Multiple Linear Regression Approach Research Articles

The Application of Reference Dose Prediction Model to Human Health Water Quality Criteria and Risk Assessment.

Oral reference dose (RfD) is a key parameter for deriving the human health ambient water quality criteria (AWQC) for non-carcinogenic substances. In this study, a non-experimental approach was used to calculate the RfD values, which explore the potential correlation between toxicity and physicochemical characteristics and the chemical structure of pesticides. The molecular descriptors of contaminants were calculated using T.E.S.T software from EPA, and a prediction model was developed using a stepwise multiple linear regression (MLR) approaches. Approximately 95% and 85% of the data points differ by less than 10-fold and 5-fold between predicted values and true values, respectively, which improves the efficiency of RfD calculation. The model prediction values have certain reference values in the absence of experimental data, which is beneficial to the advancement of contaminant health risk assessment. In addition, using the prediction model constructed in this manuscript, the RfD values of two pesticide substances in the list of priority pollutants are calculated to derive human health water quality criteria. Furthermore, an initial assessment of the health risk was performed by the quotient value method based on the human health water quality criteria calculated by the prediction model.

Pengajaran dan Pendapatan Industri untuk Meningkatkan Pandangan Internasional pada Universitas-Universitas di Indonesia

This study aims to provide empirical evidence on the influence of teaching, research, citation, and industry income variables on the international outlook of universities in Indonesia. The study uses a multiple linear regression approach, using data from the World University Ranking provided by The Times Higher Education. The sample consists of Indonesian universities listed in the World University Ranking during the period from 2019 to 2023. The results show a significant positive impact of teaching and industry income variables on the international outlook of universities. However, the study also reveals that research and citation variables do not have a significant impact on the international outlook of universities.

Evaluate the granite waste efficiency in the construction using statistical indicators

Due to the expansion of industrial operations globally in recent years, waste output has risen. So these wastes must be reduced by recycling and reusing to achieve environmentally friendly buildings and find various alternative materials in critical cases. The statistical indicators are used as practical study including Multiple linear regression (MLR) and artificial neural network (ANN) models. The study's goals were to assess the effectiveness of granite waste (GW) as a replacement for cement, sand, plastic, and binder in specific building applications and the relationships between MLR and ANN approaches. Results show the efficiency of adding granite waste to some construction stages and replacing it with cement in the mixture and examining its strength, it gave excellent results in addition to good results for its use as a binder in cement mortar, while the results were weak when used as a substitute for sand and plastic in insulator because it's classified as fine sand, Therefore, it cannot be used as a substitute for sand in the construction. The statistical models give an effective indicator to use GW as an alternative material ( binder and cement) based on the coefficient of correlation (R2) for the two models MLR and ANN equal to 83.4 % and 80 % respectively.

Performance of Statistical and Intelligent Methods in Estimating Rock Compressive Strength

This research was conducted to forecast the uniaxial compressive strength (UCS) of rocks via the random forest, artificial neural network, Gaussian process regression, support vector machine, K-nearest neighbor, adaptive neuro-fuzzy inference system, simple regression, and multiple linear regression approaches. For this purpose, geo-mechanical and petrographic characteristics of sedimentary rocks in southern Iran were measured. The effect of petrography on geo-mechanical characteristics was assessed. The carbonate and sandstone samples were classified as mudstone to grainstone and calc-litharenite, respectively. Due to the shallow depth of the studied mines and the low amount of quartz minerals in the samples, the rock bursting phenomenon does not occur in these mines. To develop UCS predictor models, porosity, point load index, water absorption, P-wave velocity, and density were considered as inputs. Using variance accounted for, mean absolute percentage error, root-mean-square-error, determination coefficient (R2), and performance index (PI), the efficiency of the methods was evaluated. Analysis of model criteria using multiple linear regression allowed for the development of a user-friendly equation, which proved to have adequate accuracy. All intelligent methods (with R2 > 90%) had excellent accuracy for estimating UCS. The percentage difference of the average of all six intelligent methods with the measured value was equal to +0.28%. By comparing the methods, the accuracy of the support vector machine with radial basis function in predicting UCS was (R2 = 0.99 and PI = 1.92) and outperformed all the other methods investigated.

