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Search Results (204)

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Keywords = adaptive digital filter

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18 pages, 3666 KB  
Article
Reinforcement Learning Enabled Intelligent Process Monitoring and Control of Wire Arc Additive Manufacturing
by Allen Love, Saeed Behseresht and Young Ho Park
J. Manuf. Mater. Process. 2025, 9(10), 340; https://doi.org/10.3390/jmmp9100340 (registering DOI) - 18 Oct 2025
Abstract
Wire Arc Additive Manufacturing (WAAM) has been recognized as an efficient and cost-effective metal additive manufacturing technique due to its high deposition rate and scalability for large components. However, the quality and repeatability of WAAM parts are highly sensitive to process parameters such [...] Read more.
Wire Arc Additive Manufacturing (WAAM) has been recognized as an efficient and cost-effective metal additive manufacturing technique due to its high deposition rate and scalability for large components. However, the quality and repeatability of WAAM parts are highly sensitive to process parameters such as arc voltage, current, wire feed rate, and torch travel speed, requiring advanced monitoring and adaptive control strategies. In this study, a vision-based monitoring system integrated with a reinforcement learning framework was developed to enable intelligent in situ control of WAAM. A custom optical assembly employing mirrors and a bandpass filter allowed simultaneous top and side views of the melt pool, enabling real-time measurement of layer height and width. These geometric features provide feedback to a tabular Q-learning algorithm, which adaptively adjusts voltage and wire feed rate through direct hardware-level control of stepper motors. Experimental validation across multiple builds with varying initial conditions demonstrated that the RL controller stabilized layer geometry, autonomously recovered from process disturbances, and maintained bounded oscillations around target values. While systematic offsets between digital measurements and physical dimensions highlight calibration challenges inherent to vision-based systems, the controller consistently prevented uncontrolled drift and corrected large deviations in deposition quality. The computational efficiency of tabular Q-learning enabled real-time operation on standard hardware without specialized equipment, demonstrating an accessible approach to intelligent process control. These results establish the feasibility of reinforcement learning as a robust, data-efficient control technique for WAAM, capable of real-time adaptation with minimal prior process knowledge. With improved calibration methods and expanded multi-physics sensing, this framework can advance toward precise geometric accuracy and support broader adoption of machine learning-based process monitoring and control in metal additive manufacturing. Full article
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17 pages, 414 KB  
Article
DQMAF—Data Quality Modeling and Assessment Framework
by Razan Al-Toq and Abdulaziz Almaslukh
Information 2025, 16(10), 911; https://doi.org/10.3390/info16100911 - 17 Oct 2025
Viewed by 63
Abstract
In today’s digital ecosystem, where millions of users interact with diverse online services and generate vast amounts of textual, transactional, and behavioral data, ensuring the trustworthiness of this information has become a critical challenge. Low-quality data—manifesting as incompleteness, inconsistency, duplication, or noise—not only [...] Read more.
In today’s digital ecosystem, where millions of users interact with diverse online services and generate vast amounts of textual, transactional, and behavioral data, ensuring the trustworthiness of this information has become a critical challenge. Low-quality data—manifesting as incompleteness, inconsistency, duplication, or noise—not only undermines analytics and machine learning models but also exposes unsuspecting users to unreliable services, compromised authentication mechanisms, and biased decision-making processes. Traditional data quality assessment methods, largely based on manual inspection or rigid rule-based validation, cannot cope with the scale, heterogeneity, and velocity of modern data streams. To address this gap, we propose DQMAF (Data Quality Modeling and Assessment Framework), a generalized machine learning–driven approach that systematically profiles, evaluates, and classifies data quality to protect end-users and enhance the reliability of Internet services. DQMAF introduces an automated profiling mechanism that measures multiple dimensions of data quality—completeness, consistency, accuracy, and structural conformity—and aggregates them into interpretable quality scores. Records are then categorized into high, medium, and low quality, enabling downstream systems to filter or adapt their behavior accordingly. A distinctive strength of DQMAF lies in integrating profiling with supervised machine learning models, producing scalable and reusable quality assessments applicable across domains such as social media, healthcare, IoT, and e-commerce. The framework incorporates modular preprocessing, feature engineering, and classification components using Decision Trees, Random Forest, XGBoost, AdaBoost, and CatBoost to balance performance and interpretability. We validate DQMAF on a publicly available Airbnb dataset, showing its effectiveness in detecting and classifying data issues with high accuracy. The results highlight its scalability and adaptability for real-world big data pipelines, supporting user protection, document and text-based classification, and proactive data governance while improving trust in analytics and AI-driven applications. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining for User Classification)
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26 pages, 19488 KB  
Article
A Joint Method on Dynamic States Estimation for Digital Twin of Floating Offshore Wind Turbines
by Hao Xie, Ling Wan, Fan Shi, Jianjian Xin, Hu Zhou, Ben He, Chao Jin and Constantine Michailides
J. Mar. Sci. Eng. 2025, 13(10), 1981; https://doi.org/10.3390/jmse13101981 - 16 Oct 2025
Viewed by 147
Abstract
Dynamic state estimation of floating offshore wind turbines (FOWTs) in complex marine environments is a core challenge for digital twin systems. This study proposes a joint estimation framework that integrates windowed dynamic mode decomposition (W-DMD) and an adaptive strong tracking Kalman filter (ASTKF). [...] Read more.
