Cyber security Approach for Programs and Networks

It is the process of protecting systems, networks, and programs against digital attacks. These cyber attacks usually aim to access, alter or destroy sensitive information; For the purpose of extorting money from users or interrupting normal business operations. Implementing cyber security measures is a huge challenge today because there are more devices than people and attackers are becoming more innovative. A successful cyber security approach has multiple layers of protection spread across computers, networks, programs or the data one wants to preserve. People, processes and technology must complement each other within an organization to create an effective defense against cyber attack

Electronic Governance that depends on Technology

Many dimensions and factors influence the definition of e-governance. The word “electronic” in the term electronic governance means that governance that depends on technology. E-governance is the use of Information and Communication Technology (ICT) to deliver government services, exchange information of telecom transactions, and the integration of various stand-alone systems and services between government and citizen (G2C), between government and business (G2B) and between governments and each other (1).                      (G2G) as well as departmental operations and interactions within the whole-of-government framework. Through e-governance, government services available to citizens are provided in a convenient, efficient and transparent manner. The three main target groups that can be distinguished in the concepts of governance are government, citizens and corporations/interest groups. There are no clear boundaries in governance .                                                                                                              &nbsp

Monitoring des activités et détection de chute chez les personnes âgées

National audienceStand formations : Masters électronique, informatique et LPSI

Enzymatic Electrochemical Biosensors for Neurotransmitters Detection: Recent Achievements and Trends

Neurotransmitters (NTs) play a crucial role in regulating the behavioral and physiological functions of the nervous system. Imbalances in the concentrations of NT have been directly linked to various neurological diseases (e.g., Parkinson’s, Huntington’s, and Alzheimer’s disease), in addition to multiple psychotic disorders such as schizophrenia, depression, dementia, and other neurodegenerative disorders. Hence, the rapid and real-time monitoring of the NTs is of utmost importance in comprehending neurological functions and identifying disorders. Among different sensing techniques, electrochemical biosensors have garnered significant interest due to their ability to deliver fast results, compatibility for miniaturization and portability, high sensitivity, and good controllability. Furthermore, the utilization of enzymes as recognition elements in biosensing design has garnered renewed attention due to their unique advantages of catalytic biorecognition coupled with simultaneous signal amplification. This review paper primarily focuses on covering the recent advances in enzymatic electrochemical biosensors for the detection of NTs, encompassing the importance of electrochemical sensors, electrode materials, and electroanalytical techniques. Moreover, we shed light on the applications of enzyme-based biosensors for NTs detection in complex matrices and in vivo monitoring. Despite the numerous advantages of enzymatic biosensors, there are still challenges that need to be addressed, which are thoroughly discussed in this paper. Finally, this review also presents an outlook on future perspectives and opportunities for the development of enzyme-based electrochemical biosensors for NTs detection

Accidents exposing blood to the staff of a hospital and university establishment in Algeria: Assessment and risk factors

Background: Accidents exposing to blood AEB represent real public health problem in healthcare establishments. The objective of our study was to estimate the frequency of AEB As at our establishment as well as the risk factors that determine their occurrence. Patients and Methods: A cross-sectional descriptive survey was conducted at a hospital university establishment over period from October 16 to December 3, 2018. The survey concerned accident exposing blood to the staff of our establishment. Data entry and analysis was carried out using Epi-Info software. Results: A clear predominance of women was noted (79.2%) among the study population with a Sex ratio equal to 0.26. The average age was 27.7 ± 6.2 years.The frequency of exposure to AEB among hospital staff was 48.5%. Needlestick injuries were the most common accident (88.3%), followed by splashing blood or body fluids (51.7%), and cutting with a sharp object (10.0%). Among the risk factors significantly associated with the occurrence of AEB, we can cite the medical profession (OR = 3.94; p<0.001), the surgical specialty (OR = 3.3; p <0.01), the male sex (OR = 3.7; p <0.01). Likewise, risk of AEB increased significantly with age (p <0.01) and professional seniority (p <0.02). Keywords: Accidents exposing blood; hospital staff; Algeria

