R-squared Research Articles

The Impact of Human Resource Management Practices on Public Institutions’ Performance: Case of Muhanga District, Rwanda

This research set out to examine the impact of human resource management practices on organizational performance, with a case study of Muhanga District in Rwanda. This study was guided by the Human Capital Theory and the Abilities, Motivation, and Opportunity (AMO) Theory. For this study, 243 participants were chosen at random from 20 public organizations. The inquiry was conducted using an explanatory research design. Employees at all levels of management were surveyed using a five-point Likert scale. The data obtained were analyzed descriptively using frequencies and percentages, as well as inferentially using correlations and regressions. The findings show that human resource practices significantly impact organizational performance. Recruitment and selection (β = 0.827, p = 0.000), compensation and rewards (β = 0.695, p = 0.000), and training and development (β = 0.698, p = 0.000) all show positive effects on performance. The R-squared value of 0.7942 indicates that 79.42% of the variation in performance is explained by these practices. Correlation analysis also reveals strong positive relationships between HR practices and organizational outcomes. Control variables, education (p = 0.046) and marital status (p = 0.035), are statistically significant. It is thus evident that training and development, compensation and rewards, and selection and recruitment are three separate factors that have considerable impacts on public organizational effectiveness. Therefore, it was concluded that institutions in Rwanda should prioritize government entities and other organizations consistently investing in their employees' professional growth through training and development programs. This study also recommended that organizations devise a continual incentive and pay program for their staff, with the Human Resources team being responsible for all hiring decisions.

Moderating Effect of Trust on Determinants of Customer Satisfaction Factors

In connection with customer satisfaction of mobile banking users, there are several problems, including being vulnerable to “smishing” fraud, one of which involves rogue mobile applications. This study aims to determine the role of trust in moderating the effect of security level, and service features on customer satisfaction of mobile banking users of Bank Sumsel Babel Syariah UIN Raden Fatah Palembang Branch Office. This type of research is quantitative using statistical techniques to test the hypothesis. The data used is primary data with data collection techniques using questionnaires distributed directly to 100 respondents as samples with accidental sampling techniques while secondary data uses literature studies. The data analysis technique in this study uses SmartPLS with outer model testing including convergent validity, average variance extracted (AVE) and composite reliability and inner model testing including R square and hypothesis testing. The results showed that based on testing convergent validity, average variance extracted (AVE) and composite reliability, it is known that all variables including their indicators are declared valid and reliable, then the R-square value is 0.579 which means that customer satisfaction can be explained by 57.9% by the variables of security level, service features and trust, while the remaining 42.1% is explained by other variables outside the study. Then the results of hypothesis testing show that the variables of security level, service features and trust directly have a positive and significant effect on customer satisfaction. Then the trust variable is unable to moderate the effect of the level of security and service features on customer satisfaction.

Pengaruh Literasi Keuangan, Inklusi Keuangan dan Financial Technology Terhadap Prilaku Keuangan pada Mahasiswa Universitas Bina Darma Palembang

This research aims to analyze the influence of Financial Literacy, Financial Inclusion, and Financial Technology on the Financial Behavior of postgraduate students at Bina Darma University. The main problem identified is how these three variables influence students' financial management and how much they contribute to their financial behavior. The method used was a quantitative approach with data analysis using SmartPLS, involving 227 respondents. The research results show that Financial Technology has the most significant influence on Financial Behavior, with a Path Coefficient of 0.337. Financial Literacy also makes a positive contribution with a Path Coefficient of 0.258, while Financial Inclusion has a positive effect but with a smaller contribution, namely a Path Coefficient of 0.157. The R-Square value for Financial Behavior is 0.411, which indicates that the independent variables explain 41% of the variation in student Financial Behavior. The validity and reliability test of the model shows that all variables have good validity and reliability, without multicollinearity problems. This research concludes that Financial Technology and Financial Literacy play a key role in influencing Financial Behavior, while Financial Inclusion has a smaller but still significant impact. Keywords: Financial Literacy, Inclusion, Fintech, Financial Behavior

Enhancing Prediction of Electron Affinity and Ionization Energy in Liquid Organic Electrolytes for Lithium-Ion Batteries Using Machine Learning

