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Lightweight materials with better resistance to sliding wear are prominent candidates for automobile brake drums, clutch pads and cylinder block applications to facilitate fuel economy. This attempt is reserved to cater to materials with higher tribological quality needs. Less dense foamy pumice stone particles were involved in three different percentages (5, 10, and 15 wt%) to reinforce lightweight AZ31 Mg alloy. A stir-assisted squeeze casting technique was pursued to process the composite and refine the grain structure. A phase detection, elemental mapping and microstructure study were done through X-ray diffraction (XRD), energy dispersive spectroscopy (EDS), and scanning electron microscopy (SEM), respectively. An experimental dry sliding wear scrutiny was administered using a pin-on-disc apparatus by considering: (i) ambient and elevated temperature environments and (ii) three different levels of loads. The results reveal a significant drop in wear loss and a frictional coefficient for 15% pumice-loaded composite than the base alloy. Post-wear examination acknowledges the fact that the ambient temperature wear is governed by adhesive-abrasive wear and high temperature is by abrasive wear mechanisms. Worn-out scrutiny authenticates the presence of oxide layers and their role in lubrication. A comparative study with previous works upholds the novel magnesium composite is the right candidate for the mentioned automobile applications.

The present investigation focuses on the microstructural behavior and mechanical properties of Aluminium alloy 2024 reinforced with SiC nanoparticles by applying the Friction Stir Processing (FSP) technique. Taguchi L9 orthogonal array was used to find the optimal process parameters. The experiment used the expected best process parameters, which confirmed the predicted highest value for the mechanical characteristic. Traverse speed, axial load, and rotating speed are the main factors affecting aluminum metal matrix composite. Hence, this experiment examined the effects of five different tool spinning speeds, specifically 800, 900, 1000, 1100, and 1200 rpm. Weld samples are fabricated wherein the stir zone contains SiC nanoparticles. The results of the study indicate that the optimal welding rotational speed of 1000 rpm has a significant impact on the microstructure, wear rate, and microhardness. Specifically, it was observed that the stirred zone exhibited enhanced wear resistance compared to other zones. The study's findings revealed that after implementing friction stir processing (FSP), a well-defined shear zone was observed on the advancing side of the stir zone. Additionally, it is found that the uniform dispersion and strong bonding of SiC particles with an aluminium matrix further enhance wear resistance.

In this study, biocrude was successfully produced by the hydrothermal liquefaction of municipal solid waste collected from the landfill site of Lahore, the capital of Punjab, Pakistan, boasting a population of 12 million and an annual waste collection of 10 million tons. The hydrothermal liquefaction process was performed at reaction parameters of 350 °C and 165 bars with 15 min of residence time. The solid waste was found to have 78 % dry matter, 22 % moisture contents, 22.2 % ash, 22.69 MJ/kg higher heating value, 52.062 % C, 8.007 % H, 0.764 % N, and 39.164 % O. Non-catalytic process only produced 10.57 % oil, however when using the catalytic process, the biocrude yield improved to 17.61 %, with 22.61 % energy recovery for biocrude and 12.14 % for solids, when using 2 g dose of K2CO3. The resultant biocrude has a 28.61 MJ/kg higher heating value, having 60.28 % C and 9.28 % H. In contrast, the aqueous phase generated had 4.43 pH, 71.5 g/L TOC, and 1.35 g/L Total Nitrogen. TGA indicated that biocrude contains approximately 80 % of volatile fractions of different fuels. The organic compounds having the six highest peak areas in GC-MS were Ethyl ether 25.74 %, 2-pentanone, 4-hydroxy-4-methyl 9.08 %, 2-propanone, 1,1-dimethoxy 5.62 %, Silane, dimethyl (docosyloxy) butoxy 5.08 %, 1-Hexanol, 2-ethyl 4.53 %, and. Phenol 4.07 %. This work makes the first-ever successful use of indigenous solid waste from a landfill dumping site in Lahore to successfully produce useful biocrude with aims of waste reduction and management, circular economy, and energy recovery.

This paper focuses on the derivation of a new two-parameter discrete probability distribution. The new model is derived by mixing Poisson and Loai distributions and is named “Poisson Loai Distribution”. The paper explores various mathematical properties of the new model, introducing a count-regression model based on this distribution. The parameters of the model are estimated using the maximum likelihood estimation method. A comprehensive simulation study is utilized to assess the behavior of derived estimators. The importance of the proposed distribution is confirmed through the analysis of three real datasets. It is found that the suggested distribution has the greatest match when compared to all rival distributions, and it may be a viable alternative for assessing dispersed count data.

The human JC polyomavirus (JCV) is a widespread, neurotropic, opportunistic pathogen responsible for progressive multifocal leukoencephalopathy (PML) as well as other diseases in immunosuppressed individuals, including granule cell neuronopathy, JCV-associated nephropathy, encephalitis, and meningitis in rare cases. JCV classification is still unclear, where the ICTV (International Committee on Taxonomy of Viruses) has grouped all the strains into human polyomavirus 2, with no classification on clade and subclade levels. Therefore, JCV strains were previously classified using different genomic regions, e.g., full-length, VP1, and the V-T intergenic region etc., and the strains were grouped into several types related to various geographic locations and human ethnicities. However, neither of these classifications and nomenclature contemplates all the groups described so far. Herein, we evaluated all the available full-length coding genomes, VP1, and large T antigen nucleotide sequences of JCV reported during 1993–2023 and classified them into four major phylogenetic clades, i.e., GI-GIV, where GI is further grouped into two types GI.1 and GI.2 with five sub-clades each (GI.1/GI.2 a-e), GII into three (GII a-c), GIII as a separate clade, and GIV into seven sub-clades (GIV a-g). Similarly, the phylogeographic network analysis indicated four major clusters corresponding to GI-GIV clades, each with multiple subclusters and mutational sub-branches corresponding to the subclades. GI and GIV clusters are connected via GI.1-e reported from Europe and America, GII, GIII and GIV clusters are connected by GII-b and GII-c strains reported from Africa, while GIV cluster strains are connected to the Russia-Italy JCV haplotype. Furthermore, we identified JCV-variant-GS/B-Germany-1997 (GenBank ID: AF004350.1) as an inter-genotype recombinant having major and minor parents in the GI.1-e and GII-a clades, respectively. Additionally, the amino acid variability analysis revealed high entropy across all proteins. The large T antigen exhibited the highest variability, while the small t antigen showed the lowest variability. Our phylogenetic and phylogeographic analyses provide a new approach to genotyping and sub-genotyping and present a comprehensive classification system of JCV strains based on their genetic characteristics and geographic distribution, while the genetic recombination and amino acid variability can help identify pathogenicity and develop effective preventive and control measures against JCV infections.

In a variety of industries, including transportation, agriculture, and manufacturing, diesel engines are often employed. Due of rising prices and environmental concerns, researchers examined whether biodiesels might replace diesel. The current study looks into American Saffron Oil's feasibility as a feedstock for biodiesel production. The transesterification technique is used to extract American saffron oil methyl este(ASOME), which is then examined for its physical and chemical properties in accordance with ASTM standards. Diesel fuel and American Saffron Oil methyl ester are mixed on a volume basis to create a variety of fuel blends, including B20, B40, and B60. The test results showed that the 20 % ASOME mix had better performance and reduced emissions. Also, utilizing DIESEL-RK simulation software, diesel engine tests are conducted for the B20, B40, and B60 under identical operating circumstances. Moreover, diesel engine testing for the B20, B40, and B60 are carried out using DIESEL-RK simulation software under comparable operating circumstances. Results of simulation software tests show improved engine performance and reduced pollutants. When experimental data is compared to DIESEL-RK modeling software, it is found that brake thermal efficiency increased by 5.7 % and emissions of hydrocarbon and carbon monoxide decreased by 2.5 % and 14.3 %, respectively.

