The proliferation of electric vehicles (EVs) and the advancement of Vehicle-to-Everything (V2X) energy trading systems are expected to play a crucial role in alleviating the stress on the electric grid during peak hours. However, the wide adoption of these paradigms requires intelligent mechanisms that protect the security and privacy of EV users. This paper proposes a novel Federated Reinforcement Learning (FRL) system combined with blockchain technology to maximize EV users' utility while preserving the security and privacy of trading transactions. Furthermore, we develop the concept of Proof of State of Charge (PoSOC) as a consensus mechanism to determine the winning EVs and reward them as block miners in the blockchain. The proposed system is validated through comprehensive simulation experiments utilizing a real-world dataset. The model is implemented on the Avalanche blockchain platform to demonstrate its real-world feasibility. The test results show that the proposed scheme improves EV users' utility significantly compared to the existing studies. The obtained simulation results indicate the effectiveness and robustness of the proposed system.

This paper sheds light on the complexity of designing Internet of Things (IoT) ecosystems where a high number of things reside and thus must collaborate despite their reduced size, restricted connectivity, and constrained storage limitations. To address this complexity, a novel concept referred to as thing artifact is devised abstracting the roles that things play in an IoT ecosystem. The abstraction focuses on 3 crosscutting aspects, namely functionality in terms of what to perform, life cycle in terms of how to behave, and interaction flow in terms of with whom to exchange. Building upon the concept of data artifact commonly used in data-driven business applications design, thing artifacts engage in relations with peers to coordinate their individual behaviors and hence avoid conflicts that could result from the quality of exchanged data. Putting functionality, life cycle, interaction flow, and relation together contributes to abstracting IoT ecosystems design. A system implementing a thing artifact-based IoT ecosystem along with some experiments is presented in the paper as well.

Commercial polymer membranes are largely utilized to separate oil/water mixtures; however, membrane fouling, flux decline, and short lifetime often inhibit their high performance. In order to resolve these drawbacks of the commercial membranes, we introduce a surface modification strategy following the electrospinning method. Electrospun fibers of polysulfone (PSf)/iron oxide (FeO)/halloysite nanotubes (HNT) nanocomposite are applied to modify the polyether sulfone (PES) standard membrane support surface for developing highly efficient oil/water emulsion separating membranes. This facile and simple spinning process for shorter periods ensures nanocomposite coatings on the standard PES membranes and allows a better oil/water separation. We analyze the structural and morphological characteristics of the modified membrane surface using scanning electron microscopy, Fourier transformation infrared spectroscopy, and X-ray diffraction studies and hydrophilicity from contact angle studies. FeO nanoparticles of 2–5 nm and HNTs of < 50 nm size mixed in PSf produce fibers of 531 ± 162 nm average diameter at a relatively lower applied electrical voltage of 14.5 kV, compared to PSf. Underwater and under-oil contact angle values are used to prove the surface characteristics of the membranes and total organic content (TOC) values for the emulsion separation performance. From PES support to PSf and PSf/HNT-FeO, the TOC values respectively change from 67 to 75 and 79%. We find moderately hydrophilic membranes (PSf/HNT-FeO) resulting in a higher permeate flux (28,447 Lm−2·h−1) and quicker separation performance. We believe this study provides a notable solution to modify the surface of commercial membranes for better emulsion separation performance.

A centralized secondary control is utilized in a DC islanded microgrid to fine-tune voltage levels following the implementation of droop control. This is done to avoid conflicts between current allocation and voltage adjustments. However, because it introduces a single point of failure, a distributed secondary control is preferred. This paper introduces a consensus-based secondary distributed control approach to restore critical bus voltages to their nominal values and properly distribute current among converters. The critical bus takes the lead in voltage adjustments, with only connected energy resources contributing to regulation. The microgrid is represented as an undirected graph to facilitate consensus building. Two adjustment terms, δv and δi, are generated to assist in returning voltage to its nominal level and correctly allocating current among energy resources. To enhance consistency and improve controller performance compared to those reported in existing literature, all buses are connected to a leader node. In the event of the failure of all converters except one, voltage can still be effectively restored. MATLAB-Simulink simulations are conducted on two medium-voltage DC (MVDC) microgrids to validate the efficiency of the proposed control method. The results confirmed that the proposed control method can effectively maintain voltage stability and enhance the precise distribution of current among agents by 8%.

