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Schistosomiasis is a neglected tropical disease that affects developing countries worldwide and is caused by several species of parasites from the Schistosoma genus. Chronic infection is characterized by the formation of granulomas around the parasite eggs, the leading cause of pathology. The hepatosplenic clinical form is one of the most common, but urogenital schistosomiasis is another relevant clinical presentation responsible for infertility in men and women. Inflammatory response, anatomical deformations, and endocrine/biochemical changes are involved in the development of infertility. Schistosome parasites can synthesize catechol estrogen-like molecules and affect the sexual hormone balance in their host. Here, we review many aspects of the pathology of urogenital schistosomiasis, specifically infertility, and point to the biochemical and endocrinal elements that must be investigated in the future.

Brucellosis is a bacterial zoonosis worldwide and a potential health risk in Pakistan due to socio-economic reasons and lack of awareness, veterinary health and modern dairy-processing facilities. The aim of this study was to determine the burden of brucellosis in hospitalized patients of selected districts of Punjab, Pakistan. To this end, a total of 280 serum samples were collected from suspected patients in districts Lahore, Okara and Khushab between 2016 and 2018. Anti-Brucella antibodies were detected in 89 (31.8%) sera by Rose Bengal plate test (RBPT), and Brucella abortus DNA was amplified by polymerase chain reaction (PCR) in 42 (15%) samples. In univariable analysis, the seroprevalence varied significantly (p < 0.05) between districts with Lahore indicating 41/101 (40.6%) Odds ratio (OR) = 53.98 (7.22–403.67; 95% CI), Okara 47/99 (47.5%) OR = 71.4 (9.55–533.65; 95% CI) and Khushab 1/80 (1.3%) as reference. Similarly, exposure to aborted materials and a history of blood transfusion did associate significantly (p < 0.05), posing OR = 2.91 (1.58–5.36; 95% CI) and OR = 4.48 (2.56–7.84; 95% CI) risk. However, consumption of raw milk and history of blood transfusion did associate significantly (p < 0.05) in multivariable analysis indicating OR = 1.91 (1.09–3.34; 95% CI) and OR = 4.92 (2.76–8.75; 95% CI), respectively. The association of history of blood transfusion as a brucellosis risk factor underscores the need to create brucellosis awareness programs both at public and institutional levels in the medical and veterinary fields, as well as facilities and capacity building to delve deeper into the epidemiology of brucellosis in the country. Particular attention should be paid to chronically infected patients. Therefore, laboratory modernization, standardization and validation of the diagnostic tests, and staff trainings are required. Finally, pasteurization of the milk is recommended before consumption.

The oxidation step in Oolong tea processing significantly influences its final flavor and aroma. In this study, a gas sensors detection system based on 13 metal oxide semiconductors with strong stability and sensitivity to the aroma during the Oolong tea oxidation production is proposed. The gas sensors detection system consists of a gas path, a signal acquisition module, and a signal processing module. The characteristic response signals of the sensor exhibit rapid release of volatile organic compounds (VOCs) such as aldehydes, alcohols, and olefins during oxidative production. Furthermore, principal component analysis (PCA) is used to extract the features of the collected signals. Then, three classical recognition models and two convolutional neural network (CNN) deep learning models were established, including linear discriminant analysis (LDA), k-nearest neighbors (KNN), back-propagation neural network (BP-ANN), LeNet5, and AlexNet. The results indicate that the BP-ANN model achieved optimal recognition performance with a 3–4–1 topology at pc = 3 with accuracy rates for the calibration and prediction of 94.16% and 94.11%, respectively. Therefore, the proposed gas sensors detection system can effectively differentiate between the distinct stages of the Oolong tea oxidation process. This work can improve the stability of Oolong tea products and facilitate the automation of the oxidation process. The detection system is capable of long-term online real-time monitoring of the processing process.

Geranium (Pelargonium graveolens L’Hér.) is an important commercial horticultural plant extensively used in outdoor landscaping. Seed emergence has always remained a problem in geranium due to its hard seed nature. Hence its germination and other emergence-related attributes need to be adequately tested. The purpose of this study was to evaluate the germination and seedling growth of geraniums under different types of containers. In this regard, the seeds of two varieties of geranium viz. Large FID mixed and Star mixed were planted in different types of containers. The containers included black trays plastic pots, plastic bags, and nonwoven fabric bags. The seed emergence and other germination related parameters were significantly affected by the different types of containers. However, varieties exhibited similar responses for most germination and growth characteristics. Data were collected for seed germination, mean germination time, germination index and seedling vigor index. The type of containers showed a significant impact on seedling growth and development. The taller plants with more leaves and maximum biomass production were recorded from seeds sown in nonwoven fabric type bags. The results pertaining to varieties demonstrated that Star mixed showed better emergence and vigorous seedlings in comparison with large FID mixed. The taller seedlings with maximum leaves and shoot biomass were also recorded from Star mixed grown in nonwoven fabric type bags. Based on the results, it is concluded that germanium may be raised in nonwoven fabric bags for better seed emergence and seedling growth and development.

Staphylococcus aureus is one of the major pathogens responsible for causing food poisoning worldwide. The emergence of antibiotic resistance in this bacterium is influenced by various factors. Among them, bacterial acquired defense systems described as clustered regularly interspaced short palindromic repeats (CRISPR)-cas system might be involved in antibiotic resistance development in bacteria. The current study was designed to assess the prevalence of S. aureus and its antibiotic resistance profile and identify the relationship of the CRISPR-cas system with antimicrobial resistance, followed by phylogenetic analysis. Total samples (n = 188) of poultry meat were collected from the poultry bird market of Lahore, Punjab, Pakistan. We used both phenotypic (antibiotic disc diffusion) and genotypic methods (PCR) to identify multi-drug resistant (MDR) strains of S. aureus. Additionally, the role of the CRISPR-Cas system in the isolated MDR S. aureus was also assessed. In addition, real-time quantitative PCR (qRT-PCR) was used to evaluate the association of the CRISPR-cas system with antimicrobial resistance. All of the S. aureus isolates showed 100% resistance against erythromycin, 97.5% were resistant to tetracycline, and 75% were resistant to methicillin. Eleven isolates were MDR in the current study. The CRISPR system was found in all MDR isolates, and fifteen spacers were identified within the CRISPR locus. Furthermore, MDR S. aureus isolates and the standard strain showed higher expression levels of CRISPR-associated genes. The correlation of said system with MDR isolates points to foreign gene acquisition by horizontal transfer. Current knowledge could be utilized to tackle antibiotic-resistant bacteria, mainly S. aureus.

Alzheimer’s disease (AD), is a progressive neurodegenerative disorder that involves the deposition of β-amyloid plaques and the clinical symptoms of confusion, memory loss, and cognitive dysfunction. Despite enormous progress in the field, no curative treatment is available. Therefore, the current study was designed to determine the neuroprotective effects of N-methyl-(2S, 4R)-Trans-4-hydroxy-L-proline (NMP) obtained from Sideroxylon obtusifolium, a Brazilian folk medicine with anti-inflammatory and anti-oxidative properties. Here, for the first time, we explored the neuroprotective role of NMP in the Aβ1–42-injected mouse model of AD. After acclimatization, a single intracerebroventricular injection of Aβ1–42 (5 µL/5 min/mouse) in C57BL/6N mice induced significant amyloidogenesis, reactive gliosis, oxidative stress, neuroinflammation, and synaptic and memory deficits. However, an intraperitoneal injection of NMP at a dose of (50 mg/kg/day) for three consecutive weeks remarkably decreased beta secretase1 (BACE-1) and Aβ, activated the astrocyte and microglia expression level as well as downstream inflammatory mediators such as pNF-ĸB, TNF-α, and IL-1β. NPM also strongly attenuated oxidative stress, as evaluated by the expression level of NRF2/HO-1, and synaptic failure, by improving the level of both the presynaptic (SNAP-25 and SYN) and postsynaptic (PSD-95 and SNAP-23) regions of the synapses in the cortexes and hippocampi of the Aβ1–42-injected mice, contributing to cognitive improvement in AD and improving the behavioral deficits displayed in the Morris water maze and Y-maze. Overall, our data suggest that NMP provides potent multifactorial effects, including the inhibition of amyloid plaques, oxidative stress, neuroinflammation, and cognitive deficits.

