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Search Results (720)

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Keywords = e-learning platform

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32 pages, 4222 KiB  
Article
AI-Driven Anomaly Detection in E-Commerce Services: A Deep Learning and NLP Approach to the Isolation Forest Algorithm Trees
by Pascal Muam Mah, Iwona Skalna and Tomasz Pelech-Pilichowski
J. Theor. Appl. Electron. Commer. Res. 2025, 20(3), 214; https://doi.org/10.3390/jtaer20030214 - 14 Aug 2025
Abstract
The accelerated development of e-commerce has given rise to sophisticated systems defined by significant user interaction, a variety of product offerings, and considerable quantities of structured and unstructured data. Upholding trust and operational security is becoming ever more essential. E-commerce platforms are susceptible [...] Read more.
The accelerated development of e-commerce has given rise to sophisticated systems defined by significant user interaction, a variety of product offerings, and considerable quantities of structured and unstructured data. Upholding trust and operational security is becoming ever more essential. E-commerce platforms are susceptible to deceptive practices, including counterfeit reviews, dubious transactions, and anomalous usage behaviors. This research introduces a framework for anomaly detection powered by artificial intelligence, integrating deep learning and natural language processing (NLP) with the isolation forest algorithm tree to enhance the identification of unusual activities on e-commerce platforms. We leveraged customer feedback, transaction logs, and user interaction data obtained from Kaggle. Textual reviews were interpreted using natural language processing (NLP), while deep learning was utilized to discern behavioral patterns. The isolation forest algorithm tree was employed to detect statistical anomalies in multidimensional data. The hybrid model surpassed conventional techniques in terms of detection accuracy, recall, and interpretability. It successfully detects suspicious actions and clarifies anomalies in their relevant context. The application of AI techniques, particularly natural language processing, deep learning, and isolation forest algorithm trees, establishes a solid foundation for anomaly detection in the realm of e-commerce. This approach fosters a more secure and trustworthy experience for online consumers. Full article
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7 pages, 188 KiB  
Proceeding Paper
Lightweight Post-Quantum Cryptography: Applications and Countermeasures in Internet of Things, Blockchain, and E-Learning
by Chin-Ling Chen, Kuang-Wei Zeng, Wei-Ying Li, Chin-Feng Lee, Ling-Chun Liu and Yong-Yuan Deng
Eng. Proc. 2025, 103(1), 14; https://doi.org/10.3390/engproc2025103014 - 12 Aug 2025
Viewed by 104
Abstract
With the rapid advancement of quantum computing technology, traditional encryption methods are encountering unprecedented challenges in the Internet of Things (IoT), blockchain systems, and digital learning (e-learning) platforms. Therefore, we systematically reviewed the applications and countermeasures of lightweight post-quantum cryptographic techniques, focusing on [...] Read more.
With the rapid advancement of quantum computing technology, traditional encryption methods are encountering unprecedented challenges in the Internet of Things (IoT), blockchain systems, and digital learning (e-learning) platforms. Therefore, we systematically reviewed the applications and countermeasures of lightweight post-quantum cryptographic techniques, focusing on the requirements of resource-constrained IoT devices and decentralized systems. We compared the encryption methods based on ring learning with errors (Ring-LWE), Binary Ring-LWE, ring-ExpLWE, the collaborative critical generation framework Q-SECURE, and hardware accelerators for the CRYSTALS-dilithium digital signature scheme. According to the high security and efficiency demands for data transmission and user interaction in e-learning platforms, we developed lightweight encryption schemes. By reviewing existing research achievements, we analyzed the application challenges in IoT, blockchain, and e-learning scenarios and explored strategies for optimizing post-quantum encryption schemes for effective deployment. Full article
20 pages, 1735 KiB  
Article
Multilingual Named Entity Recognition in Arabic and Urdu Tweets Using Pretrained Transfer Learning Models
by Fida Ullah, Muhammad Ahmad, Grigori Sidorov, Ildar Batyrshin, Edgardo Manuel Felipe Riverón and Alexander Gelbukh
Computers 2025, 14(8), 323; https://doi.org/10.3390/computers14080323 - 11 Aug 2025
Viewed by 128
Abstract
The increasing use of Arabic and Urdu on social media platforms, particularly Twitter, has created a growing need for robust Named Entity Recognition (NER) systems capable of handling noisy, informal, and code-mixed content. However, both languages remain significantly underrepresented in NER research, especially [...] Read more.
