Comparative Analysis of Supervised Learning Algorithms for Delivery Status Prediction in Big Data Supply Chain Management

This study addresses the problem of predicting delivery status in supply chain data, a critical task for optimizing logistics and operations. The dataset, which includes multiple features like order details, product specifications, and customer information, was pre-processed using oversampling to address class imbalance, ensuring that the model could handle rare cases of late or canceled deliveries. The data cleaning process involved handling missing values, removing irrelevant columns, and transforming categorical variables into numerical formats. After pre-processing and cleaning, five machine learning models were applied: Logistic Regression, Random Forest, SVM, K-Nearest Neighbors (KNN), and XGBoost. Each model was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results showed that XGBoost outperformed the other models, achieving the highest accuracy and providing the most reliable predictions for the delivery status. This makes XGBoost the best choice for supply chain data analysis in this context. This study contributes to the growing application of machine learning in supply chain optimization by identifying XGBoost as a robust model for delivery status prediction in large datasets. For future research, exploring hybrid models and advanced feature engineering techniques could further improve prediction accuracy and address additional challenges in supply chain optimization, especially in the context of real-time data processing and dynamic supply chain environments.

Effects of Contaminated Soil on the Survival and Growth Performance of European (Populus tremula L.) and Hybrid Aspen (Populus tremula L. × Populus tremuloides Michx.) Clones Based on Stand Density

This study was conducted to assess the survival rates, growth, and chlorophyll fluorescence (Fv/Fm) of four hybrid aspen (14, 191, 27, 291) and two European aspen (R3 and R4) clones cultivated in creosote- and diesel oil-contaminated soil treatments under three different plant densities: one plant per pot (low density), two plants per pot (medium density), and six plants per pot (high density) over a period of two years and three months. Evaluating the survival, growth, and Fv/Fm values of different plants is a prerequisite for phytoremediation to remediate polluted soils for ecological restoration and soil health. The results revealed that contaminated soils affected all plants’ survival rates and growth. However, plants grown in the creosote-contaminated soil displayed a 99% survival rate, whereas plants cultivated in the diesel-contaminated soil showed a 22–59% survival rate. Low plant density resulted in a higher survival rate and growth than in the other two density treatments. In contrast, the medium- and high-density treatments did not affect the plant survival rate and growth to a greater extent, particularly in contaminated soil treatments. The effects of clonal variation on the survival rate, growth, and Fv/Fm values were evident in all treatments. The results suggested that hybrid aspen clones 14 and 291, and European aspen clone R3 were suitable candidates for the phytoremediation experiment, as they demonstrated reasonable survival rates, growth, and Fv/Fm values across all treatments. A superior survival rate for clone 291, height and diameter growth, and stem dry biomass production for clone 14 were observed in all soil treatments. Overall, a reasonable survival rate (~75%) and Fv/Fm value (>0.75) for all plants in all treatments, indicating European aspen and hybrid aspen have considerable potential for phytoremediation experiments. As the experiment was set up for a limited period, this study deserves further research to verify the growth potential of different hybrid aspen and European aspen clones in different soil and density treatment for the effective phytoremediation process to remediate the contaminated soil

Pengaruh Ukuran Perusahaan, Debt To Equity Ratio, Tingkat Suku Bunga, Peringkat Obligasi Terhadap Yield To Maturity (Ytm) Obligasi Perusahaan

Obligasi merupakan salah satu investasi efek/ sekuritas  berpendapatan tetap yang bertujuan untuk memberikan tingkat pertumbuhan nilai investasi yang relatif stabil, hal ini dicerminkan nilai YTM obligasi. Penelitian ini bertujuan mengetahui apakah terdapat pengaruh atas ukuran perusahaan, DER, tingkat suku bunga dan peringkat obligasi terhadap YTM obligasi perusahaan. Penelitian dilakukan atas  dari perusahaan 8 perbankan yang terdaftar di Bursa Efek Indonesia dari tahun 2016 sampai dengan tahun 2019 .Penelitian ini membuktikan bahwa ukuran perusahaan mempunyai pengaruh negatif terhadap YTM obligasi, DER tidak berpengaruh.terhadap nilai YTM obligasi. Sedangkan tingkat suku bunga berpengaruh negatif terhadap YTM Obligasi, dan peringkat obligasi berpengaruh positif terhadap YTM Obligas

