scispace - formally typeset
Search or ask a question

Answers from top 8 papers

More filters
Papers (8)Insight
Mechanically, the novel fiber has considerable tensile strength of more than 200 MPa.
This fiber has considerable fabrication tolerance.
This has important implications in future fiber research.
These differences most likely affect the physiological differences seen among dietary fiber sources.
Proceedings ArticleDOI
08 Sep 1993
49 Citations
In particular, strong “pristine” fiber can behave quite differently from weaker fiber.
This view has been subject to considerable criticism in recent years, but it does, nevertheless, remain the most effective basis for describing many fiber properties.
Some of these plants can be novel sources of dietetic fiber, which is considered a functional ingredient.
Finally, compounds such as resistant starch and inulin, which do not qualify as dietary fiber under current AOAC methods could be considered Functional Fiber if they show beneficial physiological effects in humans.

See what other people are reading

How does synthetic data improve training results?
5 answers
Synthetic data enhances training results by providing a valuable alternative to real-world datasets, addressing challenges like data annotation efforts and limited generalization capabilities. Various techniques have been proposed to generate high-quality synthetic datasets, such as creating digital twins of real-world data, employing Generative Adversarial Networks with post-processing techniques, and utilizing frameworks like GRADE for realistic environment simulation. These approaches aim to reduce domain gaps between synthetic and real data, leading to improved model performance. By leveraging synthetic data for training, models can benefit from increased diversity, reduced annotation burdens, and enhanced generalization capabilities, ultimately resulting in better training outcomes and more robust algorithms for various computer vision tasks.
What factors govern the tear strength of paper?
5 answers
The tear strength of paper is primarily governed by the fiber properties, including fiber length and strength. Fiber length, when combined with fiber strength, plays a crucial role in determining tear strength, especially in well-bonded networks. Synthetic fibers, like PLA and viscose, can significantly enhance tear strength when mixed with wood fibers due to their high length and strength, leading to well-entangled networks. Additionally, the presence of a heat activatable bonding agent, such as starch, can improve tearing strength by enhancing bonding between fibers during the paper formation process. Furthermore, the incorporation of non-cellulosic synthetic fibers with tear strength enhancing properties, along with a binder material, can result in a paper product with high tear strength and tensile strength comparable to cotton cloth.
In entrepreneurial ecosystem domains, what is finance?
5 answers
In entrepreneurial ecosystem domains, finance plays a critical role in facilitating entrepreneurial activities and fostering growth. It is essential for start-ups to access financial resources to support their innovative ventures and sustain their operations. The interaction between entrepreneurial firms and various sources of finance, especially under conditions of innovation, is crucial for the development and success of these ecosystems. The recycling of entrepreneurial resources within ecosystems creates a self-enhancing cycle of finance that contributes to ecosystem enhancement and member empowerment. Additionally, an improved entrepreneurial finance ecosystem acts as a catalyst for innovation by connecting public funds with industry, promoting effective finance escalators, and impacting scientific research commercialization. Overall, finance in entrepreneurial ecosystems is a key driver of growth, innovation, and sustainability.
What is the current stae of DNA testing for detecting diseases?
4 answers
The current state of DNA testing for detecting diseases involves a significant focus on nucleic acid tests (NATs) for infectious diseases, genetic disorders, and human diseases. These tests offer high sensitivity, specificity, and the ability to multiplex, allowing for simultaneous detection of multiple markers. Advances in genetic testing have revolutionized the diagnosis of genetic syndromes and rare diseases affecting pediatric populations, transitioning from conventional methods to whole-genome approaches. Automated nucleic acid detection systems are crucial for high-throughput testing, especially in the context of outbreaks like COVID-19, emphasizing the importance of rapid and accurate results. Nanopore-based sequencing shows promise in providing high-resolution, single-molecule-based DNA sequencing, aiding in uncovering complex structural variants and disease-specific genomic sequences. These advancements in DNA testing are paving the way for more precise and efficient disease detection and personalized treatments.
What are the most commonly used evaluation metrics for stock price prediction models in the finance industry?
5 answers
The most commonly used evaluation metrics for stock price prediction models in the finance industry include accuracy, mean squared error (MSE), RMSE, and R2 score. These metrics are crucial for assessing the performance and efficiency of various prediction algorithms. Studies have compared the performance of different models like Support Vector Machine (SVM), Long Short Term Memory (LSTM), Decision Tree, Random Forest, and XGBoost, using these metrics to determine the best approach for stock market predictions. While accuracy is a key metric for evaluating the efficiency of algorithms, mean squared error helps in understanding the prediction errors. Additionally, RMSE and R2 score are used to evaluate the models' efficiency, with lower values indicating higher accuracy and precision in predicting stock market returns.