The increasing use of Arabic and Urdu on social media platforms, particularly Twitter, has created a growing need for robust Named Entity Recognition (NER) systems capable of handling noisy, informal, and code-mixed content. However, both languages remain significantly underrepresented in NER research, especially in social media contexts. To address this gap, this study makes four key contributions: (1) We introduced a manual entity consolidation step to enhance the consistency and accuracy of named entity annotations. In the original datasets, entities such as person names and organization names were often split into multiple tokens (e.g., first name and last name labeled separately). We manually refined the annotations to merge these segments into unified entities, ensuring improved coherence for both training and evaluation. (2) We selected two publicly available datasets from GitHub—one in Arabic and one in Urdu—and applied two novel strategies to tackle low-resource challenges: a joint multilingual approach and a translation-based approach. The joint approach involved merging both datasets to create a unified multilingual corpus, while the translation-based approach utilized automatic translation to generate cross-lingual datasets, enhancing linguistic diversity and model generalizability. (3) We presented a comprehensive and reproducible pseudocode-driven framework that integrates translation, manual refinement, dataset merging, preprocessing, and multilingual model fine-tuning. (4) We designed, implemented, and evaluated a customized XLM-RoBERTa model integrated with a novel attention mechanism, specifically optimized for the morphological and syntactic complexities of Arabic and Urdu. Based on the experiments, our proposed model (XLM-RoBERTa) achieves 0.98 accuracy across Arabic, Urdu, and multilingual datasets. While it shows a 7–8% improvement over traditional baselines (RF), it also achieves a 2.08% improvement over a deep learning (BiLSTM = 0.96), highlighting the effectiveness of our cross-lingual, resource-efficient approach for NER in low-resource, code-mixed social media text. Full article
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26 pages, 1444 KiB  
Article
Enhancing Neural Collaborative Filtering for Product Recommendation by Integrating Sales Data and User Satisfaction
by Haoyang Xia and Yuanyuan Wang
Electronics 2025, 14(16), 3165; https://doi.org/10.3390/electronics14163165 - 8 Aug 2025
Viewed by 231
Abstract
The rapid growth of e-commerce has made it increasingly difficult for users to select appropriate products due to the overwhelming amount of available information. Although many platforms, such as Amazon and Rakuten, encourage users to leave reviews, effectively utilizing this information for personalized [...] Read more.
The rapid growth of e-commerce has made it increasingly difficult for users to select appropriate products due to the overwhelming amount of available information. Although many platforms, such as Amazon and Rakuten, encourage users to leave reviews, effectively utilizing this information for personalized recommendations remains a challenge. To address this issue, we propose a multi-task product recommender system that supports both new users without purchase histories and existing users with interaction records. For new users without purchase histories, we introduce a ranking-based method that combines three market-oriented features: sales volume, sales period, and user satisfaction. User satisfaction is quantified using sentiment analysis of product reviews. These three factors are integrated into a composite score to identify products with a strong market presence and positive customer feedback. For existing users, we developed an enhanced neural collaborative filtering (NCF) method that incorporates a product bias factor. This model, named bias neural collaborative filtering (BNCF), utilizes multilayer perceptrons to learn latent user–product interactions while also capturing item popularity bias. We evaluated the proposed approaches using a real-world dataset from Rakuten. The results show that our multi-task system effectively improves recommendation quality for users in both cold-start and data-rich scenarios. Full article
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27 pages, 851 KiB  
Article
From Lemon Market to Managed Market: How Flagship Entry Reshapes Sellers’ Composition in the Online Market
by Liang Ping, Yanying Chen and Qianhui Yu
J. Theor. Appl. Electron. Commer. Res. 2025, 20(3), 208; https://doi.org/10.3390/jtaer20030208 - 8 Aug 2025
Viewed by 396
Abstract
With the rapid development of e-commerce, ensuring product quality on online platforms has become increasingly important, especially in developing countries where market regulations are still underdeveloped. By treating different sellers offering the same brand’s products as an industry, this study examines the impact [...] Read more.