Dynamic state estimation of floating offshore wind turbines (FOWTs) in complex marine environments is a core challenge for digital twin systems. This study proposes a joint estimation framework that integrates windowed dynamic mode decomposition (W-DMD) and an adaptive strong tracking Kalman filter (ASTKF). W-DMD extracts dominant modes under stochastic excitations through a sliding-window strategy and constructs an interpretable reduced-order state-space model. ASTKF is then employed to enhance estimation robustness against environmental uncertainties and noise. The framework is validated through numerical simulations under turbulent wind and wave conditions, demonstrating high estimation accuracy and strong robustness against sudden environmental disturbances. The results indicate that the proposed method provides a computationally efficient and interpretable tool for FOWT digital twins, laying the foundation for predictive maintenance and optimal control. Full article
(This article belongs to the Section Ocean Engineering)
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19 pages, 4859 KB  
Article
A Dual-Mode Adaptive Bandwidth PLL for Improved Lock Performance
by Thi Viet Ha Nguyen and Cong-Kha Pham
Electronics 2025, 14(20), 4008; https://doi.org/10.3390/electronics14204008 - 13 Oct 2025
Viewed by 263
Abstract
This paper proposed an adaptive bandwidth Phase-Locked Loop (PLL) that integrates integer-N and fractional-N switching for energy-efficient RF synthesis in IoT and mobile applications. The architecture exploits wide-bandwidth integer-N mode for rapid lock acquisition, then seamlessly transitions to narrow-bandwidth fractional-N mode for high-resolution [...] Read more.
This paper proposed an adaptive bandwidth Phase-Locked Loop (PLL) that integrates integer-N and fractional-N switching for energy-efficient RF synthesis in IoT and mobile applications. The architecture exploits wide-bandwidth integer-N mode for rapid lock acquisition, then seamlessly transitions to narrow-bandwidth fractional-N mode for high-resolution synthesis and noise optimization. The architecture features a bandwidth-reconfigurable loop filter with intelligent switching control that monitors phase error dynamics. A novel adaptive digital noise filter mitigates ΔΣ quantization noise, replacing conventional synchronous delay lines. The multi-loop structure incorporates a high-resolution digital phase detector to enhance frequency accuracy and minimize jitter across both operating modes. With 180 nm CMOS technology, the PLL consumes 13.2 mW, while achieving 119 dBc/Hz in-band phase noise and 1 psrms integrated jitter. With an operating frequency range at 2.9–3.2 GHz from a 1.8 V supply, the circuit achieves a worst case fractional spur of −62.7 dBc, which corresponds to a figure of merit (FOM) of −228.8 dB. Lock time improvements of 70% are demonstrated compared to single-mode implementations, making it suitable for high-precision, low-power wireless communication systems requiring agile frequency synthesis. Full article
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20 pages, 2608 KB  
Article
Influence of Vibration on Servo Valve Performance and Vibration Suppression in Electro-Hydraulic Shaking Table
by Tao Wang, Sizhuo Liu, Zhenyu Guo and Yuelei Lu
Machines 2025, 13(10), 913; https://doi.org/10.3390/machines13100913 - 3 Oct 2025
Viewed by 236
Abstract
With the rapid progress of industrial technology in recent years, servo controllers have the characteristics of precise control and short response time and are widely used in different industrial fields. As for the electro-hydraulic servo valve being an important control element of the [...] Read more.