The electron affinity (EA) and ionization energy (IE) of battery electrolyte molecules are pivotal in determining the electrochemical properties of electrode materials, significantly impacting the performance and safety of batteries. In this study, we employed a suite of machine learning algorithms, including linear regression, gradient boosting, CatBoost, XGBoost, and random forest regression, to predict these properties. Our analysis leveraged a comprehensive dataset of 24432 electrolyte molecules extracted from the Joint Center for Energy Storage Research (JCESR) Molecules database, and used RDKit and Mordred tools to generate a series of molecular descriptors. Through meticulous feature engineering and selection using the maximum relevance minimum redundancy (mRMR) algorithm, we identified the most influential molecular features for predicting EA and IE. The CatBoost model emerged as the most accurate, outperforming other models with its ability to handle complex nonlinear relationships and provide robust predictions with lower errors. The model's predictions were further validated through 10-fold cross-validation, demonstrating its generalization capability and resistance to overfitting. The CatBoost model achieved a root mean squared error (RMSE) of 0.239 eV for IE and 0.427 eV for EA, with R-squared (R2) values of 0.944 and 0.879, respectively. SHapley Additive exPlanations (SHAP) value analysis elucidated the contribution of each feature to the model's predictions, highlighting the molecular characteristics that significantly influence EA and IE. Our findings offer valuable insights for the design of novel electrolyte materials with tailored electronic properties, advancing the development of high-performance battery technologies.

Pengaruh Kompensasi Manajemen dan Tingkat Utang Terhadap Manajemen Pajak Pada Perusahaan Industri Yang Terdaftar di BEI Tahun 2019 – 2023

This study aims to determine the effect of management compensation and debt levels on tax management in industrial companies listed on the Indonesia Stock Exchange (IDX) for the 2019-2023 period. The study uses a quantitative method with multiple regression analysis techniques through the Eviews 13 application. The sample used consisted of 13 companies that met the research criteria with a purposive sampling technique, namely the selection of samples based on certain criteria that are relevant to the research objectives. The results of the study indicate that management compensation has a negative and significant effect on tax management with a probability value of 0.0216 (<0.05). Conversely, the debt level has a positive and significant effect on tax management with a probability value of 0.0004 (<0.05). Simultaneously, the variables of management compensation and debt levels are able to explain 51.7% of the variation in tax management, as indicated by the adjusted R-squared value of 0.516880. The results of the F test also show that this research model is significant overall with a probability of 0.000001. These findings support agency theory which explains that managerial incentives and debt structure can influence tax management practices. This study provides implications that companies need to consider compensation policies and debt structures to optimize tax management efficiently and in accordance with regulations.

Using tree-based models to identify factors contributing to trait negative affect in adults

BackgroundIndividuals with high levels of negative affect (NA) are at an increased risk of experiencing distress and negative self-views. Theoretical models suggest that NA plays a critical role in psychopathology, particularly in Major Depressive Disorder (MDD), and is linked to cognitive-perceptual and affective regulation issues.ObjectiveDetermine whether maladaptive cognitive schemas, attributional style, childhood adversity, and lifestyle factors (including alcohol and drug use and physical activity) could effectively predict negative affect (NA) in adults.MethodsA secondary data analysis was performed on a sample of 342 depressed and non-depressed adults. Beta regression and regression tree analyses were conducted to identify the principal risk factors and their interactions. The regression tree model was trained with 5-fold cross-validation on 75% of the sample, with 25% of observations held for testing.ResultsThe findings revealed that the cognitive schemas of disconnection and rejection and impaired autonomy had a significant impact on the likelihood of higher scores on the State Depression Inventory (IDER) test (p < 0.001), as indicated by both beta regression and regression tree analyses. Additionally, childhood adversity emerged as a crucial factor in determining high levels of NA. The regression tree model achieved strong performance metrics, including an R-squared value of 0.77.ConclusionsThis study represents a significant step forward in the understanding of NA, as it considers a broad range of individual factors, such as cognitive schemas, lifestyle, and demographics, to predict its impact on NA, with potential implications for prevention programs aimed at reducing NA.