A multiple linear regression approach to the estimation of carboxylic acid ester and lactone alkaline hydrolysis rate constants

ABSTRACT Pesticides, pharmaceuticals, and other organic contaminants often undergo hydrolysis when released into the environment; therefore, measured or estimated hydrolysis rates are needed to assess their environmental persistence. An intuitive multiple linear regression (MLR) approach was used to develop robust QSARs for predicting base-catalyzed rate constants of carboxylic acid esters (CAEs) and lactones. We explored various combinations of independent descriptors, resulting in four primary models (two for lactones and two for CAEs), with a total of 15 and 11 parameters included in the CAE and lactone QSAR models, respectively. The most significant descriptors include pK a, electronegativity, charge density, and steric parameters. Model performance is assessed using Drug Theoretics and Cheminformatics Laboratory’s DTC-QSAR tool, demonstrating high accuracy for both internal validation (r 2 = 0.93 and RMSE = 0.41–0.43 for CAEs; r 2 = 0.90–0.93 and RMSE = 0.38–0.46 for lactones) and external validation (r 2 = 0.93 and RMSE = 0.43–0.45 for CAEs; r 2 = 0.94–0.98 and RMSE = 0.33–0.41 for lactones). The developed models require only low-cost computational resources and have substantially improved performance compared to existing hydrolysis rate prediction models (HYDROWIN and SPARC).

Determinants of Human Resources Accounting Disclosure and their Impact on Financial Performance as Intervening Variables

The purpose of this study is to most of which indicate that human resource accounting disclosures have an influence on company performance, which refers to the research of al Mamun (2009) and Widodo (2014). Seeing that there are not many studies in Indonesia that examine the disclosure practices of human resource accounting in Indonesian companies and their relationship to company performance, it is very appropriate to conduct further research in Indonesia and contribute to novelty to this research. Research that can provide empirical evidence to practitioners and regulators that human resource accounting has great benefits for companies on financial performance. This research is a descriptive quantitative study with the type of describing the factors that affect the level of human resource accounting disclosure. The data used is secondary data from annual financial reports (annual reports) published on the Indonesia Stock Exchange (IDX) through the website www.idx.co.id in the 2017-2018 period. The population that is the object of this research is manufacturing and financing companies listed on the IDX in 2017-2018. The sample data used in this study were 113 manufacturing and financial companies listed on the Indonesia Stock Exchange (IDX) from 2017-2018. The statistical analysis used is the multiple linear regression approach.

International Trade and its Impact on Global Economic Development

The benefit of international trade had not been noticed in the economic growth of Nigeria because some of the goods imported into the country were those that can cause damage to local industries by rendering their products inferior and being neglected, thereby reducing the growth rate of output of such industries which later spread to the aggregate economy. The work investigated the extent to which international trade impacted global economic development with particular reference to the Nigerian economy between 1986 and 2018. The data employed for this study are annual time series data covering 1986-2018. The data were obtained from the World Bank data outlook and the central bank of Nigeria statistical bulletin. This study adopts the statistical method of multiple linear regression approach using ordinary least squares to examine the relationship between RGDP as a dependent variable and degree of openness, foreign exchange rate, and interest rate as independent variables. The paper revealed that a relationship exists between international trade and economic growth and that while some components of international trade exerted a positive and significant effect on growth, INTR exerted a positive but insignificant effect. The result further shows that all the regressors except interest rates were statistically significant at a 5% level of significance. Some policy recommendations which would be helpful and applicable to the Nigerian economy were suggested. For the degree of openness, Nigeria should adopt more policies on trade liberalization like reducing non-tariff barriers, reducing barriers, and eliminating quotas that will enable the economy to grow at a spectacular rate. The finding for exchange rate implies that the policymakers should adopt long-term policies because, in the long run, a strong currency depends on economic fundamentals. To have a strong exchange rate, countries will need a combination of a low inflation rate, productivity growth, and economic and political stability.