How do autonomous underwater vehicles use computer vision algorithms to detect subsea cables visually?
5 answers
Autonomous underwater vehicles (AUVs) utilize computer vision algorithms for visual detection of subsea cables. These algorithms face challenges like poor lighting, sediment interference, and biofouling mimicry. To enhance detection accuracy, lightweight convolutional neural networks are employed, achieving high precision and recall rates. Additionally, AUVs can assist in subsea pipeline inspection by analyzing images for potential damage through anomaly detection methods. Synthetic data generation based on risk analysis insights helps overcome the lack of training data, improving the reliability of AUV inspections for damage detection. Furthermore, AUVs can detect artificial objects through semi-supervised frameworks using Variational Autoencoders, achieving a precision of 0.64 on unlabelled datasets. This integration of computer vision algorithms in AUV systems enables efficient and accurate visual detection of subsea cables and other subsea infrastructure.
How ontology improve recommendation systems?
5 answers
Ontologies enhance recommendation systems by providing a structured representation of domain knowledge. They address challenges like cold-start problems and data sparsity by incorporating collaborative and content-based filtering approaches. Ontologies aid in suggesting precise and relevant products by considering user preferences based on social network associations. By integrating domain ontologies into recommendation engines, complex semantic relationships can be captured, leading to more accurate suggestions. Tools like OntoSeer monitor ontology development, offering real-time suggestions on naming conventions, vocabulary reuse, and ontology design patterns, thereby improving the quality of the ontology and subsequently enhancing recommendation systems. Overall, ontologies play a crucial role in refining recommendation algorithms and optimizing the user experience in e-commerce portals.
How to implement MiMO in Leo satellites?
4 answers
To implement MIMO in LEO satellites, novel technologies like Distributed Massive MIMO (DM-MIMO) and Holographic MIMO (HMIMO) are proposed. DM-MIMO allows ground user terminals to connect to a cluster of satellites, optimizing network throughput and minimizing handover rates through a cross-layer approach. On the other hand, HMIMO utilizes reconfigurable holographic surfaces (RHS) integrated with user terminals to achieve high directive gain with small antenna sizes, enhancing data services in LEO satellite networks. Additionally, a double-layer precoder and cluster-based power allocation design are suggested for LEO satellite communication with massive MIMO, improving spectral efficiency while considering hardware complexity and channel information. These advanced techniques offer efficient ways to implement MIMO in LEO satellite systems, enhancing connectivity and performance.
What is the difference between software over-the-air update and firmware over-the-air update on ADAS?
5 answers
Software over-the-air (OTA) updates and firmware over-the-air updates play distinct roles in Advanced Driver Assistance Systems (ADAS). Software OTA updates primarily focus on updating applications and higher-level software components, enhancing functionalities and fixing bugs. On the other hand, firmware OTA updates are crucial for updating the embedded software that directly controls hardware components in ADAS, ensuring optimal performance and security. While software updates are more frequent and address application-level changes, firmware updates are essential for maintaining the integrity and efficiency of the underlying system software in ADAS. Both types of updates are vital for the overall performance, safety, and longevity of ADAS solutions, with software updates catering to higher-level functionalities and firmware updates focusing on core system operations and hardware control.
What is the difference between automotive software over-the-air update and firmware over-the-air update on ADAS?
4 answers
Automotive software over-the-air (OTA) updates focus on enhancing vehicle functionalities and addressing security issues remotely, ensuring higher update frequencies and customer satisfaction. These updates are crucial for connected vehicles, requiring secure access to the in-vehicle communication network. In contrast, firmware over-the-air updates for Advanced Driver Assistance Systems (ADAS) specifically target embedded software on electronic control units, aiming to upgrade functionalities and fix bugs remotely. To optimize OTA updates for ADAS, a novel technique called STRIDE ensures secure and scalable updates through cloud and cellular networks, reducing computation overheads and propagation delays significantly. Additionally, the proposed ScalOTA architecture introduces a network of update stations at Electric Vehicle charging stations to enhance download speeds and reduce cellular bandwidth overhead for efficient ADAS firmware updates.
Can graph curvature be used as a feature for anomaly detection in complex networks?
5 answers
Graph curvature can indeed be utilized as a feature for anomaly detection in complex networks. Various methods leverage graph attributes and structures to detect anomalies effectively, with some focusing on node outlier detection in attributed graphs. One approach involves using knowledge graph technology to extract graph feature parameters reflecting node and network characteristics, followed by a two-stage unsupervised anomaly analysis method for abnormal changes in these features. Graph neural networks (GNNs) have been extensively studied for anomaly detection, utilizing graph attributes and structures to score anomalies appropriately. These models have shown success in tasks like node classification and link prediction, making them valuable for detecting anomalies in graphs with non-conforming patterns.