Current Issue : July-September Volume : 2025 Issue Number : 3 Articles : 5 Articles
To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models. In the theoretical section, the model introduction and estimation algorithms are provided. In the empirical analysis section, global air quality data from 2022 to 2024 are used, and the proposed method is applied. Specifically, principal component analysis (PCA) is first conducted, and then VAR and Random Forest methods are used for prediction on the reduced-dimensional data. The results show that the RMSE of the hybrid model is 45.27, significantly lower than the 49.11 of the VAR model alone, verifying its superiority. The stability and predictive performance of the model are effectively enhanced....
Accurate millet appearance quality assessment is critical for fair procurement pricing. Traditional manual inspection is time-consuming and subjective, necessitating an automated solution. This study proposes a machine-vision-based approach using deep learning for dense-scene millet detection and quality evaluation. High-resolution images of standardized millet samples were collected via smartphone and annotated into seven categories covering impurities, high-quality grains, and various defects. To address the challenges with small object detection and feature loss, the YOLO11s model with an overlap- partitioning strategy were introduced, dividing the high-resolution images into smaller patches for improved object representation. The experimental results show that the optimized model achieved a mean average precision (mAP) of 94.8%, signicantly outperforming traditional whole-image detection with a mAP of 15.9%. The optimized model was deployed in a custom-developed mobile application, enabling low-cost, realtime millet inspection directly on smartphones. It can process full-resolution images (4608 × 3456 pixels) containing over 5000 kernels within 6.8 s. This work provides a practical solution for on-site quality evaluation in procurement and contributes to real-time agricultural inspection systems....
This study examines the role of artificial intelligence in enhancing quality management practices within Algerian startups, focusing specifically on how AI-driven business intelligence contributes to competitive advantage through quality improvements in emerging market contexts. The research employs quantitative methodology, utilizing structural equation modeling (CB-SEM) to analyze data collected from 357 Algerian startups. The study implements a comprehensive measurement framework incorporating quality management practices, AI implementation status, and competitive advantage indicators, validated through confirmatory factor analysis. The analysis reveals that quality management supported by artificial intelligence has a moderate positive impact on competitive advantage (correlation coefficient 0.346, p < 0.001). Organizations implementing AI-enabled quality management systems achieved a 52.4% improvement in overall quality metrics. Customer response capability scored highest among quality dimensions (mean score 2.86), while product-market alignment showed room for improvement (mean score 2.53). The research identified three critical areas of AI integration success: quality control automation, predictive quality management, and customer response systems. The study provides actionable insights for startups in emerging markets implementing AI-driven quality management systems. The findings suggest a staged approach to technology adoption, emphasizing the importance of foundational quality management practices before advanced AI integration. Results indicate that successful implementation requires balanced investment in both technological infrastructure and organizational capabilities. While Algerian startups demonstrate awareness of and commitment to AI-enabled quality management, with 50.8% showing a positive disposition toward adoption, actual implementation remains at moderate levels. The study highlights significant opportunities for enhancement in quality management through strategic AI integration, particularly in emerging market contexts where technological infrastructure and resource constraints present unique challenges....
This research investigates the positioning performance of the L-band Digital Aeronautical Communications System (LDACS) and presents a system architecture based on carrier-smoothed ground-to-air pseudoranges (PRs), along with clock corrections derived from asymmetric two-way time and frequency transfer (A-TWTFT) filters. The objective is to achieve required positioning accuracy and integrity for aviation operations, addressing the complexities associated with utilizing a terrestrial communications system for complementary positioning, navigation, and timing (CPNT). Through error covariance analysis, this study assesses the steady-state value, convergence time, and bounding performances of the filters. The positioning performance highlights the benefits provided by the proposed architecture....
Unplanned urbanization and economic development can deteriorate water quality (WQ) and alter its beneficial usage. Continuous monitoring of biotic and abiotic parameters describing the WQ is essential to track changes and classify water resources to protect public health. Various invest significant effort, money, and time in monitoring programs. Using data from those sources is challenging due to the large number of observations, and inconsistencies in sampling time, date, stations, and gaps. This study aims to design different water quality index (WQI) models to provide policymakers, stakeholders, and water managers with a more comprehensive assessment by converting complex datasets from over 10 years, processed with the statistical software R, into consistent data sets. These datasets are then transformed into small principal components. WQ datasets of lakes and reservoirs in the western USA were chosen as case studies. The strategy of data processing is explained, and the results organized as a descriptive summary of the 12,000 observations for 31 parameters are discussed. Outputs of principal component analysis (PCA) are used to create relative and absolute WQI models for water irrigation usage and protecting cold- and warm-water species of game fish. Weighted arithmetic water quality indices are applied, and the relation between different models is examined....
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