalanced data by using an XGBoost binary classification model. This model predicts the likelihood of buying an electric vehicle using the Sigmoid function on tree node weights. It evaluates the model using Precision, Recall, and Area Under the Receiver Operating Characteristic Curve (AUC), which works well even with imbalanced data. The Precision and Recall rates for all three brands are above 90%, with AUC over 85% for Brands 1 and 2, and over 75% for Brand 3. This shows that the model is effective and can be used in real-world situations.

The impact of different types and levels of construction noise on physiological responses: Focusing on standardization and habituation

Construction noise is a pervasive and influential source of stress for urban residents and can lead to various health problems, including hormonal dysfunction and cardiovascular diseases. Despite considerable research on the general impact of urban noise, studies that specifically address physiological responses to construction noise are limited. This study explores the relationship between different types of construction noise (tonal and impulsive) and physiological responses, focusing on electrodermal activity (EDA) at varying decibel levels (40, 60, and 80 dBA). The experimental results demonstrated that the z-score standardization method excels in EDA analysis in response to construction noise, particularly showing that noise levels significantly affect the phasic EDA of subjects exposed to tonal noise. Additionally, it was found that EDA responses recorded during the initial 30 s decisively influenced the subjective rating of annoyance. These results highlight the importance of considering both noise type and exposure duration in managing construction noise to mitigate its negative impacts on urban residents. This study can provide practical implications for urban planning, potentially minimizing complaints from urban residents and the impact of construction noise on communities.

Financial Distress Analysis of Top 100 Malaysian Public Listed Companies during COVID-19 Pandemic using Altman Z-Score Analysis

The outbreaks of the COVID-19 pandemic at the beginning of 2020 have led to considerable economic pressure worldwide, including in Malaysia. The Malaysian government has issued a series of Movement Control Orders (MCOs) requiring companies in most sectors to be closed to the public, with the exception of essential services and certain sectors. The objective of this study is to analyse whether there is a difference in the financial distress of Malaysian companies before and during the COVID-19 pandemic by using the Altman Z-score method. In this study, the annual reports of 83 different companies from 2017 to 2019 and the post-pandemic period (2020 to 2021) were analysed. This study shows that the global COVID-19 pandemic scenario has a significant impact on the logistics and transport sectors, the consumer goods market, and the manufacturing sector. A significant number of companies have been shaken and are now struggling with difficult circumstances as a direct result of the COVID-19 outbreak. This study also shows that almost half of the companies face the possibility of going bankrupt within this period. Surprisingly, there are companies that seem to have improved their performance despite the widespread COVID-19 epidemic. The findings of this study can provide valuable insights for regulators looking to develop strategies to help listed companies cope with future pandemic-related problems.

Application research of BP-neutral network model in forecasting sewage volume in Beijing city

Abstract In order to meet the urgent demand for sewage treatment in Beijing, three methods-PLSR, GM (1,1) and BP-Network were used to fit the sewage volume of Beijing from 2010 to 2019, and the fitting conditions of the three were compared in this paper. In this study, the application of the three methods was analyzed, and the Z-score method was used to eliminate the impact of data dimension and standardize the data. At the same time, the normal distribution of the data was tested, as was the correlation between the data, Which improved the accuracy of the three fitting models. The final result is that the realistic BP neural network has the highest fitting accuracy and can improve the most accurate sewage volume prediction for Beijing.

Differentially Expressed Proteins in the Serum of Elderly Patients Who Experienced Perioperative Neurocognitive Disorders Following Transurethral Resection of the Prostate.

Perioperative neurocognitive disorders (PND) are a group of prevalent neurological complications that often occur in elderly individuals following major or emergency surgical procedures. The etiologies are not fully understood. This study endeavored to investigate novel targets and prediction methods for the occurrence of PND. A total of 229 elderly patients diagnosed with prostatic hyperplasia who underwent transurethral resection of the prostate (TURP) combined with spinal cord and epidural analgesia were included in this study. The patients were divided into two groups, the PND group and non-PND group, based on the Z-score method. According to the principle of maintaining consistency between preoperative and intraoperative conditions, three patients from each group were randomly chosen for serum sample collection. isobaric tags for relative and absolute quantification (iTRAQ) proteomics technology was employed to analyze and identify the proteins that exhibited differential expression in the serum samples from the two groups. Bioinformatics analysis was performed on the proteins that exhibited differential expression. Among the 1101 serum proteins analyzed in the PND and non-PND groups, eight differentially expressed proteins were identified in PND patients. Of these, six proteins showed up-regulation, while two proteins showed down-regulation. Further bioinformatics analysis of the proteins that exhibited differential expression revealed their predominant involvement in cellular biological processes, cellular component formation, as well as endocytosis and phagocytosis Additionally, these proteins were found to possess the RING domain of E3 ubiquitin ligase. The iTRAQ proteomics technique was employed to analyze the variation in protein expression in serum samples from patients with PND and those without PND. This study successfully identified eight proteins that exhibited differential expression levels between the two groups. Bioinformatics analysis indicates that proteins exhibiting differential expression are primarily implicated in the biological processes associated with microtubules. Investigating the microtubule formation process as it relates to neuroplasticity and synaptic formation may offer valuable insights for enhancing our comprehension and potential prevention of PND. Registered (ChiCTR2000028836). Date (20190306).

