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Abstract Colistin is one of the last-resort antibiotics in treating infections caused by multidrug-resistant (MDR) pathogens. Unfortunately, the emergence of colistin-resistant gram-negative strains limit its clinical application. Here, we identify an FDA-approved drug, valnemulin (Val), exhibit a synergistic effect with colistin in eradicating both colistin-resistant and colistin-susceptible gram-negative pathogens both in vitro and in the mouse infection model. Furthermore, Val acts synergistically with colistin in eliminating intracellular bacteria in vitro. Functional studies and transcriptional analysis confirm that the combinational use of Val and colistin could cause membrane permeabilization, proton motive force dissipation, reduction in intracellular ATP level, and suppression in bacterial motility, which result in bacterial membrane disruption and finally cell death. Our findings reveal the potential of Val as a colistin adjuvant to combat MDR bacterial pathogens and treat recalcitrant infections.

Summary: We recently developed an explosion-propeller hybrid driving underwater robot combined with an AI-based concrete damage detection technique for concrete overhaul in real marine environments. Here, we describe steps for establishing a detection dataset, optimizing and testing the algorithm, and preparing the explosive module. We detail procedures for setting up the robot body, assembling, and preparing other tools. Finally, we outline steps for testing in the designated sea area, collecting data, and processing and analysis.For complete details on the use and execution of this protocol, please refer to Zhang et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Rosa persica is considered a clonal plant because it is mainly propagated by clonal growth. Due to environmental degradation and habitat devastation, R. persica has been listed as a national second-class protected plant in China. However, the absence of research on wild populations of R. persica has impeded progress in formulating efficient conservation strategies. In this study, we investigated the clonal dispersal distance of R. persica to accurately determine the genetic diversity and population structure of the wild population in Xinjiang. We suggested that 20 m was the threshold distance with which to distinguish between different genets of plants. Based on this, we collated sequencing data from a total of 70 different genets of plants from 117 test samples. Eight populations of R. persica were primarily categorized into three subgroups: BL (Bole), TC (Tacheng) and CG (Changji). Of these, the CG subgroup exhibited the most genetic diversity. This research is the first to illustrates the clonal dispersal distance of R. persica, thus providing valuable reference guidelines for understanding the reproductive characteristics of clonal plants. In addition, the genetic diversity of R. persica provides a theoretical foundation for the formulation of conservation policies.

With the improvement of urbanization and living standards, waste disposal has become an urgent environmental problem. In recent years, object detection networks based on deep learning have greatly improved classification accuracy and speed by learning feature expression and object detection through deep neural networks. However, with the increase of model depth and number of parameters, object detection models based on deep neural networks require a lot of computing and storage resources, and are difficult to deploy on edge devices. To solve this problem, this paper proposes a lightweight garbage classification object detection algorithm based on improved YOLOv7. Firstly, the DW-SPPFCSPC module is constructed according to the depth-separable volume product, replacing the original SPPCSPC module. Second, the ECA channel attention mechanism was added to the backbone of YOLOv7. By paying more attention to important feature channels, the model can better capture important information in images and thus improve the performance of target detection. Finally, the network channel is pruned by analyzing the network dependency graph. Experimental results show that the model size is greatly reduced by this method. Compared with the original YOLOv7 model, the number of parameters is reduced to 6.05% and floating-point operations (FLOPs) are reduced to 1/16 with a loss of only 0.56% in average accuracy. The method is tested on VOC and H2O data sets, and good compression results are obtained.

In existing methods, the data distribution between different domains may have large differences, which can lead to performance degradation in the target domain, and the modelling of feature variability between different domains is not sufficient. Aiming at the problem of severe performance degradation during cross-domain migration, this paper proposes a cross-domain person re-identification method based on normalized IBN-Net. The normalized IBN-Net network introduces instance normalization and batch normalization to handle the feature maps at different scales. First, this study employed the normalized IBN-Net network as the backbone of the ResNet50 network. Second, the SimAM attention mechanism is integrated into the backbone network, which is an attention mechanism for inter-modal fusion,that is mainly used in multimodal data processing tasks,and it learns the spatial attention weights in the person images to obtain person information with more discriminative features. Finally, supervised learning was performed using the cross-entropy loss function during source domain training. Meanwhile, it can obtain the details of the source domain samples using the triplet loss function, thereby improving the classification performance. During target domain training, adaptation to the challenges of viewpoint, illumination, background, and feature distribution differences between samples is achieved by learning variations between the source and target domains. In the test stage, comparative experiments were conducted on two large-scale public data sets, Market-1501 and DukeMTMC-reID, achieving Rank-1 accuracies of 86.6% and 79.3%, respectively, with mean average precision mAPs of 68.7% and 62.6%, respectively. The experimental results show that the proposed method performs better in terms of improving the generalization ability of the model.

