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This review is devoted to presenting the history of the CARDIOSIM© software simulator platform, which was developed in Italy to simulate the human cardiovascular and respiratory systems. The first version of CARDIOSIM© was developed at the Institute of Biomedical Technologies of the National Research Council in Rome. The first platform version published in 1991 ran on a PC with a disk operating system (MS-DOS) and was developed using the Turbo Basic language. The latest version runs on PC with Microsoft Windows 10 operating system; it is implemented in Visual Basic and C++ languages. The platform has a modular structure consisting of seven different general sections, which can be assembled to reproduce the most important pathophysiological conditions. One or more zero-dimensional (0-D) modules have been implemented in the platform for each section. The different modules can be assembled to reproduce part or the whole circulation according to Starling’s law of the heart. Different mechanical ventilatory and circulatory devices have been implemented in the platform, including thoracic artificial lungs, ECMO, IABPs, pulsatile and continuous right and left ventricular assist devices, biventricular pacemakers and biventricular assist devices. CARDIOSIM© is used in clinical and educational environments.

CentOS Stream 9 Essentials is designed to provide detailed information on the installation, use, and administration of the CentOS 9 distribution. For beginners, the book covers topics such as operating system installation, the basics of the GNOME desktop environment, configuring email and web servers, and installing packages and system updates using App Streams. Additional installation topics, such as dual booting with Microsoft Windows, are also covered, together with all important security topics, such as configuring a firewall and user and group administration. For the experienced user, topics such as remote desktop access, the Cockpit web interface, logical volume management (LVM), disk partitioning, swap management, KVM virtualization, Secure Shell (SSH), Linux Containers, and file sharing using both Samba and NFS are covered in detail to provide a thorough overview of this enterprise class operating system.

Get more out of your Windows 11 computer with easy-to-follow advice Powering 75% of the PCs on the planet, Microsoft Windows is capable of extraordinary things. And you don't need to be a computer scientist to explore the nooks and crannies of the operating system! With Windows 11 All-in-One For Dummies, anyone can discover how to dig into Microsoft's ubiquitous operating system and get the most out of the latest version. From securing and protecting your most personal information to socializing and sharing on social media platforms and making your Windows PC your own through personalization, this book offers step-by-step instructions to unlocking Windows 11's most useful secrets.  With handy info from 10 books included in the beginner-to-advanced learning path contained within, this guide walks you through how to:  Install, set up, and customize your Windows 11 PC in a way that makes sense just for you  Use the built-in apps, or download your own, to power some of Windows 11's most useful features  Navigate the Windows 11 system settings to keep your system running smoothly   Perfect for anyone who's looked at their Windows PC and wondered, \"I wonder what else it can do?\", Windows 11 All-in-One For Dummies delivers all the tweaks, tips, and troubleshooting tricks you'll need to make your Windows 11 PC do more than you ever thought possible. 

Software-Defined Networks (SDN) provides more control and network operation over a network infrastructure as an emerging and revolutionary paradigm in networking. Operating the many network applications and preserving the network services and functions, the SDN controller is regarded as the operating system of the SDN-based network architecture. The SDN has several security problems because of its intricate design, even with all its amazing features. Denial-of-service (DoS) attacks continuously impact users and Internet service providers (ISPs). Because of its centralized design, distributed denial of service (DDoS) attacks on SDN are frequent and may have a widespread effect on the network, particularly at the control layer. We propose to implement both MLP (Multilayer Perceptron) and CNN (Convolutional Neural Networks) based on conventional methods to detect the Denial of Services (DDoS) attack. These models have got a complex optimizer installed on them to decrease the false positive or DDoS case detection efficiency. We use the SHAP feature selection technique to improve the detection procedure. By assisting in the identification of which features are most essential to spot the incidents, the approach aids in the process of enhancing precision and flammability. Fine-tuning the hyperparameters with the help of Bayesian optimization to obtain the best model performance is another important thing that we do in our model. Two datasets, InSDN and CICDDoS-2019, are utilized to assess the effectiveness of the proposed method, 99.95% for the true positive (TP) of the CICDDoS-2019 dataset and 99.98% for the InSDN dataset, the results show that the model is highly accurate.

DESCRIPTIONUltimate SwiftUI Handbook for iOS Developers is your comprehensive introduction to SwiftUI, Apple's powerful UI framework. Designed for both aspiring app developers and seasoned programmers, this book equips you with the knowledge and skills to build stunning user interfaces and robust app functionalities. Starting from the basics, you'll learn the core concepts of SwiftUI and its seamless integration with the Swift programming language. With step-by-step tutorials and practical examples, you'll gain hands-on experience in creating interactive apps for iOS, macOS, watchOS, and tvOS.Not only does this book cover the fundamental principles of SwiftUI, but it also goes beyond the basics. Explore advanced topics such as networking with async-await, enabling smooth and responsive data fetching from remote servers. Dive into local storage techniques using UserDefault, CoreData, and File Manager to persist and manage data within your apps.With a focus on practical application, you'll discover how to design responsive layouts, handle user input, and implement state management techniques in your SwiftUI apps. Furthermore, you'll leverage SwiftUI's powerful animation capabilities to create visually appealing and engaging user experiences. i i TABLE OF CONTENTS Chapter 1: Swift LanguageChapter 2: Introduction to View in SwiftUIChapter 3: Implementing Layout in SwiftUIChapter 4: State, Binding, Property Wrapper, and Property ObserverChapter 5: Design Patterns with MVVMChapter 6: Tab Bar, Navigation, and Compositional LayoutChapter 7: Networking with SwiftUI - Part 1Chapter 8: Networking with SwiftUI - Part 2Chapter 9: Local Storage with UserDefaults, CoreData, and File ManagerChapter 10: Construct Beautiful Charts with Swift Charts iOS 17 Appendix Index

