Observable Variables Research Articles

Potential of The Biological Agent Bacillus spp in Inhibiting Fusarium Wild Disease and its Effects on The Growth and Production of Cayenne Papper (Capsicum frutescens L.)

Cayenne pepper (Capsicum frutescens L.) is a horticultural crop in Indonesia that often has many obstacles. The main obstacle that is often faced is wilt disease caused by the fungus Fusarium oxysporum. Bacillus spp. bacteria have the potential to control the fungus F. oxysporum. The purpose of this study was to determine the ability of Bacillus spp. bacteria in controlling fusarium wilt disease. This research was conducted from August to November 2023 at the Plant Health Laboratory and screen house of the Faculty of Agriculture, National Development University "Veteran" East Java. The study used a Factorial Complete Randomized Block Design with three replications and nine treatments so that there were 27 experimental units. The observation variables included incubation period, disease intensity, plant height, number of leaves, first flower appearance and fruit wet weight. The research data were analyzed using ANOVA analysis of variance procedure to determine whether there is an effect of each treatment. If it is known that there is a significantly different effect from the treatment, it will be continued with the DMRT test at the 5% level. The results showed that the combination of Bacillus spp bacteria and dosage had an effect on the incubation period and disease intensity. The results showed that the combination of Bacillus spp bacteria and doses had an effect on vegetative phase parameters, namely plant height and number of leaves. However, the combination of Bacillus spp and dosage showed that the generative phase parameters did not significantly affect the first flower appearance and fruit wet weight

Automated mitral inflow Doppler peak velocity measurement using deep learning

Doppler echocardiography is a widely utilised non-invasive imaging modality for assessing the functionality of heart valves, including the mitral valve. Manual assessments of Doppler traces by clinicians introduce variability, prompting the need for automated solutions. This study introduces an innovative deep learning model for automated detection of peak velocity measurements from mitral inflow Doppler images, independent from Electrocardiogram information. A dataset of Doppler images annotated by multiple expert cardiologists was established, serving as a robust benchmark. The model leverages heatmap regression networks, achieving 96% detection accuracy. The model discrepancy with the expert consensus falls comfortably within the range of inter- and intra-observer variability in measuring Doppler peak velocities. The dataset and models are open-source, fostering further research and clinical application.

Polish version of the questionnaire for measuring post-traumatic stress disorders PTSD-8 in studies of four groups of medical professions

There are several techniques to diagnose post-traumatic stress disorder (PTSD) symptoms that correspond to the Diagnostic and Statistical Manual of Mental Disorders (DSM) classification of mental disorders. This study aimed at testing the reliability and construct validity of the Polish version of the PTSD-8. The results obtained in four professional groups of healthcare workers (physicians, nurses, paramedics, other occupations) were compared. The PTSD-8 questionnaire includes questions on stress reactions manifested by intrusion (4 items), avoidance (2 items), or hypervigilance (2 items). Respondents were asked about their reactions to the traumatic work-related experiences occurring during the COVID-19 pandemic. The nationwide survey conducted in 2022 included 2303 employees, among whom 746 had such an experience and completed the PTSD-8 questionnaire. Based on confirmatory factor analysis (CFA) results, it was shown that a single-factor model is acceptable, but under the condition of modification by correlation of random errors at selected observable variables. The reliability of this scale is very good (McDonald's Ω = 0.890), and the goodness-of-fit of the overall CFA model is confirmed by: CMIN/DF = 3.969, NFI = 0.982, RFI = 0.964, IFI = 0.986, TLI = 0.983, RMSEA = 0.063. The model estimated for female nurses has the best psychometric properties. In the study group of 2303 respondents, 16.76% met the criteria for PTSD, accounting for 52.74% of those who were traumatized by a work-related event. The PTSD-8 index is M±SD 20.01±5.55 pts (range: 8-32 pts). The PTSD-8 index is significantly higher in women than in men, and higher in nurses compared to other professions. However, the differences between professional groups are significant only for women, and the largest when comparing female physicians and nurses. The analyses confirmed the rationale of further implementation of the PTSD-8 questionnaire in Poland, both as a tool for identifying PTSD and for studying the severity of the syndrome, its determinants and health effects. Med Pr Work Health Saf. 2024;75(1):45-56.