Evaluating Generalization of Methods for Artificially Generating NDVI from UAV RGB Imagery in Vineyards

High-resolution NDVI maps derived from UAV imagery are valuable in precision agriculture, supporting vineyard management decisions such as disease risk and vigor assessments. However, the expense and complexity of multispectral sensors limit their widespread use. In this study, we evaluate Generative Adversarial Network (GAN) approaches—trained on either multispectral-derived or true RGB data—to convert low-cost RGB imagery into NDVI maps. We benchmark these models against simpler, explainable RGB-based indices (RGBVI, vNDVI) using Botrytis bunch rot (BBR) risk and vigor mapping as application-centric tests. Our findings reveal that both multispectral- and RGB-trained GANs can generate NDVI maps suitable for BBR risk modelling, achieving R-squared values between 0.8 and 0.99 on unseen datasets. However, the RGBVI and vNDVI indices often match or exceed the GAN outputs, for vigor mapping. Moreover, model performance varies with sensor differences, vineyard structures, and environmental conditions, underscoring the importance of training data diversity and domain alignment. In highlighting these sensitivities, this application-centric evaluation demonstrates that while GANs can offer a viable NDVI alternative in some scenarios, their real-world utility is not guaranteed. In many cases, simpler RGB-based indices may provide equal or better results, suggesting that the choice of NDVI conversion method should be guided by both application requirements and the underlying characteristics of the subject matter.

Interpretable data-driven framework for life prediction of homogenous lead-free solder joints in ball grid array packages

Abstract Designing reliable electronic devices depends heavily on the reliability of solder joints. However, due to the ongoing advancement of novel lead-free solder materials, assessing their reliability for various applications, particularly in extreme conditions, often involves costly and time-consuming tests. Multiple factors, including board, package, assembly parameters, solder material properties, and process parameters, all affect the reliability of solder joints. Leveraging the available reliability test data as well as the simulated data, an interpretable data-driven framework is proposed by integrating linear mixed-effects (LME) models and artificial neural network (ANN) with sensitivity analysis to achieve accurate life predictions of solder materials and improve interpretability for understanding new lead-free materials. Multiple linear regression (MLR) and decision tree (DT) models have also been included for comparison. For thermal cycling (TC) tests, ANN, LME, and MLR achieve high prediction accuracy (R-squared values of 88.95%, 91.79%, and 90.07%, respectively), which are approximately 10%, 14%, and 13% higher than DT. Moreover, the interpretation derived from the random effects of LME and the sensitivity analysis of ANN, along with the insights from MLR and DT, successfully extract valuable information about the life of solder materials, which aligns with experimental testing results and empirical physics knowledge. For instance, it is found that the impact of aging duration on reliability depends on the configuration of the tested electronic package. Furthermore, as the temperature range of the TC test increases, the significance of the solder alloy material’s influence on reliability decreases. This reduction in relevance is associated with a transition to the failure mode under more extreme temperature conditions. Therefore, the interpretable data-driven framework not only models the complex relationships among influencing factors and solder joint reliability but also enables understanding the solder joint reliability. This framework can contribute to accelerating the design and adoption of new lead-free solder materials in various electronic applications and utilizing data-driven models to assess the reliability of electronic packages in future research.

FINANCIAL INCLUSION AND PERFORMANCE OF LIVESTOCK MARKET IN NIGER STATE

he Naira redesign policy, despite its intended excellence, has exacerbated business stagnation and heightened the suffering of the masses. This policy, implemented without a corresponding infusion of the new notes after removing over 70% of the cash in circulation, resulted in significant financial losses and a downturn in business performance. In response to this pressing issue, this study assesses the impact of financial inclusion on the sales performance of livestock market stakeholders in Niger South Senatorial Area. The evaluation of financial inclusion encompasses access to financial services, usage of financial services, and the quality of financial services. Data collection employed a structured closed-ended questionnaire, with research instrument validity assessed using the Average Variance Extracted at the 0.5 level, and reliability verified through a Cronbach's alpha value of 0.7 for all constructs. Structural Equation Modeling on Smart PLS 4.0 was employed for data analysis. Results unveiled a positive and significant relationship for access, usage, and quality at p-values of 0.037, 0.000, and 0.025, with corresponding path coefficients of 0.126, 0.536, and 0.099, respectively. The study further revealed an R-square value of 48.0%, signifying that financial inclusion, through its dimensions, explains a substantial 48% change in the performance of livestock market stakeholders in Niger South Senatorial Area. As a recommendation, emphasis on the usage of financial services is advocated for, as it stands out as a crucial driver for achieving higher performance among livestock market stakeholders.