Background: Gynecological issues and sexually transmitted infections (STIs) pose significant challenges to women's health, particularly in developing nations. These challenges are exacerbated by limited access to modern reproductive healthcare facilities, economic constraints, and entrenched cultural norms. Consequently, most of the Pakistani population relies on traditional ethno-medicinal healthcare systems. This preference stems from the ease of access, affordability, widespread availability, and inherent trust placed in these alternative healthcare methods. Aim/objective: The inquiry aimed to report details on the application of conventional uses of plants in the health field in rural areas that could contribute to advancing the natural discovery of drugs. The objective of this analysis is to provide researchers with information on conventional and empirical knowledge of plant species concerning women's diseases. Methodology: Information on the common use of medicinal plants in treating women's diseases was gathered from electronic databases. As a keyword for the quest, ethnobotany, and ethnopharmacology were used together with gynecological complications. Result: The work of the current analysis has revealed that 217 plant species belonging to 89 families have been used in Pakistan's rural communities. The majority of plant species belong to the Apiaceae family, followed by the Asteraceae, Fabaceae, Solanaceae, Rosaceae, Lamiaceae, and other families. The biological interpretation of plants used in rural communities of Pakistan revealed that herbs and trees are the dominant forms with 58 % and 23 % respectively while shrubs and sub-shrubs with a low percentage of 17 % and 2 %. In natural preparation, leaves 29 %, flowers 22 %, seeds 14 %, fruits 14 %, roots 13 %, bark 7 %, and stems 5 % were the most used parts respectively and aerial parts, dried pericarp, bulb, bud, berry, latex, wood, rhizome, husk, fruit coat, oil, resins, twigs, and shoot were also used in minimum percentage. A multitude of plant species have found extensive application in the management of diverse women's health issues. These encompass concerns such as fatigue, mood fluctuations attributed to menstrual problems, gonorrhea, complications related to pregnancy, cravings for specific foods, throbbing breast pain, abdominal and pelvic cramps, excessive vaginal discharge, mastitis, irritability, abortion-related matters, headaches, uterine hemorrhage, Menorrhagia, Amenorrhea, Menopause, Vomiting Abortion, infertility and lactation challenges, as well as the regulation of lochia flow. Conclusion: This review provides remarkable information about the use of medicinal plants against women's diseases in the rural communities of Pakistan. It opens the gateway for the discovery of natural drug development.

Non-Fourier relation elucidates conduction of heat subjected to finite thermal wave proliferation speed whereas non-Fick relation reports anomalous diffusion differing from orthodox Fickian conduct. Their applications encompass the modeling of heat-mass transference in biological tissues, environmental engineering and micro/nanoscale systems. These models improve prediction precision in heterogeneous, complex or non-equilibrium environments. Here an attempt is made to model Darcy-Forchheimer dual convected viscoplastic material flow confined by stratified slippery surface. Modeling is based on magnetohydrodynamics, thermal stratification, temperature-dependent conductivity, temperature-dependent diffusivity, chemical reaction and solutal stratification. Deploying similarity variables, the nonlinear coupled complex partial differential expressions are transfigured into a system of coupled ordinary differential expressions. This process streamlines the problem, making it more convenient and assisting the utilization of numerical schemes to solve the simplified differential system. This system is then computed numerically deploying bvp4c algorithm. The outcomes related to drag force are derived and compared with previously reported findings in the existing literature. The comparison demonstrates a high level of agreement between the results obtained in this study and those from prior research. This study provides a comprehensive inspection of specific parameters and their characteristics on concentration, velocity and temperature. Results show that the Hartman number, inertia coefficient parameter, Casson parameter, porosity parameter and velocity slip parameter reduce Casson fluid velocity while reverse scenario is witnessed for mixed convection parameter. Besides the concentration distribution is an escalating function of Hartman number, solutal ratio, mass diffusivity and chemical reaction parameters whereas a reducing trend is found for solutal relaxation time, power index, solutal stratified parameters and Schmidt number.

This study aims to investigate the blends of supplementary cementitious ground granulated blast furnace slag (GGBFS) with limestone powder (LSP) as mineral admixtures to partially replace cement (up to 30 %) in self-consolidating paste (SCP) systems made at respective water demands. Besides the control formulation (100 % cement), the first set of SCP systems includes GGBFS alone as a partial substitute of cement (5 %, 10 %, 15 %, and 20 %), whereas the second set contains blends of 10 % GGBFS with 10 % and 20 % LSP. For all the seven formulations (1 control, 4 binary and 2 ternary), tests were performed to determine their water demand, setting times and flow behavior, hydration kinetics, mechanical strengths and microstructural properties. Results showed that the fresh and hardened state properties of SCP systems incorporating GGBFS and LSP led to an increase in strength, hydration kinetics were slightly improved and hence enhancement of strength and microstructure of SCP systems. Reduction in initial and final setting times recorded with increased LSP from 10 % to 20 % in ternary SCP formulations. Early compressive strength due to LSP increased in ternary SCP systems as compared to binary formulations, which reflected synergy between GGBFS and LSP in SCP systems. Moreover, environmental assessment results indicate a significant reduction in CO2 emissions by substituting cement with GGBFS and LSP. These findings indicate that both GGBFS and LSP can be successfully used in developing high performance and environment friendly self-compacting concrete (SCC).

Ordinary Portland cement (OPC) is proving to be hazardous to the environment. To replace the OPC, geopolymers (GPs) are introduced. However, to fully replace the OPC by GPs extensive laboratory tests are required to assess the long-term and short-term properties of GPs in different scenarios. Given the shortage of time for performing such extensive testing, artificial intelligence (AI) is used to analyze the properties of GPs. In this study, different AI techniques such as artificial neuro network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP) are used to obtain the predictive models for estimating the compressive strength of fly ash and ground granulated blast furnace slag-based GP concrete. Different statistical parameters are used to evaluate the performance of predictive models. Similarly, sensitivity and parametric analysis are also conducted on the input parameters. Additionally, multiple linear regression was also performed on the whole database. After comparing all the results, it was concluded that GEP is the best AI technique to predict the compressive strength of GP-based concrete.

Sustainable development in the building industry can be achieved through the use of versatile cementitious composites. Thus, incorporating nanoparticles into cement composites can create materials with enhanced performance and numerous applications. The utilization of carbon nanotubes (CNTs) in the construction industry has great promise for developing efficient solutions to establish a sustainable ecosystem with diverse characteristics. However, forecasting the characteristics of these composites is a significant challenge due to their intricate composite structure and nonlinear behavior. Designing and conducting laboratory experiments on diverse samples and across multiple age groups is challenging, time-consuming, and costly. Moreover, there is presently a lack of a forecasting model that can predict concrete's compressive strength (fc') with nanoparticles. Three machine learning (ML) techniques, K-nearest neighbor (KNN), linear regression (LR), and artificial neural network (ANN), were used to predict the fc' of nanocomposites containing CNTs in this research. A thorough database consisting of 282 data entities for the CNTs-based concrete and the model's reliability was assessed using the R2 test and statistical error analysis. The ANN model had the most outstanding R2 value of 0.885, while the KNN and LR models had R2 values of 0.838 and 0.744, respectively. Moreover, RReliefF analysis is utilized to evaluate the primary components in predicting concrete outcomes. This research shows that the properties of CNT-based concrete composites are greatly affected by the water-to-binder ratio, followed by the proportions of cement and coarse aggregates. The ML algorithms exhibited superior generalization capabilities, suggesting their enhanced potential for accurate predictions of CNTs-based concrete properties.

Alkali-activated composites (AACs) have attracted considerable interest as a promising alternative to reduce CO2 emissions from Portland cement production and advance the decarbonisation of concrete construction. This study describes the data-driven predictive modelling to anticipate the compressive strength (CS) of AACs. Four different modelling techniques have been chosen to forecast the CS of AACs using the selected data set. The decision tree (DT), multi-layer perceptron (MLP), bagging regressor (BR), and AdaBoost regressor (AR) were employed to investigate the precision level of each model. When it comes to predicting the CS of AACs, the results show that the AR model performs better than the BR model, the MLP model, and the DT model by providing a higher value for the coefficient of determination, which is equal to 0.91, and a lower MAPE value, which is equal to 13.35%. However, the accuracy level of the BR model was very near to that of the AR model, with the R2 value suggesting a value of 0.90 and the MAPE value indicating a value of 14.43%. Moreover, the graphical user interface has also been developed for the strength prediction of alkali-activated composites, making it easy to get the required output from the selected inputs.