Noninvasive diagnosis of liver cirrhosis: qualitative and quantitative imaging biomarkers.

A diagnosis of cirrhosis initiates a shift in the management of chronic liver disease and affects the diagnostic workflow and treatment decision of primary liver cancer. Liver biopsy remains the gold standard for cirrhosis diagnosis, but it is invasive and susceptible to sampling bias and observer variability. Various qualitative and quantitative imaging biomarkers based on ultrasound, CT and MRI have been proposed for noninvasive diagnosis of cirrhosis. Qualitative imaging features are easy to apply but have moderate diagnostic sensitivity. Elastography techniques allow quantitative assessment of liver stiffness and are highly accurate for cirrhosis diagnosis. Ultrasound elastography are widely used in clinical practice, while MR elastography has narrower availability. Although not applicable in clinical practice yet, other quantitative imaging features, including liver surface nodularity, linear and volumetric measurement, extracellular volume fraction, liver enhancement on hepatobiliary phase, and parameters derived from diffusion-weighted imaging, can provide additional information of liver morphology, perfusion, and function, thus may increase diagnosis performance. The introduction of radiomics and deep learning has further improved diagnostic accuracy while reducing subjectivity. Several imaging features may also help to assess liver function and outcomes in patients with cirrhosis. In this review, we summarize the qualitative and quantitative imaging biomarkers for noninvasive cirrhosis diagnosis, and the assessment of liver function and outcomes, and discuss the challenges and future directions in this field.

Investigasi Aspek-aspek Promosi Penjualan

In today's highly developed e-commerce era, sales promotions have become a reliable marketing communication technique for many companies. There is also much research in this field. However, many researchers need to define and operationalize sales promotions as a research variable correctly. With critical review techniques, this study found that aspects of sales promotions that can be used as observation variables are perceived value, credibility, fairness, time frame, uncertainty, congruence, requirements, and consequences. Every sales promotion technique requires different aspects. The researchers should match the sales promotions technique properly with their relevant aspects.

Forest Canopy Height Estimation by Integrating Structural Equation Modeling and Multiple Weighted Regression

As an important component of forest parameters, forest canopy height is of great significance to the study of forest carbon stocks and carbon cycle status. There is an increasing interest in obtaining large-scale forest canopy height quickly and accurately. Therefore, many studies have aimed to address this issue by proposing machine learning models that accurately invert forest canopy height. However, most of the these approaches feature PolSAR observations from a data-driven viewpoint in the feature selection part of the machine learning model, without taking into account the intrinsic mechanisms of PolSAR polarization observation variables. In this work, we evaluated the correlations between eight polarization observation variables, namely, T11, T22, T33, total backscattered power (SPAN), radar vegetation index (RVI), the surface scattering component (Ps), dihedral angle scattering component (Pd), and body scattering component (Pv) of Freeman-Durden three-component decomposition, and the height of the forest canopy. On this basis, a weighted inversion method for determining forest canopy height under the view of structural equation modeling was proposed. In this study, the direct and indirect contributions of the above eight polarization observation variables to the forest canopy height inversion task were estimated based on structural equation modeling. Among them, the indirect contributions were generated by the interactions between the variables and ultimately had an impact on the forest canopy height inversion. In this study, the covariance matrix between polarization variables and forest canopy height was calculated based on structural equation modeling, the weights of the variables were calculated by combining with the Mahalanobis distance, and the weighted inversion of forest canopy height was carried out using PSO-SVR. In this study, some experiments were carried out using three Gaofen-3 satellite (GF-3) images and ICESat-2 forest canopy height data for some forest areas of Gaofeng Ridge, Baisha Lizu Autonomous County, Hainan Province, China. The results showed that T11, T33, and total backscattered power (SPAN) are highly correlated with forest canopy height. In addition, this study showed that determining the weights of different polarization observation variables contributes positively to the accurate estimation of forest canopy height. The forest canopy height-weighted inversion method proposed in this paper was shown to be superior to the multiple regression model, with a 26% improvement in r and a 0.88 m reduction in the root-mean-square error (RMSE).