With the rapid progress of industrial technology in recent years, servo controllers have the characteristics of precise control and short response time and are widely used in different industrial fields. As for the electro-hydraulic servo valve being an important control element of the entire hydraulic system, the quality of its own characteristics has a significant impact on the normal operation and safety of the mechanical equipment. Therefore, the working stability of the servo valve in actual operation is of great importance to its body and the overall servo system. Similarly, during the vibration test of the electro-hydraulic servo shaking table, servo valve inevitably experiences various vibrations and shocks, which requires the servo system to be able to withstand the test and assessment under the extreme conditions in actual operation to ensure the smooth operation. This paper takes function of the shaker as the research target and studies the servo valve under various vibration conditions by constructing a digital modeling system. On this basis, an adaptive format filter is established, and corresponding vibration suppression methods are adopted for the vibration conditions inside the system. Finally, simulation examples are used to prove that this method can more effectively control the vibration in the servo valve and suppress the interference with shaking table function. Full article
(This article belongs to the Section Machine Design and Theory)
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22 pages, 15219 KB  
Article
Integrating UAS Remote Sensing and Edge Detection for Accurate Coal Stockpile Volume Estimation
by Sandeep Dhakal, Ashish Manandhar, Ajay Shah and Sami Khanal
Remote Sens. 2025, 17(18), 3136; https://doi.org/10.3390/rs17183136 - 10 Sep 2025
Viewed by 668
Abstract
Accurate stockpile volume estimation is essential for industries that manage bulk materials across various stages of production. Conventional ground-based methods such as walking wheels, total stations, Global Navigation Satellite Systems (GNSSs), and Terrestrial Laser Scanners (TLSs) have been widely used, but often involve [...] Read more.
Accurate stockpile volume estimation is essential for industries that manage bulk materials across various stages of production. Conventional ground-based methods such as walking wheels, total stations, Global Navigation Satellite Systems (GNSSs), and Terrestrial Laser Scanners (TLSs) have been widely used, but often involve significant safety risks, particularly when accessing hard-to-reach or hazardous areas. Unmanned Aerial Systems (UASs) provide a safer and more efficient alternative for surveying irregularly shaped stockpiles. This study evaluates UAS-based methods for estimating the volume of coal stockpiles at a storage facility near Cadiz, Ohio. Two sensor platforms were deployed: a Freefly Alta X quadcopter equipped with a Real-Time Kinematic (RTK) Light Detection and Ranging (LiDAR, active sensor) and a WingtraOne UAS with Post-Processed Kinematic (PPK) multispectral imaging (optical, passive sensor). Three approaches were compared: (1) LiDAR; (2) Structure-from-Motion (SfM) photogrammetry with a Digital Surface Model (DSM) and Digital Terrain Model (DTM) (SfM–DTM); and (3) an SfM-derived DSM combined with a kriging-interpolated DTM (SfM–intDTM). An automated boundary detection workflow was developed, integrating slope thresholding, Near-Infrared (NIR) spectral filtering, and Canny edge detection. Volume estimates from SfM–DTM and SfM–intDTM closely matched LiDAR-based reference estimates, with Root Mean Square Error (RMSE) values of 147.51 m3 and 146.18 m3, respectively. The SfM–intDTM approach achieved a Mean Absolute Percentage Error (MAPE) of ~2%, indicating strong agreement with LiDAR and improved accuracy compared to prior studies. A sensitivity analysis further highlighted the role of spatial resolution in volume estimation. While RMSE values remained consistent (141–162 m3) and the MAPE below 2.5% for resolutions between 0.06 m and 5 m, accuracy declined at coarser resolutions, with the MAPE rising to 11.76% at 10 m. This emphasizes the need to balance the resolution with the study objectives, geographic extent, and computational costs when selecting elevation data for volume estimation. Overall, UAS-based SfM photogrammetry combined with interpolated DTMs and automated boundary extraction offers a scalable, cost-effective, and accurate approach for stockpile volume estimation. The methodology is well-suited for both the high-precision monitoring of individual stockpiles and broader regional-scale assessments and can be readily adapted to other domains such as quarrying, agricultural storage, and forestry operations. Full article
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26 pages, 2173 KB  
Article
RAMHA: A Hybrid Social Text-Based Transformer with Adapter for Mental Health Emotion Classification
by Mahander Kumar, Lal Khan and Ahyoung Choi
Mathematics 2025, 13(18), 2918; https://doi.org/10.3390/math13182918 - 9 Sep 2025
Cited by 1 | Viewed by 668
Abstract
Depression, stress, and anxiety are mental health disorders that are increasingly becoming a huge challenge in the digital age; at the same time, it is critical that they are detected early. Social media is a rich and complex source of emotional expressions that [...] Read more.
