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The tidal power has the potential to play a vital role in a sustainable energy future. The main objective of this paper is to investigate the performance and fatigue life of tidal current turbine (TCT) using fluid structure interaction (FSI) modeling. The performance of TCT was predicted using Ansys CFX. The performance curve, pressure distribution on the blade, and velocity streamline were visualized for eight repetitive analyses at different tip speed ratio. The hydrodynamic load calculated from CFD analysis was transferred to FEA model for investigation of the structural response of TCT. Modal analysis was performed to examine the mode shapes and natural frequencies of TCT. The fatigue analysis were performed and number of cycles and safety factor at different equivalent alternating stresses were investigated. The results of the simulation confirm that the turbine has a maximum value of the coefficient of performance at λ = 5, the turbine operating frequency is not close to its natural frequency, and it is safe under the applied fatigue loads with a high factor of safety. Keywords: Tidal current turbine, Fluid–structure interaction, Fatigue performance, Computational Fluid Dynamic, Blade element

Heavy metals (HMs) contamination in agricultural soils is a major concern for global food safety and human health. Although, various in-situ and ex-situ remediation methods have been used for the treatment of HMs contaminated soils, however, they also have many drawbacks viz., capital investment, toxicity, and environmental health hazards. Consequently, there is an urgent need to develop a novel method to ameliorate the toxicity of HMs in agricultural soils. In recent years, nanoparticles (NPs) have gained significant attention due to their potential applications in the environment and agriculture fields. Nanoremediation employs NPs that effectively reduce the contents of toxic HMs in the soil-plant system. Several studies have reported that the application of NPs in HMs-polluted soils, which reduced plant-available HMs concentration soils. However, the long-term efficiency of NPs immobilization is still unclear. Here, we provide details about the toxicity of HMs to environmental systems and potential applications NPs to alleviate the accumulation of HMs in agricultural soils. Finally, we present the mechanistic route of HMs-toxicity alleviation in plants by NPs application as well as their long-term efficiency and future prospects.

ABSTRACT Background: Different risk variables that lead to mortality of a burn victim. The most important categories for assessment of severity in burns among pediatric age group is, total body surface area (TBSA) involved, age and type of burn which can lead to increased deaths. The current study was performed to analyze the various factors of causing risk of burns and leading to mortality. Methods: It is a cross-sectional non-probability descriptive study, performed of Pediatric Burns unit Ruth Pfau Civil hospital, Karachi. Clinical recordof 227 Patients of both sexes135 males and 92 females was analyzed after receiving approval from Burn Center, Ruth Pfau Civil Hospital Karachi. Statistical analyzes was carried out using version 20.00 of SPSS. Results: Largest percentage of mortality was in age between 1 and 5 years. In 139 patients, Scalding due to hot water was the most common form of burn. Major cause of mortality was due to multiple burns with > 50% involvement of TBSA was the most common category constituting 68.7% among the patients in the current study. Significant association was analyzed between the nutritional status of patients as assessed by albumin levels and mortality (p=0.004). However, total mortality among the reported cases in between 2016 until 2018 was 28.2% being in 1-5years of age with above-mentioned associations. Conclusion: Current research indicates that an increasing mortality in pediatric burn patients is linked with the proportion of total body surface area (TBSA) exposed to burns, duration of the exposure, decrease total protein content due to damage of tissues and muscles. This leads to prolonged protein and intravenous fluid replacement. One of the most common complication is progression to respiratory failure. Keywords: Burns, burns etiology, burns mortality rate.

Modulation of pulsatile gonadotropin-releasing hormone (GnRH) secretion across postnatal development in higher primates is not fully understood. While gonadotropin-inhibitory hormone (GnIH) is reported to suppress reproductive axis activity in birds and rodents, little is known about the developmental trajectory of GnIH expression in rhesus monkeys throughout the pubertal transition. This study was aimed at examining the variation in GnIH immunoreactivity (-ir) and associated changes among GnIH, GnRH, and Kiss1 mRNA expression in the hypothalamus of infant, juvenile, prepubertal, and adult male rhesus monkeys. The brains from rhesus macaques were collected from infancy until adulthood and were examined using immunofluorescence and RT-qPCR. The mean GnIH-ir was found to be significantly higher in prepubertal animals (p < 0.01) compared to infants, and significantly reduced in adults (p < 0.001). Significantly higher (p < 0.001) GnRH and Kiss1 mRNA expression was noted in adults while GnIH mRNA expression was the highest at the prepubertal stage (p < 0.001). Significant negative correlations were seen between GnIH-GnRH (p < 0.01) and GnIH-Kiss1 (p < 0.001) expression. Our findings suggest a role for GnIH in the prepubertal suppression of the reproductive axis, with disinhibition of the adult reproductive axis occurring through decreases in GnIH. This pattern of expression suggests that GnIH may be a viable target for the development of novel therapeutics and contraceptives for humans.

