Complete catalogue for cable (By Shen Tai Electric Cable Co., Ltd)

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The document discusses the results of a study on the impact of COVID-19 lockdowns on air pollution. The study found that lockdowns led to short-term reductions in nitrogen dioxide and fine particulate matter concentrations globally. However, the impacts on air quality were temporary and levels returned to pre-pandemic levels once restrictions were lifted and activity resumed.

Engineering

Complete catalogue for cable (By Shen Tai Electric Cable Co., Ltd)

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In this study, using the fuzzy logic method, a stress detection tool was created with body temperature and blood pressure parameters as indicators to determine a person's stress level. This tool uses the LM35DZ sensor to detect body temperature, the MPX5100GP sensor to read blood pressure values, and Arduino Uno as a data processor from sensor readings which are then calculated using the fuzzy logic method as a stress level decisionmaker. The resulting output measures blood pressure, body temperature, and the stress level experienced by a person, which will be displayed on the liquid crystal display. Based on the results of testing the body temperature parameter, the highest error generated was 1.17%, and for the blood pressure parameter, the highest error was 2.5% for systole and 0.93% for diastole. Furthermore, testing the stress level displayed on the tool is compared to the depression, anxiety, and stress scales 42 (DASS 42), a psychological stress measuring instrument. From the results of testing the tool with the questionnaire, the average conformity level is 74%.

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We investigated the applicability and efficiency of the MLMC approach to the Henry-like problem with uncertain porosity, permeability and recharge. These uncertain parameters were modelled by random fields with three independent random variables. Permeability is a function of porosity. Both functions are time-dependent, have multi-scale behaviour and are defined for two layers. The numerical solution for each random realisation was obtained using the well-known ug4 parallel multigrid solver. The number of random samples required at each level was estimated by calculating the decay of the variances and the computational cost for each level. The MLMC method was used to compute the expected value and variance of several QoIs, such as the solution at a few preselected points $(t,\bx)$, the solution integrated over a small subdomain, and the time evolution of the freshwater integral. We have found that some QoIs require only 2-3 mesh levels and samples from finer meshes would not significantly improve the result. Other QoIs require more grid levels.

litvinenko_Henry_Intrusion_Hong-Kong_2024.pdf

Alexander Litvinenko

Explore the revolutionary concept of microkernel architecture in operating systems with this comprehensive presentation. From its inception to its contemporary applications, this PowerPoint provides a deep dive into the essence and significance of microkernels. Covering the following key aspects: Understanding Microkernel Architecture: Delve into the core principles of microkernel design, distinguishing it from monolithic kernels. Learn how microkernels streamline the operating system by delegating essential functions to user-space processes. Advantages of Microkernels: Uncover the myriad benefits of microkernel architecture, including increased system stability, modularity, and flexibility. Explore how microkernels facilitate easier maintenance, extensibility, and customization of operating systems. Challenges and Trade-offs: Navigate through the challenges and trade-offs associated with microkernel design, such as performance overhead and complexity. Gain insights into strategies for mitigating these challenges and optimizing microkernel-based systems. Real-world Implementations: Explore notable examples of operating systems built on microkernel architecture, including GNU Hurd, MINIX, and QNX. Understand how these systems leverage microkernels to achieve specific goals in terms of reliability, security, and real-time performance. Whether you're a budding operating system enthusiast or a seasoned developer exploring alternative system architectures, this PowerPoint presentation offers a comprehensive overview of microkernel technology. Download now and embark on a journey to reimagine the future of operating systems with microkernels.

Microkernel in Operating System | Operating System

Sampad Kar

Breast cancer is the most fatal women’s cancer, and accurate diagnosis of this disease in the initial phase is crucial to abate death rates worldwide. The demand for computer-aided disease diagnosis technologies in healthcare is growing significantly to assist physicians in ensuring the effectual treatment of critical diseases. The vital purpose of this study is to analyze and evaluate the classification efficiency of several machine learning algorithms with hyperparameter optimization techniques using grid search and random search to reveal an efficient breast cancer diagnosis approach. Choosing the optimal combination of hyperparameters using hyperparameter optimization for machine learning models has a straight influence on the performance of models. According to the findings of several evaluation studies, the k-nearest neighbor is addressed in this study for effective diagnosis of breast cancer, which got a 100.00% recall value with hyperparameters found utilizing grid search. k-nearest neighbor, logistic regression, and multilayer perceptron obtained 99.42% accuracy after utilizing hyperparameter optimization. All machine learning models showed higher efficiency in breast cancer diagnosis with grid search-based hyperparameter optimization except for XGBoost. Therefore, the evaluation outcomes strongly validate the effectiveness and reliability of the proposed technique for breast cancer diagnosis.

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According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.

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In this short lecture, I explain the fundamentals of electromagnetic compatibility (EMC), the basic coupling model and coupling paths via cables, electric fields, magnetic fields and wave fields. We also look at electric vehicles as an example of systems with many conducted EMC problems due to power electronic devices such as rectifiers and inverters with non-linear components such as diodes and fast switching components such as MOSFETs or IGBTs. After a brief review of circuit analysis fundamentals and an experimental investigation of the frequency-dependent impedance of resistors, capacitors and inductors, we look at a simple low-pass filter. The transfer function is derived and measured.

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Exploring AI's Impact: Key Features in Due Diligence In the realm of due diligence, AI emerges as a game-changer, revolutionizing traditional methods with its advanced features. AI-powered algorithms excel in data analysis, swiftly sifting through vast amounts of information for crucial insights. Automation streamlines document review processes, ensuring accuracy and efficiency. Moreover, AI enables predictive analytics, forecasting potential risks and opportunities with precision. With machine learning capabilities, AI continuously improves its performance, adapting to evolving trends and patterns. By integrating AI into due diligence practices, businesses gain a competitive edge, maximizing efficiency and making informed decisions swiftly. AI in due diligence is not just a tool; it's a transformational force driving businesses into the future. https://www.leewayhertz.com/ai-in-due-diligence/

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