BackgroundThe emergence of COVID-19 interrupted education worldwide and educational institutions were forced to switch to an online learning (E-learning) environment. ObjectivesTo explore the perceived educational experiences, worries, and preparedness to enter clinical practice among final-year nursing students and new graduate nurses after studying during the COVID-19 pandemic. DesignA cross-sectional survey design. Settingand participants: An online survey of final-year undergraduate bachelor of nursing students at a single university in Qatar and new graduates across 14 health facilities in Qatar was undertaken between May 2022 and July 2022. MethodsParticipant demographics, experiences of E-learning and perceptions of readiness for practice were collected using validated instruments and open-ended questions. Descriptive statistics and thematic exploration were used to analyze the data. ResultsFifty-nine final-year students and 23 new graduates with an average age of 23.95 years (SD = 3.89) responded to the survey. Face-to-face clinical placement learning was preferred by 50 % of students and 66.1 % of graduates. During COVID-19, the majority of the participants indicated that strategies like practice kits and online simulations were implemented. While 61 % of new graduates felt well-prepared in their nursing skills, and 87 % felt confident, less than half of the students still in their final year of education felt prepared or confident. Increased stress and a perceived impact on education quality were reported due to changes in learning modalities. Despite these challenges, 81.1 % of students and 95.7 % of graduates felt they had developed sufficient professional values, with most looking forward to becoming registered nurses. The qualitative themes that emerged related to 1) adjusting to online learning, 2) experiencing restrictions in clinical learning skills, 3) feeling less confident and 4) experiencing increased stress. ConclusionsDespite experiencing emotional and educational challenges, the participants in this study felt that their education prepared them for clinical practice. Innovative strategies and unique educational experiences used by educators enhanced participants' clinical skills and readiness for practice.

Health literacy (HL) is both a direct determinant and a mediator of health outcomes. Research on the prevalence and determinants of HL in terms of its functional, communicative, and critical domains is scarce in the state of Qatar and its surrounding regions. Thus, this study aims to fill the knowledge gap in this area, estimate the levels of functional, communicative, and critical health literacy among the general adult population, and identify its determinants in the state of Qatar. An analytical cross-sectional study with a disproportionate stratified random sampling technique was conducted in 2022. A representative sample of phone numbers was obtained from the Cerner database at Hamad Medical Corporation and approached via well-trained data collectors. A socio-demographic and health-relevant factor questionnaire and the validated All Aspects of Health Literacy scale (AAHLS) were used to collect the data on functional, communicative, and critical HL and their determinants. Descriptive analysis, independent sample t-test, ANOVA, and linear regression were used and yielded the outcomes on HL levels as low, adequate, and high in percentages and the HL determinants. A total of 770 participants were included. The study found that 41.5%, 29.3%, and 29.2% of them have adequate, high, and low overall HL levels consecutively. People who participated in the study are older adult, are of Arabic ethnicity, are of Qatari ethnicity, have a lower level of education, have close relatives with a lower level of education, have a lower income, are non-migrants, are not living within a family, sought medical care within the last week, and who do not know if they have a chronic disease or do not have lower overall HL levels compared to the other groups. After linear regression analysis, only the participant's level of education and "last time sought medical care within last week" variable predict the overall HL score. Almost half of Qatar's adult population has an adequate HL level, comparable to the HL levels in other regions, despite the limitation in comparison due to variation in context and the HL measurement tools used. The possible determinants are amenable factors to focus on while designing HL interventions and providing healthcare.

The concept of smart city architecture requires a comprehensive solution that can combine real-time response applications for cyber-physical systems. However, the architecture faces challenges that can obstruct the operations in terms of systems, processes, and data flow as far as the breach risk is concerned. Though the field has been researched with the existence of centralized and distributed architectures to support smart cities. Research gaps regarding security concerns, platform assistance, and resource management continue to persist. This research article presents a novel blockchain-based architecture that proposes expansion in the non-fungible tokens (NFTs) to cater to the expansion of IoT-enabled smart assets. It enables NFTs to employ fog computing for all users and smart devices connected to a fog node in a cyber-physical system. The proposed expansion suggested in Non-Fungible Tokens (NFTs) for IoT assets representation in a cyber-physical system, provides devices and user identification and authentication functionality. The proposed NFT architecture has been designed to provide a smart city solution for cyber-physical systems that ensures robust security features (such as CIA) by introducing new attributes and functions for Owner, User, Fog, and IoT device/s authentication. The validation and rigor of the security services, efficiency, and latency have been achieved by deployments on private and public ledgers. The efficiency, and cost-effectiveness of the suggested functions and components have been evaluated in terms of evaluation cost and time complexity which resulted in promising results, obtained and validated on a testnet. The evaluation cost for the devised mint component was approximately 81%, and devised approve() was approximately 23% more efficient than other solutions.

