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.

Parkhomov published decay rate measurements of 90Sr/90Y and 60Co beta decay sources with Geiger–Müller counters which showed annual cyclic deviations with less than 0.2% amplitude from a purely exponential slope. He investigated instrument instability induced by environmental parameters, yet did not find a clear coincidence with local temperature, atmospheric pressure, and relative humidity. Parkhomov hypothesised that gravitationally-focussed ‘slow’ cosmic neutrinos influenced beta decay. In the current work, environmental conditions in the Moscow area at the time of the experiment are presented. There appears to be a resemblance of the shape of the annual 90Sr/90Y decay rate anomalies with the inverse of the absolute air humidity, albeit with an apparent time shift of 0.05–0.15 year. Humidity may have influenced the range of beta particles in air, as well as geometric and electronic properties of the detection set-up, however causality could not be unambiguously demonstrated. The instabilities in the 60Co data were more difficult to correlate with environmental data, except for some similarities with temperature and external dew point.

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.

—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.

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.

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.

The aim of this paper is to investigate the state of performance of audit firms registered in the Republic of Serbia, as well as the factors that manage performance. The research is based on the entire population of audit firms, based on financial reports for the period 2019-2020. that are publicly available. The performance of audit firms will be investigated from the aspect of profitability performance, where the indicators of return on assets nad return on equity is most often used. Performance analysis will be presented through descriptive statistical analysis. Performance management will be analyzed in terms of the impact of independent factors on ROA and ROE. The following will be defined as independent factors: the size of the audit firm, liquidity, indebtedness, belonging to the big four, growth and the like. The research of the influence of independent factors on the dependent variables of the performance of audit firms will be conducted on the basis of regression analysis. The results of descriptive analysis should indicate the state and trend of performance of audit firms, while the results of regression analysis should indicate the nature of the factors that drive the performance of audit firms.

The virus SARS-Co V -2 that has caused a pandemic of COVID-19 in 2019 is still a major concern for health care systems. The reason for this is the fact that the outcome of the disease is difficult to predict, as deadly complications can occur in all people. Diagnosing COVID-19 relies on polymerase chain reaction (PCR) testing and antigen testing, both of which require special referral. The aim of this study was to develop artificial intelligence (AI) expert system which will facilitate COVID-19 diagnosis based on parameters that can be readily collected from blood specimens. The database contains 1000 samples, divided into 2 categories: (1) healthy and (2) sick subjects The following parameters were used: CRP, LDH, SE, AST, ALT, D-dimer and IL-6. The sensitivity of the developed system was 100%, specificity 98.33%, and accuracy 99.67%, on the basis of which we can conclude that the use of AI in the diagnosis of COVID19 has a significant potential.

Diagnosis of anemia is a time intensive and medically expensive procedure requiring a multitude of tests to establish a final diagnosis. Classification is an even more complex procedure that often takes years to complete thus delaying proper treatment and worsening the prognosis. This paper presents the application of machine learning, K-nearest neighbors (KNN), in order to diagnose and classify anemia. Monitoring parameters used as input for diagnosis were: age, sex, ferritin, transferrin, vitamin B12, erythrocyte count, iron, folic acid, hemoglobin, while the parameter relevant for classification was MCV. The results of the study indicate significant possibilities for the application of this system in the field of medical diagnostics.

Pulmonary emphysema is a complicated disease caused by irreversible damage to the wall of the pulmonary alveoli and causes 5% of the total mortality worldwide. This paper presents the development of an artificial neural network (ANN) for the diagnosis of pulmonary emphysema. Following biomarkers were used for the development of the ANN: AAT (alphal-antitrypsin), FEV1 (forced expiratory volume in 1 second), FVC (forced vital capacity) and FEV1/FVC (ratio forced expiratory volume in 1 second / forced vital capacity). The dataset consisted of 300 patients: 210 healthy subjects and 90 subject with disease. The neural network has 4 input parameters and 1 output parameter. For the final architecture, a neural network with 13 neurons in hidden layer was chosen based on the training results. The developed ANN has shown good performance and has a potential for use in this field.