Pengaruh Analisis Fundamental, Teknikal dan Bandarmologi terhadap Harga Saham Syariah yang Terdaftar di Jakarta Islamic Index Periode 2018-2021

Equity-based sharia stocks that comply with Islamic principles. One of the sharia stock indices is the Jakarta Islamic Index (JII). JII is an index consisting of the 30 most liquid Islamic stocks on the IDX which was launched in 2000. To select 30 Islamic stocks to be included in the index calculation, there are additional criteria carried out by IDX, namely liquidity criteria. The liquidity indicators used are based on market capitalization and daily transaction value. Usually calculated based on the last one year period and every day there must always be transactions. This study uses a quantitative descriptive research method, namely research that emphasizes the analysis of numerical data processed by statistical models. This method is called the quantitative method because the research data uses numbers that indicate the value of the observation variable that leads to a certain time period. This research results that from the results of the t test table it is known that the fundamental analysis variables partially influence the prices of Islamic stocks listed on the Jakarta Islamic Index (JII) in 2018-2021. From the results of the t test table it is known that the technical analysis variable has a partial effect on the prices of Islamic stocks listed on the Jakarta Islamic Index (JII) for 2018-2021. From the results of the t test table it is known that the bandarmological analysis variable has a partial effect on the prices of Islamic stocks listed on the Jakarta Islamic Index (JII) for 2018-2021. From the results of the f test table it is known that the variables of fundamental analysis, technical analysis, and bandarmological analysis simultaneously influence the prices of Islamic stocks listed on the Jakarta Islamic Index (JII) for 2018-2021.
 Keywords: Fundamental Analysis, Technical, Bandarmology, Sharia Stocks, Sharia Market

Machine learning prediction of factors affecting Major League Baseball (MLB) game attendance: algorithm comparisons and macroeconomic factor of unemployment

PurposeThis study aims to identify an automated machine learning algorithm with high accuracy that sport practitioners can use to identify the specific factors for predicting Major League Baseball (MLB) attendance. Furthermore, by predicting spectators for each league (American League and National League) and division in MLB, the authors will identify the specific factors that increase accuracy, discuss them and provide implications for marketing strategies for academics and practitioners in sport.Design/methodology/approachThis study used six years of daily MLB game data (2014–2019). All data were collected as predictors, such as game performance, weather and unemployment rate. Also, the attendance rate was obtained as an observation variable. The Random Forest, Lasso regression models and XGBoost were used to build the prediction model, and the analysis was conducted using Python 3.7.FindingsThe RMSE value was 0.14, and the R2 was 0.62 as a consequence of fine-tuning the tuning parameters of the XGBoost model, which had the best performance in forecasting the attendance rate. The most influential variables in the model are “Rank” of 0.247 and “Day of the week”, “Home team” and “Day/Night game” were shown as influential variables in order. The result was shown that the “Unemployment rate”, as a macroeconomic factor, has a value of 0.06 and weather factors were a total value of 0.147.Originality/valueThis research highlights unemployment rate as a determinant affecting MLB game attendance rates. Beyond contextual elements such as climate, the findings of this study underscore the significance of economic factors, particularly unemployment rates, necessitating further investigation into these factors to gain a more comprehensive understanding of game attendance.

Assessment of left ventricular ejection fraction in artificial intelligence based on left ventricular opacification.