Soybean is an important oilseed crop that is used as a feed for livestock and has several industrial uses. Lipid biosynthesis and accumulation primarily occur during seed development in plants. This process is regulated by several transcription factors and interconnected biochemical pathways. This study investigated the role of glycine max LEAFY COTYLEDON 1 (GmLEC1) in soybean seed development and the accumulation of storage reserves. The overexpression of GmLEC1 significantly increased the amount of triacylglycerol (TAG) in transgenic Arabidopsis seeds compared to the wild-type and an atlec1 mutant. Similarly, the high expression of GmLEC1 led to a 12% increase in TAG content in transgenic soybean hairy roots compared to the control. GmLEC1 also altered the fatty acid composition in transgenic Arabidopsis seeds and soybean hairy roots. Additionally, the overexpression of GmLEC1 resulted in a reduction in starch accumulation in seeds and vegetative tissues, as well as changes in cotyledon and seed morphology. The cotyledons of the atlec1 mutant displayed abnormal trichome development, and the seeds were smaller and less tolerant to desiccation. A complementation assay in Arabidopsis restored normal cotyledon phenotype and seed size. The main downstream targets of LEC1 are GL2 and WRI1, which were found to participate in fatty acid biosynthesis and trichome formation through the regulation of phytohormones and various transcription factors involved in seed development and maturation. The findings of this study suggest that GmLEC1 controls seed development and regulates the accumulation of seed storage compounds. Furthermore, these results demonstrate that GmLEC1 could be a reliable target for the genetic improvement of oil biosynthesis in soybean.

Lung diseases rank third in terms of mortality and represent a significant economic burden globally. Scientists have been conducting research to better understand respiratory diseases and find treatments for them. An ideal in vitro model must mimic the in vivo organ structure, physiology, and pathology. Organoids are self-organizing, three-dimensional (3D) structures originating from adult stem cells, embryonic lung bud progenitors, embryonic stem cells (ESCs), and induced pluripotent stem cells (iPSCs). These 3D organoid cultures may provide a platform for exploring tissue development, the regulatory mechanisms related to the repair of lung epithelia, pathophysiological and immunomodulatory responses to different respiratory conditions, and screening compounds for new drugs. To create 3D lung organoids in vitro, both co-culture and feeder-free methods have been used. However, there exists substantial heterogeneity in the organoid culture methods, including the sources of AT2 cells, media composition, and feeder cell origins. This article highlights the currently available methods for growing AT2 organoids and prospective improvements to improve the available culture techniques/conditions. Further, we discuss various applications, particularly those aimed at modeling human distal lung diseases and cell therapy.

Polychlorinated dibenzofurans (PCDFs) are persistent toxic compounds that are ubiquitous in the environment. Nanocomposites of titanium(IV) oxide-vanadium(III) oxide (Ti3V2O7) and titanium(IV) oxide-silicon dioxide (Ti2Si7O30) were prepared and spectroscopically analyzed as potential decontaminants for dioxin-like materials. The analysis confirmed a homogeneous morphology with nanoscale particle size. The Ti-Si sample was found to have a high surface area compared to the Ti-V composite. Vanadium(III) oxide (V2O5) and silicon dioxide (SiO2) were chosen as materials for the formation of heterogeneous compounds with titanium(IV) oxide (TiO2) because they possess a suitable band alignment with TiO2, thus forming effective photocatalysts. This study evaluated the photodegradation of 2,3,7,8-tetrachlorodibenzo-furan (TCDF) in the presence of Ti-Si and Ti-V oxide composites, which was tested using high- (254 nm) and midenergy (302 nm) UV irradiation sources. While Ti-Si showed success in the photodegradation of 2,3,7,8-TCDF dissolved in a (1:1) methanol–tetrahydrofuran (MeOH-THF) solution, the Ti-V composite proved to be a powerful material in adsorbing TCDF with a high capacity immediately upon mixing. Ti-Si oxide was found to decompose TCDF under the two irradiation sources with 98–99% degradation occurring after 70 min. The use of 254 nm as an irradiation source in the presence of Ti-Si was 4.3 times faster than the analogue reaction irradiated without a catalyst. Byproducts of the degradation were evaluated using gas chromatography–mass spectrometry (GC–MS), resulting in a lower chlorinated congener and less toxicity, as the main degradation product.

Lumpy skin disease (LSD) is a contagious infection of cattle caused by a virus of the Poxviridae family, genus Capripoxvirus. In Pakistan, recent outbreaks have resulted in significant nationwide mortality and economic losses. A 20-day prospective cohort study was performed on sixty infected cattle with the aim to evaluate LSD-induced oxidative stress’s genotoxic role and to determine the ameliorative effect of antioxidant therapy using principal component analysis (PCA) and a multivariable ordinal logistic regression model. LSDV was identified from scab samples and nodular lesions using RPO30-specific gene primers. The infected cattle were divided into control and treated groups. The animals were observed initially and finally on day 20 to evaluate the homeostatic, oxidative, and genotoxic changes. The animals in the treated group were administered a combination of selenium (Se) and vitamin E at the standard dose rate for five consecutive days. A substantial (p < 0.05) improvement in the hematological indices was observed in the treated group. The treated group also showed a significant (p < 0.05) reduction in levels of serum nitric oxide (NO) and malondialdehyde (MDA) post-therapy. The PCA at the final sampling data of the treated group showed that Principal Component (PC1 eigenvalue 1.429) was influenced by superoxide dismutase (SOD; 0.3632), catalase (CAT; 0.2906), and glutathione (GSH; 0.0816) and PC2 (eigenvalue 1.200) was influenced by CAT (0.4362), MDA (0.2056), and NO (0.0693). A significant correlation between serum NO (76%) and MDA levels (80%) was observed with genetic damage index (GDI) scores. The ordinal logistic regression model regarding the use of antioxidant therapy revealed 73.95-times (95%CI; 17.36–314.96) improvement in the GDI in treated animals. The multivariable ordinal logistic regression showed that each unit increase in NO and MDA resulted in a 13% increase in genotoxicity in infected individuals. In conclusion, our study revealed that LSD-induced oxidative stress and lipid peroxidation product causes genotoxicity in affected animals. Furthermore, the combined Se and vitamin E therapy significantly alleviated oxidative stress and genotoxicity in LSD-affected cattle.

The intricate and multifaceted nature of diabetes disrupts the body’s crucial glucose processing mechanism, which serves as a fundamental energy source for the cells. This research aims to predict the occurrence of diabetes in individuals by harnessing the power of machine learning algorithms, utilizing the PIMA diabetes dataset. The selected algorithms employed in this study encompass Decision Tree, K-Nearest Neighbor, Random Forest, Logistic Regression, and Support Vector Machine. To execute the experiments, two software tools, namely Waikato Environment for Knowledge Analysis (WEKA) version 3.8.1 and Python version 3.10, were utilized. To evaluate the performance of the algorithms, several metrics were employed, including true positive rate, false positive rate, precision, recall, F-measure, Matthew’s correlation coefficient, receiver operating characteristic area, and precision–recall curves area. Furthermore, various errors such as Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, and Root Relative Squared Error were examined to assess the accuracy of the models. Upon conducting the experiments, it was observed that Logistic Regression outperformed the other techniques, exhibiting the highest precision of 81 percent using Python and 80.43 percent using WEKA. These findings shed light on the efficacy of machine learning in predicting diabetes and highlight the potential of Logistic Regression as a valuable tool in this domain.

Background and Objectives: Dermatological disorders are highly prevalent among children in Pakistan. The present cross-sectional study aims to identify the spectrum of dermatological conditions among children and adolescents in Pakistan. Materials and Methods: A total of 582 patients (50.9% males; 49.1% females) were included in the study based on their age (5.7 ± 4.1 years), dermatological condition, and epidemiology. The youngest patient was aged ten days, whereas the eldest was seventeen. Age criteria were further stratified into three categories: infants and toddlers (≤5 years), children (≥5 to Results: Scabies was the highest reported skin condition with 281 (45.55%) patients, followed by 114 (19.6%) with eczema, 60 (10.3%) with dermatitis, 33 (5.7%) with tinea capitis, 17 (2.9%) with tinea corporis, 16 (2.7%) with impetigo, and 15 (2.6%) with folliculitis. Other conditions include urticaria, burns, infections, pediculosis, tinea inguinalis, tinea faciei, nappy rashes, alopecia, warts, tinea incognito, tinea cruris, and acne vulgaris. The chi-squared test showed a high prevalence of tinea corporis and acne among adolescents (12–17 years), whereas eczema, dermatitis, and impetigo were more prevalent among infants and toddlers. Conclusions: Pets or livestock and poor hygiene were found to be highly reported risk factors for many dermatological conditions like scabies and fungal infections. Dermatological conditions are common in younger individuals, but unfortunately, many children do not receive the desired medical assistance.