With the rapid development of e-commerce, ensuring product quality on online platforms has become increasingly important, especially in developing countries where market regulations are still underdeveloped. By treating different sellers offering the same brand’s products as an industry, this study examines the impact of flagship store entry on online product quality by constructing a multiple period difference-in-difference model and conducts detailed empirical tests using full-category and large-span data from Taobao. The empirical results demonstrate that flagship store entry not only prompts the exit of incumbent sellers and deters potential new entrants due to the competition effect, but also facilitates the exit of low-quality sellers while attracting high-quality sellers as a result of a consumer-learning effect. Consequently, the overall quality of the industry is improved, and this effect is more pronounced in high-priced and durable goods industries. The findings of this study have important implications for market structure design and online quality governance in online marketplaces. Full article
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8 pages, 214 KiB  
Proceeding Paper
Evaluation of E-Learning Websites Using Additive Rank Probability Method: Case Study of C Programming Website
by Muhamad Rasydan Mokhtar
Eng. Proc. 2025, 103(1), 7; https://doi.org/10.3390/engproc2025103007 - 7 Aug 2025
Viewed by 120
Abstract
E-learning is conducted by using electronic media and resources for learning activities. Recently, e-learning platforms have received more attention than traditional learning methods. Advances in information and communication systems have resulted in e-learning websites becoming interactive and flexible. However, the rapid increase in [...] Read more.
E-learning is conducted by using electronic media and resources for learning activities. Recently, e-learning platforms have received more attention than traditional learning methods. Advances in information and communication systems have resulted in e-learning websites becoming interactive and flexible. However, the rapid increase in the use of e-learning websites leads to the problem of e-learning website evaluation and selection. In this study, e-learning websites were evaluated by adopting multi-criteria decision-making (MCDM). A new MCDM method, namely additive rank probability (ARP), was developed in this study to select the best e-learning website. To verify the effectiveness of the ARP method, the best C programming website was selected using five alternatives and ten criteria. The ranking of the C programming websites exactly matched those derived by other MCDM methods. However, there was a difference in the ranking by the ARP method with the weighted Euclidean distance-based approximation (WEDBA) method. ARP was proven as a simple and efficient method for identifying the best e-learning website for an effective learning process. Full article
24 pages, 3559 KiB  
Article
Advancing Online Road Safety Education: A Gamified Approach for Secondary School Students in Belgium
by Imran Nawaz, Ariane Cuenen, Geert Wets, Roeland Paul and Davy Janssens
Appl. Sci. 2025, 15(15), 8557; https://doi.org/10.3390/app15158557 - 1 Aug 2025
Viewed by 320
Abstract
Road traffic accidents are a leading cause of injury and death among adolescents, making road safety education crucial. This study assesses the performance of and users’ opinions on the Route 2 School (R2S) traffic safety education program, designed for secondary school students (13–17 [...] Read more.