Regeneration of a part of the diseased liver after surgical resection is mainly achieved by the proliferation of the remaining healthy liver cells. However, in case of extreme loss of liver cells or in the final stages of chronic liver disease, most liver cells are depleted or lose their ability to proliferate. Therefore, to foster liver regeneration, it is of great clinical and scientific significance to improve the survival and proliferation ability of residual hepatocytes. In this study, we conducted experiments on a zebrafish model of targeted ablation of liver cells to clarify the role of fibroblast growth factor 21 (FGF21). We found that FGF21 increased the regeneration area of the damaged liver and improved the survival rate of damaged liver cells by inhibiting cell apoptosis and reducing oxidative stress. Our results also showed that administration of FGF21 upregulated autophagy, and the beneficial effects of FGF21 were reversed by the well-known autophagy inhibitor chloroquine (CQ), indicating that FGF21-activated autophagy played a central role in the treatment. We further showed that the enhancement of autophagy induced by FGF21 was due to the activation of the AMPK-mTOR signaling pathway. Taken together, these data provide new evidence that FGF21 is an effective autophagy regulator that can significantly improve the survival of damaged livers.

Aim: The gut microbiome plays a crucial role in colorectal cancer (CRC) tumorigenesis, but compositions of microorganisms have been inconsistent in previous studies due to the different types of specimens. We investigated the microbiomes and resistomes of CRC patients with colonic biopsy tissue and intestinal lavage fluid (IVF).Methods: Paired samples (biopsy tissue and IVF) were collected from 20 patients with CRC, and their gut microbiomes and resistomes were measured by shotgun metagenomics. Clinical and laboratory data were recorded. Bioinformatics (KneadData, Kraken2, and FMAP) and statistical analysis were done using the R (v4.0.2) software.Results: Bacterial diversity in IVF was higher than in tissue samples, and bacterial operational taxonomic units (OTUs) were 2,757 in IVF vs. 197 in tissue. β-diversity showed distinct clusters in paired samples. The predominant bacteria in IVF were phylum Proteobacteria, while the predominant bacteria of tissue were phylum Actinobacteria. Twenty-seven representative bacteria were selected to form six bacterial clusters, which showed only Firmicutes Cluster 1, and the Bacteroidetes Cluster 1 were significantly more abundant in the IVF group than those in the tissue group (p < 0.05). The Firmicutes Cluster 2, Bacteroidetes Cluster 2, Pathogen Cluster, and Prevotella Cluster were not significantly different between IVF and tissue (p > 0.05). Correlation analysis revealed that some bacteria could have effects on metabolic and inflammatory parameters of CRC patients. A total of 1,295 antibiotic resistance genes (ARGs) were detected in the gut microbiomes, which conferred multidrug resistance, as well as resistance to tetracycline, aminoglycoside, and more. Co-occurrence patterns revealed by the network showed mainly ARG-carrying bacteria to be similar between IVF and tissue, but leading bacteria located in the hub differed between IVF and tissue.Conclusion: Heterogeneity of microbiota is particularly evident when studied with IVF and tissue samples, but bacterial clusters that have close relationships with CRC carcinogenesis are not significantly different, using IVF as an alternative to tissue for gut microbiome, and resistome assessment may be a feasible method.

