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.

Abstract Worldwide, cities are implementing circular economy (CE) strategies to reduce the resources they consume and their environmental impact. However, the evidence of the intended and unintended social consequences of the transition to “circular cities” is scattered. The lack of a coherent overview of the evidence on the subject can hinder effective decision-making in policy and practice. This study examines the extent to which the current literature addresses the social impacts that a transition to a CE produces in cities. We used a methodological approach related to systematic mapping to collate the evidence published over the past decade globally. The study finds that social impacts have rarely been considered in studies of circular cities, and where they have been discussed, the scope has been quite limited, only covering employment (mostly of informal sector workers) and governance practices. This scoping review highlights the need to further analyse and integrate social impact considerations into decision-making connected to transitions towards circular cities.

Highlights • The overall severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) secondary attack rate in this study was 17%.• Adults were more likely to be secondary cases than children.• Particular care should be taken if primary cases present with cough and rhinorrhoea.• Kissing a SARS-CoV-2 case or sharing a meal with a SARS-CoV-2 case increased the risk of infection.• Reducing contact in the household immediately is key to prevent onward transmission.

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.

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.

Nowadays there are ham and spam messages that are sent to the users via SMS. The aim of this article is to show how machine learning and text processing technologies can be used in order to predict the trustworthiness of SMS messages. The data we are going to use is collected from Kaggle. This study is very important because it helps us to understand how machine learning and text processing can be used in order to predict message trustworthiness. At the time of writing this article, there was not an article explaining how this can be done using the Multinomial Naive Bayes algorithm. The methodology we used in this article consists of dataset collection, data cleaning, data analysis, text preparation, and training model. This will be seen in the methodology section in great detail. At the end of this article, we will show to u the accuracy that we have got when implementing a Multinomial Naive Bayes algorithm for the classification of SMS messages. This study was quite beneficial because anyone can see how Multinomial Naive Bayes algorithm usage can be beneficial in order to predict the trustworthiness of SMS messages.

: Construction materials in the form of any products are subject to unintentional or harmful changes, occurrences and processes that reduce their usability. The destruction of construction materials is aimed to be slowed down or prevented by measures and procedures of a special technological discipline - material protection, which is usually called surface protection, since harmful occurrences and processes mostly begin on the surface of the product. In addition to the many protective methods that are used, corrosion inhibitors have a special place due to their specificity and widespread use. Based on the performed tests and their analysis, it was determined that the inhibition efficiency obtained by electrochemical measurements is in good correlation with the results obtained by the FTIR method. Impedance measurements of steel St 37-4 Pectin C in the tested media show corrosion resistance. Pectin C in 3.5% HCl at a concentration of 2.0 g / l increases the value of the charge transfer resistance and the increases of the size of the absolute impedance in the Bode diagram, which further confirms the improved resistance to corrosion of steel.

Water quality is deteriorating over the years, and the main source of water pollution is industrial, agricultural and municipal wastewater. Heavy metals, organic compounds and microorganisms, present even in traces, can be very dangerous to human health, aquatic organisms and the environment. Therefore, in this study was investigate the possibility of modified and unmodified plum pits as biosorbents for Pb (II) ions removal from aqueous solution. Experimental data have shown that these bisorbents show a certain potential for application in the metal removal process. The feasibility was tested for an unmodified and modified biosorbent based on plum pits in the range of concentrations 150-200 mg/l (unmodified sample) and 100-200 mg/l (modified sample) at a contact time of 30 and 60 minutes . Adsorption parameters were determined using the Freundlich isotherm. The results showed that unmodified biosorbent based on plum pits with increasing concentration from 150 mg/L to 200 mg/L leads to a large increase in the percentage of removal of Pb (II) ions, with no significant effect on contact time. In contrast to the unmodified sample, the modified biosorbent based on plum pits % of removed Pb (II) ions significantly increases the contact time at the initial Pb (II) concentration of 100 mg/L, while at the initial concentration of 150 mg/L and longer mixing, the removal efficiency increases and amounts to 86.032 %. The calculated values of the parameters used in the Freundlich isotherm indicated the existence of high-energy sorption centers in the unmodified bisorbent based on plum pits, while the calculated values of the parameters used in the Freundlich isotherm for the modified biosorbent based on plum pits showed moderate mode adsorption.

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.