Left ventricular opacification (LVO) improves the accuracy of left ventricular ejection fraction (LVEF) by enhancing the visualization of the endocardium. Manual delineation of the endocardium by sonographers has observer variability. Artificial intelligence (AI) has the potential to improve the reproducibility of LVO to assess LVEF. The aim was to develop an AI model and evaluate the feasibility and reproducibility of LVO in the assessment of LVEF. This retrospective study included 1305 echocardiography of 797 patients who had LVO at the Department of Ultrasound Medicine, Union Hospital, Huazhong University of Science and Technology from 2013 to 2021. The AI model was developed by 5-fold cross validation. The validation datasets included 50 patients prospectively collected in our center and 42 patients retrospectively collected in the external institution. To evaluate the differences between LV function determined by AI and sonographers, the median absolute error (MAE), spearman correlation coefficient, and intraclass correlation coefficient (ICC) were calculated. In LVO, the MAE of LVEF between AI and manual measurements was 2.6% in the development cohort, 2.5% in the internal validation cohort, and 2.7% in the external validation cohort. Compared with two-dimensional echocardiography (2DE), the left ventricular (LV) volumes and LVEF of LVO measured by AI correlated significantly with manual measurements. AI model provided excellent reliability for the LV parameters of LVO (ICC > 0.95). AI-assisted LVO enables more accurate identification of the LV endocardium and reduces observer variability, providing a more reliable way for assessing LV function.

Alternative Methods for Handling Iron (Fe) Deficiency of Oil Palms (Elaeis guineensis Jacq.) Grown in Sandy Soil

Iron (Fe) deficiency is one of the general problems that occur in Oil Palms (Elaeis guineensis Jacq.) which are planted on sandy soil. Fe is an important micronutrient in enzyme activities, including cytochromes in the process of electron transfer, chlorophyll synthesis, and in maintaining the chloroplast structure. The research was conducted in 2 series of field observations. The first observation was conducted on the application of clay mineral soils (Plinthic Kanhapludults and Lithic Dystrudepts) in a silt pit, while the second observation was on the application of liquid nano-sized Fe fertilizer through root infusion and trunk injection methods, in addition, Fe chelated fertilizer application were also observed. The observation variable was the level of Fe in leaves 17th. The results showed all treatments significantly increased levels of Fe in leaves with different plant response rates. Root infusion treatment shows a faster response than clay mineral soil treatment, namely at 2 - 4 months after application (50.3 ppm and 38.2 ppm). Meanwhile, clay mineral soil treatment has a slower plant response rate on increasing Fe level in the leaves with a significant difference against the control, precisely at 24 months after application. Normally, regular Fe fertilization is enough to fulfill plant nutrient needs. Application of Fe through trunk injection or root infusion can be an alternative treatment for short-term use, whereas the application of clay mineral (iron-rich) soil can be optional for long-term needs.

Digital Microscopy Augmented by Artificial Intelligence to Interpret Bone Marrow Samples for Hematological Diseases.

Analysis of bone marrow aspirates (BMAs) is an essential step in the diagnosis of hematological disorders. This analysis is usually performed based on a visual examination of samples under a conventional optical microscope, which involves a labor-intensive process, limited by clinical experience and subject to high observer variability. In this work, we present a comprehensive digital microscopy system that enables BMA analysis for cell type counting and differentiation in an efficient and objective manner. This system not only provides an accessible and simple method to digitize, store, and analyze BMA samples remotely but is also supported by an Artificial Intelligence (AI) pipeline that accelerates the differential cell counting process and reduces interobserver variability. It has been designed to integrate AI algorithms with the daily clinical routine and can be used in any regular hospital workflow.

Two is better than one: longitudinal detection and volumetric evaluation of brain metastases after Stereotactic Radiosurgery with a deep learning pipeline.