Summary: Marine activities typically face various risk factors such as marine animal attacks or unexpected collisions. In this paper, we develop underwater smart glasses (USGs) based on visual-tactile fusion for underwater hazard detection in real-time, ensuring operational safety. The proposed USG is composed of the vision module by artificial intelligence (AI)-enabled optical sensing and the tactile module by triboelectric metamaterials-enabled mechanical sensing. The vision module is obtained based on the underwater target detection algorithm you only look once-underwater hazard (YOLO-UH) developed by the dataset to detect toxic marine organisms in the visual field. The tactile module is designed with the kirigami tribo-materials (KTMs) to sensitively detect and warn of collisions outside the visual field. Through numerical simulations, laboratory tests, and real-world experiments, we validated the performance of both modules. The reported USG with its visual-tactile fusion concept enables near-far all-around hazard detection and reduces the danger for divers working underwater.

Summary: Large-scale complex underwater concrete structures have structural damage and the traditional damage detection method mostly uses manual identification, which is inaccurate and inefficient. Therefore, robotic detection systems have been proposed to replace manual identification for underwater concrete structures in ocean engineering. However, the highly corrosive and disruptive environment of the ocean poses great difficulties for the application. Here, we develop a manta ray-inspired underwater robot with well controllability to establish the damage datasets of underwater concrete structures, proposing the YOLOX-DG algorithm to improve the damage detection accuracy, and integrating the model into the robotic detection systems for underwater concrete damages. Eventually, the system is used for ocean testing in real applications (i.e., underwater marine harbors around the East China Sea), and satisfactory detection performance is obtained. The reported manta ray-inspired robotic detection system can be used to accurately monitor and analyze the underwater regions.

Abstract Background Improving the accuracy of estimated fetal weight (EFW) calculation can contribute to decision-making for obstetricians and decrease perinatal complications. This study aimed to develop a deep neural network (DNN) model for EFW based on obstetric electronic health records. Methods This study retrospectively analyzed the electronic health records of pregnant women with live births delivery at the obstetrics department of International Peace Maternity & Child Health Hospital between January 2016 and December 2018. The DNN model was evaluated using Hadlock’s formula and multiple linear regression. Results A total of 34824 live births (23922 primiparas) from 49896 pregnant women were analyzed. The root-mean-square error of DNN model was 189.64 g (95% CI 187.95 g—191.16 g), and the mean absolute percentage error was 5.79% (95%CI: 5.70%—5.81%), significantly lower compared to Hadlock’s formula (240.36 g and 6.46%, respectively). By combining with previously unreported factors, such as birth weight of prior pregnancies, a concise and effective DNN model was built based on only 10 parameters. Accuracy rate of a new model increased from 76.08% to 83.87%, with root-mean-square error of only 243.80 g. Conclusions Proposed DNN model for EFW calculation is more accurate than previous approaches in this area and be adopted for better decision making related to fetal monitoring.

ABSTRACTOver 3 billion doses of inactivated vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been administered globally. However, our understanding of the immune cell functional transcription and T cell receptor (TCR)/B cell receptor (BCR) repertoire dynamics following inactivated SARS-CoV-2 vaccination remains poorly understood. Here, we performed single-cell RNA and TCR/BCR sequencing on peripheral blood mononuclear cells at four time points after immunization with the inactivated SARS-CoV-2 vaccine BBIBP-CorV. Our analysis revealed an enrichment of monocytes, central memory CD4+ T cells, type 2 helper T cells and memory B cells following vaccination. Single-cell TCR-seq and RNA-seq comminating analysis identified a clonal expansion of CD4+ T cells (but not CD8+ T cells) following a booster vaccination that corresponded to a decrease in the TCR diversity of central memory CD4+ T cells and type 2 helper T cells. Importantly, these TCR repertoire changes and CD4+ T cell differentiation were correlated with the biased VJ gene usage of BCR and the antibody-producing function of B cells post-vaccination. Finally, we compared the functional transcription and repertoire dynamics in immune cells elicited by vaccination and SARS-CoV-2 infection to explore the immune responses under different stimuli. Our data provide novel molecular and cellular evidence for the CD4+ T cell-dependent antibody response induced by inactivated vaccine BBIBP-CorV. This information is urgently needed to develop new prevention and control strategies for SARS-CoV-2 infection. (ClinicalTrials.gov Identifier: NCT04871932).