Depression, stress, and anxiety are mental health disorders that are increasingly becoming a huge challenge in the digital age; at the same time, it is critical that they are detected early. Social media is a rich and complex source of emotional expressions that requires intelligent systems that can decode subtle psychological states from natural language. This paper presents RAMHA (RoBERTa with Adapter-based Mental Health Analyzer), a hybrid deep learning model that combines RoBERTa, parameter-efficient adapter layers, BiLSTM, and attention mechanisms and is further optimized with focal loss to address the class imbalance problem. When tested on three filtered versions of the GoEmotions dataset, RAMHA shows outstanding results, with a maximum accuracy of 92% in binary classification and 88% in multiclass tasks. A large number of experiments are performed to compare RAMHA with eight standard baseline models, including SVM, LSTM, and BERT. In these experiments, RAMHA is able to consistently outperform the other models in terms of accuracy, precision, recall, and F1-score. Ablation studies further confirm the contributions of the individual components of the architecture, and comparative analysis demonstrates that RAMHA outperforms the best previously reported F1-scores by a substantial margin. The results of our study not only indicate the potential of the adapter-enhanced transformer in emotion-aware mental health screening but also establish a solid basis for its use in clinical and social settings. Full article
(This article belongs to the Section E1: Mathematics and Computer Science)
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23 pages, 2699 KB  
Article
Leveraging Visual Side Information in Recommender Systems via Vision Transformer Architectures
by Arturo Álvarez-Sánchez, Diego M. Jiménez-Bravo, María N. Moreno-García, Sergio García González and David Cruz García
Electronics 2025, 14(17), 3550; https://doi.org/10.3390/electronics14173550 - 6 Sep 2025
Viewed by 638
Abstract
Recommender systems are essential tools in the digital age, helping users discover products, content, and services across platforms like streaming services, online stores, and social networks. Traditionally, these systems have relied on methods such as collaborative filtering, content-based, and knowledge-based approaches, using data [...] Read more.
Recommender systems are essential tools in the digital age, helping users discover products, content, and services across platforms like streaming services, online stores, and social networks. Traditionally, these systems have relied on methods such as collaborative filtering, content-based, and knowledge-based approaches, using data like user–item interactions and demographic details. With the rise of big data, an increasing amount of “side information”, like contextual data, social behavior, and metadata, has become available, enabling more personalized and effective recommendations. This work provides a comparative analysis of traditional recommender systems and newer models incorporating side information, particularly visual features, to determine whether integrating such data improves recommendation quality. By evaluating the benefits and limitations of using complex formats like visual content, this work aims to contribute to the development of more robust and adaptive recommender systems, offering insights for future research in the field. Full article
(This article belongs to the Special Issue Application of Data Mining in Social Media)
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21 pages, 8775 KB  
Article
Speckle Noise Reduction in Digital Holography by 3D Adaptive Filtering
by Andrey A. Kerov, Alexander V. Kozlov, Pavel A. Cheremkhin, Anna V. Shifrina, Rostislav S. Starikov, Evgenii Y. Zlokazov, Elizaveta K. Petrova, Vsevolod A. Nebavskiy and Nikolay N. Evtikhiev
Sensors 2025, 25(17), 5402; https://doi.org/10.3390/s25175402 - 1 Sep 2025
Viewed by 635
Abstract
Digital holography enables the reconstruction of both 2D and 3D object information from interference patterns captured by digital cameras. A major challenge in this field is speckle noise, which significantly degrades the quality of the reconstructed images. We propose a novel speckle noise [...] Read more.