Modeling of Aquatic Toxicity of a Set of Phenols in Silico

A structure / lethal dose 50 (pCIC50) relationship was researched for a set of phenols while favoring a hybrid genetic algorithm (GA) / multiple linear regression (MLR) approaches to the structural parameters being computed with (E-calc) which calcula the Kier–Hall Electrotopological state indices (E- state) and Hyperchem software. Among the more than 100 simple models with two explanatory variables acquired, we chose the model with the best values of the prediction parameter (Q2) and the coefficient of determination (R2). The reliability of the proposed model has also been illustrated using various techniques of evaluation: leave-many out, cross-validation, randomization test, and validation by the test set. pCIC50 = - 0.0835 ± (0.07006) +0.112 ± (0.007408 (logkow)2 - 0.116 ± (0.01797) s-CH3 ntot = 81 ; S= 0.3296 log unit ; Q2(%) = 74.26 ; R2 (%)= 79.24 ; F= 118.3193; P=0,000.

Modeling of COVID‐19 death rate using various air pollutants: A multiple linear regression approach

AbstractAir pollution is a significant health risk, especially for vulnerable populations such as children, people with chronic illnesses, the elderly, and the economically and socially disadvantaged. Furthermore, air pollution has enormous social costs that we all bear in the form of premature deaths, low productivity, sick leave, and other strains on the healthcare system. The primary sources of air pollution are traffic, home fires, and industry. Measuring NO2 levels in air pollution reveals the extent of pollution caused by traffic, particularly diesel vehicles, which are the primary source of NO2. COVID‐19 rates are rising in areas with high levels of air pollution, according to mounting evidence. Toxic contaminants can make people more susceptible to COVID‐19. The causal relationship between air pollution and COVID‐19 cases has yet to be established, but experts warn that long‐term exposure will undoubtedly make people more susceptible to lung infections. Air pollution has been linked to an increase in cancer, heart disease, stroke, diabetes, asthma, and other comorbidities by inducing cellular damage and inflammation throughout the body. All of these factors increase the risk of death in COVID‐19 patients. As a result, air quality parameters must be predicted and monitored. To predict results, this study proposes a statistical‐based machine learning approach. Using multiple linear regression (MLR), Decision Tree (D.T.), and Random Forest (R.F.), the experimental results achieved 80%, 73%, and 65% accuracy on the dataset, respectively.

Evaluation of pedotransfer functions to estimate some of soil hydraulic characteristics in North Africa: A case study from Morocco

Soil hydraulic properties are an important factor to optimize and adapt water management for a given crop. Pedotransfer functions (PTFs) present a solution to predict soil variables such as hydraulic properties, using fundamental soil properties. In this research, we compared two sources of soil information: iSDAsoil data and field data, in four regions in Morocco. We then used this data to evaluate published data and developed new PTFs using soil information to estimate soil gravimetric moisture content at saturation (w0), field capacity (w330) and permanent wilting point (w15000). A total of 331 samples were collected from four regions: Doukkala, Gharb-Loukous, Moulouya and Tadla. The data was divided into calibration and validation datasets. For development of different PTFs, we used simple linear regression, multiple linear regression, regression tree, Cubist algorithm, and random forest approaches. PTFs developed by Dijkerman (Geoderma, 1988, 42, 29–49) presented the best performance, showing lower RMSE, Bias and MAE compared to other PTFs. Using multiple linear regression to develop PTFs, models based on clay, silt and soil organic matter as input variables showed the best performance after calibration (R2 of 0.590, 0.785, 0.786 for w0, w330, and w15000, respectively). Regarding the techniques based on machine learning, random forest showed the best performance after calibration compared with other algorithms (R2 of 0.930, 0.955, 0.954 for w0, w330, and w15000, respectively). PTFs represent a low cost and easy technique to estimate soil hydraulic properties, to improve water management efficiency for the farmers.