Upravljanje otporno na kvarove asinkronog motora zasnovano na deskriptorskom observeru

This paper presents an active Fault Tolerant Control (FTC) strategy for induction motor (IM) that ensures Field Oriented Control (FOC) and offset the effect of the sensor faults despite of the load torque disturbance. The proposed approach uses a fuzzy descriptor approach to estimate simultaneously the system state and the sensor fault. The physical model of IM is approximated by the Takagi-Sugeno (T-S) fuzzy technique in the synchronous d-q rotating frame with field-oriented control strategy. The stability conditions are analyzed using Lyapunov theory. The controller and observers gains are calculated by solving a set of Linear Matrix Inequalities (LMIs). Finally, the effectiveness of the proposed strategy have been illustrated in simulation and experimental results.U ovom radu je predstavljena strategija upravljanja otpornog na kvarove za asinkroni motor koja omogućuje vektorsko upravljanje bez pogreške uslijed kvara senzora i postojećeg poremećaja momenta tereta. Predloženi pristup koristi neizraziti deskriptor za estimaciju stanja sustava i kvara senzora. Fizikalni model asinkronog motora s vektorskim upravljanjem aproksimiran je korištenjem Takagi-Sugeno modela u rotirajućem d-q koordinatnom sustavu. Uvjeti stabilnosti analizirani us korištenjem Ljapunovljeve teorije. Konstante pojačanja regulatora i obzervera su izračunati rješavanjem skupa linearnih matričnih nejednadžbi. Učinkovitost predložene strategije je ilustrirana simulacijskim i eksperimentalnim rezultatima

CNS Involvement at Initial Diagnosis and Risk of Relapse After Allogeneic HCT for Acute Lymphoblastic Leukemia in First Complete Remission

Outcomes of allogeneic hematopoietic cell transplantation (allo-HCT) for adult acute lymphoblastic leukemia (ALL) have improved over time. Studies have shown that total body irradiation (TBI) is the preferable type of myeloablative conditioning (MAC). However, outcomes based on central nervous system (CNS) involvement, namely CNS-positive versus CNS-negative, have not been compared. Here, we evaluated outcomes of 547 patients (CNS-positive = 96, CNS-negative = 451) who were allografted in the first complete remission (CR1) between 2009 and 2019. Primary endpoint was leukemia-free survival (LFS). Median follow-up was not different between the CNS-positive and CNS-negative groups (79 versus 67.2 months, P = 0.58). The CNS-positive group were younger (median age 31.3 versus 39.7 years, P = 0.004) and were allografted more recently (median year 2012 versus 2010, P = 0.003). In both groups, MAC was the preferred approach (82.3% versus 85.6%, P = 0.41). On multivariate analysis, the CNS-positive group had higher incidence of relapse (RI) (hazard ratio [HR] = 1.58 [95% confidence interval (CI) = 1.06-2.35], P = 0.025), but no adverse effect on LFS (HR = 1.38 [95% CI = 0.99-1.92], P = 0.057) or overall survival (OS) (HR = 1.28 [95% CI = 0.89-1.85], P = 0.18). A subgroup multivariate analysis limited to CNS-positive patients showed that a TBI-based MAC regimen resulted in better LFS (HR = 0.43 [95% CI = 0.22-0.83], P = 0.01) and OS (HR = 0.44 [95% CI = 0.21-0.92], P = 0.03) and lower RI (HR = 0.35 [95% CI = 0.15-0.79], P = 0.01). Another subgroup analysis in CNS-negative patients showed that MAC-TBI preparative regimens also showed a lower RI without a benefit in LFS or OS. While a MAC-TBI allo-HCT regimen may not be suitable to all, particularly for older patients with comorbidities, this approach should be considered for patients who are deemed fit and able to tolerate.Peer reviewe

Canvass: a crowd-sourced, natural-product screening library for exploring biological space