Biofuel derived from halophytic biomass is getting attention owing to the concerns of energy versus food crisis. The disadvantages associated with edible bioenergy resources necessitate the need to explore new feedstocks for sustainable biofuel production. In this study, biomass from locally available abundant halophytes (Panicum antidotale, Phragmites karka, Halopyrum mucronatum, and Desmostachya bipinnata) was screened for saccharification by an enzyme cocktail composed of cellulase, xylanase, and pectinase from Brevibacillus borstelensis UE10 and UE27, Bacillus aestuarii UE25, Aneurinibacillus thermoaerophilus UE1, and Bacillus vallismortis MH 1. Two types of pretreatment, i.e., with dilute acid and freeze-thaw, were independently applied to the halophytic biomass. Saccharification of acid-pretreated P. karka biomass yielded maximum reducing sugars (9 mg g–1) as compared to other plants. Thus, the factors (temperature, pH, substrate concentration, and enzyme units) affecting its saccharification were optimized using central composite design. This statistical model predicted 49.8 mg g–1 of reducing sugars that was comparable to the experimental value (40 mg g–1). Scanning electron microscopy and Fourier-transform infrared spectroscopy showed significant structural changes after pretreatment and saccharification. Therefore, halophytes growing in saline, arid, and semi-arid regions can be promising alternative sources for bioenergy production.

The negative effects of salinity on plant growth and physiology are well-established, which is one of the major threats to food security in semi-arid and arid regions of the world. The current research focuses on biosynthesis of copper nanoparticles (CuNPs) from a bacterial strain NST2, which was genetically identified as Klebsiella pneumoniae based on taxonomic identity of 16S rRNA gene. The strain was selected for bioprospecting of CuNPs owing to its Cu tolerance potential. The biologically-synthesized CuNPs were confirmed in culture by using ultraviolet visible spectroscopy. The material characteristics of green CuNPs were further investigated by using Fourier transform infrared spectroscopy, X-ray diffractometer, scanning electron microscopy and transmission electron microscopy, where crystallite size was ranged from 22.44 nm to 44.26 nm and particles were stabilized by various functional groups, such as carbonyl and amine groups. When 100 mg kg−1 of green CuNPs were mixed in saline soil in a pot experiment, the maize plants showed increased root and shoot length (43.52% and 44.06%, respectively), fresh weight (46.05% and 51.82%, respectively) and dry weight (47.69% and 30.63%, respectively) in comparison to control maize plants without CuNPs application. Moreover, green CuNPs at their highest treatment level (100 mg kg−1 of soil) counteracted the lipid peroxidation and oxidative damage in maize plants by promoting the activities of antioxidants and demoting the cellular levels of reactive oxygen species and ionic contents of Na+ and Cl-. Conclusively, biogenic CuNPs is an emerging and promising technique, which could replace traditional methods of salinity management in agricultural soils.

The adulteration of plant raw materials used for the preparation of herbal drugs with foreign plant material is one of the important issues in the quality control of herbal products. Chemical fingerprinting is a well-known approach for the characterization of secondary metabolites associated with the plant species and can be used for quality control of plant material. The current study centred on the development of chemical fingerprinting of three medicinal plants of genus Anemone including A. obtusiloba, A. falconeri and A tetrasepala through identification of their metabolites using LC-ESI-QTOF-MS/MS analysis. Thirty compounds were identified by using high-resolution positive and negative electrospray- ionization (ESI) modes and MS/MS analysis. The identified compounds belong to diterpenoids, alkaloids, phenols, flavonoids and other classes and their distribution among the analysed species was studied using different statistical tools. Moreover, an LC-HR-ESI-MS/MS method was developed to detect the cross mixing of A. obtusiloba with Ziziphus jujuba. Seven chromatographically differentiative peaks confined to A. obtusiloba were selected to detect its contamination in adulterated samples. The method was able to detect as low as 20% mixing of A. obtusiloba in Z. jujuba. This study can play a significant role to manage the quality control of herbal medicines and to identify lead natural products of these plants.