S-triazines are a group of herbicides that are extensively applied to control broadleaf weeds and grasses in agricultural production. They are mainly taken up through plant roots and are transformed by xylem tissues throughout the plant system. They are highly persistent and have a long half-life in the environment. Due to imprudent use, their toxic residues have enormously increased in the last few years and are frequently detected in food commodities, which causes chronic diseases in humans and mammals. However, for the safety of the environment and the diversity of living organisms, the removal of s-triazine herbicides has received widespread attention. In this review, the degradation of s-triazine herbicides and their intermediates by indigenous microbial species, genes, enzymes, plants, and nanoparticles are systematically investigated. The hydrolytic degradation of substituents on the s-triazine ring is catalyzed by enzymes from the amidohydrolase superfamily and yields cyanuric acid as an intermediate. Cyanuric acid is further metabolized into ammonia and carbon dioxide. Microbial-free cells efficiently degrade s-triazine herbicides in laboratory as well as field trials. Additionally, the combinatorial approach of nanomaterials with indigenous microbes has vast potential and considered sustainable for removing toxic residues in the agroecosystem. Due to their smaller size and unique properties, they are equally distributed in sediments, soil, water bodies, and even small crevices. Finally, this paper highlights the implementation of bioinformatics and molecular tools, which provide a myriad of new methods to monitor the biodegradation of s-triazine herbicides and help to identify the diverse number of microbial communities that actively participate in the biodegradation process.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has affected billions of lives and is expected to impose a significant burden on the economy worldwide. Vaccination is the only way to prevent the infection. However, convincing people to get themselves vaccinated is challenging in developing countries such as Pakistan. Therefore, a cross-sectional questionnaire-based study was conducted (n = 982 participants) all over Pakistan to evaluate the perception, knowledge, attitude, and acceptance of the general public towards the SARS-CoV-2 vaccine, in general, and a booster dose of SARS-CoV-2, in particular. The highest number of participants were from the province of Punjab (84.5%), followed by Islamabad (3.8%), Sindh (3.7%), Khyber Pakhtunkhwa (2.7%), Baluchistan (2.6%), Gilgit Baltistan (1.4%), and Azad Jammu and Kashmir (1.4%). A total of 915 participants were vaccinated against COVID-19, out of which 62.2% received one booster dose, followed by double booster doses (25.5%) and single vaccine shots (12.3%). The highest number of vaccinated participants were from Punjab (85.8%), followed by Islamabad (3.9%), Sindh (2.8%); Khyber Pakhtunkhwa (2.6%); Baluchistan (2.3%); Gilgit-Baltistan (1.3%); and Azad, Jammu, and Kashmir (1.2%). Among the vaccinated individuals, 71.4% were unemployed, 27.4% were employed (653), and 1.2% were retired from service. However, no significant association was observed among genders and educational levels in regard to acceptance of the booster vaccine. The outcomes of the study revealed that the increased acceptance of booster doses of the SARS-CoV-2 vaccines among the public was associated with the intent of personal and family protection. Moreover, individuals with low socioeconomic status and pregnant females showed the least acceptance towards the vaccine inoculation. The study also revealed a decline trend of accepting SARS-CoV-2 vaccine among children.

Silver nanoparticles (Ag-NPs) are most effective against pathogens and have widely been studied as antibacterial agents in commodity clothing, medical textile, and other hygiene products. However, prolonged utilization of silver and rapid mutation in bacterium stains has made them resistant to conventional silver agents. On the other hand, strict compliance against excessive utilization of toxic reagents and the current sustainability drive is forcing material synthesis toward green routes with extended functionality. In this study, we proposed an unprecedented chemical-free green synthesis of bioactive Ag-NPs without the incorporation of any chemicals. Cinnamon essential oil (ECO) was used as a bio-reducing agent with and without the mediation of lime extract. A rapid reaction completion with better shape and size control was observed in the vicinity of lime extract when incorporated into the reaction medium. The interaction of natural metabolites and citrus compounds with nanoparticles was established using Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy. The application of as-prepared nanoparticles on textiles encompasses extended bioactivity to treated fabric with infused easy-care performance. To the best of our knowledge, this is the first reported instance of utilizing bioactive silver nanoparticles as a functional finish, both as an antimicrobial and as for easy care in the absolute absence of toxic chemicals. The easy-care performance of fabric treated with lime-mediated nanoparticles was found to be 141O, which is around 26% better than bare cotton without any significant loss in fabric strength. Furthermore, to enlighten the sustainability of the process, the development traits were mapped with the United Nations Sustainable Development Goals (SDGs), which show significant influence on SDGs 3, 8, 9, and 14. With the effective suspension of microorganisms, added functionality, and eco-mapping with SDGs with the chemical-free synthesis of nanoparticles, widespread utilization can be found in various healthcare and hygiene products along with the fulfillment of sustainability needs.

Diclofenac sodium was extensively used for treating arthritis, osteoarthritis and skeletal muscular injuries, which ultimately caused troubles for aquatic organisms as well as human beings. In this study, homogeneous catalytic advanced oxidation processes, including Fe2+/persulfate, Fe2+/peroxymonosulfate and Fe2+/H2O2, were used for the degradation of diclofenac sodium in water, without using UV-C light. About 89, 82 and 54% DCF sodium was decomposed by Fe2+/persulfate, Fe2+/peroxymonosulfate and Fe2+/H2O2, respectively, in 60 min. The degradation of diclofenac sodium followed the pseudo first-order kinetics, in all cases. The degradation efficiency of diclofenac sodium was significantly affected in the presence of various anions, such as NO3−, HCO3− and SO42−. The mineralization studies revealed 62, 45 and 32% total carbon removal by Fe2+/persulfate, Fe2+/peroxymonosulfate and Fe2+/H2O2, respectively, in 60 min. In addition, the degradation byproducts of diclofenac sodium were determined by FTIR analysis. The results revealed that the Fe2+/oxidant system, particularly Fe2+/persulfate, was a promising technology for the elimination of toxic pharmaceuticals, such as diclofenac sodium, from the water environment.

Ultra-thin two-dimensional (2D) materials have attained huge interest for biosensing applications because of their strong electrostatic coupling with target molecules such as spike proteins and DNA. One such 2D material is graphene, which is extremely thin and flexible and has a strong non-covalent interaction with the supporting constructs needed to detect biomolecules. This work aimed to develop a way to efficiently functionalize the surface of 2D material using a pyrene-based supporter construct to detect the target protein. For this purpose, high-quality, pristine graphene was grown via the chemical vapor deposition (CVD) method and transferred over the Si/SiO2 substrate for its functionalization using our engineered pyrene–lysine-based supporter construct (PLB). The construct was synthesized using the solid-phase peptide synthesis (SPPS) method and utilized to functionalize the graphene-channel-based field-effect transistor (FET) device via non-covalent π−π stacking interaction. The optimum concentration of the functionalized PLB was evaluated via atomic force microscopy (AFM), Raman spectroscopy, and real-time electrical measurements. The characterization techniques successfully provide an overview of the effect of the concentration of PLB used for functionalization. Moreover, the performance was tested and compared in terms of the percentage response of the device generated after the detection of various concentrations of the streptavidin protein. This research could be useful in determining how to functionalize any 2D material by designing a supporter construct without material degradation and owing to over-stacking or bypassing surface screening effects.