Road traffic accidents are a leading cause of injury and death among adolescents, making road safety education crucial. This study assesses the performance of and users’ opinions on the Route 2 School (R2S) traffic safety education program, designed for secondary school students (13–17 years) in Belgium. The program incorporates gamified e-learning modules containing, among others, podcasts, interactive 360° visuals, and virtual reality (VR), to enhance traffic knowledge, situation awareness, risk detection, and risk management. This study was conducted across several cities and municipalities within Belgium. More than 600 students from school years 3 to 6 completed the platform and of these more than 200 students filled in a comprehensive questionnaire providing detailed feedback on platform usability, preferences, and behavioral risk assessments. The results revealed shortcomings in traffic knowledge and skills, particularly among older students. Gender-based analysis indicated no significant performance differences overall, though females performed better in risk management and males in risk detection. Furthermore, students from cities outperformed those from municipalities. Feedback on the R2S platform indicated high usability and engagement, with VR-based simulations receiving the most positive reception. In addition, it was highlighted that secondary school students are high-risk groups for distraction and red-light violations as cyclists and pedestrians. This study demonstrates the importance of gamified, technology-enhanced road safety education while underscoring the need for module-specific improvements and regional customization. The findings support the broader application of e-learning methodologies for sustainable, behavior-oriented traffic safety education targeting adolescents. Full article
(This article belongs to the Special Issue Technology Enhanced and Mobile Learning: Innovations and Applications)
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23 pages, 5330 KiB  
Article
Explainable Reinforcement Learning for the Initial Design Optimization of Compressors Inspired by the Black-Winged Kite
by Mingming Zhang, Zhuang Miao, Xi Nan, Ning Ma and Ruoyang Liu
Biomimetics 2025, 10(8), 497; https://doi.org/10.3390/biomimetics10080497 - 29 Jul 2025
Viewed by 469
Abstract
Although artificial intelligence methods such as reinforcement learning (RL) show potential in optimizing the design of compressors, there are still two major challenges remaining: limited design variables and insufficient model explainability. For the initial design of compressors, this paper proposes a technical approach [...] Read more.
Although artificial intelligence methods such as reinforcement learning (RL) show potential in optimizing the design of compressors, there are still two major challenges remaining: limited design variables and insufficient model explainability. For the initial design of compressors, this paper proposes a technical approach that incorporates deep reinforcement learning and decision tree distillation to enhance both the optimization capability and explainability. First, a pre-selection platform for the initial design scheme of the compressors is constructed based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The optimization space is significantly enlarged by expanding the co-design of 25 key variables (e.g., the inlet airflow angle, the reaction, the load coefficient, etc.). Then, the initial design of six-stage axial compressors is successfully completed, with the axial efficiency increasing to 84.65% at the design speed and the surge margin extending to 10.75%. The design scheme is closer to the actual needs of engineering. Secondly, Shapley Additive Explanations (SHAP) analysis is utilized to reveal the influence of the mechanism of the key design parameters on the performance of the compressors in order to enhance the model explainability. Finally, the decision tree inspired by the black-winged kite (BKA) algorithm takes the interpretable design rules and transforms the data-driven intelligent optimization into explicit engineering experience. Through experimental validation, this method significantly improves the transparency of the design process while maintaining the high performance of the DDPG algorithm. The extracted design rules not only have clear physical meanings but also can effectively guide the initial design of the compressors, providing a new idea with both optimization capability and explainability for its intelligent design. Full article
(This article belongs to the Special Issue Advances in Biological and Bio-Inspired Algorithms)
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27 pages, 2966 KiB  
Article
Identifying Weekly Student Engagement Patterns in E-Learning via K-Means Clustering and Label-Based Validation
by Nisreen Alzahrani, Maram Meccawy, Halima Samra and Hassan A. El-Sabagh
Electronics 2025, 14(15), 3018; https://doi.org/10.3390/electronics14153018 - 29 Jul 2025
Viewed by 399
Abstract
While prior work has explored learner behavior using learning management systems (LMS) data, few studies provide week-level clustering validated against external engagement labels. To understand and assist students in online learning platforms and environments, this study presents a week-level engagement profiling framework for [...] Read more.
