Potenciális COVID-19 fertőzés automatikus felismerésé hagyományos véranalízis alapján: Automatic detection of potential COVID-19 infection based on conventional blood analysis

To control the spread of the COVID-19 it is very important to identify those who have been already infected by this new type of virus. The rRT-PCR (reverse transcription polymerase chain reaction) testing is the golden standard for COVID-19 detection, but it is time consuming, laborious manual process and it is very short in supply. To reduce the number of tests, in this article we will present a possible solution for COVID-19 preliminary patient filtering based on regular blood tests, using artificial intelligence (AI) models. The most appropriate AI model will be selected using our auto-adaptive AI platform, AutomaticAI. The hyperparameters of the selected algorithm will also be adjusted automatically by this platform to match the context of the problem. Kivonat A COVID-19 terjedésének megfékezése érdekében nagyon fontos azonosítani azokat a személyeket, akiket már megfertőzött ezen új típusú vírus. Az rRT-PCR (reverse transcription polymerase chain reaction) teszt a COVID-19 detektálásának leghatékonyabb eszköze, ám időigényes, fárasztó kézi folyamat, és nagyon szűk a készlet belőle. A tesztek számának csökkentése érdekében, ebben a cikkben a COVID-19 előzetes betegszűrésének lehetséges megoldását mutatjuk be hagyományos vérvizsgálatok alapján, mesterséges intelligencia (AI) modellek felhasználásával. A leghatékonyabb AI-modellt automatikusan alkalmazkodó AI-platformunk, az AutomaticAI segítségével választjuk ki. A kiválasztott algoritmus hiperparamétereit platformunk képes automatikusan beállítani, ezáltal megfelelve a probléma kontextusának. &nbsp

Edge Offloading in Smart Grid

The energy transition supports the shift towards more sustainable energy alternatives, paving towards decentralized smart grids, where the energy is generated closer to the point of use. The decentralized smart grids foresee novel data-driven low latency applications for improving resilience and responsiveness, such as peer-to-peer energy trading, microgrid control, fault detection, or demand response. However, the traditional cloud-based smart grid architectures are unable to meet the requirements of the new emerging applications such as low latency and high-reliability thus alternative architectures such as edge, fog, or hybrid need to be adopted. Moreover, edge offloading can play a pivotal role for the next-generation smart grid AI applications because it enables the efficient utilization of computing resources and addresses the challenges of increasing data generated by IoT devices, optimizing the response time, energy consumption, and network performance. However, a comprehensive overview of the current state of research is needed to support sound decisions regarding energy-related applications offloading from cloud to fog or edge, focusing on smart grid open challenges and potential impacts. In this paper, we delve into smart grid and computational distribution architec-tures, including edge-fog-cloud models, orchestration architecture, and serverless computing, and analyze the decision-making variables and optimization algorithms to assess the efficiency of edge offloading. Finally, the work contributes to a comprehensive understanding of the edge offloading in smart grid, providing a SWOT analysis to support decision making.Comment: to be submitted to journa

Colonization, Infection and Risk Factors for Death in an Infectious Disease ICU in Romania

Knowing the bacterial strains in the intensive care unit (ICU) is important for reducing the rate of bacterial transmission and the risk of healthcare-associated infections (HAIs), allowing for targeted interventions to reduce the risk of death by HAIs. We performed a retrospective case-control study in a single center that included 320 bacteriologically screened patients from the ICU of the Infectious Diseases Hospital in Constanta between September 2017 and March 2020. Sixty-five secondary bacterial infections were identified as the cause of hospital admission and 60 bacterial colonizations. There were 20 cases and 300 controls for the mortality rate and risk factors for death. Multivariate analysis identified that hospitalization of patients for HIV infection (OR 11.82, 95% CI: 1.69-83.62, P ≤0.05) and Clostridioides difficile infection (OR 7.38, 95% CI: 1.39 -39.22, P ≤ 0.05) were independent risk factors associated with death. We observed that the number of colonizations or secondary infections in the ICU was similar, and the mortality rate in the ICU was influenced by HIV infection or Clostridioides difficile infection

Anomaly detection techniques in cyber-physical systems

Nowadays, when multiple aspects of our life depend on complex cyber-physical systems, automated anomaly detection, prevention and handling is a critical issue that inuence our security and quality of life. Recent catastrophic events showed that manual (human-based) handling of anomalies in complex systems is not recommended, automatic and intelligent handling being the proper approach. This paper presents, through a number of case studies, the challenges and possible solutions for implementing computer-based anomaly detection systems