Lumpy skin disease (LSD) is a highly infectious disease of cattle caused by a virus of the Poxviridae family, genus Capripoxvirus. The present study was designed to determine the prognostic ability of serum IL-6 in LSD using a binary logistic regression model at baseline sampling. A 17-day cohort study was conducted on a recent outbreak of LSD among cattle in the Lodhran District of Punjab, Pakistan. Infected cattle were divided into two categories based on their clinical status on day 17 as recovered (n = 33) or unrecovered (n = 17). Nodular lesions and scab specimens (n = 50) were used for the isolation of the lumpy skin disease virus and were confirmed by PCR. In recovered animals, hematological results showed marked leukocytosis, eosinophilia, lymphocytosis, neutrophilia, and monocytopenia. However, marked erythrocytosis, leukopenia, and thrombocytopenia were observed in the unrecovered animals at the final sampling point of the study. Serum levels of total protein, albumin, and glucose were significantly higher in the recovered animals. Meanwhile, aspartate aminotransferase, alkaline phosphatase, lactate dehydrogenase, creatinine phosphokinase, total bilirubin, and direct bilirubin were found considerably higher in the unrecovered group. Receiver-operating characteristic curve analysis for serum IL-6 at baseline predicts the extended clinical conditions at the cut-off value of 85.16 pg/mL (55% specificity, 94% sensitivity, area under the curve 0.8039, respectively). In conclusion, the disease-induced hematological and biochemical alterations were significantly ameliorated in the recovered animals. In addition, the study revealed that serum IL-6 can be used as a valid marker for predicting the clinical worsening of LSD in cattle.

This study reports the removal of hydrocarbon (HC) pollutants from petroleum refinery wastewater by integrated photocatalytic oxidation and adsorption using a TiO2/AC hybrid material. The hybrid adsorbent/catalyst was prepared by the impregnation of TiO2 over AC and characterized by FTIR, SEM, EDX, and XRD analyses. Under the optimized reaction conditions of pH 3, 30 °C, and 1000 mg TiO2/AC per 500 mL of sample in 50 min, the integrated photocatalytic oxidation-adsorption achieved a net percentage removal of benzene, toluene, aniline, and naphthalene of 91% from model HC solutions. Under these conditions, for the treatment of real refinery wastewater, TiO2/AC caused a 95% decrease in chemical oxygen demand (COD). The integrated photocatalytic oxidation and adsorption using TiO2/AC showed a clear advantage over the individual adsorption and photocatalytic oxidation using AC and TiO2, whereby about the same level of removal of model HCs and a decrease in the COD of refinery wastewater was attained in 105 min and 90 min, respectively, utilizing larger adsorbent/catalyst dosages. GC-MS analysis revealed that during the integrated process of adsorption-photocatalytic oxidation, all the parent HCs and oxidation byproducts were completely removed from the refinery wastewater. Based on the outstanding performance, cost-effectiveness, and environmental greenness, the newly designed TiO2/AC via the integrated adsorption-photocatalytic oxidation can be counted as an effective alternative route for the large-scale processing of refinery wastewater.

Concrete is a heterogeneous material that is extensively used as a construction material. However, to improve the toughness and mechanical properties of concrete, various ingredients (fillers) have been added in the past. The addition of nanomaterials for the improvement of the aforementioned properties has attracted many researchers worldwide. The high surface area, high reactivity, and finer size of various nanomaterials have made them preferable for the enhancement of durability, as well as compressive and flexural strength. The aim of the current research is focused on the estimation of compressive strength for the concrete modified with various nanomaterials using two machine learning techniques, namely decision tree technique (DTT) and random forest technique (RFT), and comparison with existing models. The database is collected for different percentages of four major widely used nanomaterials in concrete, i.e., carbon nanotubes, nano silica, nano clay, and nano alumina. The other four input variables used for the calibration of the models are: cement content (CC); water–cement ratio (W/C); fine aggregate, i.e., sand (FA); and coarse aggregate (CA). Both DTT and RFT models were developed for 94 collected experimental datasets from the published literature. The predicted results are further validated through K-fold cross-validation using correlation coefficient (R2), mean absolute error (MAE), root mean square error (RMSE), relative root mean square error, relative square error (RRMSE), and performance index factor (PiF). The RFT model was found to have the lowermost MAE 3.253, RMSE 4.387, RRMSE 0.0803, and performance index factor (PiF) 0.0061. In comparison, predicted results overall revealed better performance and accuracy for the RFT-developed models than for DTT and gene expression programming (GEP) models, as illustrated by their high R2 value, equal to 0.96, while the R2 value for DTT and GEP was found 0.94 and 0.86, respectively.

Incorporating scientific research into clinical practice via clinical informatics, which includes genomics, proteomics, bioinformatics, and biostatistics, improves patients’ treatment. Computational pathology is a growing subspecialty with the potential to integrate whole slide images, multi-omics data, and health informatics. Pathology and laboratory medicine are critical to diagnosing cancer. This work will review existing computational and digital pathology methods for breast cancer diagnosis with a special focus on deep learning. The paper starts by reviewing public datasets related to breast cancer diagnosis. Additionally, existing deep learning methods for breast cancer diagnosis are reviewed. The publicly available code repositories are introduced as well. The paper is closed by highlighting challenges and future works for deep learning-based diagnosis.

Yellow rust (YR) epidemics have affected wheat productivity worldwide. YR resistance (Yr) is eminent in wheat; however, it is continuously invaded by evolving YR pathogen Puccinia striiformis (Pst.). Understanding the Yr genes’ diversity among the available germplasm is paramount to developing YR-resistant cultivars. In this study, 14 wheat genotypes were screened for their relative resistance index (RRI) and Yr genes/QTL via linked microsatellite markers. RRI screening categorized the studied genotypes into susceptible (7; 8.45 ± 0.25) bulks (p < 0.001). Genetic analysis using 19 polymorphic microsatellite markers revealed 256 alleles, which were divergent among the three resistance bulks. Markers Xbarc7 and Xgwm429 showed the highest allelic diversity in comparison to Xbarc181, Xwmc419, SCAR1400, and Xgwm130. Resistant bulk showed associated alleles at Yr18 gene-linked markers Xgwm295, cssfr6, and csLV34. Other RRI-associated alleles at markers Xbarc7 and Xbarc101 showed weak and moderate linkages, respectively, with the Yr5 gene; whereas, a moderate association was noted for the Yr15 gene-linked marker Xgwm11. Marker Xwe173 linked with the Yr26 gene showed associated alleles among the susceptible bulk. Cross combinations of the parental lines forming recombinant inbred lines (RILs) demonstrated net higher RRI implying favorable allelic recombination. These results support reports and field observations on novel Pst. races that triggered Yr26, Yr5, and Yr15 busts in recent past. This study further implies that pyramiding all stage resistance genes (Yr5, Yr10, Yr15, and Yr26) with adult plant resistance genes (Yr18 and Yr62) should provide sustained YR resistance. The associated alleles at Yr genes-linked markers provide a basis for marker-assisted YR resistance breeding in wheat.

Kari sheep inhabiting the Chitral district of Pakistan show variation in gestation length. In this study, we have analyzed the genetic differences between the three subtypes of Kari sheep (based on variation in gestation length) using microsatellite markers. Kari sheep samples were collected from their breeding tract and were characterized for gestation length and genetic diversity using microsatellite markers. A total of 78 Kari ewes were grouped into three categories based on gestation length (GL), i.e., Kari-S (with a shorter GL), Kari-M (with a medium GL), and Kari-L (with a longer GL). DNA from these samples was used to amplify 31 ovine-specific microsatellite loci through PCR. Of the total 78 Kari specimens, 24 were grouped in Kari-S (GL = 100.7 ± 1.8), 26 were from the Kari-M subtype (GL = 123.1 ± 1.0), and 28 were Kari-L (GL = 143.8 ± 1.5). Microsatellite analysis revealed an association of genotypes at two marker sites (MAF214 and ILSTS5) with variation in GL. A total of 158 alleles were detected across the 22 polymorphic loci with an average of 7.18 alleles per locus. Unique alleles were found in all three subtypes. The highest number of unique alleles was observed in Kari-L (15), followed by Kari-S (10) and Kari-M (8). The results indicated that Kari-S is a genetically distinct subtype (with higher genetic differentiation and distance) from Kari-M and Kari-L. The genetic uniqueness of Kari-S is important for further exploration of the genetic basis for shorter gestation length, and exploitation of their unique values.