NCATS thanks Dingyin Tao for assistance with compound characterization. This research was supported by the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH). R.B.A. acknowledges support from NSF (CHE-1665145) and NIH (GM126221). M.K.B. acknowledges support from NIH (5R01GM110131). N.Z.B. thanks support from NIGMS, NIH (R01GM114061). J.K.C. acknowledges support from NSF (CHE-1665331). J.C. acknowledges support from the Fogarty International Center, NIH (TW009872). P.A.C. acknowledges support from the National Cancer Institute (NCI), NIH (R01 CA158275), and the NIH/National Institute of Aging (P01 AG012411). N.K.G. acknowledges support from NSF (CHE-1464898). B.C.G. thanks the support of NSF (RUI: 213569), the Camille and Henry Dreyfus Foundation, and the Arnold and Mabel Beckman Foundation. C.C.H. thanks the start-up funds from the Scripps Institution of Oceanography for support. J.N.J. acknowledges support from NIH (GM 063557, GM 084333). A.D.K. thanks the support from NCI, NIH (P01CA125066). D.G.I.K. acknowledges support from the National Center for Complementary and Integrative Health (1 R01 AT008088) and the Fogarty International Center, NIH (U01 TW00313), and gratefully acknowledges courtesies extended by the Government of Madagascar (Ministere des Eaux et Forets). O.K. thanks NIH (R01GM071779) for financial support. T.J.M. acknowledges support from NIH (GM116952). S.M. acknowledges support from NIH (DA045884-01, DA046487-01, AA026949-01), the Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program (W81XWH-17-1-0256), and NCI, NIH, through a Cancer Center Support Grant (P30 CA008748). K.N.M. thanks the California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board for support. B.T.M. thanks Michael Mullowney for his contribution in the isolation, elucidation, and submission of the compounds in this work. P.N. acknowledges support from NIH (R01 GM111476). L.E.O. acknowledges support from NIH (R01-HL25854, R01-GM30859, R0-1-NS-12389). L.E.B., J.K.S., and J.A.P. thank the NIH (R35 GM-118173, R24 GM-111625) for research support. F.R. thanks the American Lebanese Syrian Associated Charities (ALSAC) for financial support. I.S. thanks the University of Oklahoma Startup funds for support. J.T.S. acknowledges support from ACS PRF (53767-ND1) and NSF (CHE-1414298), and thanks Drs. Kellan N. Lamb and Michael J. Di Maso for their synthetic contribution. B.S. acknowledges support from NIH (CA78747, CA106150, GM114353, GM115575). W.S. acknowledges support from NIGMS, NIH (R15GM116032, P30 GM103450), and thanks the University of Arkansas for startup funds and the Arkansas Biosciences Institute (ABI) for seed money. C.R.J.S. acknowledges support from NIH (R01GM121656). D.S.T. thanks the support of NIH (T32 CA062948-Gudas) and PhRMA Foundation to A.L.V., NIH (P41 GM076267) to D.S.T., and CCSG NIH (P30 CA008748) to C.B. Thompson. R.E.T. acknowledges support from NIGMS, NIH (GM129465). R.J.T. thanks the American Cancer Society (RSG-12-253-01-CDD) and NSF (CHE1361173) for support. D.A.V. thanks the Camille and Henry Dreyfus Foundation, the National Science Foundation (CHE-0353662, CHE-1005253, and CHE-1725142), the Beckman Foundation, the Sherman Fairchild Foundation, the John Stauffer Charitable Trust, and the Christian Scholars Foundation for support. J.W. acknowledges support from the American Cancer Society through the Research Scholar Grant (RSG-13-011-01-CDD). W.M.W.acknowledges support from NIGMS, NIH (GM119426), and NSF (CHE1755698). A.Z. acknowledges support from NSF (CHE-1463819). (Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH); CHE-1665145 - NSF; CHE-1665331 - NSF; CHE-1464898 - NSF; RUI: 213569 - NSF; CHE-1414298 - NSF; CHE1361173 - NSF; CHE1755698 - NSF; CHE-1463819 - NSF; GM126221 - NIH; 5R01GM110131 - NIH; GM 063557 - NIH; GM 084333 - NIH; R01GM071779 - NIH; GM116952 - NIH; DA045884-01 - NIH; DA046487-01 - NIH; AA026949-01 - NIH; R01 GM111476 - NIH; R01-HL25854 - NIH; R01-GM30859 - NIH; R0-1-NS-12389 - NIH; R35 GM-118173 - NIH; R24 GM-111625 - NIH; CA78747 - NIH; CA106150 - NIH; GM114353 - NIH; GM115575 - NIH; R01GM121656 - NIH; T32 CA062948-Gudas - NIH; P41 GM076267 - NIH; R01GM114061 - NIGMS, NIH; R15GM116032 - NIGMS, NIH; P30 GM103450 - NIGMS, NIH; GM129465 - NIGMS, NIH; GM119426 - NIGMS, NIH; TW009872 - Fogarty International Center, NIH; U01 TW00313 - Fogarty International Center, NIH; R01 CA158275 - National Cancer Institute (NCI), NIH; P01 AG012411 - NIH/National Institute of Aging; Camille and Henry Dreyfus Foundation; Arnold and Mabel Beckman Foundation; Scripps Institution of Oceanography; P01CA125066 - NCI, NIH; 1 R01 AT008088 - National Center for Complementary and Integrative Health; W81XWH-17-1-0256 - Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program; P30 CA008748 - NCI, NIH, through a Cancer Center Support Grant; California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board; American Lebanese Syrian Associated Charities (ALSAC); University of Oklahoma Startup funds; 53767-ND1 - ACS PRF; PhRMA Foundation; P30 CA008748 - CCSG NIH; RSG-12-253-01-CDD - American Cancer Society; RSG-13-011-01-CDD - American Cancer Society; CHE-0353662 - National Science Foundation; CHE-1005253 - National Science Foundation; CHE-1725142 - National Science Foundation; Beckman Foundation; Sherman Fairchild Foundation; John Stauffer Charitable Trust; Christian Scholars Foundation)Published versionSupporting documentatio

