Controlling quantum materials with light is of fundamental and technological importance. By utilizing the strong coupling of light and matter in optical cavities [1–3], recent studies were able to modify some of thei...
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The explosive spread of Corona Virus Disease-19 (COVID-19) in late December 2019, requires the health authorities worldwide to enforced stricter standard operating procedures (SOP) for mass gathering events, which eve...
The explosive spread of Corona Virus Disease-19 (COVID-19) in late December 2019, requires the health authorities worldwide to enforced stricter standard operating procedures (SOP) for mass gathering events, which eventually caused postponement and cancellation. This has led to a great loss and bankruptcy for most event organizers. As an effort to offer solution to this matter, a professional touch producing “soft landing” such as “Mathematical Modelling of Physical Distancing Policy for Mass Gathering Event Organizer” was proposed. Therefore, a holistic understanding about the issues related to physical distancing in mass gathering event is required. This systematic review paper summarizes current practice of physical distancing among mass gathering event organizers. This study reviewed thirteen articles using two leading databases namely Scopus and Google Scholar. Based on thematic analysis, this review finalized four themes: 1) physical distancing method; 2) the importance of physical distancing; 3) challenges in physical distancing enforcement and 4) approach to monitor the compliance of physical distancing.
Interaction is a powerful resource for quantum computation. It can be utilized in applications ranging from the verification of quantum algorithms all the way to verifying quantum mechanics itself. Here, we present th...
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Previous observations suggest the existence of ‘Active sleep’ in cephalopods. To investigate in detail the behavioral structure of cephalopod sleep, we video-recorded four adult specimens of Octopus insularis and qu...
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Abrupt time variations of the properties of optical materials have been at the center of intense research efforts in recent years, with the prospect of enabling extreme wave transformations and of leveraging time as a...
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Recently, Vehicular Cloud Communication (VCC) has been gaining momentum targeting intelligent and efficient data transmission. VCC is a type of mobile ad-hoc network comprising heterogeneous vehicles sharing their res...
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Schistosomiasis is an infectious disease caused by the parasite Schistosoma mansoni , and it is a significant health concern in many underdeveloped tropical regions. The traditional method of diagnosing schistosomiasi...
Schistosomiasis is an infectious disease caused by the parasite Schistosoma mansoni , and it is a significant health concern in many underdeveloped tropical regions. The traditional method of diagnosing schistosomiasis involves fecal examinations under a microscope for parasite eggs, a process that is time consuming, error-prone, and requires specialized training. Artificial intelligence encompasses a broad range of techniques, including both machine learning and its subset, deep learning, which have been successful in solving such problems. Therefore, this study aimed to propose an automated solution that combines DL-based object detection methods with classical ML techniques and HOG feature extraction to identify S. mansoni eggs in images obtained using the Kato-Katz parasitological technique. A real database of 1100 images was created, processed, and annotated by three parasitologists. The proposed system achieved an Average Precision of 0.884 for an @[IoU=0.50] using a Faster R-CNN method with ResNet-50 architecture over the best-annotated data, outperforming other detection models. Based on a threshold analysis, we suggest an integrated approach with a voting scheme of machine learning models to improve results in terms of false positives and false negatives. Comparative analysis with external data and a commercial tool confirmed that our approach offers promising results and applicability to assist health professionals in diagnosing schistosomiasis in public health systems, such as the Brazilian Unified Health System (SUS), potentially improving diagnosis speed and accuracy in endemic regions.
The Internet of Things (IoT) paradigm aims to bring continuous interaction between small embedded devices and humans. The IoT has the potential to affect our daily lives and bring many benefits to society. Low-Power W...
ISBN:
(数字)9781728139494
ISBN:
(纸本)9781728139500
The Internet of Things (IoT) paradigm aims to bring continuous interaction between small embedded devices and humans. The IoT has the potential to affect our daily lives and bring many benefits to society. Low-Power Wide-Area Networks (LPWAN) is a new IoT technology that offers long distance connectivity for a massive number of devices. LPWAN is a promising solution to enable complex IoT scenarios, such as smart cities and smart healthcare. LoRa is currently one of the leading LPWAN solutions available for public use. Due to the great number of connected devices and, in some cases, sensitive data transmitted in IoT networks, security is one of the main concerns in LPWAN. In this paper, we focus on the issues of key management in LoRaWAN. We propose a secure architecture for key management based on private blockchain and smart contracts in order to increase the levels of security and availability in LoRaWAN environments. In order to show the feasibility of the proposed architecture, a working prototype was implemented using open-source tools and commodity hardware.
The successful implementation of algorithms on quantum processors relies on the accurate control of quantum bits (qubits) to perform logic gate operations. In this era of noisy intermediate-scale quantum (NISQ) comput...
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The successful implementation of algorithms on quantum processors relies on the accurate control of quantum bits (qubits) to perform logic gate operations. In this era of noisy intermediate-scale quantum (NISQ) computing, systematic miscalibrations, drift, and crosstalk in the control of qubits can lead to a coherent form of error that has no classical analog. Coherent errors severely limit the performance of quantum algorithms in an unpredictable manner, and mitigating their impact is necessary for realizing reliable quantum computations. Moreover, the average error rates measured by randomized benchmarking and related protocols are not sensitive to the full impact of coherent errors and therefore do not reliably predict the global performance of quantum algorithms, leaving us unprepared to validate the accuracy of future large-scale quantum computations. Randomized compiling is a protocol designed to overcome these performance limitations by converting coherent errors into stochastic noise, dramatically reducing unpredictable errors in quantum algorithms and enabling accurate predictions of algorithmic performance from error rates measured via cycle benchmarking. In this work, we demonstrate significant performance gains under randomized compiling for the four-qubit quantum Fourier transform algorithm and for random circuits of variable depth on a superconducting quantum processor. Additionally, we accurately predict algorithm performance using experimentally measured error rates. Our results demonstrate that randomized compiling can be utilized to leverage and predict the capabilities of modern-day noisy quantum processors, paving the way forward for scalable quantum computing.
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