Throughout the world, the construction industry produces significant amounts of by-products and hazardous waste materials. The steel-making industry generates welding waste and dusts that are toxic to the environment and pose many economic challenges. Water-based drilling fluids (WBDF) are able to remove the drill cuttings in a wellbore and maintain the stability of the wellbore to prevent formation damage. To the best of our knowledge, this is the first study that reports the application of welding waste and its derived graphene oxide (GO) as a fluid-loss additive in drilling fluids. In this research, GO was successfully synthesized from welding waste through chemical exfoliation. The examination was confirmed using XRD, FTIR, FESEM and EDX analyses. The synthesized welding waste-derived GO in WBDF is competent in improving rheological properties by increasing plastic viscosity (PV), yield point (YP) and gel strength (GS), while reducing filtrate loss (FL) and mud cake thickness (MCT). This study shows the effect of additives such as welding waste, welding waste-derived GO and commercial GO, and their amount, on the rheological properties of WBDF. Concentrations of these additives were used at 0.01 ppb, 0.1 ppb and 0.5 ppb. Based on the experiment results, raw welding waste and welding waste-derived GO showed better performance compared with commercial GO. Among filtration properties, FL and MCT were reduced by 33.3% and 39.7% with the addition of 0.5 ppb of raw welding-waste additive, while for 0.5 ppb of welding waste-derived GO additive, FL and MCT were reduced by 26.7% and 20.9%, respectively. By recycling industrial welding waste, this research conveys state-of-the-art and low-cost drilling fluids that aid in waste management, and reduce the adverse environmental and commercial ramifications of toxic wastes.

The emergence of antibiotic resistance in foodborne pathogens isolated from meat pro-ducts and their producing environment has been an increasing and leading threat to public health. The aim of the study was to identify pathogens and their antimicrobial resistance isolated from pig production to pork meat distribution phases. Through this study, food spoilage and foodborne or clinical pathogenic bacteria were isolated and identified from pork (belly and neck) meat product and its related environmental samples that include pig swabs, diets, feces, liquid manure, workers’ gloves, dust fan swabs, carcass swabs, floor swabs, and drain water in the affiliated farm, slaughterhouse, meat processing plant, and in retail stores. All carcasses at the slaughterhouse and meat products at the meat processing plant were tracked from pigs at a targeted farm. Nine different selective media agars were used to effectively isolate various pathogenic bacteria. A total of 283 presumptive pathogenic bacteria isolated from 126 samples were selected and identified using MALDI-ToF MS. Twenty-three important foodborne pathogens were identified, and some of them, Shiga-toxin-producing E. coli (STEC), Listeria monocytogenes, Staphylococcus aureus, and Yersinia enterocolitica, were further confirmed using PCR. The PFGE patterns of 12 STEC isolates were grouped by sample source or site. All the foodborne pathogens used in the study were not resistant to amoxicillin/clavulanate, ciprofloxacin, and gentamicin, whereas some of the STEC, L. monocytogenes, and S. aureus isolates were resistant to various antibiotics, including ampicillin, erythromycin, tetracycline, and vancomycin. The most common antimicrobial resistance pattern in the pathogenic STEC isolates was AMP-KAN-STR-SXT-TET. Consequently, this study provides valuable information for the distribution of antimicrobial-resistant pathogens along the pork meat production chain and can assist farmers and stakeholders to develop a systematic strategy for reducing the current emergence and spread of antimicrobial resistance in the different phases of pig production and distribution.

This study examined the bibliographic data on fiber-reinforced geopolymers (FRGPs) using scientometrics to determine their important features. Manual review articles are inadequate in their capability to connect various segments of literature in an ordered and systematic manner. Scientific mapping, co-citation, and co-occurrence are the difficult aspects of current research. The Scopus database was utilized to find and obtain the data needed to achieve the study’s aims. The VOSviewer application was employed to assess the literature records from 751 publications, including citation, bibliographic, keyword, and abstract details. Significant publishing outlets, keywords, prolific researchers in terms of citations and articles published, top-cited documents, and locations actively participating in FRGP investigations were identified during the data review. The possible uses of FRGP were also highlighted. The scientometric analysis revealed that the most frequently used keywords in FRGP research are inorganic polymers, geopolymers, reinforcement, geopolymer, and compressive strength. Additionally, 27 authors have published more than 10 articles on FRGP, and 29 articles have received more than 100 citations up to June 2022. Due to the graphical illustration and quantitative contribution of scholars and countries, this study can support scholars in building joint ventures and communicating innovative ideas and practices.

In recent decades, a variety of organizational sectors have demanded and researched green structural materials. Concrete is the most extensively used manmade material. Given the adverse environmental effect of cement manufacturing, research has focused on minimizing environmental impact and cement-based product costs. Metakaolin (MK) as an additive or partial cement replacement is a key subject of concrete research. Developing predictive machine learning (ML) models is crucial as environmental challenges rise. Since cement-based materials have few ML approaches, it is important to develop strategies to enhance their mechanical properties. This article analyses ML techniques for forecasting MK concrete compressive strength (fc’). Three different individual and ensemble ML predictive models are presented in detail, namely decision tree (DT), multilayer perceptron neural network (MLPNN), and random forest (RF), along with the most effective factors, allowing for efficient investigation and prediction of the fc’ of MK concrete. The authors used a database of MK concrete mechanical features for model generalization, a key aspect of any prediction or simulation effort. The database includes 551 data points with relevant model parameters for computing MK concrete’s fc’. The database contains cement, metakaolin, coarse and fine aggregate, water, silica fume, superplasticizer, and age, which affect concrete’s fc’ but were seldom considered critical input characteristics in the past. Finally, the performance of the models is assessed to pick and deploy the best predicted model for MK concrete mechanical characteristics. K-fold cross validation was employed to avoid overfitting issues of the models. Additionally, ML approaches were utilized to combine SHapley Additive exPlanations (SHAP) data to better understand the MK mix design non-linear behaviour and how each input parameter’s weighting influences the total contribution. Results depict that DT AdaBoost and modified bagging are the best ML algorithms for predicting MK concrete fc’ with R2 = 0.92. Moreover, according to SHAP analysis, age impacts MK concrete fc’ the most, followed by coarse aggregate and superplasticizer. Silica fume affects MK concrete’s fc’ least. ML algorithms estimate MK concrete’s mechanical characteristics to promote sustainability.

This research examined machine learning (ML) techniques for predicting the compressive strength (CS) of self-compacting concrete (SCC). Multilayer perceptron (MLP), bagging regressor (BR), and support vector machine (SVM) were utilized for analysis. A total of 169 data points were retrieved from the various published articles. The data set was based on 11 input parameters, such as cement, limestone, fly ash, ground granulated blast-furnace slag, silica fume, rice husk ash, coarse aggregate, fine aggregate, superplasticizers, water, viscosity modifying admixtures, and one output with compressive strength of SCC. In terms of properly predicting the CS of SCC, the BR technique outperformed both the SVM and MLP models, as determined by the research results. In contrast to SVM and MLP, the coefficient of determination (R2) for the BR model was 0.95, whereas for SVM and MLP, the R2 was 0.90 and 0.86, respectively. In addition, a k-fold cross-validation approach was adopted to check the accuracy of the employed models. The statistical measures mean absolute percent error, mean absolute error, and root mean square error ensure the validity of the model. Using sensitivity analysis, the influence of input factors on the intended CS of SCC was also explored. This analysis reveals that the highest contributing parameter towards the CS of SCC was cement with 16.2%, while rice husk ash contributed the least with 4.25% among all the input variables.

This research employed machine learning (ML) and SHapley Additive ExPlanations (SHAP) methods to assess the strength and impact of raw ingredients of cement mortar (CM) incorporated with waste glass powder (WGP). The data required for this study were generated using an experimental approach. Two ML methods were employed, i.e., gradient boosting and random forest, for compressive strength (CS) and flexural strength (FS) estimation. The performance of ML approaches was evaluated by comparing the coefficient of determination (R2), statistical checks, k-fold assessment, and analyzing the variation between experimental and estimated strength. The results of the ML-based modeling approaches revealed that the gradient boosting model had a good degree of precision, but the random forest model predicted the strength of the WGP-based CM with a greater degree of precision for CS and FS prediction. The SHAP analysis revealed that fine aggregate was a critical raw material, with a stronger negative link to the strength of the material, whereas WGP and cement had a greater positive effect on the strength of CM. Utilizing such approaches will benefit the building sector by supporting the progress of rapid and inexpensive approaches for identifying material attributes and the impact of raw ingredients.