The optimized use of nanomaterials in concrete for developing resilient and eco-friendly infrastructure is vital for our built environment. Therefore, the current study aims to optimize the dosages and particle sizes of colloidal nano-silica in developing high-performance, sustainable cement mortar. For this purpose, compressive strength, water absorption, porosity, and microstructure tests were performed on mortars containing different dosages and particle sizes (NS80, NS250, NS250Al, NS500) of colloidal nano-silica. For comparison, tests were also performed on control mortars and mortars containing metakaolin and silica fume. The microstructure of cement pastes was examined through SEM-based BSE imaging with elemental mapping through EDS and FTIR analyses. Additionally, global warming potential (GWP) as well as embodied energy (EE) were also analyzed for all mortar mixtures. The results demonstrated that smaller nano-sized particles (NS500) at a lower dosage (3%) showed improved compressive strength and densified microstructure compared to all other mixes. However, larger nano-sized particles (NS80) at medium (5%) and high dosages (10%) had the highest impact on compressive strengths due to the formation of low and medium Ca-based C-S-H and C-A-S-H phases. Furthermore, the results of greater compressive strength of NS80 mortars under medium and high dosages were supported by their lowest water absorption and apparent porosity. When compared with the control and all other mixes, the mortar mixes containing medium and high doses of NS80 had lower embodied energy/MPa and CO2-eq intensity/MPa, indicating their significant contribution to producing green concrete with a lower environmental impact.

The reduction in compressive strength (CS) of cementitious composites incorporating waste plastic is the main concern limiting its applicability in the building sector. Using industrial wastes as cement substitutes to enhance the CS of plastic mortar is a sustainable approach. This study used fine powdered waste materials such as silica fume (SF), marble powder (MP), and glass powder (GP) in plastic-based mortar for their effectiveness in enhancing CS. Plastic mortar specimens were cast using plastic waste in 5–25 % contents as sand replacement by mass, and their 28-day CS was recorded as a reference. SF, GP, and MP were utilized in plastic mortar mixtures separately in proportions of 5–25 %, with a 5 % increment, substituting cement by mass. These waste powders were also used in combinations of two (SF+GP, SF+MP, and GP+MP) and three (SF+GP+MP) in plastic mortar mixtures. Moreover, prediction models were built using the experimental database for the CS of plastic mortar. Gradient boosting and bagging ensemble machine learning (ML) techniques were chosen for model development. The decrease in CS was limited by substituting SF, GP, and MP for cement in plastic mortar. It was determined that the most effective substitution levels for SF, GP, and MP in plastic mortar, according to the strength enhancement, were 15, 10, and 15 wt.% of cement, respectively. The ML models closely matched experimental results, and in terms of R2 and error evaluations, bagging model outputs were more accurate than gradient boosting. The gradient boosting and bagging models had R2 of 0.89 and 0.94, respectively, with average absolute errors of 0.87 and 0.65 MPa.

The study proposed the removal of Brilliant Green, a cationic dye, by adsorption process from wastewater solution utilizing a low-cost adsorbent such as Nutraceutical Industrial Pepper Seed Spent (NIPSS). The study comprises an investigation of the parametric influence on the adsorption process. The parameters identified are pH, dye concentration, process temperature, quantity of the adsorbent, and particle size. The study of statistics found from experiments was carried out by incorporating Freundlich, Brouers-Sotolongo, Langmuir, Toth, Sips, Jovanovic, and Redlich-Peterson isotherm models. The adsorption kinetics were determined by implementing pseudo-first-order and second-order models, diffusion film models, and Dumwald-Wagner and Weber-Morris models. The experimental adsorption capacity qe was found to be about 130 mg/g. This value was closest to the maximum adsorption of 144.6mg/g predicted by the Brouers-Sotolongo isotherm which had a correlation coefficient (R2) of 0.998. The adsorption kinetics data was confirmed to be a pseudo-second-order model. The change in free energy, enthalpy change, and entropy change were vital thermodynamic factors in concluding that adsorption is almost spontaneous and endothermic process. Change in enthalpy (ΔH°) reduced value indicates the physical nature of the process. The adsorption of BG dye on the adsorbent surface was authenticated by FTIR spectroscopy and SEM imaging. A Central Composite Design (CCD) Quadratic model under Response Surface Methodology (RSM) was implemented for statistical optimization of adsorption capacity for the five parameters studied, namely, time, temperature, concentration of the dye, weight of the adsorbent, and pH. Software Design Expert 7.0 was used to evaluate 3D contour plots. The process of optimization yielded a value of 350 mg/g. Thus, incrementing the adsorption process by 84.2 %. The study provides insights on various dye and adsorbent interaction possibilities and derives that NIPSS is an efficient adsorbent to extract BG dye from wastewater solutions.

The rapid improvement of artificial intelligence (AI) in the educational domain has opened new possibilities for enhancing the learning experiences for students. This research discusses the critical need for personalized education in higher education by integrating deep learning (DL) techniques to create customized learning pathways for students. This research intends to bridge the gap between constant educational content and dynamic student needs. This research presents an AI-driven adaptive learning platform implemented across four different courses and 300 students at a university in Faisalabad-Pakistan. A controlled experiment compares student outcomes between those using the AI platform and those undergoing traditional instruction. Quantitative results demonstrate a 25 % improvement in grades, test scores, and engagement for the AI group, with a statistical significance of a p-value of 0.00045. Qualitative feedback highlights enhanced experiences attributed to personalized pathways. The DL analysis of student performance data highlights key parameters, including enhanced learning outcomes and engagement metrices over time. Surveys reveal increased satisfaction compared to one-size-fits-all content. Unlike prior AI research lacking rigorous validation, our methodology and significant results deliver a concrete framework for institutions to implement personalized, AI-driven education at scale. This data-driven approach builds on previous attempts by tying adaptations to actual student needs, yielding measurable improvements in key outcomes. Overall, this work empirically validates that AI platforms leveraging robust analytics to provide customized and adaptive learning can significantly enhance student academic performance, engagement, and satisfaction compared to traditional approaches. These findings have insightful consequences for the future of higher education. The research contributes to the growing demand for AI in education research and provides a practical framework for institutions seeking to implement more adaptive and student-centric teaching methodologies.

Background: COVID-19 is primarily known as a respiratory illness; however, many patients present to hospital without respiratory symptoms. The association between non-respiratory presentations of COVID-19 and outcomes remains unclear. We investigated risk factors and clinical outcomes in patients with no respiratory symptoms (NRS) and respiratory symptoms (RS) at hospital admission. Methods: This study describes clinical features, physiological parameters, and outcomes of hospitalised COVID-19 patients, stratified by the presence or absence of respiratory symptoms at hospital admission. RS patients had one or more of: cough, shortness of breath, sore throat, runny nose or wheezing; while NRS patients did not. Results: Of 178,640 patients in the study, 86.4 % presented with RS, while 13.6 % had NRS. NRS patients were older (median age: NRS: 74 vs RS: 65) and less likely to be admitted to the ICU (NRS: 36.7 % vs RS: 37.5 %). NRS patients had a higher crude in-hospital case-fatality ratio (NRS 41.1 % vs. RS 32.0 %), but a lower risk of death after adjusting for confounders (HR 0.88 [0.83–0.93]). Conclusion: Approximately one in seven COVID-19 patients presented at hospital admission without respiratory symptoms. These patients were older, had lower ICU admission rates, and had a lower risk of in-hospital mortality after adjusting for confounders.