Degradability of organic micropollutants with sonolysis—Quantification of the structural influence through QSPR modelling

Local and climate-driven challenges combined with an increasing anthropogenic pollution of the water compartment all around the world make a sustainable handling of wastewater imperative. New additional treatment methods are under examination, including cavitation-based advanced oxidation processes. To quantify structural influences on chemical processes, quantitative structure-property relationship (QSPR) modelling can be used, which calculates a correlation between a defined endpoint and structural properties expressed by molecular descriptors. In this study, QSPR modelling has been applied to investigate the structural influence on the degradability of organic micropollutants with high-frequency sonolysis. The dataset of a previous study on 32 phenol derivates was expanded by 60 mostly aromatic compounds, whose kinetic degradation constants were obtained in a standardized experimental setup. QSPR modelling was conducted using the software PaDEL for descriptor calculation and QSARINS for the modelling process using a multiple linear regression approach and genetic algorithm. All five OECD-requirements for applicable QSPR models were respected. The obtained model included 12 model descriptors, was evaluated with numerous statistical quality parameters, and shows good regression abilities as well as robustness and predictability (R2 = 0.8651, CCCtr = 0.9277, Q2loo = 0.8010, R2ext = 0.7836, CCCext = 0.8838, Q2F1 = 0.7697). The interpretation of selected model descriptors showed interesting connections between the model results and the experimental background. A strong influence of the polarity of organic compounds on their degradability with high-frequency sonolysis could been quantified, as more nonpolar molecules are degraded faster. Additionally, the impact of specific fingerprints, including for example substituents with heteroatoms, the number of fused and non-fused aromatic rings as well as the numerical appearance of secondary carbon could be identified as relevant for this cavitation-based treatment method.

Ecotoxicological QSAR modelling of the acute toxicity of fused and non-fused polycyclic aromatic hydrocarbons (FNFPAHs) against two aquatic organisms: Consensus modelling and comparison with ECOSAR

Fused and non-fused polycyclic aromatic hydrocarbons (FNFPAHs) are a type of organic compounds widely occurring in the environment that pose a potential hazard to ecosystem and public health, and thus receive extensive attention from various regulatory agencies. Here, quantitative structure-activity relationship (QSAR) models were constructed to model the ecotoxicity of FNFPAHs against two aquatic species, Daphnia magna and Oncorhynchus mykiss. According to the stringent OECD guidelines, we used genetic algorithm (GA) plus multiple linear regression (MLR) approach to establish QSAR models of the two aquatic toxicity endpoints: D. magna (48 h LC50) and O. mykiss (96 h LC50). The models were established using simple 2D descriptors with explicit physicochemical significance and evaluated using various internal/external validation metrics. The results clearly show that both models are statistically robust (QLOO2 = 0.7834 for D. magna and QLOO2 = 0.8162 for O. mykiss), have good internal fitness (R2 = 0.8159 for D. magna and R2 = 0.8626 for O. mykiss and external predictive ability (D. magna: Rtest2 = 0.8259, QFn2 = 0.7640∼0.8140, CCCtest = 0.8972; O. mykiss:Rtest2 = 0.8077, QFn2 = 0.7615∼0.7722, CCCtest = 0.8910). To prove the predictive performance of the developed models, an additional comparison with the standard ECOSAR tool obviously shows that our models have lower RMSE values. Subsequently, we utilized the best models to predict the true external set compounds collected from the PPDB database to further fill the toxicity data gap. In addition, consensus models (CMs) that integrate all validated individual models (IMs) were more externally predictive than IMs, of which CM2 has the best prediction performance towards the two aquatic species. Overall, the models presented here could be used to evaluate unknown FNFPAHs inside the domain of applicability (AD), thus being very important for environmental risk assessment under current regulatory frameworks.