Despite advanced health facilities in many developed countries, diabetic patients face multifold health challenges. Type 2 diabetes mellitus (T2DM) go along with conspicuous symptoms due to frequent peaks, hypoglycemia <=70 mg/dL (while fasting), or hyperglycemia >=180 mg/dL two hours postprandial, according to the American Diabetes Association (ADA)). The worse effects of Type 2 diabetes mellitus are precisely associated with the poor lifestyle adopted by patients. In particular, a healthy diet and nutritious food are the key to success for such patients. This study was done to help T2DM patients improve their health by developing a favorable lifestyle under an AI-assisted Continuous glucose monitoring (CGM) digital system. This study aims to reduce the blood glucose level fluctuations of such patients by rectifying their daily diet and maintaining their exertion vs. food consumption records. In this study, a well-precise prediction is obtained by training the ML model on a dataset recorded from CGM sensor devices attached to T2DM patients under observation. As the data obtained from the CGM sensor is time series, to predict blood glucose levels, the time series analysis and forecasting are done with XGBoost, SARIMA, and Prophet. The results of different Models are then compared based on performance metrics. This helped in monitoring various trends, specifically irregular patterns of the patient's glucose data, collected by the CGM sensor. Later, keeping track of these trends and seasonality, the diet is adjusted accordingly by adding or removing particular food and keeping track of its nutrients with the intervention of a commercially available all-in-one AI solution for food recognition. This created an interactive assistive system, where the predicted results are compared to food contents to bring the blood glucose levels within the normal range for maintaining a healthy lifestyle and to alert about blood glucose fluctuations before the time that are going to occur sooner. This study will help T2DM patients get in managing diabetes and ultimately bring HbA1c within the normal range (<= 5.7%) for diabetic and pre-diabetic patients, three months after the intervention. [ABSTRACT FROM AUTHOR]

Objective: Depression is an disease that seriously endangers the physical and mental health of human body. However, in view of the limitation of existing antidepressants, it's urgent to find new antidepressants from natural products. The aim of this study was to evaluate the effect of Paecilomyces tenuipes, which is an important entomogenous fungus in China. Methods: This research was intent to investigate the antidepressant actions of P. tenuipes M98 extracts using a battery of behavioural models including tail suspension test (TST) and forced swimming test (FST), 5-HTP-induced head twitch response and chronic unpredictable mild stress (CUMS) in mice. Results: Fifteen days treatment with aqueous and ethanol extracts signifificantly decreased the duration of immobility in TST and FST, without signifificant changes in locomotor activity. Moreover, chronic application of extracts for 21 days signifificantly improved the depressive-like behaviours of CUMS mice, including reduced body weight and sucrose preference and lengthened immobility time in TST and FST. In addition, extracts produced a signifificant increase in 5-hydroxytryptamine and dopamine, but not noradrenaline, levels in hypothalamus. These fifindings suggested that this action of P. tenuipes might be related to the regulation of serotonergic and dopaminergic systems, which were further confirmed via 5-HTP induced head-twitch test. In addition, P. tenuipes M98 extracts also displayed anti-oxidative effffects. Conclusion: P. tenuipes M98 extracts present excellent antidepressant-like activities, which might be explained by regulation of neurotransmitters and alleviation of oxidative stress.

Abstract Background It is becoming imperative to develop renewable fuels such as biodiesel which are sustainable and environmentally friendly. Exploiting non-edible oils is more necessary to reduce dependency of edible oils for biodiesel production. The current study investigated biodiesel production from non-edible Salvadora persica seed oil (SPSO) and crude coconut oil (CCO) by Burkholderia cepacia lipase acting as a biocatalyst in a solvent-free system. The biodiesel yield produced from these feedstocks was compared and the effect of ethanol (acyl acceptor) vs. SPSO and CCO in various ratios on biodiesel production was determined. Results The presence of medium-chain fatty acids in majority was confirmed for SPSO and CCO while the average molecular weight was calculated as 749.53 g/mol and 664.57 g/mol, respectively. Thin Layer Chromatography indicated ethyl esters in the produced Salvadora and coconut biodiesel samples. Maximum biodiesel yield (around 70%) was obtained at 1:4 oil-to-ethanol molar ratio from both oils followed by a decline at higher ratios. The gas chromatographic analysis of Salvadora biodiesel at 1:4 molar ratio showed that the yield of individual esters was mostly of medium- and long-chain fatty acids. The analysis of coconut biodiesel at 1:4 molar ratio revealed that it consists mainly of the esters of medium-chain fatty acids. A comparison of estimated properties of biodiesel from both the parent oils with the international standard showed that it meets most of the requirements. Conclusion The study paves the way for a green route for biodiesel production and would promote the use of non-edible vegetable oils over edible ones to produce biodiesel. Further, it is a right step to use lipases in biodiesel production as compared to chemical catalysts. Ethanol, which can also be produced from biomass fermentation, can be used as acyl acceptor to produce biodiesel and this makes the process eco-friendly. Moreover, Burkholderia cepacia lipase is a good choice among lipases to get high biodiesel yields successfully from SPSO and CCO at low oil-to-ethanol molar ratios.