Drought is one of the main abiotic stress factors affecting yield of Paeonia ostii. In this study, we conducted bioinformatics and differential expression analyses of P. ostii ‘Feng Dan’ ptc-miR396g-5p in leaf samples under different drought stress. ptc-miR396g-5p belongs to the miR396 family. Among the 271 plant species registered in the miRBase database, at least one miR396 member was found in 48 Angiospermae species, 3 in Gymnospermae species, and 1 in Pteridophy. Mature sequence alignment showed that P. ostii ‘Feng Dan’ ptc-miR396g-5p had high sequence similarity with miR396 from other species. Secondary structure prediction showed that the precursor sequence of ‘Feng Dan’ ptc-miR396g-5p could form a stable stem-loop structure, and the mature sequence was located on the 5′ arm of the secondary structure. Phylogenetic tree analysis showed that ‘Feng Dan’ was closely related to 20 species such as Glycine max, Medicago truncatula, Populus trichocarpa, Citrus sinensis, Vitis vinifera, and Theobroma cacao. The predicted target gene of the ‘Feng Dan’ ptc-miR396g-5p encodes a Signal Transducer and Activator of Transcription (STAT) transcription factor. The negative correlation of expression between the miRNA and its target gene was confirmed by qRT-PCR. Our data indicate that ‘Feng Dan’ ptc-miR396g-5p′s expression decreases under drought, leading to an expression increase of the STAT transcription factor.

The Dingqing ophiolite represents a significant allochthonous ophiolite nappe in the eastern segment of the Bangong–Nujiang suture zone in southeastern Tibet. The microanalytical data of associated podiform chromitites classify them into two distinct varieties: high-Al and high-Cr. The coexistence of both high-Cr and high-Al chromitites in the Dingqing ophiolite suggests a complex or multistage evolutionary history of the host rocks. New petrological and geochemical analyses are used herein to unravel the interrelationships between the chromitite ores and host rocks and assess the mechanism of formation. The Dingqing ophiolitic nappe is made up mainly of harzburgite, dunite, and less abundant pyroxenite and gabbro. Several small lens-shaped bodies of chromitite ore are mostly confined to the harzburgite rocks, with ore textures varying from massive to sparsely disseminated chromite. In addition to magnesiochromite, the orebodies contain minor amounts of olivine, amphibole, and serpentine. The textural relationships provide compelling evidence of plastic deformation and partial melting of the associated peridotites. Detailed examination of the Cr-spinel grains reveals a wide range of composition, spanning from high-Al (Cr# = 3.18–59.5) to high-Cr (Cr# 60.3–87.32). The abundances of the platinum-group element (PGE) in chromitites are significantly variable (93 to 274 ppb). Formation of the Dingqing peridotites most likely took place in a mid-ocean ridge (MOR) setting, and subsequent modifications by supra-subduction zone (SSZ) melts resulted in heterogenous or mixed geochemical characteristics of these rocks. Chemistry of the spinel–olivine–clinopyroxene assemblage demonstrates multiple stages of partial melting of the source mantle rocks, including an early phase of restricted partial melting (~20%–30%) and a later phase of extensive partial melting (>40%). The formation of the high-Al chromitite type was associated with the early phase (constrained melting), whereas extensive partial melting in the late stages likely led to the accumulation of high-Cr podiform chromitite bodies.