Close MRI surveillance of patients with brain metastases following Stereotactic Radiosurgery (SRS) treatment is essential for assessing treatment response and the current disease status in the brain. This follow-up necessitates the comparison of target lesion sizes in pre- (prior) and post-SRS treatment (current) T1W-Gad MRI scans. Our aim was to evaluate SimU-Net, a novel deep-learning model for the detection and volumetric analysis of brain metastases and their temporal changes in paired prior and current scans. SimU-Net is a simultaneous multi-channel 3D U-Net model trained on pairs of registered prior and current scans of a patient. We evaluated its performance on 271 pairs of T1W-Gad MRI scans from 226 patients who underwent SRS. An expert oncological neurosurgeon manually delineated 1,889 brain metastases in all the MRI scans (1,368 with diameters > 5mm, 834 > 10mm). The SimU-Net model was trained/validated on 205 pairs from 169 patients (1,360 metastases) and tested on 66 pairs from 57 patients (529 metastases). The results were then compared to the ground truth delineations. SimU-Net yielded a mean (std) detection precision and recall of 1.00±0.00 and 0.99±0.06 for metastases > 10mm, 0.90±0.22 and 0.97±0.12 for metastases > 5mm of, and 0.76±0.27 and 0.94±0.16 for metastases of all sizes. It improves lesion detection precision by 8% for all metastases sizes and by 12.5% for metastases < 10mm with respect to standalone 3D U-Net. The segmentation Dice scores were 0.90±0.10, 0.89±0.10 and 0.89±0.10 for the above metastases sizes, all above the observer variability of 0.80±0.13. Automated detection and volumetric quantification of brain metastases following SRS have the potential to enhance the assessment of treatment response and alleviate the clinician workload.

Implementasi Algoritma Centroid Linkage dan K-Medoids dalam Mengelompokkan Kabupaten/Kota di Sulawesi Selatan Berdasarkan Indikator Pendidikan

Cluster analysis is a multivariate analysis technique that aims to cluster the observational data or variables into clusters in such a way that each cluster is homogeneous according to the factors used for clustering. This study used the Centroid linkage algorithm that was useful for forming groups based on the distance between centroids and the K-Medoids algorithm that was based on the use of the most centered object (medoid) to group districts/cities and obtained comparison results based on the education indicator data in South Sulawesi. The implementation of the Centroid Linkage Algorithm and K-Medoids on the education indicator data in South Sulawesi in 2018, showed that the grouping of districts/cities in South Sulawesi produced 2 clusters with cluster 1 of 21 districts/cities, and cluster 2 of 3. To determine the best method, it was seen from the value of the Standard Deviation ratio in the cluster 〖(S〗_W) and Standard Deviation between Clusters 〖(S〗_B) showed the same standard deviation ratio (S) in the Centroid Linkage algorithm and K-Medoids that was equal to 104,967.

Teach to comply? Evidence from a taxpayer education program in Rwanda

AbstractThere is virtually no evidence on the role of taxpayer education to improve tax compliance. We address this gap by providing the first evaluation of a taxpayer education program on compliance behavior, as well as taxpayer knowledge and perceptions. Using a unique dataset of administrative and survey data, we show that training new taxpayers leads to a large and significant improvement on three compliance outcomes: the probability to file declarations, to report a zero-tax amount, and tax due. These results are robust to three estimation strategies: a simple regression exploiting baseline balance across a wide set of observable variables, propensity score matching, and an IV strategy using random assignment to take part in our survey. We identify reduced compliance costs as the key mechanism at play in explaining the relationship between tax education and compliance—particularly increased knowledge and better perceptions on complexity. The effects we document persist over time, beyond the year of implementation.

The significant influence of the Atlantic multidecadal variability to the abrupt warming in Northeast Asia in the 1990s

Northeast Asia experienced unprecedented abrupt warming in the 1990s since the last century. Based on a robust time series and rank frequency evaluation, the Max Planck Institute for Meteorology Grand Ensembles of CMIP5 (MPI-GE5), CMIP6 (MPI-GE6), EC-Earth3 and IPSL-CM6A-LR were identified as the models that best simulate the external forcing and internal variability in observations and represent observations most adequately. The negative-to-positive phase transition of the Atlantic multidecadal variability (AMV), combined with the external forcing, can explain 88% [60%−111%] of the 1990s warming. With prescribed anthropogenic emissions in the near future, a phase shift in the AMV to +2 (-2) standard deviation will amplify (weaken) the warming over Northeast Asia by 37% [29%−49%] (19% [15%−25%]). This highlights the importance of natural climate variability in Northeast Asia’s government decision-making and risk management, and emphasizes that only climate models with an adequate representation of forced warming can quantify these contributions correctly.

