Objectives To assess the severity of symptoms, duration of infection and viral loads of health-care workers (HCWs) who tested positive for Coronavirus disease 2019 (COVID-19) during Omicron’s prevalence, in regard to vaccination and previous infection. Methods During 2 weeks of highest rate of COVID-19 cases in Bosnia and Herzegovina, the positive nasopharyngeal swabs were analysed in 141 HCWs by reverse transcription quantitative PCR, targeting four different genes: RdRp, E, N and nsp14. Uniformed questionnaire was used to collect relevant sociodemographic and epidemiological data from HCWs divided into four groups: unvaccinated/not previously infected (group 1); unvaccinated/previously infected (group 2); vaccinated/not previously infected (group 3); and vaccinated/previously infected (group 4). Results We observed that occurrence of fever and smell or taste loss were more frequent in group 1 (86.4% and 25%) and group 3 (76.9% and 19.2%), in comparison to group 2 (64.4% and 6.7%) and group 4 (69.2% and 3.8%), ( p  = 0.023 and p  = 0.003). Although statistically not significant, group 2 (61.9%), group 3 (65.4%), and group 4 (70.8%) experienced negativization within 7 days of positive RT-qPCR test, whereas 51.2% of HCWs from group 1 tested negative later on. There is no significant difference between all four groups regarding Ct values of analysed genes. Conclusion During Omicron’s prevalence, the vaccination had less substantial effect on symptomatic disease among HCWs, while fever and loss of smell or taste were considerably less likely to occur upon reinfection. Since viral loads and negativization periods do not seem to significantly vary, irrespective of pre-existing immunity, systemic vaccination and mask-wearing should still be considered among HCWs.

We propose a control protocol based on the prescribed performance control (PPC) methodology for a quadro-tor unmanned aerial vehicle (UAV). Quadrotor systems belong to the class of underactuated systems for which the original PPC methodology cannot be directly applied. We introduce the necessary design modifications to stabilize the considered system with prescribed performance. The proposed control protocol does not use any information of dynamic model parameters or exogenous disturbances. Furthermore, the stability analysis guarantees that the tracking errors remain inside of designer-specified time-varying functions, achieving prescribed performance independent from the control gains’ selection. Finally, simulation results verify the theoretical results.

The increasing integration of renewable energy resources (RERs) such as wind and solar onto the electric power grid through power electronic interface is challenging safe and reliable grid operation. Particularly, the high penetration of the inverter-based RERs (IB-RERs) may drive the grid towards weak grid conditions, which may cause grid stability issues. Grid strength assessment is helpful to identify these weak grid issues. However, it is challenging to assess grid strength while considering the impact of uncertain renewable generation. This paper presents an approach for quantifying the probabilistic characteristics of grid strength under uncertain renewable generation based on the probabilistic collocation method, which is a computationally efficient technique to reduce the computational burden without compromising the result accuracy compared with traditional Monte Carlo simulation. The efficacy of the proposed approach is demonstrated on the modified IEEE 9-bus system.

This study aims to ascertain the significance of the basketball game parameters which discriminated between winning and losing teams in matches played. The study sample comprises matches played at the men’s basketball tournament at the XXXII Olympic Games in Tokyo. Four regression models were formed. Due to the size of the sample, the number of explaining variables was reduced using factor analysis, followed by stepwise regression to ascertain the statistical significance of the obtained models summarily, which were then broken down into individual parameters. This study indicates: (1) one of the four set regression models was summarily highly statistically significant; (2) out of the remaining models, two were eliminated due to the presence of multicollinearity, and one model did not exhibit high statistical significance; (3) the final score was most influenced by the variables of two- and three-point shot percentages, number of three-point shots, turnovers, defensive rebounds, and true shooting percentage. The results of the study corroborated the results of other studies which were carried out in recent years, that the game of basketball is trending towards three-point shots and lay-ups, reduction of turnovers when passing, and defensive rebounds have been confirmed to be greatly significant.

Sustainable irrigation expansion over water limited croplands is an important measure to enhance agricultural yields and increase the resilience of crop production to global warming. While existing global assessments of irrigation expansion mainly illustrate the biophysical potential for irrigation, socioeconomic factors such as weak governance or low income, that demonstrably impede the successful implementation of sustainable irrigation, remain largely underexplored. Here we provide five scenarios of sustainable irrigation deployment in the 21st century integrated into the framework of Shared Socioeconomic Pathways, which account for biophysical irrigation limits and socioeconomic constraints. We find that the potential for sustainable irrigation expansion implied by biophysical limits alone is considerably reduced when socioeconomic factors are considered. Even under an optimistic scenario of socio-economic development, we find that additional calories produced via sustainable irrigation by 2100 might reach only half of the maximum biophysical potential. Regions with currently modest socioeconomic development such as Sub-Saharan Africa are found to have the highest potential for improvements. In a scenario of sustainable development, Sub-Saharan Africa would be able to almost double irrigated food production and feed an additional 70 million people compared to 2020, whereas in a scenario where regional rivalry prevails, this potential would be halved. Increasing sustainable irrigation will be key for countries to meet the projected food demands, tackle malnutrition and rural poverty in the context of increasing impacts of anthropogenic climate change on food systems. Our results suggest that improving governance levels for example through enhancing the effectiveness of institutions will constitute an important leverage to increase adaptive capacity in the agricultural sector.