Digital holography enables the reconstruction of both 2D and 3D object information from interference patterns captured by digital cameras. A major challenge in this field is speckle noise, which significantly degrades the quality of the reconstructed images. We propose a novel speckle noise reduction method based on 3D adaptive filtering. Our technique processes a stack of holograms, each with an uncorrelated speckle pattern, using an adapted 3D Frost filter. Unlike conventional filtering techniques, our approach exploits statistical adaptivity to enhance noise suppression while preserving fine image details in the reconstructed holograms. Both numerical simulations and optical experiments confirm that our 3D filtering technique significantly enhances reconstruction quality. Specifically, it reduces the normalized standard deviation by up to 40% and improves the structural similarity index by up to 60% compared to classical 2D, 3D median, BM3D, and BM4D filters. Optical experiments validate the method’s effectiveness in practical digital holography scenarios by local and global image quality estimation metrics. These results highlight adaptive 3D filtering as a promising approach for mitigating speckle noise while maintaining structural integrity in digital holography reconstructions. Full article
(This article belongs to the Section Optical Sensors)
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20 pages, 9232 KB  
Article
Anomaly-Detection Framework for Thrust Bearings in OWC WECs Using a Feature-Based Autoencoder
by Se-Yun Hwang, Jae-chul Lee, Soon-sub Lee and Cheonhong Min
J. Mar. Sci. Eng. 2025, 13(9), 1638; https://doi.org/10.3390/jmse13091638 - 27 Aug 2025
Viewed by 519
Abstract
An unsupervised anomaly-detection framework is proposed and field validated for thrust-bearing monitoring in the impulse turbine of a shoreline oscillating water-column (OWC) wave energy converter (WEC) off Jeju Island, Korea. Operational monitoring is constrained by nonstationary sea states, scarce fault labels, and low-rate [...] Read more.
An unsupervised anomaly-detection framework is proposed and field validated for thrust-bearing monitoring in the impulse turbine of a shoreline oscillating water-column (OWC) wave energy converter (WEC) off Jeju Island, Korea. Operational monitoring is constrained by nonstationary sea states, scarce fault labels, and low-rate supervisory logging at 20 Hz. To address these conditions, a 24 h period of normal operation was median-filtered to suppress outliers, and six physically motivated time-domain features were computed from triaxial vibration at 10 s intervals: absolute mean; standard deviation (STD); root mean square (RMS); skewness; shape factor (SF); and crest factor (CF, peak divided by RMS). A feature-based autoencoder was trained to reconstruct the feature vectors, and reconstruction error was evaluated with an adaptive threshold derived from the moving mean and moving standard deviation to accommodate baseline drift. Performance was assessed on a 2 h test segment that includes a 40 min simulated fault window created by doubling the triaxial vibration amplitudes prior to preprocessing and feature extraction. The detector achieved accuracy of 0.99, precision of 1.00, recall of 0.98, and F1 score of 0.99, with no false positives and five false negatives. These results indicate dependable detection at low sampling rates with modest computational cost. The chosen feature set provides physical interpretability under the 20 Hz constraint, and denoising stabilizes indicators against marine transients, supporting applicability in operational settings. Limitations associated with simulated faults are acknowledged. Future work will incorporate long-term field observations with verified fault progressions, cross-site validation, and integration with digital-twin-enabled maintenance. Full article
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18 pages, 16407 KB  
Article
An Integrated AI Framework for Personalized Nutrition Using Machine Learning and Natural Language Processing for Dietary Recommendations
by Sena Karamanlı Aydın, Raja Hashim Ali, Shan Faiz and Talha Ali Khan
Appl. Sci. 2025, 15(17), 9283; https://doi.org/10.3390/app15179283 - 23 Aug 2025
Viewed by 2009
Abstract
Nutrition plays a pivotal role in preventive health, yet existing digital solutions often lack personalization and accessibility. This study presents an AI-driven framework that integrates machine learning (ML) and natural language processing (NLP) to deliver dynamic, user-centric dietary recommendations. A gradient boosting model, [...] Read more.