Dividend policy/payout (DP) is one the areas of finance where extensive research has been con-ducted to find out the determinants that why firms pay the dividend and what motivates them to share their earnings with shareholders. This situation is termed as a puzzle by researchers. To solve this puzzle, researchers have outlined various financial and non-financial factors. This research is carried out to find the effect of financial factors viz. size, profitability, risk, leverage, and liquidity over the DP of the firms listed on Pakistan Stock Exchange (PSX) in the cement sector. Firms involved in the manufacturing of ce-ment are selected and other firms of the sector are excluded. Data for ten years i.e. from 2009 to 2018 has been extracted from the published annual financial statements of the firms. To verify the relationship between dependent and independent variables, the bivariate correlation has been applied and to find the best-fit regression model, backward multiple regression has been applied. According to the findings from backward multiple regression, profitability and liquidity are the factors that influence the dividend payouts of the firm positively and significantly. Whereas, size, risk, and leverage have failed to show their significance over the dividend payment of the sector.

The technological transformation has led to the introduction of mini computers as wearable computers which facilitate users to use the Internet, different applications, messaging, and for calls all on one platform. The focus of these wearable computers in earlier studies has been on consumers and technical aspects, and their application has not been explored from an organizational perspective. This means that the recent introduction of wearable technology has great potential, which is untapped in the organizational context, especially for supply chain collaboration (SC). In this study, wearable technology is thought to offer huge potential for organizations in SC. Therefore, the purpose of this study is to empirically examine the role of wearable technology i.e. Smartwatch in enhancing collaboration among supply chain members with mediating roles of green training (GT) and emotional intelligence (EI). The population of this study is the pharmaceutical industry in Pakistan. Simple random sampling method has been used to collect data from 150 sample size. Data have been collected through offline and online survey method. Data have been analyzed through partial least square-structure equation modeling (PLS-SEM) technique. The results of this study have empirically tested the established conceptual framework. Continuance intention to use smartwatch has a positive effect on SC. Furthermore, GT and EI have mediating roles between continuance intention to use smartwatch and SC. It is concluded that the continuance intention to use smartwatch will increase SC. GT and EI will strengthen the relationship between continuance intention to use smartwatch and SC. The use of wearable technology will motivate employees to communicate rapidly and to monitor their supply chain activities using web-based and other applications. This study has practical implications in organizations and theoretical contribution to literature.

Increasing supply chain performance (SCP) has always been a common phenomenon in the history of organizations. Recent technological advancements [including, radio frequency identification (RFID) in combination with big data predictive analytics (BDPA)] have been able to significantly leverage SCP. In this respect, there is an urgent need for acceptance of RFID technology as well as BDPA to be empirically investigated in order to better understand the enhancement of SCP. Therefore, this paper aims at establishing and empirically investigating the relationship between BDPA acceptance, RFID acceptance, and SCP, respectively. The population of this paper comprises the pharmaceutical logistics industry in China. The sample was obtained from employees through the use of a simple random sampling method. Data were collected through an adapted questionnaire. SmartPLS 3.0 software was used for structural equation modeling analysis. Partial least square-structural equation modeling (PLS–SEM), the value of path coefficient, was found to be 0.700. This showed a direct positive effect of BDPA acceptance on SCP. Moreover, RFID acceptance was also tested through the “variance accounted for” method of mediation. Results showed a partial mediation of 57% between BDPA and SCP. Theoretically, this paper fills the knowledge gap by finding relationships between BDPA and SCP via a mediation role of RFID technology. Practically, it would be beneficial for supply chain managers in the pharmaceutical industry to adopt and implement BDPA and RFID technology so as to enhance SCP.