This study aimed to identify efficient Trichoderma isolate(s) for the management of Fusarium wilt in peas. Four different pea germplasms (Sarsabz, Pea-09, Meteor and Supreme) were evaluated for resistance against Fusarium oxysporum in pot assay. Resistant germplasm exhibits a varying range of disease severity (23%) and percent disease index (21%), whereas susceptible and highly susceptible germplasm exhibit maximum disease severity (44–79%) and percent disease index (47–82%). The susceptible germplasm Meteor was selected for in vivo experiment. Five different Trichoderma spp. (Trichoderma koningii, T. hamatum, T. longibrachiatum, T. viride, and T. harzianum) were screened for the production of hydrolytic extracellular enzymes under in vitro. In-vitro biocontrol potential of Trichoderma spp. was assayed by percentage inhibition of dry mass of Fusarium oxysporum pisi (FOP) with Trichoderma spp. metabolite filtrate concentrations. Maximum growth inhibition was observed by T. harzianum (50–89%). T. harzianum metabolites in filtrate conc. (40%, 50%, and 60%) exhibited maximum reduction in biomass and were thus used for in vivo management of the disease. The pot experiment for in-vivo management also confirmed the maximum inhibition of FOP by T. harzianum metabolites filtrate at 60% by reducing disease parameters and enhancing growth, yield, and physiochemical and stress markers. Trichoderma strains led to an increase in chlorophyll and carotenoids (34-26%), Total phenolic 55%, Total protein content 60%, Total Flavonoid content 36%, and the increasing order of enzyme activities were as follows: CAT > POX > PPO > PAL in all treatments. These strains demonstrate excellent bio-control of Fusarium wilt in pea via induction of defense-related enzymes. The present work will help use Trichoderma species in disease management programme as an effective biocontrol agent against plant pathogens.

High-performance concrete is an excellent choice for modern engineering structures due to its superior performance, low maintenance costs, and long service life. Therefore, this study was aimed to produce high-performance concrete by incorporating a high volume of basaltic volcanic ash along with metakaolin (MK), micro-silica (MS), and nano-silica (NS). An investigation was performed in regard to the mechanical and durability properties, autogenous and drying shrinkage, and microstructural and pore-structure properties. A control mix, binary mixes, and ternary mixes were examined. The binary mixes contained fine (VA) and ultra-fine (VAF) volcanic ashes, and the ternary mixes contained combinations of VA with MK, MS, and NS. Test results show that ternary mixes with 30% and 40% cement substitution demonstrated better strength development and higher resistance to chloride penetration and water absorption. All binary and ternary mixes displayed lower autogenous shrinkage, as well as lower or comparable drying shrinkage than the control mix. Moreover, the combination of VA with MK, MS, and NS up to 40% resulted in micro and pore structure refinement by forming high-density gels. The findings indicate that VA in combination with MK, MS, and NS, can effectively substitute cement at up to 40% for the production of sustainable high-performance concrete.

Cardiovascular diseases, particularly coronary artery disease, pose big challenges to human life. Deployment of the stent is a preferable treatment for the above-mentioned disease. However, stents are usually made up of shape memory alloy called Nitinol. The poorer surface finish on the machined nitinol stents accelerates the migration of Nickel ions from the implanted nitinol stent, which is considered toxic and can lead to stenosis. The current study deals with controlling surface quality by minimising surface roughness and improving corrosion resistance. Femtosecond laser (fs-laser 10−15 s) micromachining was employed to machine the Nitinol surface to achieve sub-micron surface roughness. The Grey relational analysis (GRA)-coupled design of the experimental technique was implemented to determine optimal levels of four micromachining parameters (laser power, pulse frequency, scanning speed, and scanning pattern) varied at three levels to achieve minimum surface roughness and to maximise the volume ablation. The results show that to yield minimum surface roughness and maximum volume ablation, laser power and scanning speed are in a higher range. In contrast, the pulse frequency is lower, and the scanning pattern is in a zig-zag manner. ANOVA results manifest that scanning speed is the predominant factor in minimising surface roughness, followed by pulse frequency. Furthermore, the corrosion behaviour of the machined nitinol specimens was evaluated, and the results show that specimens with lower surface roughness had lower corrosion rates.

The present study used the techniques of gene expression programming (GEP) and multi-expression programming (MEP) to assess the compressive strength (CS) and flexural strength (FS) and develop predictive models of sustainable mortar modified with waste eggshell powder (WEP) and waste glass powder (WGP) as a replacement of cement. In order to get more insights into the impact and relation of raw components on the CS and FS of a developed sustainable mortar, a comprehensive study using the SHapley Additive exPlanations (SHAP) methodology was performed. When comparing the efficiency of both employed models, it was seen that the MEP model exhibited superior performance with an R2 value of 0.871 and 0.894 for CS and FS, as compared to the GEP model, which had an R2 value of 0.842 and 0.845 for CS and FS respectively.

The incorporation of nanomaterials (NMs) in concrete will produce the utmost properties. Nevertheless, investigational work takes a great deal of time with efforts to measure the strength of concrete. Thus, the process of estimating the properties of cement paste can be accelerated by employing soft machine learning (SML) techniques. Individual machine learning approaches (MLAs) like random forest (RF), decision tree (DT), and support vector machine (SVM) were utilized. These individual MLAs models then ensemble with two approaches namely bagging and boosting. The outcome of the model depicts a robust performance by showing significant correlations (R2) as compared to individual MLAs. Data points with 205 having contribution factors (e.g., curing time (days), water to binder (w/b), micro-silica (%), and nano-silica (%) were used as input parameters, and compressive strength as output for MLAs modeling. To ensure that each model is as accurate as possible, cross-validation with K-folding and statistical error analysis (i.e., MAE, MAE, and RMSE) were utilized. Thus, interpretable approaches with AdaBoost give a strong correlation of R2 ranging from 0.85 to 0.90 respectively with fewer errors. Out of which DT with ensemble Adaboost and RF give a robust performance. Moreover, the results of the cross-validation show that the response was accurate, with fewer errors. Therefore, the ensemble model represents a robust correlation. Therefore, its usage with individual ML would enhance the model's performance. In addition, the graphical user interface (GUI) is created by training a model that can predict the values of the desired outputs when the necessary input parameters are provided. It simplifies the procedure and provides a handy tool for employing the model's capabilities in the civil engineering domain.

This research used gene expression programming (GEP) and multi expression programming (MEP) to determine the compressive strength (CS) of alkali-activated material (AAM) to compare and develop more reliable genetic algorithm-based prediction models. To learn more about how raw ingredients affect and interact with the CS of AAM, a SHapley Additive exPlanations (SHAP) analysis was conducted. A comprehensive dataset containing 676 points with fifteen influential parameters was formulated from the previously published literature. According to this study, considering the impact of 15 input variables, both genetic algorithms produced results close to the experimental CS (retrieved from the literature). When the performance of the GEP and MEP models were compared, it was found that the MEP model, with an R2 of 0.86, performed better than the GEP model, with an R2 of 0.82. The assessment of the statistical parameters of generated models revealed that the MEP model was more effective. Additionally, SHAP analysis revealed that slag content, followed by the specimen's age, sodium silicate, and curing temperature, showed a positive correlation with CS of AAM, which were the most important parameters. The results also revealed the importance of chemical contents, i.e., CaO, SiO2, Al2O3, of FA and slag on the CS of AAM. The built models might be used to compute the CS of AAMs with varying input parameter values, minimizing the effort, time, and cost of unnecessary lab tests. Furthermore, the outcomes of the SHAP study might help researchers and the industry determine the quantity or composition of raw ingredients when producing AAMs.

Alkali-activated materials (AAMs) have gained significant attention in recent years due to their potential for sustainable construction and outstanding strength and durability characteristics. However, their wide application is constrained by their weak behavior under flexure and tensile loading, leading to brittleness. This study examines the formulation of an alkali-activated fiber-reinforced mortar, using fly ash and hydrated lime as main precursors, and investigates the capacity when reinforced with polyvinyl alcohol (PVA) and polypropylene (PP) fibers. The performance was measured through experimental investigation in terms of flowability, setting time, compressive strength (CS), water absorption, flexure strength (FS), and load-displacement curve under flexure loading. The durability was assessed through acid and sulfate attack tests after 120 days. The findings showed that adding both types of fibers improved the mechanical and durability performance of the one-part AAM. The highest CS of 44.95 MPa and 42.04 MPa and FS of 7.8 MPa and 6.9 MPa were achieved for PVA and PP fibers at a 1.5% fiber content. The FS was increased by 18.94% and 28.2% for PP1.5 and PVA1.5 fibers respectively. The control sample, PP 1.5 and PVA 1.5 recorded 31.1%, 12.5%, and 11% loss in CS after the acid attack test, respectively, after 120 days. A reduction of 15.3% and 13.3% was recorded in flow diameter with the addition of PP and PVA fibers at 2%. Overall, mortars reinforced with PVA fibers have shown better flowability, mechanical strength, and chemical resistance than PP fibers.