The Effect of Ease of Use, Product Quality, and Service Quality of SmartQ Queueing Services on Users’ Satisfaction

The present research study is aimed to evaluate the influential and general description of ease of use (EoU), product quality (PQ), and service quality (SQ) on users' satisfaction in SmartQ queuing service providers. The research method is quantitative research with an explanatory research paradigm. Three hypotheses are proposed. The subjects used from the survey research were 45 respondents. The eligible respondents are the ones who are partners of SmartQ services. The analysis presented in the current study has consisted of a detailed descriptive evaluation as well as a multiple linear regression approach. The findings revealed in this study indicate that the ease of use of SmartQ services affects the satisfaction of SmartQ partner users. The quality of SmartQ service products affects the satisfaction of SmartQ partner users. The three proposed hypotheses are proven to be positive and significant. The overall model shows the representation of R2 of 84.4%. The 15.6% is affected by potential outside variables not included in this study.

The Development of Empirical Correlations to Understand the Frictional Behavior of Aqueous Biomass Slurry Flows in Vertical Pipes

Highlights The frictional pressure drop correlation of agricultural residue-water slurry flows in vertical pipes is developed. Multiple linear regression with the backward elimination method was used in RStudio to obtain the optimal model. Some regression coefficients differ for different types of biomass feedstocks. The predicted pressure drops agree well with the experimental data within a 95% CI. Empirical models for the onset velocity of drag reduction of different particle sizes of biomass are proposed. Abstract. Large-scale biofuel production at levels equivalent to conventional oil refineries using long-distance pipeline hydro-transport of biomass can be a cleaner alternative to fossil fuels when it comes to economics and traffic congestion associated with the overland transportation of biomass. The transport of aqueous slurries of several saturated mass concentrations (5%-40%) and four particle sizes (from <3.2-19.2 mm) of two types of agricultural residue biomass (ARB) feedstock (corn stover and wheat straw) was studied through a vertical test section of a 29 m long, 50 mm diameter closed circuit pipeline facility, and frictional pressure drops were recorded at different flow rates (0.5-4.3 m s-1). A framework was developed in RStudio (4.0.5) to analyze the experimentally obtained frictional pressure drops of biomass slurries through a multiple linear regression approach using a backward elimination method and Akaike information criterion. An empirical model was proposed to predict slurry frictional pressure drop in terms of slurry velocity, slurry solid mass concentration, particle aspect ratio, and feedstock type. The model satisfactorily predicted the frictional pressure drops of both feedstocks of biomass-water slurry flows through pipes within a 95% confidence interval. The correlations introduced for onset velocities of drag reduction in terms of slurry solid mass concentrations seemed helpful to interpret the transition points of the corresponding slurries in vertical upward flows through pipes. The empirical correlation developed in this research could help select biomass slurry pumps and pipe dimensions when designing a typical long distance pipeline network for biofuel production at the commercial level. Keywords: Agricultural biomass wastes, Frictional loss prediction, Numerical model, Onset velocity correlation, Regression coefficients, Upward pipe flow.

Estimation of Higher Heating Value for MSW Using DSVM and BSOA

In recent decades, the generation of Municipal Solid Waste (MSW) is steadily increasing due to urbanization and technological advancement. The collection and disposal of municipal solid waste cause considerable environmental degradation, making MSW management a global priority. Waste-to-energy (WTE) using thermochemical process has been identified as the key solution in this area. After evaluating many automated Higher Heating Value (HHV) prediction approaches, an Optimal Deep Learning-based HHV Prediction (ODL-HHVP) model for MSW management has been developed. The objective of the ODL-HHVP model is to forecast the HHV of municipal solid waste, based on its oxygen, water, hydrogen, carbon, nitrogen, sulphur and ash constituents. In addition, the ODL-HHVP model contains a Deep Support Vector Machine (DSVM) regression component that can accurately predict the HHV. In addition, the Beetle Swarm Optimization (BSO) method is utilised as a hyperparameter optimizer in conjunction with the DSVM model, resulting in the highest HHV prediction accuracy. A comprehensive simulation study is conducted to validate the performance of the ODL-HHVP method. The Multiple Linear Regression (MLR), Genetic Programming (GP), Resilient backpropagation (RP), Levenberg Marquardt (LM) and DSVM approaches have attained an ineffective result with RMSEs of 4.360, 2.870, 3.590, 3.100 and 3.050, respectively. The experimental findings demonstrate that the ODL-HHVP technique outperforms existing state-of-art technologies in a variety of respects.