Chromium (Cr) is an important environmental constraint effecting crop productivity. Spermine (SPM) is a polyamine compound regulating plant responses to abiotic stresses. However, SPM-mediated tolerance mechanisms against Cr stress are less commonly explored in plants. Thus, current research was conducted to explore the protective mechanisms of SPM (0.01 mM) against Cr (100 µM) toxicity in two rice cultivars, CY927 (sensitive) and YLY689 (tolerant) at the seedling stage. Our results revealed that, alone, Cr exposure significantly reduced seed germination, biomass and photosynthetic related parameters, caused nutrient and hormonal imbalance, desynchronized antioxidant enzymes, and triggered oxidative damage by over-accretion of reactive oxygen species (ROS), malondialdehyde (MDA) and electrolyte leakage in both rice varieties, with greater impairments in CY927 than YLY689. However, seed priming with SPM notably improved or reversed the above-mentioned parameters, especially in YLY689. Besides, SPM stimulated the stress-responsive genes of endogenous phytohormones, especially salicylic acid (SA), as confirmed by the pronounced transcript levels of SA-related genes (OsPR1, OsPR2 and OsNPR1). Our findings specified that SPM enhanced rice tolerance against Cr toxicity via decreasing accumulation of Cr and markers of oxidative damage (H2O2, O2•− and MDA), improving antioxidant defense enzymes, photosynthetic apparatus, nutrients and phytohormone balance.

Data-independent acquisition (DIA) based strategies have been explored in recent years for improving quantitative analysis of metabolites. However, the data analysis is challenging for DIA methods as the resulting spectra are highly multiplexed. Thus, the DIA mode requires advanced software analysis to facilitate the data deconvolution process. We proposed a pipeline for quantitative profiling of pharmaceutical drugs and serum metabolites in DIA mode after comparing the results obtained from full-scan, Data-dependent acquisition (DDA) and DIA modes. using open-access software. Pharmaceutical drugs (10) were pooled in healthy human serum and analysed by LC-ESI-QTOF-MS. MS1 full-scan and Data-dependent (MS2) results were used for identification using MS-DIAL software while deconvolution of MS1/MS2 spectra in DIA mode was achieved by using Skyline software. The results of acquisition methods for quantitative analysis validated the remarkable analytical performance of the constructed workflow, proving it to be a sensitive and reproducible pipeline for biological complex fluids.

Vasculogenic mimicry (VM), a micro vessel-like structure formed by the cancer cells, plays a pivotal role in cancer malignancy and progression. Interleukin-1 beta (IL-1β) is an active pro-inflammatory cytokine and elevated in many tumor types, including breast cancer. However, the effect of IL-1β on the VM of breast cancer has not been clearly elucidated. In this study, breast cancer cells (MCF-7 and MDA-MB-231) were used to study the effect of IL-1β on the changes that can promote VM. The evidence for VM stimulated by IL-1β was acquired by analyzing the expression of VM-associated biomarkers (VE-cadherin, VEGFR-1, MMP-9, MMP-2, c-Fos, and c-Jun) via western blot, immunofluorescent staining, and Immunohistochemistry (IHC). Additionally, morphological evidence was collected via Matrigel-based cord formation assay under normoxic/hypoxic conditions and microvessel examination through Hematoxylin and Eosin staining (H&E). Furthermore, the STRING and Gene Ontology database was also used to analyze the VM-associated interacting molecules stimulated by IL-β. The results showed that the expression of VM biomarkers was increased in both MCF-7 and MDA-MB-231 cells after IL-1β treatment. The increase in VM response was observed in IL-1β treated cells under both normoxia and hypoxia. IL-1β also increased the activation of transcription factor AP-1 complex (c-Fos/c-Jun). The bioinformatics data indicated that p38/MAPK and PI3K/Akt signaling pathways were involved in the IL-1β stimulation. It was further confirmed by the downregulated expression of VM biomarkers and reduced formation of the intersections upon the addition of the signaling pathway inhibitors. The study suggests that IL-1β stimulates the VM and its associated events in breast cancer cells via p38/MAPK and PI3K/Akt signaling pathways. Aiming the VM-associated molecular targets promoted by IL-1β may offer a novel anti-angiogenic therapeutic strategy to control the aggressiveness of breast cancer cells.