Rice Husk ash (RHA) utilization in concrete as a waste material can contribute to the formation of a robust cementitious matrix with utmost properties. The strength of HPC when subjected to compression test is determined by the combination and quantity of the materials used in its production. Thus, making its mixed design process challenging and ambiguous. The objective of this research is to forecast the strength of HPC containing RHA, by using a diverse range of machine learning techniques, including both individual and ensemble learners such as bagging and boosting. This study will cause significant implications for sustainable construction practices by facilitating the development of an efficient and effective method for forecasting the strength of HPC. Individual machine learning (ML) algorithms are incorporated with ensemble methods such as bagging, adaptive boosting, and random forest algorithms. These ensemble techniques is use to create twenty different sub-models. Afterward, these sub-models is train and optimized for achieving the best possible value for R2. The sub-models were further fine-tuned to obtain the best value for R2. In order to assess or evaluate the quality, reliability, and generalizability of the test data, the K-Fold cross-validation method is utilized. Furthermore, the index for measuring the statistical performance of models is use to validate and compare the assessment of ensemble models with individual models. The findings indicate that using bagging and boosting techniques enhances the prediction accuracy of individual or weak models, with minimum errors and an R2 value > 0.92 is achieved using bagging with decision tree and random forest. In general, the performance of the model is optimized by using ensemble learner methods in machine learning (ML).

This study aims to develop various machine learning (ML) models to investigate the self-healing capacity of engineered cementitious composites (ECC) and to evaluate the effect of input parameters (raw materials and crack width before the healing process) on output parameters (crack width after the healing process) via Shapley additive explanations (SHAP) analysis. The combination of the individual and ensemble ML models has been introduced to check and compare the accuracy level towards the prediction of crack width after the healing process to access the most suitable model for this application. The support vector machine (SVM) from the individual, while XGBoost (XGB) and random forest (RF) from ensemble ML models have been investigated for prediction purposes. As per the obtained results, the RF model outperforms both the SVM and XGB algorithms in forecasting the fracture width after the healing process for the selected ECC in concrete material. The performance indicator, such as the coefficient of determination (R2), was reported as 0.97 for RF, 0.96 for XGB, and 0.93 for the SVM model. Statistical results and k-fold cross-validation for the employed ML models also confirm their legitimacy. It was also noted from the result of the SHAP analysis that the significant contribution was for the crack width before healing towards the prediction of crack width after the healing process. Furthermore, the ML models can also be utilized to anticipate the healing ability of the other ECC, such as soda glass powder, marble powder, and bagasse ash.

This study used three artificial intelligence-based algorithms – adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANNs), and gene expression programming (GEP) – to develop empirical models for predicting the compressive strength (CS) and slump values of fly ash-based geopolymer concrete. A database of 245 CS and 108 slump values were established from the published literature, where 17 significant parameters were chosen as input variables for the development of models. The algorithms were trained and tested using statistical measures including Nash-Sutcliffe efficiency, root-squared error, root-mean-square error, relative-root-mean-square error, mean absolute error, correlation coefficient, and regression coefficient. The comparison results showed that the GEP model was superior to the ANFIS and ANN models in terms of R-value, R2, and RMSE for both CS and slump prediction. The R-value for the CS models was 0.94 (GEP), 0.92 (ANFIS), and 0.91 (ANN), while for the slump it was 0.96 (GEP), 0.91 (ANFIS), and 0.90 (ANN). Moreover, the performance index factor values for slump and CS were found 0.03 and 0.029 for GEP-models and 0.036, 0.030 for ANFIS-models and 0.035 and 0.034 for ANN-models respectively. The sensitivity and parametric analysis were also performed for GEP-developed model. Results demonstrate that the GEP model generates more accurate prediction for the slump and CS of fly ash-based geopolymer after being rigorously trained and its hyperparameters optimized.

The shrinkage of concrete is crucial under aggressive environmental conditions to avoid short and long-term deterioration. ACI committee for hot weather concreting specifies 35 °C as the maximum limit of fresh concrete to mitigate problems associated with that. This paper aims to investigate the plastic and drying shrinkage characteristics and compressive strength development of ordinary Portland cement (OPC) by varying w/c (0.3–0.45), casting temperatures (25–45 °C), and curing methods. The results demonstrated that the characteristics of concrete are significantly influenced by w/c, as well as the conditions at which they are cast and cured. In general, the plastic/drying shrinkage strains were observed to be minimum, while compressive strength was maximum when mixtures were cast at an ambient temperature of 32 °C. An increase in the w/c results in a corresponding rise in plastic/drying shrinkage strain, coupled with a decrease in the compressive strength of the mixtures. In addition, application of curing compound was found most effective in the field for enhancing the shrinkage characteristics whilst wet burlap practice was found beneficial in long-term compressive strength gain. These findings will enhance the optimization of mix designs for harsh conditions, improve construction efficiency, and support long-term concrete structure performance and sustainability.

We report the proteomic profile of Epidermal Mucus (EM) from Labeo rohita and identified the differentially abundant proteins (DAPs) against Aeromonas hydrophila infection through label-free liquid chromatography-mass spectrometry (LC-MS/MS). Using discovery-based proteomics, a total of 2039 proteins were quantified in nontreated group and 1,328 proteins in the treated group, of which 114 were identified as DAPs in both the groups. Of the 114 DAPs, 68 proteins were upregulated and 46 proteins were downregulated in the treated group compared to nontreated group. Functional annotations of these DAPs shows their association with metabolism, cellular process, molecular process, cytoskeletal, stress, and particularly immune system. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Fisher's exact test between the two groups shows that most of the proteins were immune-related, which were significantly associated with the proteasome, phagosome, and Salmonella infection pathways. Overall, this study shows a basic and primary way for further functional research of the involvement of vitellogenin 2, alpha-2-macroglobulin-like protein, toll-like receptors (TLR-13), calpain, keratin-like proteins, and heat shock proteins against bacterial infection. Nonetheless, this first-ever comprehensive report of a proteomic sketch of EM from L. rohita after A. hydrophila infection provides systematic protein information to broadly understand the biological role of fish EM against bacterial infection.

Sustainable development of infrastructure requires the efficient and optimized utilization of nano-silica in concrete applications. Thus, current study aims to explore how the dosages of colloidal nano-silica (CNS) and its surface area affect the mechanical performance of cementitious mortar composites. To assess the compressive strength with aging (7, 28 and 56 days), CNS with different surface areas was used as cement substitutes at three different dosages levels. Additionally, to optimize nano-silica dosages and evaluate the outcomes of the experiment, statistical modelling based on Response Surface Methodology (RSM) and machine learning-based Artificial Neural Network (ANN) were applied. In comparison to the control and all other mixtures, the compressive strength was improved (51.5 MPa, 70.2 MPa, and 73.2 MPa) at 7, 28, and 56 days, respectively, by the CNS with higher surface area (CNS500) at their lower dosages (3%). However, at medium (5%) and high dosages (10%), CNS with a lower surface area (CNS80) resulted in better compressive strength (71.9 MPa, 87 MPa, and 90 MPa) at 7, 28, and 56 days, respectively, comparatively to control as well as all other mixtures. These findings were supported by both the RSM and ANN models, with the ANN model outperforming the RSM model in terms of performance. The ANN model was trained and validated using 70% and 30% of the data, respectively. For the validation data of ANN model, the Coefficient of Determination (R), the Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE) were revealed as 0.925, 2.263 and 2.974 MPa, respectively. Whereas, for the quadratic RSM models, these values were determined as 0.86, 4.485, and 5.45 MPa, respectively. The results of statistical models validated the experimental results, thus showing that the developed RSM and ANN can be used as reliable prediction model to optimize CNS use in producing stronger and more sustainable concrete.

