Fiscal sustainability should be defined in the relation between public finances and sustainable economy. It requires a fiscal policy aimed not only at a fiscal balance, but also at the well-being of future generations, while taking the economic, social, environmental and institutional dimensions into account. The objective of this paper is to examine the relationship between fiscal sustainability and efficiency on the example of large cities in Poland in the period from 2008 to 2019. In order to obtain empirical results, a data envelopment analysis and panel data analysis were applied, and a fiscal sustainability index was constructed by means of a multidimensional approach. Based on a sample of 66 large cities in Poland, the results showed that there is a negative relationship between fiscal sustainability and efficiency. These results, however, relate to a specific period in the history of Polish cities when the local debt limits were tightened. The improvement in efficiency allowed local government units to allocate saved local expenditures to the partial repayment of previously incurred local debts. By doing so, Polish cities fulfilled tightened fiscal rules, but their service-level solvency decreased.

Introduction and Objectives: There have been concerns with the level of misinformation regarding coronavirus disease 2019 (COVID-19) and its treatment, drug shortages, as well as increased use and prices of anti-malarials, antibiotics and hygiene products during the recent pandemic. Community pharmacists can play a significant role in disease prevention and treatment in the fight against COVID-19 including providing hygiene information and medicine availability across Africa and generally. Consequently, there is a need to review the role of community pharmacists in preventing unintended consequences during any pandemic as well as the impact of COVID-19 on the demand, availability and prices of suggested medicines for its management. Materials and Methods: Multiple approaches involving a qualitative review of the management of COVID-19 across countries coupled with a pilot study in Ghana among six purposely selected community pharmacists during the early stages of the pandemic assessing patterns of demand, availability and prices of medicines suggested for the management of COVID-19. Alongside this, pharmacists' future role enhancing appropriate medicine use in Ghana and wider combined with the help of senior level co-authors. Results: The majority (five out of six) of pharmacists in Ghana reported increased demand for hydroxychloroquine, antibiotics and vitamins as immune boosters resulting in shortages with price increases particularly for anti-malarials. Conclusion: The global lockdown had impacted on the supply and prices of medicines in Ghana similar to other countries. Community pharmacists can play a key role with encouraging safe medicine use, reducing self-purchasing of medicines and planning workflows during future pandemics including vaccinations. They can also help address potential misinformation and its consequences as well as the unintended consequences of pandemics including better management of non-communicable diseases.

The inception of artificial intelligence (AI) dates back to the 1950s, with the latest wave of AI featuring unprecedented “machine learning” capabilities, including “deep learning.” With these increased capacities, AI is disrupting society in both beneficial and disempowering ways, exemplified in medical and scientific advances and enabling oppressive surveillance [1]. The rate of adoption and potential of this wave of AI are novel, as are the socio-technical problems developing with emerging technologies powered by AI.

This research paper deals with the problem of Metal-Oxide Surge Arrester (MOSA) condition monitoring and a new methodology in surge arrester monitoring and diagnostics is presented. A machine learning algorithm (back propagation regression) is used to estimate the non-linearity coefficient of the surge arrester, based on operating voltage and leakage current of the arrester. Using a simulated system, this research investigates the possibility of application and efficiency of machine learning. It is shown that the applied learning algorithm results are competitive with the model results parameters calculated as R2 = 0.999 and mean absolute real error computed as 0.005 which has shown that the proposed model can be used for MOSA monitoring and diagnostic purposes.

In this study, the chemical profiles, antioxidant and antibacterial activity of Helivhrysum italicum essential oils from three plantation fields in Herzegovina were analysed. GC/MS analysis showed that all samples were rich in sesquiterpenes (45.19%-50.07%) and monoterpenes (21.15%-23.21%), followed by oxygenated monoterpenes (9.92%-14.03%). Diketones in the essential oil were detected in quantities ranging 5.72% to 6.67%. The main components in essential oils were γ-curcumene, α-pinene, β-selinene and neril-acetate. All tested essential oils exhibited relatively weak DPPH-scavenging capacity. The antimicrobial activity of the essential oil was assayed by using the disk diffusion method. E. coli was most resistant against all three tested H. italicum essential oils, while moderate inhibitory activity against S. aureus and C. albicans was detected. The L. monocytogenes was the most sensitive where all three tested samples showed inhibitory activity.

In this study, metal complex of Copper(II) with a Schiff base derived from 2,2-dihydroxyindane-1,3-dione and 2-aminoethanoic acid were synthesized. The product are characterized by spectral methods. The antimicrobial activity was tested on reference bacterial strains and the antioxidant capacity was analyzed by using the DPPH and FRAP methods. The spectral data indicates that the Schiff base coordinates the Copper(II) as a tridentate ONO donor ligand. The compounds showed weaker antimicrobial activity on certain tested microorganisms. In vitro testing of antioxidant activity showed a significant reducing ability of the complex, as well as inhibitory activity against DPPH radicals.

Glioblastoma multiforme (GBM) is the most frequent type of primary astrocytomas. We examined the association between single nucleotide polymorphisms (SNPs) in Aurora kinase A (AURKA), Aurora kinase B (AURKB), Aurora kinase C (AURKC) and Polo-like kinase 1 (PLK1) mitotic checkpoint genes and GBM risk by qPCR genotyping. In silico analysis was performed to evaluate effects of polymorphic biological sequences on protein binding motifs. Chi-square and Fisher statistics revealed a significant difference in genotypes frequencies between GBM patients and controls for AURKB rs2289590 variant (p = 0.038). Association with decreased GBM risk was demonstrated for AURKB rs2289590 AC genotype (OR = 0.54; 95% CI = 0.33–0.88; p = 0.015). Furthermore, AURKC rs11084490 CG genotype was associated with lower GBM risk (OR = 0.57; 95% CI = 0.34–0.95; p = 0.031). Bioinformatic analysis of rs2289590 polymorphic region identified additional binding site for the Yin-Yang 1 (YY1) transcription factor in the presence of C allele. Our results indicated that rs2289590 in AURKB and rs11084490 in AURKC were associated with a reduced GBM risk. The present study was performed on a less numerous but ethnically homogeneous population. Hence, future investigations in larger and multiethnic groups are needed to strengthen these results.