Modelling of Reference Evapotranspiration using Neural Network and Regression Approaches for Semi-humid Region of Sikkim

This study compared the applicability of multiple linear regression (MLR) and artificial neural network (ANN) approaches for estimating the weekly reference evapotranspiration (ET0) in a semi-humid area of Tadong in Gangtok district of Sikkim state in India. Daily meteorological data (1991 - 2020) were used for developing MLR and ANN model using various combinations of meteorological data. Predicted ET0 values were compared with the FAO-56 Penman Monteith (PM) equation estimated ET0. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and Nash Sutcliffe efficiency (NSE) were used to assess the performance of MLR and ANN models. For ANN model, one hidden layer and four neurons was found as the best ANN architecture. ANN-4 model with inputs parameters of maximum air temperature (Tmax), minimum air temperature (Tmin), maximum relative humidity (RHmax), minimum relative humidity (RHmin), and sunshine hours (SSH) had the smallest RMSE (0.609 mm.day-1 and 0.880 mm.day-1), MAE (0.473 mm.day-1 and 0.551mm.day-1) and the highest R2 (0.752 and 0.657) and NSE (0.782 and 0.696) during training and testing phases, respectively. The MLR-4 model with same combination of input parameters (Tmax, Tmin, RHmax, RHmin, SSH) resulted in smallest RMSE (0.704 mm.day-1 and 0.714 mm.day-1), MAE (0.562 mm.day-1and 0.583 mm.day-1); and the highest R2 (0.679 and 0.602) and NSE (0.708 and 0.597) during training and testing phase, respectively. Study also showed increase in model performance of ANN and MLR models with increase in number of meteorological parameters. All the ANN models gave comparable results, but ANN 4 model resulted in higher prediction accuracy for the sub-humid region of Tadong in Gangtok district of North-east India. The developed ANN-4 model could be helpful for preparing irrigation schedules and planning, and management of water resources in the data scarce northeast region of India and other identical climatic regions.

A robust hybrid methodology between applied linear regression model (alrm) and multilayer perceptron (mlp)

Background: The goal of this study is to illustrate an optimum variable selection method using established Multiple Linear Regression (MLR) models and to validate the variable using Multilayer Perceptron Neural Network (MLP) models. Initially, all selected variables will be passed through the bootstrap methodology, and they were screened for significant relationships. Objective: The goal of this work is to analyze and construct a model for the factor linked with total crime cases by combining an Applied Linear Regression Model (ALRM) and a Multilayer Perceptron (MLP). Material and Methods: Around 200 data was simulated to build the methodology. Advanced computational statistical modeling methodologies were used to evaluate data descriptions of several variables in this retrospective study, including the total victim, gender, age, marital status, social class, adult in the household, children in household, burglary’s victim, sexual’s victim, victim’s report, and household location. The case study was developed and implemented using the R-Studio program and syntax. Results: The statistical method demonstrated that regression modeling surpasses R-squared and mean square error test in most situations. Researchers observed that when data is divided into two datasets for training and testing, the hybrid model approach performs significantly better at predicting the experiment’s outcome. When it came time to determine variable validity, the well-established bootstrap-integrated MLR approach was applied. Ten characteristics are taken into consideration in this case: Gender (: -0.4369700; p< 0.25), age (: -0.0086757; p< 0.25), marital status (: 0.2646097; p< 0.25), social class ( : 0.0602540; p< 0.25), adult in household (: -0.0211293; p> 0.25), children in household (: -0.0025346; p> 0.25), burglary’s victim (: 1.3473593; p< 0.25), sexual’s victim (: 1.0382444; p< 0.25), victim’s report (: -0.3176104; p< 0.25), and location of household (: -0.1355046; p< 0.25).There is a 0.07745823 MSE for the linear model in this scenario. Conclusion: The neural network’s Predicted Mean Square Error (PMSE) was used to assess MLP’s performance (MSE-forecasts the Network). PMSE is used to determine how far our projections are from the actual data, and the lowest MSE from the MLP indicates the best achievement. The R syntax for MLR and MLP is also included in this research article.As a result, the study’s conclusion establishes the superiority of the hybrid model technique. Bangladesh Journal of Medical Science Vol. 22 No. 01 January’23 Page : 38-46