While prior work has explored learner behavior using learning management systems (LMS) data, few studies provide week-level clustering validated against external engagement labels. To understand and assist students in online learning platforms and environments, this study presents a week-level engagement profiling framework for e-learning environments, utilizing K-means clustering and label-based validation. Leveraging log data from 127 students over a 13-week course, 44 activity-based features were engineered to classify student engagement into high, moderate, and low levels. The optimal number of clusters (k = 3) was identified using the elbow method and assessed through internal metrics, including a silhouette score of 0.493 and R2 of 0.80. External validation confirmed strong alignment with pre-labeled engagement levels based on activity frequency and weighting. The clustering approach successfully revealed distinct behavioral patterns across engagement tiers, enabling a nuanced understanding of student interaction dynamics over time. Regression analysis further demonstrated a significant association between engagement levels and academic performance, underscoring the model’s potential as an early warning system for identifying at-risk learners. These findings suggest that clustering based on LMS behavior offers a scalable, data-driven strategy for improving learner support, personalizing instruction, and enhancing retention and academic outcomes in digital education settings such as MOOCs. Full article
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21 pages, 3448 KiB  
Article
A Welding Defect Detection Model Based on Hybrid-Enhanced Multi-Granularity Spatiotemporal Representation Learning
by Chenbo Shi, Shaojia Yan, Lei Wang, Changsheng Zhu, Yue Yu, Xiangteng Zang, Aiping Liu, Chun Zhang and Xiaobing Feng
Sensors 2025, 25(15), 4656; https://doi.org/10.3390/s25154656 - 27 Jul 2025
Viewed by 457
Abstract
Real-time quality monitoring using molten pool images is a critical focus in researching high-quality, intelligent automated welding. To address interference problems in molten pool images under complex welding scenarios (e.g., reflected laser spots from spatter misclassified as porosity defects) and the limited interpretability [...] Read more.
Real-time quality monitoring using molten pool images is a critical focus in researching high-quality, intelligent automated welding. To address interference problems in molten pool images under complex welding scenarios (e.g., reflected laser spots from spatter misclassified as porosity defects) and the limited interpretability of deep learning models, this paper proposes a multi-granularity spatiotemporal representation learning algorithm based on the hybrid enhancement of handcrafted and deep learning features. A MobileNetV2 backbone network integrated with a Temporal Shift Module (TSM) is designed to progressively capture the short-term dynamic features of the molten pool and integrate temporal information across both low-level and high-level features. A multi-granularity attention-based feature aggregation module is developed to select key interference-free frames using cross-frame attention, generate multi-granularity features via grouped pooling, and apply the Convolutional Block Attention Module (CBAM) at each granularity level. Finally, these multi-granularity spatiotemporal features are adaptively fused. Meanwhile, an independent branch utilizes the Histogram of Oriented Gradient (HOG) and Scale-Invariant Feature Transform (SIFT) features to extract long-term spatial structural information from historical edge images, enhancing the model’s interpretability. The proposed method achieves an accuracy of 99.187% on a self-constructed dataset. Additionally, it attains a real-time inference speed of 20.983 ms per sample on a hardware platform equipped with an Intel i9-12900H CPU and an RTX 3060 GPU, thus effectively balancing accuracy, speed, and interpretability. Full article
(This article belongs to the Topic Applied Computing and Machine Intelligence (ACMI))
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19 pages, 2378 KiB  
Article
The Necessity of Phased Research: Sentiment Fluctuations in Online Comments Caused by Product Value
by Jing Li and Junjie Shen
J. Theor. Appl. Electron. Commer. Res. 2025, 20(3), 185; https://doi.org/10.3390/jtaer20030185 - 23 Jul 2025
Viewed by 489
Abstract
In the sentiment analysis of online comments, all comments are generally considered as a whole, with little attention paid to the inevitable emotional fluctuations in comments caused by changes in product value. In this study, we analyzed the online comments related to apple [...] Read more.
In the sentiment analysis of online comments, all comments are generally considered as a whole, with little attention paid to the inevitable emotional fluctuations in comments caused by changes in product value. In this study, we analyzed the online comments related to apple sales on Chinese e-commerce platforms, and combined topic models, sentiment analysis, and transfer learning to investigate the impact of product value on emotional fluctuations in online comments. We found that as product value changes, the sentiment of online comments undergoes significant fluctuations. Among the prominent negative sentiments, the proportion of topics influenced by product value significantly increases as product value decreases. This study reveals the correlation between changes in product value and sentiment fluctuations in online comments, and demonstrates the necessity of classifying online comments based on product value as an indicator. This study offers a novel perspective for enhancing sentiment analysis by incorporating product value dynamics. Full article
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34 pages, 2648 KiB  
Review
Microfluidic Sensors for Micropollutant Detection in Environmental Matrices: Recent Advances and Prospects
by Mohamed A. A. Abdelhamid, Mi-Ran Ki, Hyo Jik Yoon and Seung Pil Pack
Biosensors 2025, 15(8), 474; https://doi.org/10.3390/bios15080474 - 22 Jul 2025
Viewed by 550
Abstract
The widespread and persistent occurrence of micropollutants—such as pesticides, pharmaceuticals, heavy metals, personal care products, microplastics, and per- and polyfluoroalkyl substances (PFAS)—has emerged as a critical environmental and public health concern, necessitating the development of highly sensitive, selective, and field-deployable detection technologies. Microfluidic [...] Read more.