Estimating concrete properties using soft computing techniques has been shown to be a time and cost-efficient method in the construction industry. Thus, for the prediction of steel fiber-reinforced concrete (SFRC) strength under compressive and flexural loads, the current research employed advanced and effective soft computing techniques. In the current study, a single machine learning method known as multiple-layer perceptron neural network (MLPNN) and ensembled machine learning models known as MLPNN-adaptive boosting and MLPNN-bagging are used for this purpose. Water; cement; fine aggregate (FA); coarse aggregate (CA); super-plasticizer (SP); silica fume; and steel fiber volume percent (Vf SF), length (mm), and diameter were the factors considered (mm). This study also employed statistical analysis such as determination coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) to assess the performance of the algorithms. It was determined that the MLPNN-AdaBoost method is suitable for forecasting SFRC compressive and flexural strengths. The MLPNN technique’s higher R2, i.e., 0.94 and 0.95 for flexural and compressive strength, respectively, and lower error values result in more precision than other methods with lower R2 values. SHAP analysis demonstrated that the volume of cement and steel fibers have the greatest feature values for SFRC’s compressive and flexural strengths, respectively.

Recently, artificial intelligence (AI) approaches have gained the attention of researchers in the civil engineering field for estimating the mechanical characteristics of concrete to save the effort, time, and cost of researchers. Consequently, the current research focuses on assessing steel-fiber-reinforced concrete (SFRC) in terms of flexural strength (FS) prediction by employing delicate AI techniques as well as to predict the raw material interaction that is still a research gap. In this study, the FS of SFRC is estimated by deploying supervised machine learning (ML) techniques, such as DT-Gradient Boosting, DT-XG Boost, DT-AdaBoost, and DT-Bagging. In addition to that, the performance model is also evaluated by using R2, root mean square error (RMSE), and mean absolute error (MAE). Furthermore, the k-fold cross-validation method is also applied to validate the model’s performance. It is observed that DT-Bagging with an R2 value of 0.95 is superior to DT-XG Boost, DT-Gradient Boosting, and DT-AdaBoost. Lesser error MAE and RMSE and higher R2 values for the DT-Bagging model show the enhanced performance of the model compared to the other ensembled approaches. Considerable conservation of time, effort, and cost can be made by applying ML techniques to predict concrete properties. The evaluation of the outcome depicts that the estimated results of DT-Bagging are closer to the experimental results, indicating the accurate estimation of SFRC flexural strength. It is further revealed from the SHapley Additive exPlanations (SHAP) study that the volumetric content of steel fiber highly and positively influences the FS of SFRC.

In this study, compressive strength (CS) of fiber-reinforced nano-silica concrete (FRNSC) was anticipated using ensemble machine learning (ML) approaches. Four types of ensemble ML methods were employed, including gradient boosting, random forest, bagging regressor, and AdaBoost regressor, to achieve the study’s aims. The validity of employed models was tested and compared using the statistical tests, coefficient of determination (R2), and k-fold method. Moreover, a Shapley Additive Explanations (SHAP) analysis was used to observe the interaction and effect of input parameters on the CS of FRNSC. Six input features, including fiber volume, coarse aggregate to fine aggregate ratio, water to binder ratio, nano-silica, superplasticizer to binder ratio, and specimen age, were used for modeling. In predicting the CS of FRNSC, it was observed that gradient boosting was the model of lower accuracy and the AdaBoost regressor had the highest precision in forecasting the CS of FRNSC. However, the performance of random forest and the bagging regressor was also comparable to that of the AdaBoost regressor model. The R2 for the gradient boosting, random forest, bagging regressor, and AdaBoost regressor models were 0.82, 0.91, 0.91, and 0.92, respectively. Also, the error values of the models further validated the exactness of the ML methods. The average error values for the gradient boosting, random forest, bagging regressor, and AdaBoost regressor models were 5.92, 4.38, 4.24, and 3.73 MPa, respectively. SHAP study discovered that the coarse aggregate to fine aggregate ratio shows a greater negative correlation with FRNSC’s CS. However, specimen age affects FRNSC CS positively. Nano-silica, fiber volume, and the ratio of superplasticizer to binder have both positive and deleterious effects on the CS of FRNSC. Employing these methods will promote the building sector by presenting fast and economical methods for calculating material properties and the impact of raw ingredients.

This study performed a scientometric-based examination of the literature on steel fiber-reinforced concrete (SFRC) to identify its key elements. Typical review papers are limited in their capacity to link distinct segments of the literature in an organized and systematic method. The most challenging aspects of current research are knowledge mapping, co-occurrence, and co-citation. The Scopus search engine was used to search for and obtain the data required to meet the goals of the study. During the data evaluation, the relevant publication sources, keyword assessment, productive authors based on publications and citations, top papers based on citations received, and areas vigorously involved in SFRC studies were recognized. The VOSviewer software tool was used to evaluate the literature data from 9562 relevant papers, which included citation, abstract, bibliographic, keywords, funding, and other information. Furthermore, the applications and constraints related to the usage of SFRC in the construction sector were examined, as well as potential solutions to these constraints. It was determined that only 17 publication sources (journals/conferences) had published at least 100 articles on SFRC up to June 2022. Additionally, the mostly employed keywords by authors in SFRC research include steel fibers, fiber-reinforced concrete, concrete, steel fiber-reinforced concrete, and reinforced concrete. The assessment of authors revealed that 39 authors had published at least 30 articles. Moreover, China, the United States, and India were found to be the most active and participating countries based on publications on SFRC research. This study can assist academics in building collaborative initiatives and communicating new ideas and techniques because of the quantitative and graphical depiction of participating nations and researchers.

A scientometric-based assessment of the literature on geopolymers was conducted in this study to determine its critical aspects. Typical review studies are restricted in their capability to link disparate segments of the literature in a systematic and exact way. Knowledge mapping, co-citation, and co-occurrence are very difficult components of creative research. This study adopted an advanced strategy of data mining, data processing and analysis, visualization and presentation, and interpretation of the bibliographic data on geopolymers. The Scopus database was used to search for and retrieve the data needed to complete the study’s objectives. The relevant sources of publications, keyword assessment, productive authors based on publications and citations, top papers based on citations received, and areas actively engaged in the research of geopolymers are recognized during the data assessment. The VOSviewer (VOS: visualization of similarities) software application was employed to analyze the literature data comprising citation, bibliographic, abstract, keywords, funding, and other information from 7468 relevant publications. In addition, the applications and restrictions associated with the use of geopolymers in the construction sector are discussed, as well as possible solutions to overcome these restrictions. The scientometric analysis revealed that the leading publication source (journal) in terms of articles and citations is “Construction and building materials”; the mostly employed keywords are geopolymer, fly ash, and compressive strength; and the top active and contributing countries based on publications are China, India, and Australia. Because of the quantitative and graphical representation of participating nations and researchers, this study can help academics to create collaborative efforts and exchange creative ideas and approaches. In addition, this study concluded that the large-scale usage of geopolymer concrete is constrained by factors such as curing regime, activator solution scarcity and expense, efflorescence, and alkali–silica reaction. However, embracing the potential solutions outlined in this study might assist in boosting the building industry’s adoption of geopolymer concrete.

Yellow rust (YR) globally affects wheat crops. It may turn into an epidemic, resulting in significant yield losses if the environment is suited to YR spread. The provision of resistant wheat cultivars is a sustainable protection strategy against YR. The current study aimed to use a combination of classical analytical tools to identify potential wheat lines through screening under YR stress for utilization in YR-resistance breeding. A total of 14 parents, which included 9 lines and 5 testers, were formed into 45 cross combinations via line × tester mating format. The tested germplasm was grown in a triplicate randomized full-block design, under optimal and yellow rust (YR)-stress conditions. Data were recorded on different morphological, physiochemical, yield and component traits at appropriate crop stages. A pre-combining ability analysis revealed significant inter-genotype variations. A combining ability study identified non-additive gene action in the inheritance of most of the investigated traits. Four potential parents (PR128, AN179, KS17 and WD17) and two crosses (PR128 × WD17 and AN179 × KS17) retained higher combining ability values for yield traits under YR-stress. A cluster analysis based on the overall performance found divergent classes among the screened genotypes. The clustering of different genotypes was shifted under YR-stress, which suggests variable genotypic response. Through factor analysis, we assessed and confirmed genotypes performing consistently under YR-stress. The identified genotypes may be used for disease-resistance breeding in wheat. Based on their positive correlation with grain yield, we suggest the use of peduncle length and tillers per plant as phenotypic markers for wheat selection and breeding. The knowledge base generated through the current study will add to the ongoing research on sustainable wheat breeding program.