Comparison of financial distress between manufacturing companies in the ceramic, porcelain, and glass sector with agriculture sector

Rapid technological development, high economic growth rates and the development of complex demands have resulted in the emergence of various industries that only answer the needs of society. The purpose of the study is to determine the influence of independent variables, namely Liquidity, Activity and Leverage, on Dependent variables, namely Financial Distress of Agricultural Sector Companies and Ceramics, Glass and Porcelain Subsector Companies in 2017-2020 and compare the level of Financial Distress between Agricultural Sector Companies and Ceramics, Glass and Porcelain Subsector Companies using the Altman Z-Score method. The population to be studied in this study is all Agricultural Sector Companies and Ceramics, Glass and Porcelain Subsector Companies listed on the Indonesia Stock Exchange in 2017-2020. The sampling technique was selected using the purposive sampling method, obtaining observation data of 100 data. The analysis technique used in this study is multiple linear regression. Coefiseien determination, F test and t-test as measuring instruments were used. The study results showed that Liquidity does not affect Financial Distress, Activity, and Leverage affect Financial Distress, and the level of Financial Distress of Ceramics, Glass and Porcelain Subsector Companies is better than Agricultural Sector Companies.Profitability is important to measure a company's ability to generate profits. In this study, the level of profitability of each company during 2017-2020 varies; some companies experienced an increase in profit and a decrease in profit, this will certainly affect the results of research on the independent variable profitability.

Analisis Financial Distress dengan Menggunakan Metode Altman Z-Score pada PT. Cahaya Bintang Medan Tbk

This research aims to determine and analiyze the Altman Z-Score mothod in assessing the level of bankruptcy PT. Cahaya Medan Tbk for The 2018- 2021 period and to provide evidence that the altman Z-score method is effectively used in assessing the level of company bankruptcy with a relatively reliable level of precision and accuracy at PT.Cahaya Bintang Medan Tbk for the 2018-2021 period. The type of research used is quantitative descriptive analysis, namely by collecting, clarifying, analyzing and interpreting data obtained from the company so that it can provide a picture of the actual situatuin the calculation technique used in this reseatch is the Altman Z-Score model formula. Based on the results of calculations using the modified Altman Z-Score method from the 2018-2021 period, it shows that PT. Cahaya Bintang Medan Tbk is in the safe zone or safe because the average Z-Score value is above 2,60 or Z2,60

The Influence of Liquidity Ratios, Leverage, Profitability and Activity on Financial Distress of Infrastructure Companies in the Building Construction Subsector for the Period 2018 – 2022

This research aims to determine the effect of liquidity, leverage, profitability and activity ratios on the financial distress of infrastructure companies in the building construction subsector listed on the Indonesia Stock Exchange in 2018 - 2022. The approach used is quantitative research. This research uses secondary data accessed through the official website of the Indonesia Stock Exchange with a population of 22 companies and a sample of 16 companies selected using the purposive sampling method and obtaining a sample of 80 observations. The data analysis technique uses panel data regression analysis with the Eviews9 test tool. The research results show that the Liquidity Ratio as proxied by the Current Ratio has a positive effect on financial distress, the Leverage Ratio as measured by the Debt to Asset Ratio (DAR) has a negative effect on financial distress, the Profitability Ratio as measured by Return on Assets (ROA) has a positive effect on financial distress, while the Activity Ratio which is proxied by Total Asset Turnover (TATO) does not affect financial distress. The calculation technique uses the Altman Z-Score method and financial ratio calculations. Therefore, researchers recommend utilizing this test model to predict financial distress because the accuracy that can be given is quite good so that companies can anticipate financial difficulties.

Highly prognostic signature for stage II colon cancer from the TOSCA trial: The PROSIT study.

3524 Background: The predictive and prognostic value of current biomarkers for guiding the treatment of stage II colon cancer (CC) is very limited. Here, we aimed to develop a prognostic transcriptional signature for high-risk stage II CC patients undergoing adjuvant treatment with either 3 or 6 months of FOLFOX4/CAPOX within the framework of the phase III TOSCA Italian trial. Methods: We conducted an observational retrospective study based on the TOSCA trial database, matching 62 patients with high-risk stage II CC who relapsed with 62 who did not. The matching criteria included the center of recruitment, pT stage, adjuvant treatment type and duration (3 or 6 months). Transcriptome sequencing was performed on material from centrally assessed FFPE blocks on an Illumina Novaseq 6000 instrument. Generated data were processed using the nf-core/rnaseq (v 3.14.0) pipeline, aligned to the human genome GRCh37/hg19, and analyzed for differential gene expression, requiring a minimum absolute log fold change of 1 and false discovery rate below 0.1. Enrichment analysis was performed using GSVA package with the z-score method. Results: Out of the 124 stage II CC cases considered for the present analysis, 60% received 3 months and 40% 6 months of adjuvant chemotherapy. The median age was 64 years (IQR 55.2-71.1), with 40% of the cancers being right-sided. Fifty-seven percent of the patients received FOLFOX, while the remaining were treated with XELOX. Of the 124 matched samples, 72 were of sufficient quality for RNA-seq.We identified a signature among the top 17 differentially expressed genes (absolute log fold-change > 1, p-value < 0.001, FDR < 0.1, see Table). Patients exhibiting a lower prognostic signature (PROSIT-L) showed worse disease-free survival (DFS) compared to those with a high prognostic signature (PROSIT-H) (HR 3.5; 95% CI 1.53 – 7.9; p = 0.002), indicating its strong and independent prognostic value, regardless of the treatment received. Conclusions: The new transcriptomic signature may help in identifying stage II CC patients who are likely to relapse despite receiving adjuvant oxaliplatin-based treatment. [Table: see text]