The Influence of Organizational Culture and Organizational Commitment on Employee Performance at a Japanese Multinational Company in Indonesia

This study aims to analyze the influence of organizational culture and organizational commitment on employee performance within a Japanese multinational corporation operating in Indonesia. The research involved a sample of 217 employees from a total population of 500 production staff, with data collected through a questionnaire that assessed their perceptions of organizational culture, commitment, and performance. The results from the multiple linear regression analysis indicate that both organizational culture and commitment significantly impact employee performance, with an R-squared value of 0.850. The coefficients for organizational culture and commitment were found to be 0.450 and 0.300, respectively, both demonstrating a positive effect on performance. The extremely low p-value (0.000) for both variables further confirms the statistical significance of their influence. These findings provide important implications for companies looking to enhance employee performance by improving organizational culture and commitment.

The Role of Customer Satisfaction as an Intervening Variable between Servicescape and Repurchase Intention: A Study on Maxx Coffee Customers in Medan

Nowadays, cafes that serve coffee drinks are competing with one anotherextremely quickly. Understanding customer behavior is one strategy to win thecompetition. For instance, keeping an eye on customer satisfaction and theservicescape of their workplace. The aim of this research was to evaluate andascertain how Servicescape and Customer Satisfaction directly affectRepurchase Intention, as well as how Customer Satisfaction functions as anintermediate variable. A sample of 150 respondents was used in the descriptiveassociative research design. Using the Smart PLS application, the data analysistechnique is (Partial Least Square) PLS. The findings revealed that Servicescapeand Customer Satisfaction have a positive and significant effect on RepurchaseIntention; Servicescape has a positive and significant effect on CustomerSatisfaction; and Servicescape has a positive and significant effect onRepurchase Intention via Customer Satisfaction at Customer Maxx Coffee inMedan. The R-squared value for the variable of repurchase intention is 0.786,whereas the variable of customer satisfaction is 0.684. The acquisition of thesevalues explains that the percentage of large intention to repurchase can beexplained by servicescape and customer satisfaction of 78.6%, while theacquisition of the value that explains the percentage of the large performance ofcustomer satisfaction can be explained by servicescape of 68.4%.

Deep learning model based prediction of vehicle CO2 emissions with eXplainable AI integration for sustainable environment

The transportation industry contributes significantly to climate change through carbon dioxide (CO2) emissions, intensifying global warming and leading to more frequent and severe weather phenomena such as flooding, drought, heat waves, glacier melting, and rising sea levels. This study proposes a comprehensive approach for predicting CO2 emissions from vehicles using deep learning techniques enhanced by eXplainable Artificial Intelligence (XAI) methods. Utilizing a dataset from the Canadian government’s official open data portal, we explored the impact of various vehicle attributes on CO2 emissions. Our analysis reveals that not only do high-performance engines emit more pollutants, but fuel consumption under both city and highway conditions also contributes significantly to higher emissions. We identified skewed distributions in the number of vehicles produced by different manufacturers and trends in fuel consumption across fuel types. This study used deep learning techniques to construct a CO2 emission prediction model, specifically a light multilayer perceptron (MLP) architecture called CarbonMLP. The proposed model was optimized by hyperparameter tuning and achieved excellent performance metrics, such as a high R-squared value of 0.9938 and a low Mean Squared Error (MSE) of 0.0002. This study employs XAI approaches, particularly SHapley Additive exPlanations (SHAP), to improve the model interpretation ability and provide information about the importance of features. The findings of this study show that the proposed methodology accurately predicts CO2 emissions from vehicles. Additionally, the analysis suggests areas for further research, such as increasing the dataset, integrating additional pollutants, improving interpretability, and investigating real-world applications. Overall, this study contributes to the design of effective strategies for reducing vehicle CO2 emissions and promoting environmental sustainability.

Institutional Policies and Procedures on Child Rights Protection in Uganda. A Study of Mityana Municipality.