The widespread and persistent occurrence of micropollutants—such as pesticides, pharmaceuticals, heavy metals, personal care products, microplastics, and per- and polyfluoroalkyl substances (PFAS)—has emerged as a critical environmental and public health concern, necessitating the development of highly sensitive, selective, and field-deployable detection technologies. Microfluidic sensors, including biosensors, have gained prominence as versatile and transformative tools for real-time environmental monitoring, enabling precise and rapid detection of trace-level contaminants in complex environmental matrices. Their miniaturized design, low reagent consumption, and compatibility with portable and smartphone-assisted platforms make them particularly suited for on-site applications. Recent breakthroughs in nanomaterials, synthetic recognition elements (e.g., aptamers and molecularly imprinted polymers), and enzyme-free detection strategies have significantly enhanced the performance of these biosensors in terms of sensitivity, specificity, and multiplexing capabilities. Moreover, the integration of artificial intelligence (AI) and machine learning algorithms into microfluidic platforms has opened new frontiers in data analysis, enabling automated signal processing, anomaly detection, and adaptive calibration for improved diagnostic accuracy and reliability. This review presents a comprehensive overview of cutting-edge microfluidic sensor technologies for micropollutant detection, emphasizing fabrication strategies, sensing mechanisms, and their application across diverse pollutant categories. We also address current challenges, such as device robustness, scalability, and potential signal interference, while highlighting emerging solutions including biodegradable substrates, modular integration, and AI-driven interpretive frameworks. Collectively, these innovations underscore the potential of microfluidic sensors to redefine environmental diagnostics and advance sustainable pollution monitoring and management strategies. Full article
(This article belongs to the Special Issue Biosensors Based on Microfluidic Devices—2nd Edition)
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18 pages, 3220 KiB  
Article
High-Throughput Microfluidic Electroporation (HTME): A Scalable, 384-Well Platform for Multiplexed Cell Engineering
by William R. Gaillard, Jess Sustarich, Yuerong Li, David N. Carruthers, Kshitiz Gupta, Yan Liang, Rita Kuo, Stephen Tan, Sam Yoder, Paul D. Adams, Hector Garcia Martin, Nathan J. Hillson and Anup K. Singh
Bioengineering 2025, 12(8), 788; https://doi.org/10.3390/bioengineering12080788 - 22 Jul 2025
Viewed by 671
Abstract
Electroporation-mediated gene delivery is a cornerstone of synthetic biology, offering several advantages over other methods: higher efficiencies, broader applicability, and simpler sample preparation. Yet, electroporation protocols are often challenging to integrate into highly multiplexed workflows, owing to limitations in their scalability and tunability. [...] Read more.