In-Storage Computation of Histograms with differential privacy

Network-attached Smart Storage is becoming increasingly common in data analytics applications. It relies on processing elements, such as FPGAs, close to the storage medium to offload compute-intensive operations, reducing data movement across distributed nodes in the system. As a result, it can offer outstanding performance and energy efficiency. Modern data analytics systems are not only becoming more distributed they are also increasingly focusing on privacy policy compliance. This means that, in the future, Smart Storage will have to offload more and more privacy-related processing. In this work, we explore how the computation of differentially private (DP) histograms, a basic building block of privacy-preserving analytics, can be offloaded to FPGAs. By performing DP aggregation on the storage side, untrusted clients can be allowed to query the data in aggregate form without risking the leakage of personally identifiable information. We prototype our idea by extending an FPGA-based distributed key-value store with three new components. First, a histogram module, that processes values at 100Gbps line-rate. Second, a random noise generator that adds noise to final histogram according to the rules dictated by DP. Third, a mechanism to limit the rate at which key-value pairs can be used in histograms, to stay within the DP privacy budget

The case for adding privacy-related offloading to smart storage

It is important to ensure that personally identifiable information (PII) is protected within large distributed systems and is used only for intended purposes. Achieving this is challenging and several techniques have been proposed for privacy-preserving analytics, but they typically focus on the end hosts only. We argue that future storage solutions should include, in addition to emerging compute offload, also privacy-related operators. Since many privacy operators, such as perturbation and anonymization, take place as the very first step before other computations, query offload to a Smart Storage device might be only feasible in the future if privacy-related operators can also be offloaded. In this work we demonstrate that privacy-preserving operators can be implemented in hardware without reducing read bandwidths. We focus on perturbations and extend an FPGA-based network-attached Smart Storage solution to show that it is possible to provide these operations at 10Gbps line-rate while using only a small amount of additional FPGA real-estate. We also discuss how future faster smart storage nodes should look like in the light of these additional requirements

Edge Offloading in Smart Grid

The management of decentralized energy resources and smart grids needs novel data-driven low-latency applications and services to improve resilience and responsiveness and ensure closer to real-time control. However, the large-scale integration of Internet of Things (IoT) devices has led to the generation of significant amounts of data at the edge of the grid, posing challenges for the traditional cloud-based smart-grid architectures to meet the stringent latency and response time requirements of emerging applications. In this paper, we delve into the energy grid and computational distribution architectures, including edge–fog–cloud models, computational orchestration, and smart-grid frameworks to support the design and offloading of grid applications across the computational continuum. Key factors influencing the offloading process, such as network performance, data and Artificial Intelligence (AI) processes, computational requirements, application-specific factors, and energy efficiency, are analyzed considering the smart-grid operational requirements. We conduct a comprehensive overview of the current research landscape to support decision-making regarding offloading strategies from cloud to fog or edge. The focus is on metaheuristics for identifying near-optimal solutions and reinforcement learning for adaptively optimizing the process. A macro perspective on determining when and what to offload in the smart grid is provided for the next-generation AI applications, offering an overview of the features and trade-offs for selecting between federated learning and edge AI solutions. Finally, the work contributes to a comprehensive understanding of edge offloading in smart grids, providing a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis to support cost–benefit analysis in decision-making regarding offloading strategies

Advanced Techniques for Monitoring and Management of Urban Water Infrastructures—An Overview

Water supply systems are essential for a modern society. This article presents an overview of the latest research related to information and communication technology systems for water resource monitoring, control and management. The main objective of our review is to show how emerging technologies offer support for smart administration of water infrastructures. The paper covers research results related to smart cities, smart water monitoring, big data, data analysis and decision support. Our evaluation reveals that there are many possible solutions generated through combinations of advanced methods. Emerging technologies open new possibilities for including new functionalities such as social involvement in water resource management. This review offers support for researchers in the area of water monitoring and management to identify useful models and technologies for designing better solutions

PD-L1, CD4+, and CD8+ Tumor-Infiltrating Lymphocytes (TILs) Expression Profiles in Melanoma Tumor Microenvironment Cells

(1) Background: Because melanoma is an aggressive tumor with an unfavorable prognosis, we aimed to characterize the PD-L1 expression in melanomas in association with T cell infiltrates because PD-1/PD-L1 blockade represents the target in treating melanoma strategy. (2) Methods: The immunohistochemical manual quantitative methods of PD-L1, CD4, and CD8 TILs were performed in melanoma tumor microenvironment cells. (3) Results: Most of the PD-L1 positive, expressing tumors, have a moderate score of CD4+ TILs and CD8+TILs (5−50% of tumor area) in tumoral melanoma environment cells. The PD-L1 expression in TILs was correlated with different degrees of lymphocytic infiltration described by the Clark system (X2 = 8.383, p = 0.020). PD-L1 expression was observed often in melanoma cases, with more than 2−4 mm of Breslow tumor thickness being the associated parameters (X2 = 9.933, p = 0.014). (4) Conclusions: PD-L1 expression represents a predictive biomarker with very good accuracy for discriminating the presence or absence of malign tumoral melanoma cells. PD-L1 expression was an independent predictor of good prognosis in patients with melanomas