This study investigates the effects of institutional policies and procedures on child rights protection in Mityana Municipality, Uganda. Grounded in pragmatic philosophy, a mixed-methods approach was employed, integrating quantitative surveys and qualitative interviews to gather comprehensive data on stakeholders' perceptions regarding child rights protection. The study targeted a population of 200 respondents, with a final sample size of 133 participants. The findings reveal a generally positive perception of institutional policies, with an average mean score of 3.29, indicating a commitment to prioritizing children's rights. However, several areas were identified for improvement, including the clarity of procedures, staff training, and effective communication among institutions. Pearson correlation analysis indicated a very strong positive relationship (r = 0.978) between institutional policies and procedures and child rights protection, with a statistically significant p-value of 0.000. Regression analysis further supported these findings, demonstrating that institutional policies and procedures significantly influence child rights outcomes, with an R-squared value of 0.097. Based on these findings, the study recommends that local government authorities enhance training programs for staff involved in child rights protection, improve communication channels among organizations, conduct regular policy reviews, enhance resource allocation, establish child protection committees, and initiate awareness campaigns focused on children's rights. These measures are essential for strengthening the frameworks that safeguard children's rights. In conclusion, the study emphasizes the necessity for ongoing development and refinement of institutional practices to effectively protect children's rights in Mityana Municipality. Strengthening these frameworks will not only improve child protection outcomes but also ensure a more secure environment for vulnerable children in the community.

Monitoring and Evaluation Practices and Performance of the Energy Projects in Rwanda: A Case of Electricity Access Rollout Programme (EARP)

Purpose: This research aims to appraise contribution of M&amp;E practices to energy projects performance, especially in the Electricity Access Rollout Programme Rwanda (EARP).It examined especially the contribution of planning for M&amp;E, project baseline Surveys implementation, and Monitoring and Evaluation information system usage to EARP performance. The research involved the Program theory, Result-based theory, and the theory of change. Methodology: For the research design, a descriptive survey helped, and the target population was 15 project staff of the completed EARP in the project management unit. During this study, the researcher used a census, the entire population was considered as a sample since its number was limited. Qualitative and quantitative approaches were applied to gather data, and through interview guide and questionnaire, the researcher gathered primary data while secondary data were gathered from various official reports. After collecting data, the researcher used SPSS V.23 for their process and analysis. The content analysis techniques served to analyze data from interviews, and reports. The frequencies, percentages, standard deviation, and means known as descriptive statistics helped the researcher in defining the data, Pearson correlational and regression statistics were used to set up relationships concerning the variables and the significance levels. The research results were finally represented using graphs and tables. Findings: According to the research findings, the majority of participants agreed that M&amp;E planning contributed to the performance of EARP (mean=4.31, Std =0.78). Furthermore, they indicated that M&amp;E planning and EARP performance were positively correlated with a Pearson correlation coefficient of r=0.632, and p-value= 0.01 &lt;0.05 significance level. Also, a great number of participants agreed that baseline implementation contributed to EARP performance (mean=4.37, Std =0.813), and a strong positive correlation was established between baseline implementation and EARP performance, the Pearson correlation coefficient was r=0.832 and the p-value=0.00&lt;0.05 the significance level. Lastly the mean and the standard deviation for the M&amp;E Information System were (mean= 4.08, and Std= 1.04) respectively, most participants agree that M&amp;E IS contributed to EARP performance. Nevertheless, the standard deviation was greater than one implying a large spread of responses from the mean, thus no consensus among respondents. The Pearson coefficient was r=0.170 with a p-value =0.544&gt;005 and this value implied that no significant correlation existed between the M&amp;E Information System usage and EARP performance as it was greater than the significance level. A regression model of the predictors against the performance of EARP yielded R-square value of 0.731, hence the three components under study could explain 73.1% of variation in EARP project performance with F=9.128, p-value=0.003&lt;0.05. M&amp;E Planning (β=0.228, P value= 0.04), Baseline Survey Implementation (β=0.437, P value= 0.020), M&amp;E Information System Usage (β=-0.54, P value= 0.74) respectively. Unique Contribution to Theory, Practice and Policy: The researcher recommends those involved in project management especially energy projects or programs to adopt a robust, comprehensive, and user-friendliness M&amp;E information system and promote M&amp;E practices. This research may benefit different project management teams as it may provide them with the fundamental information to effectively track and measure projects. Furthermore, it may benefit several energy sector stakeholders to optimize the process of project development and promote performance for energy projects.