Quantum Machine Learning for Next-G Wireless Communications: Fundamentals and the Path Ahead

A comprehensive coverage of the state-of-the-art in quantum machine learning (QML) methodologies, with a unique perspective on their applications for wireless communications, is presented. The paper begins by delving into the fundamental principles of quantum computing, and then goes through different operations and techniques that are involved in QML deployments. Subsequently, it provides an in-depth look at various methods peculiar to quantum computing, such as quantum search algorithms, and discusses their potentials towards maximizing the performance of wireless systems. The integration of quantum-based learning models into the existing machine learning methodologies, such as within the frameworks of unsupervised learning and reinforcement learning, are then examined. Taking the viewpoint of wireless communications, diverse studies in the literature that employ QML-based optimization methods are also highlighted. Finally, to ensure the applicability and feasibility of QML for optimizing wireless systems, potential solutions for deployment challenges are addressed

Flux thermique et coefficients de transfert global et partiel d'un échangeur à ailettes spiralées en graphite

La technique d'évaporation présentée dans ce travail consiste à faire ruisseler un liquide à évaporer à la surface d'ailettes enroulées en spirale autour d'un tube vertical, chauffé intérieurement par un fluide caloporteur. Le tube et les ailettes sont usinés dans un monobloc massif en graphite imprégné. Compte tenu de la grande complexité de la géométrie de cet échangeur et de l'écoulement sur ses ailettes, la détermination du coefficient de transfert thermique par convection externe est difficile à obtenir par un calcul direct. Il est déterminé ici par voie indirecte en suivant la méthode de Wilson basée sur deux étapes : une expérimentale et l'autre théorique. On détermine dans cet article la quantité de chaleur échangée et la conductance globale au transfert thermique de cet échangeur/évaporateur lors de l'échauffement. On détermine également les différentes résistances partielles ainsi que le coefficient de convection entre la paroi externe de l'ailette et le film de la solution à chauffer. La démarche proposée dans cet article a permis de valider la méthode indirecte