Tree peony (Paeonia suffruticosa Andr.) is a unique aromatic plant famous for its huge flowers, bright colors and strong fragrance, having high ornamental, medicinal, and edible value. Research on tree peony's flower fragrance has mainly focused on the comparative analysis among its varieties, leaving the inheritance of aroma compounds in this plant an overlooked area of study. Here, the volatile components of flowers at three different flowering stages of the light fragrance-type cultivar P. ostii 'Fengdan' and the strong fragrance-type cultivar P. suffruticosa 'Chunguihuawu', as well as the half-opening stage flowers of 109 F1 progeny, were collected and characterized in-depth by dynamic headspace sampling technique combined with gas chromatography-mass spectrometry (GC-MS). Diverse profiles of volatiles that included alcohols, esters, aldehydes, terpenes, benzenes, and hydrocarbons were identified from the evaluated accessions. These results revealed that the volatile components and content of parents were significantly different, and that hybridization generated more complex volatile components. Most volatile compounds in the hybrids, especially the main aromatic components, existed in at least one of the parents, being characterized by intermediate or transgressive inheritance for the floral trait; this demonstrated that volatile compounds can be inherited from parents to progeny. Further, seven de novo aroma compounds—those present in progeny yet absent in their parents—were found in progeny. This study preliminarily clarified the segregation performance of aroma traits in tree peony hybrids, which might provide a theoretical basis for selecting breeding parents and the breeding of new varieties for aroma traits.

The flowering period is the most important ornamental trait of tree peony, while industrial development of tree peony has been limited by short flowering period. miR319 plays an important regulatory role in plant flowering. In the current study, the expression characteristics and evolution of PsmiR319 in tree peony flowering was explored using ‘Feng Dan’ and ‘Lian He’, which are early-flowering and late-flowering varieties of tree peony, respectively. The structure, evolution, and target(s) of PsmiR319 were analyzed by bioinformatics. Evolution analysis showed that pre-PsmiR319 was distributed in 41 plant species, among which the length of the precursor sequence exhibited marked differences (between 52 and 308 bp). Pre-PsmiR319 of tree peony was located close to the corresponding sequences of Linum usitatissimum and Picea abies in the phylogenetic tree, and in addition, could form a typical hairpin structure including a mature body with a length of 20 bp located on the 3p arm and part of the loop sequence. The mature sequence of miR319 was highly conserved among different species. Target genes of PsmiR319 include MYB-related transcription factor in tree peony. Expression of PsmiR319, assayed by qRT-PCR, differed between ‘Feng Dan’ and ‘Lian He’ during different flower development periods. PsmiR319 and its target gene showed a negative expression regulation relationship during the periods of CE (color exposure), BS (blooming stage), IF (initial flowering), and HO (half opening) in the early-flowering ‘Feng Dan’, and the same in FB (Full blooming) periods of late-flowering ‘Lian He’. Findings from this study provide a reference for further investigation into the mechanism of miR319 in the development of different varieties of tree peony.

As a candidate national flower of China, tree peony has extremely high ornamental, medicinal and oil value. However, the short florescence and rarity of early-flowering and late-flowering varieties restrict further improvement of the economic value of tree peony. Specific miRNAs and their target genes engaged in tree peony floral florescence, development and senescence remain unknown. This report presents the integrated analysis of the miRNAome, transcriptome and degradome of tree peony petals collected from blooming, initial flowering, full blooming and decay stages in early-flowering variety Paeonia ostii ‘Fengdan’, an early-flowering mutant line of Paeonia ostii ‘Fengdan’ and late-flowering variety Paeonia suffruticosa ‘Lianhe’. Transcriptome analysis revealed a transcript (‘psu.G.00014095’) which was annotated as a xyloglucan endotransglycosylase/hydrolase precursor XTH-25 and found to be differentially expressed across flower developmental stages in Paeonia ostii ‘Fengdan’ and Paeonia suffruticosa ‘Lianhe’. The miRNA-mRNA modules were presented significant enrichment in various pathways such as plant hormone signal transduction, indole alkaloid biosynthesis, arachidonic acid metabolism, folate biosynthesis, fatty acid elongation, and the MAPK signaling pathway. Multiple miRNA-mRNA-TF modules demonstrated the potential functions of MYB-related, bHLH, Trihelix, NAC, GRAS and HD-ZIP TF families in floral florescence, development, and senescence of tree peony. Comparative spatio-temporal expression investigation of eight floral-favored miRNA-target modules suggested that transcript ‘psu.T.00024044’ and microRNA mtr-miR166g-5p are involved in the floral florescence, development and senescence associated agronomic traits of tree peony. The results might accelerate the understanding of the potential regulation mechanism in regards to floral florescence, development and abscission, and supply guidance for tree peony breeding of varieties with later and longer florescence characteristics.