A Model-Data Dual-Driven Approach for Predicting Shared Bike Flow near Metro Stations

Bike-sharing has emerged as an innovative green transportation mode, showing promising potential in addressing the ‘last-mile’ transportation challenge in an eco-friendly manner. However, shared bikes around metro stations often face supply–demand imbalance problems during peak hours, causing bike shortages or congestion that compromise user experience and bike utilization. Accurate prediction enables operators to develop rational dispatch strategies, improve bike turnover rate, and promote synergistic metro–bike integration. However, state-of-the-art research predominantly focuses on improving complex deep-learning models while overlooking their inherent drawbacks, such as overfitting and poor interpretability. This study proposes a model–data dual-driven approach that integrates the classical statistical regression model as a model-driven component and the advanced deep-learning model as a data-driven component. The model-driven component uses the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to extract periodic patterns and seasonal variations of historical data, while the data-driven component employs an Extended Long Short-Term Memory (xLSTM) neural network to process nonlinear relationships and unexpected variations. The fusion model achieved R-squared values of 0.9928 and 0.9770 for morning access and evening egress flows, respectively, and reached 0.9535 and 0.9560 for morning egress and evening access flows. The xLSTM model demonstrates an 8% improvement in R2 compared to the conventional LSTM model in the morning egress flow scenario. For the morning egress and evening access flows, which exhibit relatively high variability, classical statistical models show limited effectiveness (SARIMA’s R2 values are 0.8847 and 0.9333, respectively). Even in scenarios like morning access and evening egress, where classical statistical models perform well, our proposed fusion model still demonstrates enhanced performance. Therefore, the proposed data–model dual-driven architecture provides a reliable data foundation for shared bike rebalancing and shows potential for addressing the challenges of limited robustness in statistical regression models and the susceptibility of deep-learning models to overfitting, ultimately enhancing transportation ecosystem sustainability.

Transfers to Region on Economic Growth During The Covid-19 Pandemic

The Covid-19 pandemic that began in late 2019 has had a significant impact on economies around the world, including Indonesia. In an effort to handle the pandemic, the Indonesian government has refocused the budget, one of which is through the transfer of funds to the regions. This study aims to analyze the effect of fund transfers to regions on economic growth during the Covid-19 pandemic in Indonesia. This research uses a quantitative approach with an exploratory case study method. The secondary data used includes local government financial reports and Gross Domestic Product (GDP) data from 34 provinces in Indonesia during 2020 to 2022. The analysis was conducted using statistical techniques with the help of SPSS. The results of the analysis show that the transfer of funds to the regions has a significant positive effect on economic growth, with an R-square value of 24.8%. However, 75.2% of economic growth is influenced by factors other than transfers. Fund transfer allocations that were originally intended to support regional development have been diverted to handling the impact of the pandemic. This study indicates that while financial transfers contribute to economic growth, there are many other factors that need to be considered. Therefore, further research is needed to understand the more complex dynamics in the effect of transfers on economic growth.

Analysis of heat transfer of second-grade hybrid nanofluid and optimization using response surface methodology for thermal enhancement

The fluid flow and heat transfer applications in coaxial double-revolving disks are extensive and encompass multiple engineering and scientific disciplines. This study presents a comprehensive heat transfer analysis in a second-grade hybrid nanofluid. The fluid, influenced by variable thermal conductivity, is confined between two rotating disks in a magnetohydrodynamic Darcy-Forchheimer flow. The hybrid nanofluid combines titanium dioxide (TiO2) and cobalt ferrite (CoFe2O4) nanoparticles with engine oil. These issues have not been addressed in previous research. Similarity transformations make flow equations dimensionless. The resulting equations are solved using a semi-analytical approach called the homotopy analysis method (HAM). This approach provides flexibility in selecting the base function and an initial estimate. The temperature profile improved with higher values of the variable thermal conductivity parameter. Furthermore, the heat transfer rate was enhanced by increasing the variable thermal conductivity, the volume fraction concentration of TiO2, and the Reynolds number.The sensitivity analysis is performed using response surface methodology (RSM). Both the R-squared and adjusted R-squared values attain 100%. The heat transfer rate is found to have a maximum sensitivity of 0.50243 towards varying thermal conductivity parameters and a minimum sensitivity of -0.00666 towards magnetic field parameters. The research utilizes RSM to elucidate methods for improving heat transfer. This work offers practical solutions for developing more efficient thermal management systems. This research provides practical strategies for improving thermal management systems. These solutions are especially useful in rotating machinery-intensive industries. Designing biochemical and pharmaceutical microfluidic pumps and mixers requires understanding hybrid nanofluid flows between spinning disks.