Eco-friendly concrete is in great demand, and as a consequence, the necessity to find sustainable alternatives to ordinary cement has become critical. In recent years, there has been considerable interest in the potential benefits of utilizing rice husk ash (RHA) as a cement substitute in concrete. Evaluating concrete properties in a laboratory setup like compressive strength (CS) is a time-consuming and expensive process. The development of trustworthy and precise models for CS estimation might be a better strategy. To determine the CS of RHA concrete, this research used boosting ensemble algorithms, namely gradient boosting (GB), AdaBoost regressor (ABR), and extreme gradient boosting (XGB), over a vast literature database. The estimation models were validated using statistical measures, error distribution by plotting violin graphs, and k-fold analysis. Furthermore, SHapley Additive exPlanations (SHAP) analysis was utilized to assess the importance of contributing elements. The results showed that XGB had the greatest performance in estimating the CS of RHA concrete out of the three methods tested. While the GB and ABR models both had R2 values of 0.90 and 0.94, respectively, the XGB model achieved a value of 0.96. The violin graphs indicated that the average absolute error values for the GB, ABR, and XGB were 5.82, 4.65, and 3.53 MPa, implying the higher precision of the XGB model in estimating the CS of RHA concrete. The SHAP study demonstrated that the three most influential factors in increasing the strength were cement, specimen age, and rice husk ash. The construction sector may benefit from the application of such technologies by facilitating the development of quick and low-cost methods for identifying material qualities and the influence of input parameters.

This study used machine learning methods to predict the water absorption (W-A) of cement-based material (CBM) containing eggshell and glass powder as sand and cement substitutes. A dataset from the laboratory experiments consisting of 234 points and seven input variables was used to develop models, including multilayer perceptron neural network (MLPNN), support vector machine (SVM), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). Additionally, a SHapley Additive exPlanations (SHAP) analysis was performed to investigate the relevance and interaction of raw components. When evaluating the prediction models for the W-A of CBM, it was found that the MLPNN and SVM models were moderately accurate (R2 = 0.74 and 0.78, respectively), while the AdaBoost and XGBoost models showed good agreement with the lab test results (R2 = 0.86 and 0.91, respectively). The SHAP approach revealed that while the cement quantity had a higher negative association with W-A of CBM, the quantities of eggshell powder, sand, and glass powder showed both favourable and detrimental correlations. Therefore, eggshell and glass powder must be used in optimal proportions of around 60 kg/m3 and 80 kg/m3, respectively, for maximum resistance to W-A. The AdaBoost and XGBoost models can potentially compute the W-A of CBMs by utilising various input parameter values, which may help decrease unnecessary test trials in labs. Furthermore, the SHAP investigation revealed the impact and relationship of the inputs on the W-A of CBMs, which might potentially assist researchers and the industry in determining the appropriate amount of raw materials during CBM production.

The increase in strength and stiffness of a metal matrix composite connecting (MMC) rod when compared to conventional rods have motivated researchers in this area. This opens a new window for the present research, here Al7075 alloy and Al7075-based composite connecting rods were fabricated by machining composite blocks using computer numerical control machining centre. Al7075-based composite connecting rods comprises of 91 % Al7075, 3 % hexagonal boron nitride (h-BN) and 6 % of zirconium dioxide (ZrO2). Modal characteristics and mechanical performance of the prepared components were analysed. An experimental modal analysis was conducted on the connecting rod under fixed-free condition to identify the natural frequency and its mode shapes, further the outcomes were compared with the numerical values. The ANSYS workbench is used to estimate the component's failure load based on finite element (FE) approach. The results obtained from modal analysis revealed that the composite connecting rod is more dynamically stable than its unreinforced counterpart. Composite connecting rods also showed better load-bearing capacity as compared to Al7075 in terms of tensile and compressive loading. Failure load analysis results indicate that under compressive load the component bends along the shank (I-beam), while under tensile load, cracks originate at the section connecting the piston pin end and the I-beam. The composite connecting rod sustained 13.5 % higher compressive load and16.5 % higher tensile load than the Al7075 connecting rod, making it a suitable alternative material. The failure mechanisms have been investigated by extensive scanning electron micrography of fabricated connecting rods.

To investigate the effect of eggshell powder on the water-absorption capacity of cement mortar, this research employed experimental testing followed by machine learning (ML) modeling techniques. On cement mortar specimens, the eggshell powder was partially substituted for cement and sand in concentrations ranging from 0% to 15% with a 2.5% increment, and water absorption tests were conducted. Experimental tests were used to generate the dataset, which was then used to train ML algorithms and construct ML-based prediction models. The integration of eggshell powder lowered the water absorption capacity of cement mortar, as determined by the test findings. The highest reduction in water absorption was seen at 5% eggshell powder as a cement replacement, resulting in up to 41.7% decrease, and 7.5% eggshell powder as a sand replacement, resulting in up to 45.8% decrease in water absorption compared to the control sample. In addition, the constructed ML models exhibited good agreement with the actual results and may be utilized to determine the water absorption of cement mortar incorporating eggshell powder. Nevertheless, based on the R2 value, the disparity between experimental and predicted results, and error analysis, it was determined that the random forest model was more accurate than the decision tree model.

This study evaluates the compressive strength (C–S) of nano-silica-based fiber-reinforced concrete (NS-FRC) by using advanced machine learning (ML) individual and ensembled techniques. The employed advanced ML approaches used for the analysis are Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and eXtreme Gradient Boosting (XGB). Furthermore, the level of accuracy for the employed advanced algorithms is also evaluated by the k-fold cross-validation technique. Statistical checks, i.e., root mean square error (RMSE), mean absolute error (MAE) and mean absolute percent error (MAPE), are also applied to validate the performance of algorithms. Sensitivity analysis is also made to explore the influence of input parameters on the C–S of NS-FRC. Among all, the XGB technique is found most effective for an accurate C–S prediction of NS-FRC. In XGB model, the coefficient of determination (R2) is 0.95, which is comparatively more than that of SVM (0.90) and MLP (0.90). The MAE value of XGB algorithm is 3.3 MPa which is lower than that of SVM (4.8 MPa) and MLP (4.5 MPa). In addition, RMSE value is also less for XGB algorithm (3.8 MPa) as compared to that of SVM (5.5 MPa) and MLP (5.9 MPa). Furthermore, the employed XGB models exhibited highest R2 of 0.95 as compared to the models reported in the available literature. The sensitivity analysis revealed that the nano-silica influenced the C–S of NS-FRC by 7%. Moreover, discussion reveals that nano-silica in concrete can have several benefits, such as improved microstructure, enhanced strength, prolonged durability, reduced cement content, and less carbon emission.

Diabetes mellitus (DM) is the most prevalent endocrine disorder. Numerous individual herbs possess anti-diabetic activity. The seeds of Hordeum vulgare, Elettaria cardamomum and Cicer arietinum are traditionally used to manage DM. The ambition of this work was to formulate the poly-herbal granules (PHGs) comprising of these three functional foods and evaluate their in-vitro antioxidant and antidiabetic potential. The dried seed extracts of Hordeum vulgare, Elettaria cardamomum and Cicer arietinum were used in a ratio of 2.5:1:1 to formulate PHGs by wet granulation method. The ratio of extracts was selected on the basis of traditional phytotherapies popularly used by local Hakeems of Pakistan to achieve glycemic control in diabetic patients resistant to traditional allopathic regime of medicine. The flow properties of developed PHGs were evaluated. The UV–Visible spectroscopic, Fourier Transform Infrared (FTIR) and HPLC-DAD of all seed extracts and PHGs were performed. The in-vitro antioxidant DPPH, FRAP, total antioxidant capacity (TAC) and Nitric Oxide (NO) scavenging assays were carried out on PHGs. The in-vitro antidiabetic activity of PHGs was investigated by alpha-amylase and alpha-glucosidase enzyme inhibition activity. The developed PHGs exhibited excellent flow properties. The UV–Vis spectra of all seed extracts and PHGs demonstrated peak at 278 nm showing the presence of flavonoids and phenols. The FTIR spectra confirmed the existence of flavonoids, and phenols along with amines in seed extracts as well as PHGs. The HPLC-DAD test revealed the existence of gallic acid, ascorbic acid, Quercetin-3-(caffeoyldiglucoside)-7-glucoside, Rosmarinic acid, delphinidin-3,5-diglucosides, Kaempferol-3-feruloylsophoroside-7-glucoside and Phloroglucinol in PHGs. The PHGs exhibited IC50 of 51.23, 58.57, 55.41 and 53.13 μg/mL in DPPH assay, FRAP assay, TAC, Nitric oxide scavenging assays respectively. The PHGs also demonstrated IC50 of 49.97 and 36.16 μg/mL in alpha-amylase and in alpha-glucosidase inhibition assays respectively in dose dependent manner. The developed PHGs exhibited an excellent flow property. These exhibit significant in-vitro antioxidant and antidiabetic profile by virtue of flavonoid and phenolic acid derivatives.