Mobile edge computing (MEC) is expected to provide low-latency computation service for wireless devices (WDs). However, when WDs are located at cell edge or communication links between base stations (BSs) and WDs are blocked, the offloading latency will be large. To address this issue, we propose an intelligent reflecting surface (IRS)-assisted cell-free MEC system consisting of multiple BSs and IRSs for improving the transmission environment. Consequently, we formulate a min–max latency optimization problem by jointly designing multiuser detection (MUD) matrices, IRSs’ reflecting beamforming vectors, WDs’ offloading data size and edge computing resource, subject to constraints on edge computing capability and IRSs phase shifts. To solve it, an alternating optimization algorithm based on the block coordinate descent (BCD) technique is proposed, in which the original nonconvex problem is decoupled into two subproblems for alternately optimizing computing and communication parameters. In particular, we optimize the MUD matrix based on the second-order cone programming (SOCP) technique, and then develop two efficient algorithms to optimize IRSs’ reflecting vectors based on the semi-definite relaxation (SDR) and successive convex approximation (SCA) techniques, respectively. Numerical results show that employing IRSs in cell-free MEC systems outperforms conventional MEC systems, resulting in up to about 60% latency reduction can be attained. Moreover, numerical results confirm that our proposed algorithms enjoy a fast convergence, which is beneficial for practical implementation.

The most common type of renal cell carcinoma (RCC) is clear cell renal cell carcinoma (ccRCC), which has a high metastatic potential. Even though the International Metastatic RCC Database Consortium risk model is conventionally utilized for selection and stratification of patients with metastatic RCC (mRCC), there remains an unmet demand for novel prognostic and predictive markers. The goal of this study was to analyze the expression of Vascular endothelial growth factor (VEGF), Cluster of Differentiation 31 (CD31) to determine microvessel density, and Angiopoietin-1 (Ang-1) in primary kidney tumors, as well as their predictive and prognostic value in patients with metastatic ccRCC (mccRCC) who were treated with first-line sunitinib. The study included 35 mccRCC patients who were treated with first-line sunitinib in period between 2009 and 2019. Immunofluorescence was used to examine biomarker expression in tissue specimens of the primary tumor and surrounding normal kidney tissue. Median disease-free survival (DFS) was longer in patients with negative and low tumor VEGF score than in patients with medium tumor VEGF score (p ═ 0.02). Those with low tumor CD31 expression had a longer median DFS than patients with high tumor CD31 expression (p ═ 0.019). There was no correlation between Ang-1 expression and DFS. The expression of biomarkers in normal kidney tissue was significantly lower than in tumor tissue (p < 0.001). In conclusion, higher VEGF scores and greater CD31 expression were associated with longer DFS, but neither of these biomarkers correlated with progression-free survival or overall survival.

Filamentary resistance switching, or ReRAM, devices based on oxides suffer from device-do-device and cycle-to-cycle variability of electrical characteristics (electroforming voltages, set and reset voltages, resistance levels and cycling endurance). These are largely materials issues related to the microstructure of the switching oxide. Here we outline strategies to engineer the electrical performance of silicon oxide ReRAM by controlling the oxide microstructure at the nanometre scale through approaches including engineered interfaces and ion implantation. We demonstrate control over the distribution of switching voltages, electroforming voltages, and stable multilevel resistance states.

—For a continuous-input-continuous-output arbitrarily distributed quantum channel carrying classical information, the channel capacity can be computed in terms of the distribution of the channel envelope, received signal strength over a quantum propagation field and the noise spectral density. If the channel en-velope is considered to be unity with unit received signal strength, the factor controlling the capacity is the noise . Quantum channel carrying classical information will suffer from the combination of classical and quantum noise. Assuming additive Gaussian-distributed classical noise and Poisson-distributed quantum noise, we formulate a hybrid noise model by deriving a joint Gaussian-Poisson distribution in this letter. For the transmitted signal, we consider the mean of signal sample space instead of considering a particular distribution and study how the maximum mutual in- formation varies over such mean value. Capacity is estimated by maximizing the mutual information over unity channel envelope.

The COVID-19 pandemic has accelerated the process of digital transformation of higher education institutions. In a very short period, teachers and students abruptly switched to digital environments, which they had not used until then. As online teaching is very different from traditional teaching, teachers and students are faced with numerous new challenges. Online teaching requires a specific environment that primarily implies the availability of adequate technology as well as the skills that both teachers and students should have. Some higher education institutions have completely switched to online mode, while others have practiced a combined (online and offline) mode. The aim of this paper is, based on a questionnaire developed by Bernard et al. (2007), to examine the level of online skills, readiness for online learning and learning initiatives, attitudes about online learning, as well as the desire for online interaction with teachers and colleagues by the surveyed students.

By delivering end-to-end latencies down to 5ms, data rates of up to 20Gbps, and ultra-high reliability of 99.999%, 5G is extending the capabilities of numerous industry verticals, including the Transport & Logistics (T&L). As the T&L industry has a pivotal role in modern production and distribution systems, it is expected to leverage 5G technology to significantly increase efficiency and safety in the T&L operations, through automating and optimizing processes and resource usage. However, to be able to truly benefit from 5G, the design, the development, as well as the management, of T&L services need to specify and include 5G connectivity requirements, and the features that are tailored to the specific T&L use cases. To this end, in this paper we introduce the concept of Network Applications (NetApps), as the fundamental building blocks of T&L services in 5G, which simplify the composition of complex services, abstracting the underlying complexity and bridging the knowledge gap between the vertical stakeholders, the network experts, and the application/service providers, while specifying service-level information (vertical specific) and 5G requirements (5G slices and 5G Core services). In this paper, we exemplify the concept of NetApps leveraging one of the VITAL-5G use cases, which provides faster and safer operations of vessels in the port of Galati, the largest port on the Danube River.