Digital assessment of speech in Huntington disease.

Speech changes are an early symptom of Huntington disease (HD) and may occur prior to other motor and cognitive symptoms. Assessment of HD commonly uses clinician-rated outcome measures, which can be limited by observer variability and episodic administration. Speech symptoms are well suited for evaluation by digital measures which can enable sensitive, frequent, passive, and remote administration. We collected audio recordings using an external microphone of 36 (18 HD, 7 prodromal HD, and 11 control) participants completing passage reading, counting forward, and counting backwards speech tasks. Motor and cognitive assessments were also administered. Features including pausing, pitch, and accuracy were automatically extracted from recordings using the BioDigit Speech software and compared between the three groups. Speech features were also analyzed by the Unified Huntington Disease Rating Scale (UHDRS) dysarthria score. Random forest machine learning models were implemented to predict clinical status and clinical scores from speech features. Significant differences in pausing, intelligibility, and accuracy features were observed between HD, prodromal HD, and control groups for the passage reading task (e.g., p < 0.001 with Cohen'd = -2 between HD and control groups for pause ratio). A few parameters were significantly different between the HD and control groups for the counting forward and backwards speech tasks. A random forest classifier predicted clinical status from speech tasks with a balanced accuracy of 73% and an AUC of 0.92. Random forest regressors predicted clinical outcomes from speech features with mean absolute error ranging from 2.43-9.64 for UHDRS total functional capacity, motor and dysarthria scores, and explained variance ranging from 14 to 65%. Montreal Cognitive Assessment scores were predicted with mean absolute error of 2.3 and explained variance of 30%. Speech data have the potential to be a valuable digital measure of HD progression, and can also enable remote, frequent disease assessment in prodromal HD and HD. Clinical status and disease severity were predicted from extracted speech features using random forest machine learning models. Speech measurements could be leveraged as sensitive marker of clinical onset and disease progression in future clinical trials.

Chemical And Organoleptic Quality Of Suction Pudding With Variations In The Type And Concentration Of Carrot Extract (Daucus carota L.)

Carrots are one type of vegetable that is rich in vitamin A. In order to increase carrot consumption, it is necessary to diversify carrot-based preparations, among others, by adding carrots to the pudding suction. This study aims to determine the effect of the interaction of the type and concentration of wotel extract on the chemical and organoleptic quality of lipo pudding as functional food. The research method used is experimental laboratory with Complete Random Design (RAL) research design which is arranged factorially with 2 (two) factors. The first factor is the type of carrot extract, namely boiled carrot extract and fresh carrot extract. The second factor is the difference in carrot extract concentration, which is 7%, 10% and 13% carrot extract. Each treatment factor was repeated 3 (three) times resulting in 18 trials. Observational variables include β-carotene tests, crude fiber tests, reduction sugar tests, and organoleptic tests. Based on the results of the study showed that the interaction between different types and carrot extracts had no real effect on β-carotene levels, crude fiber levels, sugar reduction levels. The best treatment in this study was the F2B treatment, namely suction pudding with the addition of fresh carrot extract with a concentration of 10% with the highest Yield Value (NH) of 0.87. The criteria for the parameters of the study were β-carotene content = 983.0 ppm, crude fiber content = 2.1%, reduced sugar content = 14.37%, color = 5.05 (somewhat like), taste = 5.07 (somewhat like), aroma = 4.67 (somewhat like), and chewiness = 4.71 (somewhat like).