Nutrition plays a pivotal role in preventive health, yet existing digital solutions often lack personalization and accessibility. This study presents an AI-driven framework that integrates machine learning (ML) and natural language processing (NLP) to deliver dynamic, user-centric dietary recommendations. A gradient boosting model, trained on NHANES demographic and anthropometric data, predicts caloric needs with an MAE of 132 kcal, while a locally deployed LLM (Mistral 7B) interprets free-text dietary constraints with 91% accuracy. Rule-based filtering from the USDA database ensures nutritional balance. A pilot usability test (n = 5) confirmed the system’s practicality and satisfaction. The proposed framework addresses key gaps in scalability, privacy, and adaptability, demonstrating the potential of hybrid AI techniques in applied nutrition science. By bridging computational methods with food science, this work offers a reproducible, modular solution for personalized health applications. Full article
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14 pages, 1467 KB  
Article
MDKAG: Retrieval-Augmented Educational QA Powered by a Multimodal Disciplinary Knowledge Graph
by Xu Zhao, Guozhong Wang and Yufei Lu
Appl. Sci. 2025, 15(16), 9095; https://doi.org/10.3390/app15169095 - 18 Aug 2025
Viewed by 850
Abstract
With the accelerated digital transformation in education, the efficient integration of massive multimodal instructional resources and the support for interactive question answering (QA) remains a prominent challenge. This study introduces Multimodal Disciplinary Knowledge-Augmented Generation (MDKAG), a framework integrating retrieval-augmented generation (RAG) with a [...] Read more.
With the accelerated digital transformation in education, the efficient integration of massive multimodal instructional resources and the support for interactive question answering (QA) remains a prominent challenge. This study introduces Multimodal Disciplinary Knowledge-Augmented Generation (MDKAG), a framework integrating retrieval-augmented generation (RAG) with a multimodal disciplinary knowledge graph (MDKG). MDKAG first extracts high-precision entities from digital textbooks, lecture slides, and classroom videos by using the Enhanced Representation through Knowledge Integration 3.0 (ERNIE 3.0) model and then links them into a graph that supports fine-grained retrieval. At inference time, the framework retrieves graph-adjacent passages, integrates multimodal data, and feeds them into a large language model (LLM) to generate context-aligned answers. An answer-verification module checks semantic overlap and entity coverage to filter hallucinations and triggers incremental graph updates when new concepts appear. Experiments on three university courses show that MDKAG reduces hallucination rates by up to 23% and increases answer accuracy by 11% over text-only RAG and knowledge-augmented generation (KAG) baselines, demonstrating strong adaptability across subject domains. The results indicate that MDKAG offers an effective route for scalable knowledge organization and reliable interactive QA in education. Full article
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26 pages, 553 KB  
Systematic Review
Financial Education and Personal Finance: A Systematic Review of Evidence, Context, and Implications from the Spanish Language Academic Literature in Latin America
by Elena Jesús Alvarado-Cáceres, Luz Maribel Vásquez-Vásquez and Víctor Hugo Fernández-Bedoya
J. Risk Financial Manag. 2025, 18(8), 455; https://doi.org/10.3390/jrfm18080455 - 15 Aug 2025
Viewed by 2512
Abstract
The growing complexity of financial markets, driven by globalization and digitalization, has increased the need for individuals to make informed financial decisions. In this context, financial education and personal finance have become crucial areas of study. This systematic review aimed to identify and [...] Read more.
