A novel DeepMaskNet model for face mask detection and masked facial recognition

Coronavirus disease (COVID-19) has significantly affected the daily life activities of people globally. To prevent the spread of COVID-19, the World Health Organization has recommended the people to wear face mask in public places. Manual inspection of people for wearing face masks in public places is a challenging task. Moreover, the use of face masks makes the traditional face recognition techniques ineffective, which are typically designed for unveiled faces. Thus, introduces an urgent need to develop a robust system capable of detecting the people not wearing the face masks and recognizing different persons while wearing the face mask. In this paper, we propose a novel DeepMasknet framework capable of both the face mask detection and masked facial recognition. Moreover, presently there is an absence of a unified and diverse dataset that can be used to evaluate both the face mask detection and masked facial recognition. For this purpose, we also developed a largescale and diverse unified mask detection and masked facial recognition (MDMFR) dataset to measure the performance of both the face mask detection and masked facial recognition methods. Experimental results on multiple datasets including the cross-dataset setting show the superiority of our DeepMasknet framework over the contemporary models

Blockchain Empowered Federated Learning Ecosystem for Securing Consumer IoT Features Analysis

Resource constraint Consumer Internet of Things (CIoT) is controlled through gateway devices (e.g., smartphones, computers, etc.) that are connected to Mobile Edge Computing (MEC) servers or cloud regulated by a third party. Recently Machine Learning (ML) has been widely used in automation, consumer behavior analysis, device quality upgradation, etc. Typical ML predicts by analyzing customers’ raw data in a centralized system which raises the security and privacy issues such as data leakage, privacy violation, single point of failure, etc. To overcome the problems, Federated Learning (FL) developed an initial solution to ensure services without sharing personal data. In FL, a centralized aggregator collaborates and makes an average for a global model used for the next round of training. However, the centralized aggregator raised the same issues, such as a single point of control leaking the updated model and interrupting the entire process. Additionally, research claims data can be retrieved from model parameters. Beyond that, since the Gateway (GW) device has full access to the raw data, it can also threaten the entire ecosystem. This research contributes a blockchain-controlled, edge intelligence federated learning framework for a distributed learning platform for CIoT. The federated learning platform allows collaborative learning with users’ shared data, and the blockchain network replaces the centralized aggregator and ensures secure participation of gateway devices in the ecosystem. Furthermore, blockchain is trustless, immutable, and anonymous, encouraging CIoT end users to participate. We evaluated the framework and federated learning outcomes using the well-known Stanford Cars dataset. Experimental results prove the effectiveness of the proposed framework

Digital Citizenship And Mental And Physical Well-Being Of Saudi Citizens

Background: The escalating integration of technology and digital platforms in the lives of individuals raises pertinent concerns regarding its impact on mental and physical well-being. This study aims to explore the association between Digital Citizenship and the well-being of Saudi citizens, considering the multifaceted dimensions of digital behavior and their potential consequences. Method: Employing a cross-sectional descriptive research design, data was collected from a diverse sample of 400 healthcare workers in the Jizan Region, KSA. The Digital Citizenship Scale was utilized to assess online behaviors, while the Short Form 12 (SF-12) Questionnaire gauged mental and physical well-being. Descriptive statistics, Pearson Correlation, t-tests, and ANOVA were employed for data analysis. Results: The findings revealed a significant correlation between various facets of digital citizenship and mental and physical well-being. Responsible digital behaviors, such as online etiquette and security consciousness, correlated positively with enhanced well-being. Conversely, exposure to cyberbullying and security breaches was associated with lower well-being scores. Conclusion: The study underscores the vital role of responsible digital citizenship in shaping the mental and physical well-being of Saudi citizens. Promoting awareness and education regarding digital behaviors can contribute to a healthier online environment. Policymakers, educators, and healthcare professionals are encouraged to collaborate in integrating digital citizenship education and initiatives to safeguard the well-being of individuals in the digital age

Impact of Acceptor Quadrupole Moment on Charge Generation and Recombination in Blends of IDT-Based Non-Fullerene Acceptors with PCE10 as Donor Polymer

Advancing non-fullerene acceptor (NFA) organic photovoltaics requires the mitigation of the efficiency-limiting processes. Acceptor end-group and side-chain engineering are two handles to tune properties, and a better understanding of their specific impact on the photophysics could facilitate a more guided acceptor design. Here, the device performance, energetic landscape, and photophysics of rhodanine and dicyanovinyl end-capped IDT-based NFAs, namely, O-IDTBR and O-IDTBCN, in PCE10-based solar cells are compared by transient optical and electro-optical spectroscopy techniques and density functional theory calculations. It is revealed how the acceptors’ quadrupole moments affect the interfacial energetic landscape, in turn causing differences in exciton quenching, charge dissociation efficiencies, and geminate versus non-geminate recombination losses. More precisely, it is found that the open circuit voltage (VOC) is controlled by the acceptors’ electron affinity (EA), while geminate and non-geminate recombination, and the field dependence of charge generation, rely on the acceptors’ quadrupole moments. The kinetic parameters and yields of all processes are determined, and it is demonstrated that they can reproduce the performance differences of the devices’ current–voltage characteristics in carrier drift-diffusion simulations. The results provide insight into the impact of the energetic landscape, specifically the role of the quadrupole moment of the acceptor, beyond trivial considerations of the donor–acceptor energy offsets