Longitudinal vibration estimation of a mine hoist using a hybrid signal fusion method combining UKF, ND and improved DE

The monitoring of cage longitudinal vibration can directly indicate the operational status of mine hoists. However, it is always challenging to collect the sensor signals of moving cages with high dynamic characteristics in real time from complex working environments using traditional monitoring methods. In this study, a more practical hybrid signal fusion approach is proposed to realize estimation of cage longitudinal vibration from a low sampling rate acceleration acquisition signal and a low cost encoder signal for state estimation. A nonlinear differentiator is applied to extract encoder differential signals and expand observation variables. An unscented Kalman observer based on nonlinear mine hoist model is designed to estimate the unknown state. To overcome the influence of the uncertain parameters, an improved differential evolution (DE) algorithm combining parameter adaptive method, reverse learning competition scheme and multiple parallel populations strategy is proposed to find unknown parameters of the observation model and autotune the parameters of the algorithms by using low sampling rate acceleration. Sensor data of the simulated experiment platform were collected and processed by the xPC system to validate the effectiveness of the proposed strategy. The experimental results showed that the improved DE (IDE) algorithm had a faster mean time for parameter tuning and the smallest fitness value compared to the standard DE, the particle swarm optimization algorithm and the genetic algorithm. Moreover, the longitudinal vibration estimation system, after parameter tuning by the IDE optimization algorithm, could achieve the purpose of signal estimation, with a smaller estimation error and a better estimation effect.

How can organizations measure the integration of environmental, social, and governance (ESG) criteria? Validation of an instrument using item response theory to capture workers' perception

AbstractEvaluation of corporate sustainability parameters has become part of organizational management. Item Response Theory (IRT) serves as a valuable approach for quantifying and exploring observable variables associated with these parameters. Therefore, this article aims to propose, apply, and validate an instrument utilizing IRT to measure environmental, social, and governance (ESG) impacts on corporate sustainability performance, using the perspective of workers. Workers were chosen as key stakeholders for the present study, as they were pointed by previous research as central to corporate sustainability decisions. The present paper addresses a literature gap by introducing, implementing, and validating a research instrument, along with a comparative scale, to assess the effects of integrating ESG criteria on corporate sustainability performance. Workers of two major Brazilian electricity sector companies were respondents for this instrument. Multigroup IRT method was used to assess item discrimination and difficulty levels, and to construct a scale reflecting workers' perceptions of integrating ESG criteria. The questionnaire consisted of 15 items, demonstrating Content Validity Coefficients (CVC) above .97, confirming the instrument's content effectiveness. Cronbach's Alpha and McDonald's Omega were greater than .94 for both companies. Questionnaire items demonstrated satisfactory discrimination ability and varying difficulty levels, resulting in a five‐level scale to gauge the impacts of ESG criteria. This research contributes through the introduction and validation of the research instrument, the latent trait assessment scale, and the evaluation of workers' perceptions regarding ESG criteria impacts on corporate sustainability performance. In conclusion, the proposed instrument showed robust psychometric properties, displaying favorable reliability and validity parameters. It effectively measures the impacts of ESG criteria, providing interpretable levels aligned with the unique characteristics of its constituent items

The relationship between work period and hearing disorder caused by noise in workers of PT. Kayu Perkasa Raya

Noise Induced Hearing Loss (NIHL) is sensorineural deafness caused by hearing threshold values in the workplace. According to the Regulation of the Minister of Manpower and Transmigration Number 13 of 2011 it regulates the threshold and chemical factors. The threshold value is 85 dB with 8 hours/day noise exposure time. This study aimed to determine the relationship between work period and the incidence of NIHL in PT. Kayu Perkasa Raya. The type of research used was quantitative with observational analytic methods. Data was obtained from questionnaires and audiometric results. The research design used cross-sectional, a study between measurement and observation variables that were carried out simultaneously. The sampling technique was purposive sampling. The analysis used using chi-square. The distribution was based on a working period. During the working period of ≥ 5 years, there were 10 people (33.3%) who experienced NIHL, and 20 people (66.7%) who did not experience NIHL. During the working period &lt; 5 years, two people (11.1%) experienced NIHL, and 16 people (88.9%) did not experience NIHL (p=0.081). In conclusion, no significant relationship exists between the work period and NIHL at PT.Kayu Perkasa Raya.