The growing complexity of financial markets, driven by globalization and digitalization, has increased the need for individuals to make informed financial decisions. In this context, financial education and personal finance have become crucial areas of study. This systematic review aimed to identify and analyze the existing scientific evidence on these topics, determine the countries contributing to the literature, and extract key conclusions and lessons. A comprehensive search was conducted across the Scopus, Scielo, and La Referencia databases using keywords in Spanish. The initial query yielded 97 documents, which were filtered using seven inclusion and exclusion criteria, resulting in a final sample of 19 relevant articles. The reviewed studies highlight that financial education is a key factor in promoting economic well-being, reducing over-indebtedness, supporting entrepreneurship, and enhancing social inclusion. The effectiveness of financial education depends on equitable access to information, the use of digital tools, tailored approaches for diverse populations, and systematic program evaluation. The findings suggest that collaborative efforts between governments, educational institutions, and the financial sector are necessary to develop inclusive and practical financial education strategies, particularly for vulnerable populations. Financial education must be approached as a continuous, adaptive process to effectively respond to evolving economic challenges. Full article
(This article belongs to the Special Issue The New Horizons of Global Financial Literacy)
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14 pages, 2652 KB  
Article
Optimized Multi-Antenna MRC for 16-QAM Transmission in a Photonics-Aided Millimeter-Wave System
by Rahim Uddin, Weiping Li and Jianjun Yu
Sensors 2025, 25(16), 5010; https://doi.org/10.3390/s25165010 - 13 Aug 2025
Cited by 1 | Viewed by 721
Abstract
This work presents an 80 Gbps photonics-aided millimeter-wave (mm Wave) wireless communication system employing 16-Quadrature Amplitude Modulation (16-QAM) and a 1 × 2 single-input multiple-output (SIMO) architecture with maximum ratio combining (MRC) to achieve robust 87.5 GHz transmission over 4.6 km. By utilizing [...] Read more.
This work presents an 80 Gbps photonics-aided millimeter-wave (mm Wave) wireless communication system employing 16-Quadrature Amplitude Modulation (16-QAM) and a 1 × 2 single-input multiple-output (SIMO) architecture with maximum ratio combining (MRC) to achieve robust 87.5 GHz transmission over 4.6 km. By utilizing polarization-diverse optical heterodyne generation and spatial diversity reception, the system enhances spectral efficiency while addressing the low signal-to-noise ratio (SNR) and channel distortions inherent in long-haul links. A blind equalization scheme combining the constant modulus algorithm (CMA) and decision-directed least mean squares (DD-LMS) filtering enables rapid convergence and suppresses residual inter-symbol interference, effectively mitigating polarization drift and phase noise. The experimental results demonstrate an SNR gain of approximately 3 dB and a significant bit error rate (BER) reduction with MRC compared to single-antenna reception, along with improved SNR performance in multi-antenna configurations. The synergy of photonic mm Wave generation, adaptive spatial diversity, and pilot-free digital signal processing (DSP) establishes a robust framework for high-capacity wireless fronthaul, overcoming atmospheric attenuation and dynamic impairments. This approach highlights the viability of 16-QAM in next-generation ultra-high-speed networks (6G/7G), balancing high data rates with resilient performance under channel degradation. Full article
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17 pages, 2534 KB  
Article
Modeling Recommender Systems Using Disease Spread Techniques
by Peixiong He, Libo Sun, Xian Gao, Yi Zhou and Xiao Qin
Information 2025, 16(8), 687; https://doi.org/10.3390/info16080687 - 13 Aug 2025
Viewed by 521
Abstract
Recommender systems on digital platforms profoundly influence user behavior through content dissemination, and their diffusion process is similar to the spreading mechanism of infectious diseases to some extent. In this paper, we use a network-based susceptibility-infection (SI) model to model the propagation dynamics [...] Read more.
Recommender systems on digital platforms profoundly influence user behavior through content dissemination, and their diffusion process is similar to the spreading mechanism of infectious diseases to some extent. In this paper, we use a network-based susceptibility-infection (SI) model to model the propagation dynamics of recommended content, and systematically compare the differences in propagation efficiency among three recommendation strategies based on popularity, collaborative filtering, and content. We constructed scale-free user networks based on real-world clickstream data and dynamically adapted the SI model to reflect the realistic scenario of user engagement decay over time. To enhance the understanding of the recommendation process, we further simulate the visualization changes of the propagation process to show how the content spreads among users. The experimental results show that collaborative filtering performs superior in the initial dissemination, but its dissemination effect decays rapidly over time and is weaker than the other two methods. This study provides new ideas for modeling and understanding recommender systems from an epidemiological perspective. Full article
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