In order to explore the release rule of floral volatile substances and the diurnal variation of different flower development stages of Paeonia ostii ‘Fengdan’ in potted and ground-planted conditions, dynamic headspace adsorption combined with gas chromatography-mass spectrometry(GC-MS) was used to analyze the dynamic changes in floral volatile components and contents. Quantitative real-time PCR (qRT-PCR) was used to analyze changes in flower fragrance-regulating genes PsPAL, PsTPSs, and PsbHLH at different flower development stages and a daily change process at the full-blooming stage. The results show that there were differences in aroma components and contents of Paeonia ostii ‘Fengdan’ at different flower development stages and different time quantum of every day. There were 25 and 28 aroma components identified in 7 flower development stages of tree peonies planted in pots and in the field, respectively, and 23 and 22 aroma components identified at different time quantum of the day, of which the largest and highest content was alkanes. The main characteristic aroma substances were (E)-β-ocimene, 1,3,5-trimethoxybenzene, 2,4-di-tert-butylphenol, methyl jasmonate, nerol, and cinnamyl alcohol; released amounts of the abovementioned substances varied depending on the development stage and the time of the day. The expression of flower fragrance-controlling genes (PsPAL, PsTPSs, and PsbHLH) in tree peonies varied greatly in different conditions. The results of this study provide a valuable resource to investigate floral fragrance formation in tree peonies.

Abstract Background The ability of a preventive nutritional intervention to reduce the morbidity of gestational diabetes mellitus (GDM) remains controversial. We aim to assess whether GDM can be prevented by an individualised nutritional intervention in pregnant women who are at high risk for the disease based on a prediction model. Methods/design A multicentre randomised controlled trial was designed to assess the efficacy of an individualised nutritional intervention for the prevention of GDM in a high-risk population screened by a novel prediction model in the first trimester. Pregnant women evaluated to be at high risk for GDM by the prediction model at less than 14 gestational weeks will be included. Women with pre-existing chronic diseases, including pregestational diabetes, or who are currently prescribed medicines that affect glucose values will be excluded. Allocation to intervention/control at a ratio of 1:1 will be conducted by a computerized randomisation system. The intervention group will complete 3-day food records and receive 3 individualised nutritional consultations with professional dieticians before the oral glucose tolerance test. The primary intention of the intervention is to promote a long-term healthy dietary pattern and prevent excessive gestational weight gain throughout pregnancy. The control group will complete 3-day food records at designated gestational weeks and receive standard antenatal care according to local health care provisions. The primary outcome is the incidence of GDM according to the criteria of the International Association of Diabetes and Pregnancy Study Group (IADPSG). A sample of 464 participants will provide 80% power to detect a 30% reduction in GDM incidence (α = 0.05 two tailed, 10% dropout). A total of 500 participants will be recruited. Discussion To date, this is the first randomised controlled trial aimed to evaluate the protective effect of an individualised nutritional intervention against GDM based on a logistic regression prediction model. Eligibility is not limited to obese women or singleton pregnancies, as in previous studies. This pragmatic trial is expected to provide valuable information on early screening and effective GDM prevention methods. Trial registration number ChiCTR, ChiCTR1900026963 . Registered 27 October 2019.

The miRNA, a kind of endogenous non-coding small RNA, plays an essential role in regulation of gene expression in plants. Quantitative real-time PCR (qRT-PCR) assay is one of the most common methods used for quantification of miRNA expression, and levels of expression are normalized by comparing with reference genes. The present study was intended to identify the appropriate reference genes for normalizing the level of miRNA expression in various developmental stages and tissues such as the bud development process, flower development stages, and different tissues of tree peony of different flowering times. Five algorithms (Delta Ct, geNorm, NormFinder, BestKeeper, and RefFinder) were used for stability analysis. The results showed that mtr-MIR160b-p3 and gma-miR394a-5p were the most stable miRNAs expressed during the bud development process of early-flowering tree peony ‘Feng dan’; PC-5p-19095 was the most stable during the bud development process of late-flowering tree peony ‘Lian he’, followed by gma-miR394a-5p and mtr-MIR160b-p3; the mtr-miR159a was the most stable miRNA expressed in the flower development stages of different tree peony varieties. The PC-3p-871 was the most stable miRNA expressed in different tissues of early-flowering tree peony ‘Feng dan’, followed by PC-5p-4, and PC-5p-4 was the most stable in late-flowering tree peony ‘Lian he’, followed by the mtr-miR168b. The findings of this study provide a reference for studying the changes in miRNA expression, and further exploring the regulatory mechanism of miRNA in tree peony.