Forcespun polyvinylpyrrolidone/copper and polyethylene oxide/copper composite fibers and their use as antibacterial agents

Copper nanoparticles (CuNPs) embedded in polyvinylpyrrolidone (PVP) and polyethylene oxide (PEO) fiber-matrices were prepared through centrifugal spinning of PVP/ethanol and PEO/aqueous solutions, respectively. The prime focus of the current study is to investigate the antibacterial activity of composite fibers against Escherichia coli (E. coli) and Bacillus cereus (B. cereus) bacteria. During the fiber formation, the centrifugal spinning parameters such as spinneret rotational speed, spinneret to collector distance, and relative humidity were carefully chosen to obtain long and continuous fibers. The structural and morphological analyses of both composite fibers were investigated using scanning electron microscopy, X-ray diffraction, energy-dispersive X-ray spectroscopy, and thermogravimetric analysis. In the antibacterial test, PVP/Cu and PEO/Cu composite fibrous membranes exhibited inhibition efficiency of 99.98% and 99.99% against E. coli and B. cereus bacteria, respectively. Basically, CuNPs were well embedded in the fibrous membrane at the nanoscale level, which facilitated the inhibition of bacterial functions through the inactivation of the chemical structure of the cells. Such an effective antibacterial agent obtained from forcespun composite fibers could be promising candidates for biomedical applications

Examining performance of Islamic banks in Bangladesh using Stochastic Frontier analysis and Maqasid model / Md. Golzare Nabi … [et al.]

The current study uses the parametric Stochastic Frontier Approach (SFA) with the Maqasid Model, Performance Measures based on Maqasid al Shariah (PMMS) to empirically evaluate the performance of Bangladeshi Islamic commercial banks from 2005 to 2018. In accordance with the SFA model, Islamic commercial banks had an average cost efficiency value of 0.78 between 2005 and 2018, which was greater than traditional state banks’ (0.781), but lower than local private banks’ (0.879) and international banks’ (0.969). This implies that Islamic and other commercial banks can save identical stage of output with the same quantity of resources. Under the PMMS Model empirical evidences show that Islamic banks experienced a low level of performance based on the Maqasid Index which ranges from 19.16% to 23.07%. The current article provides significant information on performance gaps of Islamic commercial banks and its determinants. The regulators, policy makers and managers can adopt necessary policy actions to improve performance of Islamic commercial banks from perspective of cost efficiency and welfare issue

Rehabilitation of abandoned housing projects in Peninsular Malaysia: Lessons for housing entrepreneurs

Abandoned housing projects is one of the housing problems in Peninsular Malaysia. Even though there are laws and policies provided by the Malaysian government to govern housing industry, yet abandoned housing projects problem is still an unsettled issue for the Malaysian government to tackle.The real victims are the purchasers themselves. Virtually, there is no specific and common ways to face the problems of abandoned housing projects.This is due to the fact that in each and every abandoned housing project, the problems and issues faced by the stakeholders vary.This paper will discuss the law and practice in the rehabilitation of abandoned housing projects in Peninsular Malaysia.From the discussion, certain suggestions will be forwarded at the end part of the paper for facilitating the implementation of the rehabilitation of abandoned housing projects in Peninsular Malaysia by the housing entrepreneurs

Goal-Oriented Communications for Remote Inference under Two-Way Delay with Memory

We study the design of a goal-oriented sampling and scheduling strategy through a channel with highly variable two-way random delay, which can exhibit memory (e.g., Delay and Disruption Tolerant Networks). The objective of the communication is to optimize the performance of remote inference, where an inference algorithm (e.g., a trained neural network) on the receiver side predicts a time-varying target signal using the data samples transmitted by a sensor. Previous formulations to this problem either assumed a channel with IID transmission delay, neglecting feedback delay, or considered the monotonic relation that the performance only gets worse as the input information ages. We show how, with delayed feedback, one can effectively exploit the knowledge about delay memory through an index-based threshold policy. This policy minimizes the expected time-average inference error that can be monotone or non-monotone in age. The index function is expressed in terms of the Age of Information (AoI) on the receiver side and a parameter regarding the distribution of subsequent transmission delay, both of which can readily be tracked.9 pages, 3 figure