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Concrete is a material that is widely used in the construction market due to its availability and cost, although it is prone to fracture formation. Therefore, there has been a surge in interest in self-healing materials, particularly self-healing capabilities in green and sustainable concrete materials, with a focus on different techniques offered by dozens of researchers worldwide in the last two decades. However, it is difficult to choose the most effective approach because each research institute employs its own test techniques to assess healing efficiency. Self-healing concrete (SHC) has the capacity to heal and lowers the requirement to locate and repair internal damage (e.g., cracks) without the need for external intervention. This limits reinforcement corrosion and concrete deterioration, as well as lowering costs and increasing durability. Given the merits of SHCs, this article presents a thorough review on the subject, considering the strategies, influential factors, mechanisms, and efficiency of self-healing. This literature review also provides critical synopses on the properties, performance, and evaluation of the self-healing efficiency of SHC composites. In addition, we review trends of development in research toward a broad understanding of the potential application of SHC as a superior concrete candidate and a turning point for developing sustainable and durable concrete composites for modern construction today. Further, it can be imagined that SHC will enable builders to construct buildings without fear of damage or extensive maintenance. Based on this comprehensive review, it is evident that SHC is a truly interdisciplinary hotspot research topic integrating chemistry, microbiology, civil engineering, material science, etc. Furthermore, limitations and future prospects of SHC, as well as the hotspot research topics for future investigations, are also successfully highlighted.

Designed for undergraduate students in electrical and electronic engineering, this text covers the basics of emerging areas in power electronics and a broad range of topics such as power switching devices, conversion methods, analysis and techniques, and applications.

Ets-related gene (ERG) is overexpressed as a fusion protein in prostate cancer. During metastasis, the pathological role of ERG is associated with cell proliferation, invasion, and angiogenesis. Here, we hypothesized that miRNAs regulate ERG expression through its 3ʹUTR. Several bioinformatics tools were used to identify miRNAs and their binding sites on 3ʹUTR of ERG. The selected miRNAs expression was analyzed in prostate cancer samples by qPCR. The miRNAs overexpression was induced in prostate cancer cells (VCaP) to analyze ERG expression. Reporter gene assay was performed to evaluate the ERG activity in response to selected miRNAs. The expression of ERG downstream target genes was also investigated through qPCR after miRNAs overexpression. To observe the effects of selected miRNAs on cell proliferation and migration, scratch assay was performed to calculate the cell migration rate. miR-4482 and miR-3912 were selected from bioinformatics databases. miR-4482 and -3912 expression were decreased in prostate cancer samples, as compared to controls ( p<0 . 05 and p<0 . 001 ), respectively. Overexpression of miR-4482 and miR-3912 significantly reduced ERG mRNA ( p<0 . 001 and p<0 . 01 ), respectively ) and protein ( p<0 . 01 ) in prostate cancer cells. The transcriptional activity of ERG was significantly reduced ( p<0 . 01 ) in response to miR-4482 and-3912. ERG angiogenic targets and cell migration rate was also reduced significantly ( p<0 . 001 ) after miR-4482 and -3912 over-expression. This study indicates that miR-4482 and -3912 can suppress the ERG expression and its target genes, thereby, halt prostate cancer progression. These miRNAs may be employed as a potential therapeutic target for the miRNA-based therapy against prostate cancer.

Structural health monitoring (SHM) systems are used to analyze the health of infrastructures such as bridges, using data from various types of sensors. While SHM systems consist of various stages, feature extraction and pattern recognition steps are the most important. Consequently, signal processing techniques in the feature extraction stage and machine learning algorithms in the pattern recognition stage play an effective role in analyzing the health of bridges. In other words, there exists a plethora of signal processing techniques and machine learning algorithms, and the selection of the appropriate technique/algorithm is guided by the limitations of each technique/algorithm. The selection also depends on the requirements of SHM in terms of damage identification level and operating conditions. This has provided the motivation to conduct a Systematic literature review (SLR) of feature extraction techniques and pattern recognition algorithms for the structural health monitoring of bridges. The existing literature reviews describe the current trends in the field with different focus aspects. However, a systematic literature review that presents an in-depth comparative study of different applications of machine learning algorithms in the field of SHM of bridges does not exist. Furthermore, there is a lack of analytical studies that investigate the SHM systems in terms of several design considerations including feature extraction techniques, analytical approaches (classification/ regression), operational functionality levels (diagnosis/prognosis) and system implementation techniques (data-driven/model-based). Consequently, this paper identifies 45 recent research practices (during 2016–2023), pertaining to feature extraction techniques and pattern recognition algorithms in SHM for bridges through an SLR process. First, the identified research studies are classified into three different categories: supervised learning algorithms, neural networks and a combination of both. Subsequently, an in-depth analysis of various machine learning algorithms is performed in each category. Moreover, the analysis of selected research studies (total = 45) in terms of feature extraction techniques is made, and 25 different techniques are identified. Furthermore, this article also explores other design considerations like analytical approaches in the pattern recognition process, operational functionality and system implementation. It is expected that the outcomes of this research may facilitate the researchers and practitioners of the domain during the selection of appropriate feature extraction techniques, machine learning algorithms and other design considerations according to the SHM system requirements.

Salt stress is the major risk to the seed germination and plant growth via affecting physiological and biochemical activities in plants. Zinc nanoparticles (ZnNPs) are emerged as a key agent in regulating the tolerance mechanism in plants under environmental stresses. However, the tolerance mechanisms which are regulated by ZnNPs in plants are still not fully understood. Therefore, the observation was planned to explore the role of ZnNPs ( applied as priming and foliar) in reducing the harmful influence of sodium chloride (NaCl) stress on the development of spinach ( Spinacia oleracea L.) plants. Varying concentrations of ZnNPs (0.1%, 0.2% & 0.3%) were employed to the spinach as seed priming and foliar, under control as well as salt stress environment. The alleviation of stress was observed in ZnNPs-applied spinach plants grown under salt stress, with a reduced rise in the concentration hydrogen peroxide, melondialdehyde and anthocyanin contents. A clear decline in soluble proteins, chlorophyll contents, ascorbic acid, sugars, and total phenolic contents was observed in stressed conditions. Exogenous ZnNPs suppressed the NaCl generated reduction in biochemical traits, and progress of spinach plants. However, ZnNPs spray at 0.3% followed by priming was the most prominent treatment in the accumulation of osmolytes and the production of antioxidant molecules in plants.

Azo dyes are one of the largest classes of synthetic dyes being used in textile industries. It has been reported that 15–50% of these dyes find their way into wastewater that is often used for irrigation purpose in developing countries. The effect of azo dyes contamination on soil nitrogen (N) has been studied previously. However, how does the azo dye contamination affect soil carbon (C) cycling is unknown. Therefore, we assessed the effect of azo dye contamination (Reactive Black 5, 30 mg kg −1 dry soil), bacteria that decolorize this dye and dye + bacteria in the presence or absence of maize leaf litter on soil respiration, soil inorganic N and microbial biomass. We found that dye contamination did not induce any change in soil respiration, soil microbial biomass or soil inorganic N availability ( P  > 0.05). Litter evidently increased soil respiration. Our study concludes that the Reactive Black 5 azo dye (applied in low amount, i.e., 30 mg kg −1 dry soil) contamination did not modify organic matter decomposition, N mineralization and microbial biomass in a silty loam soil.