Cadmium and drought are the most destructive of the abiotic stresses with negative consequences in terms of impaired metabolism, restricted nutrient use efficiency and disruptive photosynthesis of plants. The present study investigated the mitigation strategy of both aforementioned stresses by the application of iron oxide (IONPs) and hydrogel nanoparticles (HGNPs) simultaneously probably for the first time. IONPs were biofabricated by using a locally identified Bacillus strain RNT1, while HGNPs were produced chemically followed by the confirmation and characterization of both NPs through nanomaterials characterization techniques. Results of FTIR and XRD showed the capping of NPs by different functional groups together with their crystalline structure, respectively. SEM and TEM analysis showed the spherical shape along with the particle size ranging from 18 to 94 nm of both NPs, while EDS analysis confirmed the elemental purity of NPs. The results revealed that IONPs-treated rice plants increased biomass, antioxidant enzyme contents, photosynthesis efficiency, nutrient acquisition together with the decrease in reactive oxygen species and acropetal Cd translocation under normal and drought stress conditions as compared with control plants. Furthermore, the expression of the Cd transporter genes, OsHMA2, OsHMA3 and OsLCT1 were curtailed in NPs-treated rice plants under normal and drought stress conditions. The overall significance of the study lies in devising the NPs-based solutions of increasing heavy metal pollution and water availability challenges being faced the farmers around the world.

Aquilegia fragrans (AF) and Aquilegia pubiflora (AP) are the two medicinally important species of genus Aquilegia used for the treatment of various diseases and infections. This paper describes the potential of fetal hemoglobin induction activity of the methanolic extracts of AF and AP in K562 cell line. AF and AP have shown 27.147 ± 1.376 and 32.786 ± 1.048 percent erythroid induction, respectively at 15.625 (µg/mL) concentration which suggested that both plants can be the source of potential fetal hemoglobin inducers and may be used for the treatment of β-thalassemia. Phytochemical analyses of both species were also evaluated by using high-resolution LC-ESI-QTOF-MS/MS techniques. A Total of thirty compounds were identified using positive and negative ionization modes. The identification was based on the matching of high-resolution masses, isotopic pattern, and MS/MS fragmentation. Several statistical analyses were performed to evaluate the distribution of compounds in both species. Identified compounds belong to various classes including flavonoids, steroids, lignans, terpenoids, benzofuran and coumarins. The established chemical fingerprints will be helpful in standardization and quality control of plant extracts.

This paper reports numerical modeling of perovskite solar cell which has been knotted with Distributed Bragg Reflector pairs to extract high energy efficiency. The geometry of the proposed cells is simulated with three different kinds of perovskite materials including CH3NH3PbI3, CH3NH3PbBr3, and CH3NH3SnI3. The toxic perovskite material based on Lead iodide and lead bromide appears to be more efficient as compared to non-toxic perovskite material. The executed simulated photovoltaic parameters with the highest efficient structure are open circuit voltage = 1.409 (V), short circuit current density = 24.09 mA/cm2, fill factor = 86.18%, and efficiency = 24.38%. Moreover, a comparison of the current study with different kinds of structures has been made and surprisingly our novel geometry holds enhanced performance parameters that are featured with back reflector pairs (Si/SiO2). The applied numerical approach and presented designing effort of geometry are beneficial to obtain results that have the potential to address problems with less efficient thin-film solar cells.

Abstract Mechanical integrity of Photovoltaic (PV) modules, which is dependent upon the materials used and the manufacturing process, plays an important role in their performance and electrical output. Besides, the mechanical integrity of the modules is also affected by environmental conditions. Mechanical loads, which include loads produced by wind, snow, rain, and hail, tend to degrade the performance of the PV module by generating stresses and enhancing micro cracks and defects. This research aims to investigate the impact of wind loads on the performance degradation of PV modules. The overall objective was to investigate the reliability of modules available in Pakistan. Wind loads were simulated through an indigenously developed setup which allowed application of uniform load of 2400 Pa on the modules as per international standards (IEC 61215 and ASTM E1830‐15). A total of 12 PV modules, from three different vendors, each of 50 W rated power, were subjected to solar flash testing and electroluminescence imaging before and after the application of three cycles of mechanical load test. Greatest effect of wind loads was observed for modules which had defects present prior to the application of load. A maximum drop of 3.19% in the power output and 8.24% increase in the series resistance was observed. The results highlight that wind load has the ability to initiate cracks, propagate the pre‐existing ones as well as damage the grid fingers.

