Multiple-input multiple-output (MIMO) systems constitute an important part of todays wireless communication standards and these systems are expected to take a fundamental role in both the access and backhaul sides of the emerging wireless cellular networks. Recently, reported measurement campaigns have established that various outdoor radio propagation environments exhibit sparsely structured channel impulse response (CIR). We propose a novel superimposed training (SiT) based up-link channels' estimation technique for multipath sparse MIMO communication channels using a matching pursuit (MP) algorithm; the proposed technique is herein named as superimposed matching pursuit (SI-MP). Subsequently, we evaluate the performance of the proposed technique in terms of mean-square error (MSE) and bit-error-rate (BER), and provide its comparison with that of the notable first order statistics based superimposed least squares (SI-LS) estimation. It is established that the proposed SI-MP provides an improvement of about 2dB in the MSE at signal-to-noise ratio (SNR) of 12dB as compared to SI-LS, for channel sparsity level of 21.5%. For BER = 10 −2 , the proposed SI-MP compared to SI-LS offers a gain of about 3dB in the SNR. Moreover, our results demonstrate that an increase in the channel sparsity further enhances the performance gain.

Antibiotics discovery was a significant breakthrough in the field of therapeutic medicines, but the over (mis)use of such antibiotics (in parallel) caused the increasing number of resistant bacterial species at an ever-higher rate. This study was thus devised to assess the multi-drug resistant bacteria present in sanitation-related facilities in human workplaces. In this regard, samples were collected from different gender, location, and source-based facilities, and subsequent antibiotic sensitivity testing was performed on isolated bacterial strains. Four classes of the most commonly used antibiotics i.e., β-lactam, Aminoglycosides, Macrolides, and Sulphonamides, were evaluated against the isolated bacteria. The antibiotic resistance profile of different (70) bacterial strains showed that the antibiotic resistance-based clusters also followed the grouping based on their isolation sources, mainly the gender. Twenty-three bacterial strains were further selected for their 16s rRNA gene based molecular identification and for phylogenetic analysis to evaluate the taxonomic evolution of antibiotic resistant bacteria (ARB). Moreover, the bacterial resistance to Sulphonamides and beta lactam was observed to be the most and to Aminoglycosides and macrolides as the least. Plasmid curing was also performed for multidrug resistant (MDR) bacterial strains, which significantly abolished the resistance potential of bacterial strains for different antibiotics. These curing results suggested that the antibiotic resistance determinants in these purified bacterial strains are present on respective plasmids. Altogether, the data suggested that the human workplaces are the hotspot for the prevalence of MDR bacteria and thus may serve as the source of horizontal gene transfer and further transmission to other environments.

Recent advances in multiple-input multiple-output (MIMO) systems have renewed the interests of researchers to further explore this area for addressing various dynamic challenges of emerging radio communication networks. Various measurement campaigns reported recently in the literature show that physical multipath MIMO channels exhibit sparse impulse response structure in various outdoor radio propagation environments. Therefore, a comprehensive physical description of sparse multipath MIMO channels is presented in first part of this paper. Superimposing a training sequence (low power, periodic) over the information sequence offers an improvement in the spectral efficiency by avoiding the use of dedicated time/frequency slots for the training sequence, which is unlike the traditional schemes. The main contribution of this paper includes three superimposed training (SiT) sequence based channel estimation techniques for sparse multipath MIMO channels. The proposed techniques exploit the compressed sensing (CS) theory and prior available knowledge of channel's sparsity. The proposed sparse MIMO channel estimation techniques are named as, SiT based compressed channel sensing (SiT-CCS), SiT based hardlimit thresholding with CCS (SiT-ThCCS), and SiT training based match pursuit (SiT-MP). Bit error rate (BER) and normalized channel mean square error (NCMSE) are used as metrics for the simulation analysis to gauge the performance of proposed techniques. A comparison of the proposed schemes with a notable first order statistics based SiT least squares (SiT-LS) estimation technique is presented to establish the improvements achieved by the proposed schemes. For sparse multipath time-invariant MIMO communication channels, it is observed that SiT-CCS, SiT-MP, and SiT-ThCCS can provide an improvement up to 2 dB, 3.5 dB, and 5.2 dB in the MSE at signal to noise ratio (SNR) of 12 dB when compared to SiT-LS, respectively. Moreover, for BER = 10 −1.9 , the proposed SiT-CCS, SiT-MP, and SiT-ThCCS, compared to SiT-LS, can offer a gain of about 1 dB, 2.5 dB, and 3.5 dB in the SNR, respectively. The performance gain in MSE and BER is observed to improve with an increase in the channel sparsity.

2Antibiotics discovery was a significant breakthrough in the field of therapeutic medicines, 3 but the over (mis)use of such antibiotics (n parallel) caused the increasing number of 4 resistant bacterial species at an ever-higher rate. This study was thus devised to assess the 5 multi-drug resistant bacteria present in sanitation-related facilities in human workplaces. 6In this regard, samples were collected from different gender, location, and source-based 7 facilities, and subsequent antibiotic sensitivity testing was performed on isolated bacterial 8 strains. Four classes of the most commonly used antibiotics i.e., β-lactam, 9 Aminoglycosides, Macrolides, and Sulphonamides, were evaluated against the isolated 10 bacteria. 11The antibiotic resistance profile of different (70) bacterial strains showed that the antibiotic 12 resistance-based clusters also followed the grouping based on their isolation sources, 13 mainly the gender. Twenty-three bacterial strains were further selected for their 16s rRNA 14 gene based molecular identification and for phylogenetic analysis to evaluate the 15 taxonomic evolution of antibiotic resistant bacteria. Moreover, the bacterial resistance to 16 Sulphonamides and beta lactam was observed to be the most and to Aminoglycosides and 17 macrolides as the least. Plasmid curing was also performed for MDR bacterial strains, 18

Correction for ‘First principles study of electronic and optical properties and photocatalytic performance of GaN–SiS van der Waals heterostructure’ by S. S. Sabir et al., RSC Adv., 2021, 11, 32996–33003, https://doi.org/10.1039/D1RA06011B.