Biodegradable materials are appropriate for the environment and are gaining immense attention worldwide. The mechanical properties (such as elongation at break, density, and failure strain) of some natural fibers (such as Coir, Hemp, Jute, Ramie, and Sisal) are comparable with those of some synthetic fibers (such as E glass, aramid, or Kevlar). However, the toughness of coconut fibers is comparatively more than other natural fibers. Numerous studies suggest coconut fibers perform better to improve the concrete mechanical properties. However, the knowledge is dispersed, making it difficult for anyone to evaluate the compatibility of coconut fibers in concrete. This study aims to perform a scientometric review of coconut fiber applications in cementitious concrete to discover the various aspects of the literature. The typical conventional review studies are somehow limited in terms of their capacity for linking different literature elements entirely and precisely. Science mapping, co-occurrence, and co-citation are among a few primary challenging points in research at advanced levels. The highly innovative authors/researchers famous for citations, the sources having the highest number of articles, domains that are actively involved, and co-occurrences of keywords in the research on coconut-fiber-reinforced cementitious concrete are explored during the analysis. The bibliometric database with 235 published research studies, which are taken from the Scopus dataset, are analyzed using the VOSviewer application. This research will assist researchers in the development of joint ventures in addition to sharing novel approaches and ideas with the help of a statistical and graphical description of researchers and countries/regions that are contributing. In addition, the applicability of coconut fiber in concrete is explored for mechanical properties considering the literature, and this will benefit new researchers for its use in concrete.

The scientometric analysis is statistical scrutiny of books, papers, and other publications to assess the “output” of individuals/research teams, organizations, and nations, to identify national and worldwide networks, and to map the creation of new (multi-disciplinary) scientific and technological fields that would be beneficial for the new researchers in the particular field. A scientometric review of 3D printing concrete is carried out in this study to explore the different literature aspects. There are limitations in conventional and typical review studies regarding the capacity of such studies to link various elements of the literature accurately and comprehensively. Some major problematic phases in advanced level research are: co-occurrence, science mapping, and co-citation. The sources with maximum articles, the highly creative researchers/authors known for citations and publications, keywords co-occurrences, and actively involved domains in 3D printing concrete research are explored during the analysis. VOS viewer application analyses bibliometric datasets with 953 research publications were extracted from the Scopus database. The current study would benefit academics for joint venture development and sharing new strategies and ideas due to the graphical and statistical depiction of contributing regions/countries and researchers.

Interface yield stress (YS) and plastic viscosity (PV) have a significant impact on the pumpability of concrete mixes. This study is based on the application of predictive machine learning (PML) techniques to forecast the rheological properties of fresh concrete. The artificial neural network (NN) and random forest (R-F) PML approaches were introduced to anticipate the PV and YS of concrete. In comparison, the R-F model outperforms the NN model by giving the coefficient of determination (R2) values equal to 0.92 and 0.96 for PV and YS, respectively. In contrast, the model’s legitimacy was also verified by applying statistical checks and a k-fold cross validation approach. The mean absolute error, mean square error, and root mean square error values for R-F models by investigating the YS were noted as 30.36 Pa, 1141.76 Pa, and 33.79 Pa, respectively. Similarly, for the PV, these values were noted as 3.52 Pa·s, 16.48 Pa·s, and 4.06 Pa·s, respectively. However, by comparing these values with the NN’s model, they were found to be higher, which also gives confirmation of R-F’s high precision in terms of predicting the outcomes. A validation approach known as k-fold cross validation was also introduced to authenticate the precision of employed models. Moreover, the influence of the input parameters was also investigated with regard to predictions of PV and YS. The proposed study will be beneficial for the researchers and construction industries in terms of saving time, effort, and cost of a project.

Incorporating waste material, such as recycled coarse aggregate concrete (RCAC), into construction material can reduce environmental pollution. It is also well-known that the inferior properties of recycled aggregates (RAs), when incorporated into concrete, can impact its mechanical properties, and it is necessary to evaluate the optimal performance. Accordingly, artificial intelligence has been used recently to evaluate the performance of concrete compressive behaviour for different types of construction material. Therefore, supervised machine learning techniques, i.e., DT-XG Boost, DT-Gradient Boosting, SVM-Bagging, and SVM-Adaboost, are executed in the current study to predict RCAC’s compressive strength. Additionally, SHapley Additive exPlanations (SHAP) analysis shows the influence of input parameters on the compressive strength of RCAC and the interactions between them. The correlation coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) are used to assess the model’s performance. Subsequently, the k-fold cross-validation method is executed to validate the model’s performance. The R2 value of 0.98 from DT-Gradient Boosting supersedes those of the other methods, i.e., DT- XG Boost, SVM-Bagging, and SVM-Adaboost. The DT-Gradient Boosting model, with a higher R2 value and lower error (i.e., MAE, RMSE) values, had a better performance than the other ensemble techniques. The application of machine learning techniques for the prediction of concrete properties would consume fewer resources and take less time and effort for scholars in the respective engineering field. The forecasting of the proposed DT-Gradient Boosting models is in close agreement with the actual experimental results, as indicated by the assessment output showing the improved estimation of RCAC’s compressive strength.

Steel-fiber-reinforced concrete (SFRC) has been introduced as an effective alternative to conventional concrete in the construction sector. The incorporation of steel fibers into concrete provides a bridging mechanism to arrest cracks, improve the post-cracking behavior of concrete, and transfer stresses in concrete. Artificial intelligence (AI) approaches are in use nowadays to predict concrete properties to conserve time and money in the construction industry. Accordingly, this study aims to apply advanced and sophisticated machine-learning (ML) algorithms to predict SFRC compressive strength. In the current work, the applied ML approaches were gradient boosting, random forest, and XGBoost. The considered input variables were cement, fine aggregates (sand), coarse aggregates, water, silica fume, super-plasticizer, fly ash, steel fiber, fiber diameter, and fiber length. Previous studies have not addressed the effects of raw materials on compressive strength in considerable detail, leaving a research gap. The integration of a SHAP analysis with ML algorithms was also performed in this paper, addressing a current research need. A SHAP analysis is intended to provide an in-depth understanding of the SFRC mix design in terms of its strength factors via complicated, nonlinear behavior and the description of input factor contributions by assigning a weighing factor to each input component. The performances of all the algorithms were evaluated by applying statistical checks such as the determination coefficient (R2), the root mean square error (RMSE), and the mean absolute error (MAE). The random forest ML approach had a higher, i.e., 0.96, R2 value with fewer errors, producing higher precision than other models with lesser R2 values. The SFRC compressive strength could be anticipated by applying the random forest ML approach. Further, it was revealed from the SHapley Additive exPlanations (SHAP) analysis that cement content had the highest positive influence on the compressive strength of SFRC. In this way, the current study is beneficial for researchers to effectively and quickly evaluate SFRC compressive strength.

Because of the extraordinary advancements in biomedical nanotechnology over the last few decades, traditional drug delivery systems have been transformed into smart drug delivery systems that respond to stimuli. These well-defined nanoplatforms can boost therapeutic targeting efficacy while reducing the side effects/toxicities of payloads, which are crucial variables for enhancing patient compliance by responding to specific internal or external triggers. Cubosomes are lipid-based nano systems that are analogous to well-known vesicular systems, such as lipo- and niosomes. They could be used as part of a unique drug delivery system that includes hydro-, lipo-, and amphiphilic drug molecules. In this review, we critically analyze the relevant literature on cubosomesregarding theories of cubosomeself-assembly, composition, and manufacturing methods, with an emphasis on tumor-targeted drug delivery applications. Due to the bioadhesive and -compatible nature of cubosome dispersion, this review also focuses on a variety of drug delivery applications, including oral, ophthalmic and transdermal.