This study reports the bactericidal activity of mucus extracts and biochemical characterization of skin mucus from five Cyprinids, including Labeo rohita, Ctenopharyngodon Idella, Gibelion catla, Hypophthalmichthys molitrix and Cirrhinus mrigala against ten different bacteria extracted from naturally infected fish. The bactericidal activity was measured based on the zone of inhibition (ZOI) and compared against Fosfomycin. Importantly, acidic mucus extracts from five fish species exhibited higher bactericidal activity than organic and aqueous extracts. The acidic skin mucus extracts of C. idella, L. rohita, and G. catla showed higher ZOI against Staphylococcus aureus, Aeromonas hydrophila, and Pseudomonas aeruginosa. The minimum inhibitory concentration (MIC) of acidic mucus extracts from C. Idella, L. rohita, and G. catla was 16 µg/mL against A. hydrophila, P. aeruginosa, and S. aureus.Further, biochemical characterization of mucus extracts showed that protein concentration was high in the acidic mucus extracts from L. rohita, C. idella, and G. catla compared to H. molitrix and C. mirigala followed by carbohydrate and lipid content. These findings suggest that skin mucus from cyprinids could be a potent source of innovative bactericidal components for fish and human-related treatments.

This research intended to increase the understanding of using modern machine intelligence techniques, including multi-expression programming (MEP) and gene expression programming (GEP), for the compressive strength (CS) prediction of rice husk ash (RHA) concrete. In addition, SHapley Additive exExplanations (SHAP) analysis was made to study the impact and interaction of raw materials on the CS of RHA concrete. A comprehensive database of 192 points with six inputs (cement, specimen age, RHA, superplasticizer, water, and fine aggregate) was used for developing prediction models. This research determined that both GEP and MEP models for the CS prediction of RHA concrete yielded reliable results, which were in close agreement with the real CS. Comparing the performance of both GEP and MEP models, it was noted that MEP, with an R2 of 0.89, outperformed the GEP model having an R2 of 0.83. Additionally, SHAP analysis indicated that specimen age was the most vital measure, followed by cement, which positively correlated with CS of RHA. The overall effect of RHA was found to be more positive, suggesting RHA utilization in the optimal range of 75–100 kg/m3 in the RHA concrete mix. The use of prediction models and SHAP analysis will help the building industry assess material properties and raw material effects faster and more economical.

In this research, test methods followed by supervised machine learning (SML) modeling were used to examine the effect of glass powder as a partial sand and cement substitute on the water absorption capability of cement mortar. This study also utilized SHapley Additive exPlanations (SHAP) analysis to determine the significance of raw materials. The dataset was derived from experiments and was used to build SML-based prediction models, including support vector machine, bagging, and AdaBoost. According to the test results, using glass powder reduced the water absorption capacity of cement mortar, and the optimum glass powder contents were noted to be 10 % as cement and 15 % as a sand substitute. Additionally, the built SML models indicated good accord with test outcomes and could be applied to calculate the water absorption of cement mortar comprising glass powder. Moreover, the AdaBoost model was found to be more accurate (R2 = 0.98) than the support vector machine (R2 = 0.95) and bagging regressor (R2 = 0.95) models in terms of R2, the difference between test and estimated findings, and the assessment of the errors.

To produce highly effective electrically conductive cementitious composites and advance non-destructive structural-health monitoring techniques, graphite-based nanomaterials (GrNs) are viable conductive fillers. A comprehensive database was created for CS and ER pertinent to GrNCC’s experimental studies from open sources. It is a challenging multi objective optimization problem (MOOP) due to the requirement of both mechanical strength and electrical resistivity for the design of Graphene nanoparticles-reinforced cementitious composites (GrNCC). The current study provides an ample data-driven approach employing machine learning (ML) techniques and a non-dominated sorting genetic algorithm (NSGA-II) to address this multi objective design optimization (MODO) problem for GrNCC. ML theories quantify how important parameters affect GrNCC’s qualities. First, using processed experimental datasets, gene expression programming (GEP) is used to develop prediction models for GrNCC’s uniaxial compressive strength (CS) and electrical resistivity (ER). The findings demonstrate its superior predictive accuracy, as evidenced by high R2 of 0.92 and 0.91 and low mean absolute error (MAE) scores of 1.57 MPa, and 60.46 kΩ.cm for CS and ER, respectively. For the MODO technique, parameter analysis was performed, which aids in identifying the parameters that need to be optimized and defining their constraints. Finally, NSGA-II is used as the foundation for the MODO technique. With the proposed prediction models as objective functions, it simultaneously optimizes the CS and ER of the GrNCC. It successfully produces a collection of Pareto solutions that make it easier to choose the right parameters for the GrNCC design. The proposed MODO technique can be efficiently applied to multiobjective design and optimization of electrical and mechanical properties of GrNCC.

Sustainable development might be promoted if waste eggshells are used in cement-based materials (CBMs) by decreasing waste disposal problems, CO2 emissions, and material costs. In this regard, experimental and machine learning (ML) strategies were used to assess the flexural strength (FS) of CBMs comprising waste eggshell powder (ESP). Initially, experimental methods were carried out to evaluate the FS of ESP-based CBMs. The acquired data was utilized to predict the FS by applying ML algorithms like a decision tree and an AdaBoost regressor. The efficacy of ML techniques was measured by comparing experimental and predicted FS and applying statistical checks and k-fold evaluations. The experiment outcomes demonstrated that the FS of CBMs was improved by the inclusion of ESP at lower replacement ratios. Additionally, based on the assessment of modeling results, the AdaBoost regressor model demonstrated a higher exactness in estimating the FS of CBMs containing ESP compared to the decision tree model. The assessment of error readings also indicated the higher precision of the AdaBoost regressor model than the decision tree. According to the feature importance analysis, the fine aggregate was the component with the lowest impact for FS, whereas cement had the prime impact. The findings showed that as opposed to using ESP as a cement replacement, using ESP as an FA replacement to keep FA in less quantity and cement in larger quantity would result in enhanced strength. The construction industry can gain from ML techniques since they allow for more time- and cost-effective approaches to assessing material attributes.

The intention of this study was to examine the use of the most prevalent industrial byproduct, silica fume, as a supplementary cementitious material (SCM). Along with the typical review, this study used the scientometric review technique to statistically examine the various features of the existing literature. A scientometric-based study can deal with the massive literature data using the applicable software tool. The bibliometric data on silica fume in concrete was gathered using the Scopus search engine between 2001 and July 2022. The top publication sources, co-occurrence of keywords, highly cited authors, and active contributing countries were all examined using scientometric analysis. Additionally, the properties of composites using silica fume as the SCM were discussed. The effect of silica fume addition on compressive, split-tensile, and flexural strength, as well as the durability and microstructure of cementitious composites, were described. It was concluded that the inclusion of silica fume as an SCM improved the material's properties while also providing environmental benefits. However, there is a limit beyond which silica fume addition showed an unfavorable effect on the performance of cementitious composites. The optimum proportion of silica fume in concrete was found to be in the range of 15–25 % by mass of cement. Moreover, literature data were utilized to develop prediction models for the strengths of cementitious composites containing silica fume, and the prediction models agreed with the actual results. These prediction models could be used to test the material strength, saving both time and cost.