Electrolyte‐gated organic field‐effect transistors with high operational stability and lifetime in practical electrolytes

A key component of organic bioelectronics is electrolyte‐gated organic field‐effect transistors (EG‐OFETs), which have recently been used as sensors to demonstrate label‐free, single‐molecule detection. However, these devices exhibit limited stability when operated in direct contact with aqueous electrolytes. Ultrahigh stability is demonstrated to be achievable through the utilization of a systematic multifactorial approach in this study. EG‐OFETs with operational stability and lifetime several orders of magnitude higher than the state of the art have been fabricated by carefully controlling a set of intricate stability‐limiting factors, including contamination and corrosion. The indacenodithiophene‐co‐benzothiadiazole (IDTBT) EG‐OFETs exhibit operational stability that exceeds 900 min in a variety of widely used electrolytes, with an overall lifetime exceeding 2 months in ultrapure water and 1 month in various electrolytes. The devices were not affected by electrical stress‐induced trap states and can remain stable even in voltage ranges where electrochemical doping occurs. To validate the applicability of our stabilized device for biosensing applications, the reliable detection of the protein lysozyme in ultrapure water and in a physiological sodium phosphate buffer solution for 1500 min was demonstrated. The results show that polymer‐based EG‐OFETs are a viable architecture not only for short‐term but also for long‐term biosensing applications

Macroalgal morphogenesis induced by waterborne compounds and bacteria in coastal seawater

Axenic gametes of the marine green macroalga Ulva mutabilis Foyn (Ria Formosa, locus typicus) exhibit abnormal development into slow-growing callus-like colonies with aberrant cell walls. Under laboratory conditions, it was previously demonstrated that all defects in growth and thallus development can be completely abolished when axenic gametes are inoculated with a combination of two specific bacterial strains originally identified as Roseo-bacter sp. strain MS2 and Cytophaga sp. strain MS6. These bacteria release diffusible morphogenetic compounds (= morphogens), which act similar to cytokinin and auxin. To investigate the ecological relevance of the waterborne bacterial morphogens, seawater samples were collected in the Ria Formosa lagoon (Algarve, Southern Portugal) at 20 sampling sites and tidal pools to assess their morphogenetic effects on the axenic gametes of U. mutabilis. Specifically the survey revealed that sterile-filtered seawater samples can completely recover growth and morphogenesis of U. mutabilis under axenic conditions. Morphogenetic activities of free-living and epiphytic bacteria isolated from the locally very abundant Ulva species (i.e., U. rigida) were screened using a multiwell-based testing system. The most represented genera isolated from U. rigida were Alteromonas, Pseudoalteromonas and Sulfitobacter followed by Psychrobacter and Polaribacter. Several naturally occurring bacterial species could emulate MS2 activity (= induction of cell divisions) regardless of taxonomic affiliation, whereas the MS6 activity (= induction of cell differentiation and cell wall formation) was species-specific and is probably a feature of difficult-to-culture bacteria. Interestingly, isolated bacteroidetes such as Algoriphagus sp. and Polaribacter sp. could individually trigger complete Ulva morphogenesis and thus provide a novel mode of action for bacterial-induced algal development. This study also highlights that the accumulation of algal growth factors in a shallow water body separated from the open ocean by barrier islands might have strong implications to, for example, the wide usage of natural coastal seawater in algal (land based) aquacultures of Ulva

Impact of magnetic field on the stability of the CMS GE1/1 GEM detector operation

The Gas Electron Multiplier (GEM) detectors of the GE1/1 station of the CMS experiment have been operated in the CMS magnetic field for the first time on the 7$^{th}$ of October 2021. During the magnetic field ramps, several discharge phenomena were observed, leading to instability in the GEM High Voltage (HV) power system. In order to reproduce the behavior, it was decided to conduct a dedicated test at the CERN North Area with the Goliath magnet, using four GE1/1 spare chambers. The test consisted in studying the characteristics of discharge events that occurred in different detector configurations and external conditions. Multiple magnetic field ramps were performed in sequence: patterns in the evolution of the discharge rates were observed with these data. The goal of this test is the understanding of the experimental conditions inducing discharges and short circuits in a GEM foil. The results of this test lead to the development of procedure for the optimal operation and performance of GEM detectors in the CMS experiment during the magnet ramps. Another important result is the estimation of the probability of short circuit generation, at 68 % confidence level, p$_{short}$$^{HV}$ $^{OFF}$ = 0.42$^{-0.35+0.94}$% with detector HV OFF and p$_{short}$$^{HV}$ $^{OFF}$ < 0.49% with the HV ON. These numbers are specific for the detectors used during this test, but they provide a first quantitative indication on the phenomenon, and a point of comparison for future studies adopting the same procedure