International wildlife trade data are frequently used by government agencies, conservation organizations, and scientific researchers to study and protect species from overexploitation and prevent the spread of invasive species and introduction of zoonotic pathogens. Inaccurate data can lead to mistaken conclusions by researchers, the development of unsuccessful remedial conservation actions, and provide government officials with incorrect views of detrimental trade. The U.S. Fish and Wildlife Service (USFWS) maintains the world's most comprehensive national dataset of legal and illegal international wildlife trade recorded by individual shipments and species in its Law Enforcement Management Information System (LEMIS). Although the importance of LEMIS data is not to be understated, the errors and inconsistencies contained therein have not previously been adequately recognized or studied. Based on firsthand experiences with the creation and application of LEMIS data, this manuscript describes a variety of errors, biases, omissions, and an overall lack of data quality assurance. An independent audit of the LEMIS wildlife trade database and the service's policies, procedures, and protocols for managing this system is needed. Additional recommendations are also offered to develop better management standards and bring greater resources for managing LEMIS, asking the nongovernmental organization and intergovernmental organization user communities to play a role.

As the wireless sensor network technology develops, precise positioning has become the key to achieving effective monitoring and data collection. This study proposes a wireless sensor network positioning technology based on improved sampling boxes and fuzzy reasoning to address the problem of low positioning accuracy and efficiency of traditional positioning methods in resource constrained environments. By using a fuzzy clustering algorithm based on time series optimization and a multidimensional Gaussian model to optimize the sampling box, the accuracy and efficiency of localization are significantly improved. The research results indicate that on the UCI and MIT Reality datasets, the Rand coefficient of the fuzzy clustering algorithm optimized for time series is stable at 0.89 when the weight allocation index is 5, which is superior to other comparative algorithms. When the proportion of beacon nodes reaches 40%, the average positioning error of the new model is as low as 0.21 m, which is lower than the comparison model. When the number of nodes is 50 and 200, the response times of the new model are 0.41 ms and 0.72 ms, respectively, which are also the fastest among all models. From this, the new model can maintain high positioning accuracy and fast response under different communication radii and beacon node ratios, especially in scenarios with a large number of nodes, demonstrating excellent versatility and efficiency. This study is of great significance for improving the application efficiency of wireless sensor networks in fields such as environmental monitoring and industrial automation, and provides an effective technical solution for achieving more accurate positioning.

Malware analysis is essential for detecting and mitigating the effects of malicious software. This study introduces a novel hybrid approach using a combination of long short-term memory (LSTM) and convolutional neural networks (CNN) to enhance malware analysis. The proposed work uses a malware classification method combining image processing and machine learning. Malware binaries are converted into grayscale images and analyzed with CNN-LSTM networks. Dynamic features are extracted, ranked, and reduced via Principal Component Analysis (PCA). Various classifiers are used, with final classification by a voting scheme, providing a robust solution for accurate malware family classification. Our approach processes binary code inputs, with the LSTM capturing temporal dependencies and the CNN performing parallel feature extraction. PCA is employed for prominent feature selection, reducing computational time. The proposed approach was evaluated on a public malware dataset and captured through network traffic, demonstrating state-of-the-art performance in identifying various malware families. It significantly reduces the resources required for manual analysis and improves system security. Our approach achieved high precision, recall, accuracy, and F1 score, outperforming existing methods. Future research directions include improving feature extraction techniques and developing real-time detection models that offer a powerful malware detection and analysis tool with promising results and potential for further advancements.

High‐frequency systems (HF) are still used in many communication applications, especially at long distances, due to their robustness. Data can be sent for hundreds of kilometres at a low power cost with only one drawback, low bitrate. To increase the bitrate, several methods were reported using the single‐tone modulation with 24 kHz bandwidth, but still not practical in real‐time applications because, the 24 kHz is unavailable due to the instability of the ionosphere layer. Here, a structured method built on the non‐orthogonal multiple access technique (NOMA) is proposed to increase the bit rate per HF narrowband channel. To increase the data rate, two parts of the data are divided and sent to one user over two different power levels. Since the ionosphere conditions prohibit the use of single‐tone channels most of the time, spread narrowband channels are used instead. The NOMA technique is used on the receiver's side to recognize the two parts of data. The results show a significant improvement (up to 45%) using NOMA in narrowband 3 kHz channels. The bit rate at the receiver has increased up to 200 kb/s concerning, while the maximum bit rate recorded by conventional methods used in previous studies is 138 kb/s. Combined with multichannel algorithms such as HF XL, the total increase in bit rate can be considerable. The system is built on the SDR platform and tested in multiple channels, weather, and power conditions in real‐time experiments to validate its reliability. (1) A key management scheme for distributed IoT devices based on blockchain is proposed; (2) The scheme combines blockchain, hash chain and other technologies; (3) The scheme can ensure the privacy of data transmission in the IoT scene.

The anomaly detection for communication networks is significant for improve the quality of communication services and network reliability. However, traditional communication monitoring methods lack proactive monitoring and real-time alerts and the prediction effect of a single machine learning model on communication data containing multiple features is not ideal. To solve the problem, A prediction-then-detection anomaly detection method was proposed, and quantitative assessment of network anomalies was developed. Specifically, anomaly-free data was obtained by eliminating outliers, and the long short-term memory (LSTM) and autoregressive integral moving average (ARIMA) were combined via residual weighting to predict the future state of the key performance indicators (KPI) without outliers. Anomalies were identified using the error comparison between the prediction and actual values, and the network condition was quantified using the scoring method. It is observed that the proposed LSTM-ARIMA hybrid model has better prediction effect, which can well represent the performance of KPIs of the future state, and the prediction-then-detection anomaly detection method has excellent performance on both precision and recall.