One of the major waste streams within the oil and gas (O&G) industry is produced and process water generated during hydrocarbon production and treatment, respectively. In gas production facilities, process water typically has lower salinity (i.e., <10,000 mg/L) which present opportunities for these wastewaters to be treated for beneficial reuse applications via advanced water treatment technologies, such as reverse osmosis (RO) membranes. One of the key challenges for RO membranes application to industrial wastewater treatment is fouling which is frequently attributed to the soluble organics present in the water and/or associated with field chemicals. In this study, a detailed organic characterization methodology, using liquid chromatography with organic carbon detector (LC-OCD) was applied to characterize the organics on a real process water collected from industrial wastewater treatment plant at a gas production facility. Additionally, a rigorous bench-scale testing procedure was implemented to assess the performance of a full-scale RO system with and without activated carbon filter (ACF) pretreatment, making this study the first one to apply LC-OCD methodology on real wastewater to evaluate the fouling of RO membranes deployed at an industrial treatment plant. Bench-scale RO results showed that in the absence of ACF pretreatment, a 12 % decline in membrane permeability (from 1.78 to 1.57 L/(m2-bar-h)) was observed, while no permeability decline was measured after ACF treatment. The organic fouling was confirmed by mass balance calculations on the bench scale experiments as well as Fourier Transform Infrared (FTIR) analysis on the membrane coupons. Moreover, LC-OCD analysis showed that the ACF inlet has a TOC concentration of approximately 3.06 mg/L; of those 1.29 mg/L (42 %) are hydrophobic, and 1.77 mg/L (58 %) are hydrophilic. After ACF treatment, the hydrophobic organics decreased to 0.65 mg/L, revealing that the ACF is removing >50 % of hydrophobic organics which are likely responsible for membrane fouling. Bench-scale operational and water quality results were comparable to the full-scale RO performance data validating the lab testing procedure implemented in this study.

Membrane distillation (MD) is a hybrid thermal-membrane desalination process that can use either low-grade waste heat and/or solar energy with hydrophobic membranes to desalinate high-salinity brines and produce high quality distillate. A research consortium was launched to investigate the application of the MD process, at lab and pilot scale, for desalination of concentrated brines. Bench scale results showed the presence of antiscalants in the concentrated brines minimized the scale precipitation potential and offered stable membrane permeability performance. Various MD technologies were screened, and two suitable technologies were selected for field-testing. Pilot unit A was based on multi-effect vacuum showed a stable flux of 6.2 LMH with excellent salt rejection (> 99.9%) from the concentrated brine discharged from thermal desalination plant in Qatar. That pilot unit was also field tested on hypersaline groundwater in Texas (USA) to generate fresh water for reservoir fracking in unconventional oil production operations. The MD unit was coupled with humidification/dehumidification (HDH) unit to achieve zero liquid discharge (ZLD) for inland applications. The MD unit was operated at 40% recovery producing distillate of < 20 mg/L total dissolved solids (TDS) and observed a stable flux of 5 LMH. Key challenges that are critical for large-scale deployment of MD technology were identified at the end of the field-testing program. Finally, a review of active MD technologies was conducted to highlight recent promising developments for full-scale applications.

BackgroundHealthcare workers (HCWs) are at a higher risk of contracting COVID-19 due to their close contact with infected patients. However, the true burden of COVID-19 among HCWs in Yemen is unknown due to the inadequate availability of healthcare and the subclinical nature of the disease. This study aims to estimate the seroprevalence of SARS-CoV-2 infection among HCWs in two Yemeni governorates and identify associated factors using a cross-sectional design.MethodA total of 404 HCWs were surveyed from June 2022 to September 2022 in Lahj and AL-Dhalea hospitals. A self-administered questionnaire collected demographic data, COVID-19 infection history, and vaccination status. A total of 404 human sera were tested using a specific electrochemiluminescence immunoassay assay. Association analysis was conducted to identify associations between antibody prevalence and demographic and vaccine-related variables.ResultThe median age of the HCWs was 31 (Range 20–64) years, with 65.0% being male and 35.0% female. Of all HCWs, 94% were SARS-CoV-2 seropositive and 77.0% had no confirmed test of COVID-19-related symptoms. There was no significant association between seropositivity and demographic factors such as age, gender, occupation, or COVID-19 vaccination (P > 0.05).ConclusionThe seroprevalence of SARS-CoV-2 was high among HCWs in Yemen, primarily due to natural infection rather than vaccination. Compliance with infection prevention and control measures did not significantly affect seropositivity. This study highlights the need for improved healthcare systems and resources to reduce the burden of COVID-19 and promote infection prevention and control (IPC) measures among HCWs in Yemen.