Fenton technology is one of advanced oxidation process (AOP) methods to treat wastewater through chemical oxidation. Due to the limitations of classical iron-based catalysts, it is still challenging to find suitable catalysts for Fenton-like reactions. Here, MoS2/Au heterojunctions were successfully synthesized by reduction of chloroauric acid in the solution of layered MoS2 prepared by hydrothermal method. As a model molecule, methylene blue (MB) was used as the species to be degraded to evaluate the performance of the catalyst. It was determined by UV–visible spectra that the optimal catalyst can be obtained when MoS2 (mg): HAuCl4 (wt. % mL) is 2:2. The Fenton-like reaction process was monitored by introducing highly sensitive surface enhanced Raman spectroscopy (SERS). The results show that MB can be degraded by 83% in the first 10 min of the reaction, indicating that MoS2/Au has good catalytic performance. In addition, as a fingerprint spectrum, SERS was used to preliminarily analyze the molecular structure changes during the degradation process. The result showed that C-N-C bond was easier to break than the C-S-C bond. NH2 group and the fused ring were destroyed at the comparable speed at the first 30 min. In terms of application applicability, it was showed that MB degradation had exceeded 95% at all the three pH values of 1.4, 5.0, and 11.1 after the reaction was carried out for 20 min. The test and analysis of the light environment showed that the catalytic efficiency was significantly improved in the natural light of the laboratory compared to dark conditions. The possible mechanism based on ·OH and ·O2− from ESR data was proposed. In addition, it was demonstrated to be a first-order reaction from the perspective of kinetics. This study made a positive contribution to broaden of the applicable conditions and scope of Fenton-like reaction catalysts. It is expected to be used as a non-iron catalyst in practical industrial applications. From the perspective of detection method, we expect to develop SERS as a powerful tool for the in situ monitoring of Fenton-like reactions, and to further deepen our understanding of the mechanism.

Cellular network operators are predicting an increase in space of more than 200 percent to carry the move and tremendous increase of total users in data traffic. The growing of investments in infrastructure such as a large number of small cells, particularly the technologies such as LTE-Advanced and 6G Technology, can assist in mitigating this challenge moderately. In this paper, we suggest a projection study in spectrum sharing of radar multi-input and multi-output, and mobile LTE multi-input multi-output communication systems near m base stations (BS). The radar multi-input multi-output and mobile LTE communication systems split different interference channels. The new approach based on radar projection signal detection has been proposed for free interference disturbance channel with radar multi-input multi-output and mobile LTE multi-input multi-output by using a new proposed interference cancellation algorithm. We chose the channel of interference with the best free channel, and the detected signal of radar was projected to null space. The goal is to remove all interferences from the radar multi-input multi-output and to cancel any disturbance sources from a chosen mobile Communication Base Station. The experimental results showed that the new approach performs very well and can optimize Spectrum Access.

Reconfigurable Intelligent Surface (RIS) is an innovative technique to precisely control the phase of incident signals with the help of low-cost passive reflective elements. It shows excellent potential in the sixth generation of mobile communication systems, which not only extends wireless coverage but also boosts channel capacity. Considering that multipath propagation and a high number of antennas are involved in RIS in assisted mega multiple-input multiple-output (MIMO) systems, it suffers from severe channel fading and multipath effects, which in turn lead to signal instability and degradation of transmission performance. To overcome this obstacle, this essay suggests an improved gradient optimization algorithm to dynamically and optimally adjust the phase of the reflective elements to counteract channel fading and multipath effects as a strategy. In order to overcome the optimization problem of falling into local minima, this paper proposes an adaptive learning rate algorithm based on Adagrad improvement, which searches for the global optimal solution more efficiently and improves the robustness of the optimization algorithm. The suggested technique helps to enhance the estimate of channel efficiency of RIS-assisted large MIMO systems, according to simulation results. [ABSTRACT FROM AUTHOR]