Analisis strategi experiential marketing dalam mempengaruhi keputusan pembelian ulang minuman Staykult di Surakarta

This study aims to find out what experiential marketing factors influence repurchasing decisions on sales of Staykult drinks in Surakarta and which experiential marketing factors are the most significant. The method used in this study is quantitative, with a multiple linear regression approach. The study results show that all Sense-Feel-Think-Act-Relate factors influence the repurchase decision. At the same time, the sense is the experiential marketing factor with the most substantial influence. The Staykult beverage shop stimulates the consumer's five senses by providing a taste in the form of drinks and food with a distinctive and high-quality taste, very comfortable room interiors, cleanliness, and excellent comfort. With fast service and the friendliness of the servers provided, customers feel comfortable in the shop to hang out with relatives for a long time, as a feeling that customers receive.

Analisis Aspek Keperilakuan Terhadap Penerapan Sistem Akuntansi Persediaan Pada Dinas PUPRD Provinsi Sulut

The purpose of this research was to determine the effect of behavioral aspects of attitudes, perceptions, and emotions on the application of the inventory accounting system at the Department of Public Works and Spatial Planning of North Sulawesi Province. The method used in this research is a quantitative method with multiple linear regression approach. The population of this research is the employees of the Department of Public Works and Regional Spatial Planning of the Province of North Sulawesi. While the research sample used by the researcher was 14 respondents. The results showed that the behavioral aspect of the attitude had no effect with a value of 0.078 on the implementation of the inventory accounting system. The behavioral aspect of perception has no effect with a value of 0.555 on the application of the inventory accounting system. Aspects of emotional behavior have no effect with a value of 0.714 on the implementation of the inventory accounting system. This is because the Department of Public Works and Spatial Planning of the Province of North Sulawesi has a good culture where this culture is the basis for employees to work based on standard operating procedures that have been set so that there is no opportunity to raise debates in aspects of behavior, attitudes, perceptions and emotions. The R Square value is 0.312 or 31.2%. This figure shows that the contribution of the behavioral aspects of attitudes, perceptions and emotions to explain the variables of the application of the inventory accounting system.

Evaluating self-reported retrospective average daily fruit, vegetable, and egg intake: Trustworthy-Sometimes!

Retrospective self-reports are commonly used to assess dietary intake. Yet, their use is criticized as it is unclear whether the underlying assumptions for valid self-reports are met: Individuals have to consider the behavior of all days in the retention interval and weigh the behavior of all days equally. This study examines whether these assumptions for retrospective self-reports are met and whether interindividual differences in self-report performance are relevant regarding these assumptions. Ninety-two participants aged 18-61 years participated in seven sequential 24-h recalls and one retrospective 7-day recall concerning their intake of fruit, vegetables, and eggs. A multiple linear regression approach was used to examine the relation between the daily reported dietary intake and the 7-day recall. In the overall sample, the requirements for retrospective self-reports were not tenable. Distinguishing good and poor self-reporters based on a rational criterion showed that the requirements can be taken as given for good self-reporters, whereas poor self-reporters base their retrospective self-reports mostly on recency effects. The underlying requirements for retrospective self-reports appear to be met in two thirds of the sample, supporting the use of retrospective self-reports to capture dietary behavior. Future research should investigate characteristics separating good from poor self-reporters.