Predictive analysis of fuel oil consumption in vessels: interpretable modelling with emphasis on load conditions

ABSTRACT Fuel oil consumption (FOC) in vessels is influenced by various factors, with vessel load conditions being a critical determinant. This research aimed to develop an interpretable regression-based machine learning model, specifically an XGBoost Regressor, to predict FOC, and leveraged the analysis with Explainable Artificial Intelligence (XAI) techniques to enhance transparency and understanding of the factors affecting FOC in maritime operations. Hyperparameter tuning optimized model parameters, achieving a high R-squared (R2) value of 0.99. XAI methods clarified how operational (e.g., speed, load, draft) and environmental factors (e.g., wind, wave, current, sea state) contribute to FOC increases. As a result, operational factors, notably average draft, exerted a substantial influence, with an average draft of 18.425 m resulting in a significant increase in predicted FOC to 1,729.40 kg/h during the laden condition, compared to the base value of 1,496.82 kg/h. Additionally, environmental factors, particularly Relative Wind Angle, significantly impacted FOC prediction, with a Mean Absolute SHAP Value of 11.97 kg/h, notably higher than in ballast (2.59 kg/h) and empty (2.85 kg/h) conditions. These findings emphasize the importance of tailored fuel efficiency strategies, as operational and environmental factors may vary in their impact across different loading conditions. ‘This article is a revised and expanded version of a paper entitled “Predictive Analysis of Fuel Oil Consumption in Vessels: Interpretable Modeling with Emphasis on Load Conditions” presented at The 11th International Conference on Logistics and Maritime Systems (LOGMS 2023) on 6 September 2023 at the Busan Port International Exhibition & Convention Center (BPEX)’

Analisis Faktor-Faktor yang Mempengaruhi Indeks Pembangunan Manusia Di Indonesia Tahun 2023

The Human Development Index (HDI) is a composite indicator that reflects the quality of life of a population through three primary dimensions: health, education, and a decent standard of living. Improvements in HDI are supported by advancements across all its constituent factors. The dimension of long and healthy life is represented by the increase in life expectancy (LE), while the education dimension is captured through the rise in the mean years of schooling (MYS) and expected years of schooling (EYS). Additionally, the standard of living dimension is indicated by improvements in adjusted per capita expenditure. This study aims to analyze the factors that determine HDI in Indonesia in 2023 using multiple linear regression analysis. The data used in this study are secondary data sourced from the Central Statistics Agency in the form of Human Development Index data and the factors that influence it. The findings reveal that variables such as Life Expectancy (LE), Expected Years of Schooling (EYS), Mean Years of Schooling (MYS), and Adjusted Real Per Capita Expenditure significantly affect the Human Development Index in 2023. This is evidenced by an R-Square value of 0.9957, indicating that the independent variables collectively explain 99.57% of the variance in the dependent variable.

What Strategy will Telkom Indonesia (TLKM) use to Increase the Price Per Share?

This study analyzes what strategies will be carried out by PT Telkom Indonesia (TLKM) Tbk in increasing the price per share focusing on NPM and DPR. The population and sample in this study are the financial statements of PT Telkom Indonesia (TLKM) Tbk for the period 2014-2023. The method used in the study is mix method research (MMR). The results show that NPM has a negative but significant effect on DPR with an Adjusted R-square value (determination coefficient) of 0.5132 or 51.32% indicating that the effect of NPM on DPR is 51.32%, the remaining 48.68% is influenced by other factors. Focus on NPM and DPR strategies that can be carried out by PT Telkom Indonesia (TLKM); Short Term: Focus on operational efficiency and optimization of dividend policy to increase investor attractiveness Medium Term: Expand the digital service portfolio and take advantage of national digital transformation opportunities, Long Term: Diversify markets and products to face global competition and reduce dependence on the domestic market.