Ultrathin SnS2 layers with high theoretical specific capacity displays promising advantages as an anode in sodium storage systems. However, their poor conductivity and large capacity loss during charging/discharging process are urgently needed to be addressed. Herein, an exotic hierarchical SnS2/carbon nanotube@reduced graphene oxide (SnS2/CNT@rGO) composite has been designed and developed to be an anode for sodium-ion batteries. Functionally, the CNT penetrates into the petals of SnS2 micro-flowers to increase the conductivity of SnS2, while the three-dimensional rGO wraps around the SnS2/CNT composite to relieve the volume expansion of SnS2 during the charging/discharging process and construct “rGO conductive bridge” to accelerate electrode reaction kinetics. Benefiting from these exotic functionalization, the SnS2/CNT@rGO anode possesses excellent reversible capacity and superior cycling stability with a high reversible capacity of 528 mA h g−1 at 50 mA g−1 and a retained capacity of 301 mA h g−1 after 1000 cycles at 1 A g−1, which are better than most of the previously reported Sn-based and carbon-based anode materials. This study offers a promising strategy for significantly improving the cycling stability in the ultra-stable electrode materials in sodium-ion batteries.

The gut microbiota is important in regulating host metabolism, maintaining physiology, and protecting immune homeostasis. Gut microbiota dysbiosis affects the development of the gut microenvironment, as well as the onset of various external systemic diseases and metabolic syndromes. Cyclophosphamide (CTX) is a commonly used chemotherapeutic drug that suppresses the host immune system, intestinal mucosa inflammation, and dysbiosis of the intestinal flora. Immunomodulators are necessary to enhance the immune system and prevent homeostasis disbalance and cytotoxicity caused by CTX. In this study, shrimp peptide hydrolysate (SPH) was evaluated for immunomodulation, intestinal integration, and microbiota in CTX-induced immunosuppressed mice. It was observed that SPH would significantly restore goblet cells and intestinal mucosa integrity, modulate the immune system, and increase relative expression of mRNA and tight-junction associated proteins (Occludin, Zo-1, Claudin-1, and Mucin-2). It also improved gut flora and restored the intestinal microbiota ecological balance by removing harmful microbes of various taxonomic groups. This would also increase the immune organs index, serum levels of cytokines (IFN-ϒ, IL1β, TNF-α, IL-6), and immunoglobin levels (IgA, IgM). The Firmicutes/Bacteroidetes proportion was decreased in CTX-induced mice. Finally, SPH would be recommended as a functional food source with a modulatory effect not only on intestinal microbiota, but also as a potential health-promoting immune function regulator.

A 32-bit chipless RFID tag operating in the 4.5–10.9 GHz band is presented in this paper. The tag has a unique multiple-arc-type shape consisting of closely packed 0.2 mm wide arcs of different radii and lengths. The specific tag geometry provides multiple resonances in frequency domain of an RCS plot. A frequency domain coding technique has also been proposed to encode the tag’s RCS signature into a 32-bit digital identification code. The tag has an overall dimension of 17.9 × 17.9 mm2, resulting in a high code density of 9.98 bits/cm2 and spectral efficiency of 5 bits/GHz. The proposed tag is built on a low loss substrate bearing a very small footprint, thereby making it extremely suitable for large-scale product identification purposes in future chipless RFID tag systems.

Unlike 2-dimensional (2D) images, direct 3-dimensional (3D) point cloud processing using deep neural network architectures is challenging, mainly due to the lack of explicit neighbor relationships. Many researchers attempt to remedy this by performing an additional voxelization preprocessing step. However, this adds additional computational overhead and introduces quantization error issues, limiting an accurate estimate of the underlying structure of objects that appear in the scene. To this end, in this article, we propose a deep network that can directly consume raw unstructured point clouds to perform object classification and part segmentation. In particular, a Deep Feature Transformation Network (DFT-Net) has been proposed, consisting of a cascading combination of edge convolutions and a feature transformation layer that captures the local geometric features by preserving neighborhood relationships among the points. The proposed network builds a graph in which the edges are dynamically and independently calculated on each layer. To achieve object classification and part segmentation, we ensure point order invariance while conducting network training simultaneously—the evaluation of the proposed network has been carried out on two standard benchmark datasets for object classification and part segmentation. The results were comparable to or better than existing state-of-the-art methodologies. The overall score obtained using the proposed DFT-Net is significantly improved compared to the state-of-the-art methods with the ModelNet40 dataset for object categorization.