Electroporation-mediated gene delivery is a cornerstone of synthetic biology, offering several advantages over other methods: higher efficiencies, broader applicability, and simpler sample preparation. Yet, electroporation protocols are often challenging to integrate into highly multiplexed workflows, owing to limitations in their scalability and tunability. These challenges ultimately increase the time and cost per transformation. As a result, rapidly screening genetic libraries, exploring combinatorial designs, or optimizing electroporation parameters requires extensive iterations, consuming large quantities of expensive custom-made DNA and cell lines or primary cells. To address these limitations, we have developed a High-Throughput Microfluidic Electroporation (HTME) platform that includes a 384-well electroporation plate (E-Plate) and control electronics capable of rapidly electroporating all wells in under a minute with individual control of each well. Fabricated using scalable and cost-effective printed-circuit-board (PCB) technology, the E-Plate significantly reduces consumable costs and reagent consumption by operating on nano to microliter volumes. Furthermore, individually addressable wells facilitate rapid exploration of large sets of experimental conditions to optimize electroporation for different cell types and plasmid concentrations/types. Use of the standard 384-well footprint makes the platform easily integrable into automated workflows, thereby enabling end-to-end automation. We demonstrate transformation of E. coli with pUC19 to validate the HTME’s core functionality, achieving at least a single colony forming unit in more than 99% of wells and confirming the platform’s ability to rapidly perform hundreds of electroporations with customizable conditions. This work highlights the HTME’s potential to significantly accelerate synthetic biology Design-Build-Test-Learn (DBTL) cycles by mitigating the transformation/transfection bottleneck. Full article
(This article belongs to the Section Cellular and Molecular Bioengineering)
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14 pages, 1893 KiB  
Article
Unlocking the Potential of Smart Environments Through Deep Learning
by Adnan Ramakić and Zlatko Bundalo
Computers 2025, 14(8), 296; https://doi.org/10.3390/computers14080296 - 22 Jul 2025
Viewed by 231
Abstract
This paper looks at and describes the potential of using artificial intelligence in smart environments. Various environments such as houses and residential and commercial buildings are becoming smarter through the use of various technologies, i.e., various sensors, smart devices and elements based on [...] Read more.
This paper looks at and describes the potential of using artificial intelligence in smart environments. Various environments such as houses and residential and commercial buildings are becoming smarter through the use of various technologies, i.e., various sensors, smart devices and elements based on artificial intelligence. These technologies are used, for example, to achieve different levels of security in environments, for personalized comfort and control and for ambient assisted living. We investigated the deep learning approach, and, in this paper, describe its use in this context. Accordingly, we developed four deep learning models, which we describe. These are models for hand gesture recognition, emotion recognition, face recognition and gait recognition. These models are intended for use in smart environments for various tasks. In order to present the possible applications of the models, in this paper, a house is used as an example of a smart environment. The models were developed using the TensorFlow platform together with Keras. Four different datasets were used to train and validate the models. The results are promising and are presented in this paper. Full article
(This article belongs to the Special Issue Multimodal Pattern Recognition of Social Signals in HCI (2nd Edition))
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15 pages, 613 KiB  
Article
Data-Driven Insights into Consumer Satisfaction in E-Learning: Implications for Sustainable Digital Marketing
by Daniel Moise, Elena Goga, Georgiana Rusu, Raluca-Giorgiana Chivu (Popa) and Mihai-Cristian Orzan
Sustainability 2025, 17(14), 6445; https://doi.org/10.3390/su17146445 - 14 Jul 2025
Viewed by 344
Abstract
This study investigates consumer satisfaction in e-learning services by addressing a specific gap in the literature: the limited integration of sustainability principles and behavioral modeling in understanding satisfaction drivers in online education. While existing studies have explored engagement and usability, few have considered [...] Read more.
This study investigates consumer satisfaction in e-learning services by addressing a specific gap in the literature: the limited integration of sustainability principles and behavioral modeling in understanding satisfaction drivers in online education. While existing studies have explored engagement and usability, few have considered how sustainability-related factors influence satisfaction in digital learning environments. Based on a conceptual model involving system quality, service quality, motivation, and cognitive engagement, we applied structural equation modeling (WarpPLS) to a sample of 312 university students from Romania, using mainstream learning management systems (LMS). Data were collected from students at the Bucharest University of Economic Studies using a convenience sampling method. The results show that service quality and cognitive engagement are the strongest predictors of satisfaction. This study offers practical recommendations for improving sustainable digital marketing strategies in e-learning, such as enhancing support services and aligning platform features with eco-conscious consumer expectations. Full article
(This article belongs to the Special Issue Sustainable Marketing: Consumer Behavior in the Age of Data Analytics)
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