Cloud Computing for Effective Cyber Security Attack Detection in Smart Cities

An astute metropolis is an urbanized region that accumulates data through diverse numerical and experiential understanding. Cloud-connected Internet of Things (IoT) solutions have the potential to aid intelligent cities in collecting data from inhabitants, devices, residences, and alternative origins. The monitoring and administration of carrying systems, plug-in services, reserve managing, H2O resource schemes, excess managing, illegal finding, safety actions, ability, numeral collection, healthcare abilities, and extra openings all make use of the processing and analysis of this data. This study aims to improve the security of smart cities by detecting attacks using algorithms drawn from the UNSW-NB15 and CICIDS2017 datasets and to create advanced strategies for identifying and justifying cyber threats in the context of smart cities by leveraging real-world network traffic data from UNSW-NB15 and labelled attack actions from CICIDS2017. The research aims to underwrite the development of more effective intrusion detection systems tailored to the unique problems of safeguarding networked urban environments, hence improving the flexibility and safety of smart cities by estimating these datasets

Securing the Skies: Cybersecurity Strategies for Smart City Cloud using Various Algorithams

As smart cities continue to evolve, their reliance on cloud computing technologies becomes increasingly apparent, enabling the seamless integration of data-driven services and urban functionalities. However, this transformation also raises concerns about the security of the vast and interconnected cloud infrastructures that underpin these cities' operations. This paper explores the critical intersection of cloud computing and cybersecurity within the context of smart cities. This research is dealing with challenges posed by the rapid expansion of smart city initiatives and their reliance on cloud-based solutions. It investigates the vulnerabilities that emerge from this technological convergence, emphasizing the potential risks to data privacy, urban services, and citizen well-being. The abstract presents a comprehensive overview of the evolving threat landscape that smart cities face in the realm of cloud computing. To address these challenges, the abstract highlights the importance of proactive cybersecurity strategies tailored specifically to the unique needs of smart cities. It underscores the significance of adopting a multi-layered approach that encompasses robust encryption protocols, intrusion detection systems, threat intelligence sharing, and collaborative efforts among stakeholders. Drawing insights from existing research and real-world case studies, the abstract showcases innovative solutions that leverage advanced technologies like artificial intelligence and blockchain to fortify the security posture of smart city cloud infrastructures. It explores the role of data governance, user authentication, and anomaly detection in creating a resilient cybersecurity framework that safeguards critical urban systems

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

IOT based Garbage Monitoring And Waste Management system for Smart Cities

Waste is an important issue, which needs to be tackled smartly. The main purpose of this paper is to develop the system which uses the information collected from sensors to manage the waste collection. In proposed system, Smart waste bins are located in several areas of city are connected to Internet wirelessly, they equipped with sensors which collects the data about level of collected waste in waste bin. Then Smart waste bin sends this information to central web portal using WIFI module. If the waste bin is filled up to its threshold value then the message is displayed on web portal and the responsible authority take proper action and it will shows the all information on to the Smart waste bin Application on the users mobile phone

IOT Based Garbage Monitoring and Waste Management System for Smart Cities

Waste is an important issue, which needs to be tackled smartly. The main purpose of this paper is to develop the system which uses the information collected from sensors to manage the waste collection. In proposed system, Smart waste bins are located in several areas of city are connected to Internet wirelessly, they equipped with sensors which collects the data about level of collected waste in waste bin. Then Smart waste bin sends this information to central web portal using WIFI module. If the waste bin is filled up to its threshold value then the message is displayed on web portal and the responsible authority take proper action and it will shows the all information on to the Smart waste bin Application on the users mobile phone

Neglected tropical diseases and omics science:Proteogenomics analysis of the promastigote stage of leishmania major parasite

Among the neglected tropical diseases, leishmaniasis is one of the most devastating, resulting in significant mortality and contributing to nearly 2 million disability-adjusted life years. Cutaneous leishmaniasis is a debilitating disorder caused by the kinetoplastid protozoan parasite Leishmania major, which results in disfiguration and scars. L. major genome was the first to be sequenced within the genus Leishmania. Use of proteomic data for annotating genomes is a complementary approach to conventional genome annotation approaches and is referred to as proteogenomics. We have used a proteogenomics-based approach to map the proteome of L. major and also annotate its genome. In this study, we searched L. major promastigote proteomic data against the annotated L. major protein database. Additionally, we searched the proteomic data against six-frame translated L. major genome. In all, we identified 3613 proteins in L. major promastigotes, which covered 43% of its proteome. We also identified 26 genome search-specific peptides, which led to the identification of three novel genes previously not identified in L. major. We also corrected the annotation of N-termini of 15 genes, which resulted in extension of their protein products. We have validated our proteogenomics findings by RT-PCR and sequencing. In addition, our study resulted in identification of 266 N-terminally acetylated peptides in L. major, one of the largest acetylated peptide datasets thus far in Leishmania. This dataset should be a valuable resource to researchers focusing on neglected tropical diseases.</p