This study utilized both experimental testing and machine learning (ML) strategies to assess the effectiveness of waste glass powder (WGP) on the compressive strength (CS) of cement mortar. The cement-to-sand ratio was kept 1:1 with a water-to-cement ratio of 0.25. The superplasticizer content was 4% by cement mass, and the proportion of silica fume was 15%, 20%, and 25% by cement mass in three different mixes. WGP was added to cement mortar at replacement contents from 0 to 15% for sand and cement with a 2.5% increment. Initially, using an experimental method, the CS of WGP-based cement mortar at the age of 28 days was calculated. The obtained data were then used to forecast the CS using ML techniques. For CS estimation, two ML approaches, namely decision tree and AdaBoost, were applied. The ML model's performance was assessed by calculating the coefficient of determination (R2), performing statistical tests and k-fold validation, and assessing the variance between the experimental and model outcomes. The use of WGP enhanced the CS of cement mortar, as noted from the experimental results. Maximum CS was attained by substituting 10% WGP for cement and 15% WGP for sand. The findings of the modeling techniques demonstrated that the decision tree had a reasonable level of accuracy, while the AdaBoost predicted the CS of WGP-based cement mortar with a higher level of accuracy. Utilizing ML approaches will benefit the construction industry by providing efficient and economic approaches for assessing the properties of materials.

While genetically modified (GM) crops bring economic benefits to human beings, their impact on non-target organisms has become an important part of environmental safety assessments. Symbiotic bacteria play an important role in eukaryotic biological functions and can adjust host communities to adapt to new environments. Therefore, this study examined the effects of Cry1B protein on the growth and development of non-target natural enemies of Pardosa astrigera (L. Koch) from the perspective of symbiotic bacteria. Cry1B protein had no significant effect on the health indicators of P. astrigera (adults and 2nd instar spiderlings). 16S rRNA sequencing results revealed that Cry1B protein did not change the symbiotic bacteria species composition of P. astrigera, but did reduce the number of OTU and species diversity. In 2nd instar spiderlings, neither the dominant phylum (Proteobacteria) nor the dominant genus (Acinetobacter) changed, but the relative abundance of Corynebacterium-1 decreased significantly; in adult spiders, the dominant bacteria genera of females and males were different. The dominant bacterial genera were Brevibacterium in females and Corynebacterium-1 in males, but Corynebacterium-1 was the dominant bacteria in both females and males feeding on Cry1B. The relative abundance of Wolbachia also increased significantly. In addition, bacteria in other genera varied significantly by sex. KEGG results showed that Cry1B protein only altered the significant enrichment of metabolic pathways in female spiders. In conclusion, the effects of Cry1B protein on symbiotic bacteria vary by growth and development stage and sex.

Human dental pulp stem cells (HDPSCs) have great potential to be used in regenerative medicine. To use these stem cells effectively for this purpose, they should be grown in a 3D cell culture that mimics their natural niches instead of a 2D conventional cell culture. The aim of this study was to grow the HDPSCs in the 3D cell culture created by Transglutaminase-crosslinked collagen hydrogels (Col-Tgel) in two different strengths to find a suitable 3D cell culture environment for these stem cells. Two stiffness of the 3D Col-Tgel were used to grow the HDPSCs: soft and medium matrix with strength of 0.9–1.5 kPa and 14–20 kPa, respectively. HDPSCs express markers similar to MSCs, therefore seven such markers were analyzed in the HDPSCs during their growth in the 2D and in the 3D soft and medium Col-Tgel. The CD105 and CD90 markers were significantly (p

New concepts for improving the performance of cementitious materials have recently surfaced due to the advancement in nanotechnology. In this context, nano silica (NS) and carbon nanotubes (CNTs) have been widely used in recent years. These nanomaterials significantly enhance the mechanical and durable performance of cement mixtures, although there are a few drawbacks related to high cost and workability issues. As a result, these components must be consumed at specific rates in order to achieve the desired qualities. This study aimed to create models to forecast the compressive strength (CS) for concrete mixed with two types of nanomaterials utilizing machine-learning-algorithms (MLA), such as the decision tree algorithm (DTA) and random forest algorithm (RFA). The results of both models were compared and verified by external K-fold cross-validation. A comprehensive database was collected containing 72 and 55 data points for the CS of CNTs and NS-modified concrete respectively. Four input variables such as fine aggregate (FA), cement content (CC), coarse aggregate (CA) and water-to-cement ratio (W/C) were used for the calibration of the models. Additionally, predicted results were checked through k-fold-cross-validation and other performance gauges such as mean-absolute-error (MAE), mean-squared-error (MSE), correlation coefficient (R2), root-mean-square-error (RMSE), relative-root-mean-square-error (RRMSE) and performance-index-factor (Pif). The RFA (CNTs and NS) models were found with better performance and accuracy than the DTA models by having the lowest MAE of 3.51, 4.17 RRMSE of 0.0783, 0.0584, and Pif of 0.0398 and 0.0299 respectively. In addition, the value of R2 for RFA models was observed higher such as 0.90 (CNTs) and 0.93 (NS), while for DTA models R2 was found as 0.88 (CNTs) and 0.86 (NS) respectively. The ensembled ML methods demonstrated a better generalization capability, which indicates their better ability for future prediction of CNTs and NS mixed concrete.

Introduction: Tracheostomy is commonly performed surgical procedure in ENT practice. Postoperative care is the most important aspect for achieving good patient outcomes. Unavailability of standard guidelines on tracheostomy management and inadequate training can make this basic practice complex. The nursing staff and doctors play a very important role in bedside management, both in the ward and in the intensive care unit (ICU) setup. Therefore, it is crucial that all healthcare providers directly involved in providing postoperative care to such patients can do this efficiently. Objectives: The objective of this study is to assess the knowledge regarding identification and management of tracheostomy-related emergencies and early complications among healthcare professionals so as to improve practice and further standardization. Methods: Cross-sectional observational study included two hundred and fifty-four doctors and nurses from four large tertiary care hospitals. The questions used were simple and straightforward regarding tracheostomy suctioning, cuff care, cuff management, tube blockage, and feeding management in patients with tracheostomy. Results: Based on evidence from our study, knowledge level regarding tracheostomy care ranges from 48% to 52% with knowledge scores above 50% being considered satisfactory. Significant gaps in knowledge exist in various aspects of tracheostomy care and management among healthcare professionals. Conclusion: Our findings demonstrated an adequate knowledge level among health care professionals ranging from 48% to 52% with knowledge scores above 50% being considered satisfactory and revealed that gaps in knowledge still exist in various aspects of tracheostomy care and management.

Our previous study revealed that the treatment of 5-aza-2-deoxycytidine (5-aza) inhibited while treatment of suberoylanilide hydroxamic acid (SAHA) enhanced the adipogenic differentiation of MG-63 cells. In this study, we examined the transcriptomic profiles of the derived adipocyte-like cells from MG-63 cells in the presence of 5-aza (Treatment 1) and SAHA (Treatment 2). Genome wide expression analysis showed high within sample variability for the adipocytes derived with 5-aza versus vehicle. Additionally, the expression profile of 5-aza derived cells was separated from the other sample groups. Differential analysis on the pairwise comparison of 5-aza versus control and SAHA versus 5-aza identified 1290 and 1086 differentially expressed (DE) genes, respectively. Furthermore, some overlap was observed between the up and down-regulated DE genes of 5-aza versus control and SAHA versus control (jaccard score 0.3) as well as between the differentially regulated genes of 5-aza versus control and 5-aza versus SAHA (jaccard score 0.29). A total of 73 transcription factors (TFs) were differentially expressed across all the pair wise comparisons with some overlap between the under and over expressed TFs of 5-aza versus control and 5-aza versus SAHA (jaccard score 0.29). Unsupervised clustering of TFs showed that the samples within the group are consistent in expression and the samples cluster in accordance with the group.Several GO terms related to enhanced adipogenesis such as neutral lipid biosynthetic process, lipid metabolic processes, cellular amide metabolic processes and cellular carbohydrate metabolic processes were enriched in the down regulated genes of 5-aza derived adipocytes versus control, indicating 5-aza inhibit the adipogenic differentiation of MG-63 cells. GSEA analysis on selected gene sets of MAPK and PI3K signaling pathway in MSigDB identified the pathways were up-regulated in 5-aza versus control. This study revealed that inhibition of MG-63 adipogenesis due to 5-aza treatment is associated with large transcriptomics changes and further research is needed to unravel the roles of these genes in the adipogenesis.