Motion-planning algorithms play a vital role in attaining various levels of autonomy for any ground or flying agent. Three-dimensional (3D) motion-planning is interesting, but rather complex, especially for flying agents such as autonomous unmanned aerial vehicles (UAVs), due to the increased dimensionality of space and consideration of dynamical constraints for a feasible trajectory. Usually, in 3D path search problems, it is hard to avoid extra expanded nodes due to increased dimensionality with various available search options. Therefore, this paper discusses and implements a modified heuristic-based A* formalism that uses a truncation mechanism in order to eradicate the mentioned problem. In this formalism, the complete motion planning is divided into shortest path search problem and smooth trajectory generation. The shortest path search problem is subdivided into an initial naïve guess of the path and the truncation of the extra nodes. To find a naïve shortest path, a conventional two-dimensional (2D) A* algorithm is augmented for 3D space with six-sided search. This approach led to the inclusion of extra expanded nodes and, therefore, it is not the shortest one. Hence, a truncation algorithm is developed to further process this path in order to truncate the extra expanded nodes and then to shorten the path length. This new approach significantly reduces the path length and renders only those nodes that are obstacle-free. The latter is ensured using a collision detection algorithm during the truncation process. Subsequently, the nodes of this shortened path are used to generate a dynamically feasible and optimal trajectory for the quadrotor UAV. Optimal trajectory generation requires that the plant dynamics must be differentially flat. Therefore, the corresponding proof is presented to ensure generation of the optimal trajectory. This optimal trajectory minimizes the control effort and ensures longer endurance. Moreover, quadrotor model and controllers are derived as preliminaries, which are subsequently used to track the desired trajectory generated by the trajectory planner. Finally, results of numerical simulations which ultimately validate the theoretical developments are presented.

Sustainable development involves the usage of alternative sustainable materials in order to sustain the excessive depletion of natural resources. Plant fibers, as a “green” material, are progressively gaining the attention of various researchers in the field of construction for their potential use in composites for stepping towards sustainable development. This study aims to provide a scientometric review of the summarized background of plant fibers and their applications as construction and building materials. Studies from the past two decades are summarized. Quantitative assessment of research progress is made by using connections and maps between bibliometric data that are compiled for the analysis of plant fibers using Scopus. Data refinement techniques are also used. Plant fibers are potentially used to enhance the mechanical properties of a composite. It is revealed from the literature that plant-fiber-reinforced composites have comparable properties in comparison to composites reinforced with artificial/steel fibers for civil engineering applications, such as construction materials, bridge piers, canal linings, soil reinforcement, pavements, acoustic treatment, insulation materials, etc. However, the biodegradable nature of plant fibers is still a hindrance to their application as a structural material. For this purpose, different surface and chemical treatment methods have been proposed in past studies to improve their durability. It can be surmised from the gathered data that the compressive and flexural strengths of plant-fiber-reinforced cementitious composites are increased by up to 43% and 67%, respectively, with respect to a reference composite. In the literature, alkaline treatment has been reported as an effective and economical method for treating plant fibers. Environmental degradation due to excessive consumption of natural resources and fossil fuels for the construction industry, along with the burning of waste plant fibers, can be reduced by incorporating said fibers in cementitious composites to reduce landfill pollution and, ultimately, achieve sustainable development.

Several review studies have been performed on nano-silica-modified concrete, but this study adopted a new method based on scientometric analysis for the keywords’ assessment in the current research area. A scientometric analysis can deal with vast bibliometric data using a software tool to evaluate the diverse features of the literature. Typical review studies are limited in their ability to comprehensively and accurately link divergent areas of the literature. Based on the analysis of keywords, this study highlighted and described the most significant segments in the research of nano-silica-modified concrete. The challenges associated with using nano-silica were identified, and future research is directed. Moreover, prediction models were developed using data from the literature for the strength estimation of nano-silica-modified concrete. It was noted that the application of nano-silica in cement-based composites is beneficial when used up to an optimal dosage of 2–3% due to high pozzolanic reactivity and a filler effect, whereas a higher dosage of nano-silica has a detrimental influence due to the increased porosity and microcracking caused by the agglomeration of nano-silica particles. The mechanical strength might enhance by 20–25% when NS is incorporated in the optimal amount. The prediction models developed for predicting the strength of nano-silica-modified concrete exhibited good agreement with experimental data due to lower error values. This type of analysis may be used to estimate the essential properties of a material, therefore saving time and money on experimental tests. It is recommended to investigate cost-effective methods for the dispersion of nano-silica in higher concentrations in cement mixes; further in-depth studies are required to develop more accurate prediction models to predict nano-silica-modified concrete properties.

Recently, the high demand for marble stones has progressed in the construction industry, ultimately resulting in waste marble production. Thus, environmental degradation is unavoidable because of waste generated from quarry drilling, cutting, and blasting methods. Marble waste is produced in an enormous amount in the form of odd blocks and unwanted rock fragments. Absence of a systematic way to dispose of these marble waste massive mounds results in environmental pollution and landfills. To reduce this risk, an effort has been made for the incorporation of waste marble powder into concrete for sustainable construction. Different proportions of marble powder are considered as a partial substitute in concrete. A total of 40 mixes are prepared. The effectiveness of marble in concrete is assessed by comparing the compressive strength with the plain mix. Supervised machine learning algorithms, bagging (Bg), random forest (RF), AdaBoost (AdB), and decision tree (DT) are used in this study to forecast the compressive strength of waste marble powder concrete. The models’ performance is evaluated using correlation coefficient (R2), root mean square error, and mean absolute error and mean square error. The achieved performance is then validated by using the k-fold cross-validation technique. The RF model, having an R2 value of 0.97, has more accurate prediction results than Bg, AdB, and DT models. The higher R2 values and lesser error (RMSE, MAE, and MSE) values are the indicators for better performance of RF model among all individual and ensemble models. The implementation of machine learning techniques for predicting the mechanical properties of concrete would be a practical addition to the civil engineering domain by saving effort, resources, and time.

Researchers and engineers are presently focusing on efficient waste material utilization in the construction sector to reduce waste. Waste marble dust has been added to concrete to minimize pollution and landfills problems. Therefore, marble dust was utilized in concrete, and its prediction was made via an artificial intelligence approach to give an easier way to scholars for sustainable construction. Various blends of concrete having 40 mixes were made as partial substitutes for waste marble dust. The ultrasonic pulse velocity of waste marble dust concrete (WMDC) was compared to a control mix without marble dust. Additionally, this research used standalone (multiple-layer perceptron neural network) and supervised machine learning methods (Bagging, AdaBoost, and Random Forest) to predict the ultrasonic pulse velocity of waste marble dust concrete. The models’ performances were assessed using R2, RMSE, and MAE. Then, the models’ performances were validated using k-fold cross-validation. Furthermore, the effect of raw ingredients and their interactions using SHAP analysis was evaluated. The Random Forest model, with an R2 of 0.98, outperforms the MLPNN, Bagging, and AdaBoost models. Compared to all the other models (individual and ensemble), the Random Forest model with greater R2 and lower error (RMSE, MAE) has a superior performance. SHAP analysis revealed that marble dust content has a positive and direct influence on and relationship to the ultrasonic pulse velocity of concrete. Using machine learning to forecast concrete properties saves time, resources, and effort for scholars in the engineering sector.

Cracking is one of the main problems in concrete structures and is affected by various parameters. The step-by-step laboratory method, which includes casting specimens, curing for a certain period, and testing, remains a source of worry in terms of cost and time. Novel machine learning methods for anticipating the behavior of raw materials on the ultimate output of concrete are being introduced to address the difficulties outlined above such as the excessive consumption of time and money. This work estimates the splitting-tensile strength of concrete containing recycled coarse aggregate (RCA) using artificial intelligence methods considering nine input parameters and 154 mixes. One individual machine learning algorithm (support vector machine) and three ensembled machine learning algorithms (AdaBoost, Bagging, and random forest) are considered. Additionally, a post hoc model-agnostic method named SHapley Additive exPlanations (SHAP) was performed to study the influence of raw ingredients on the splitting-tensile strength. The model’s performance was assessed using the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Then, the model’s performance was validated using k-fold cross-validation. The random forest model, with an R2 of 0.96, outperformed the AdaBoost models. The random forest models with greater R2 and lower error (RMSE = 0.49) had superior performance. It was revealed from the SHAP analysis that the cement content had the highest positive influence on the splitting-tensile strength of the recycled aggregate concrete and the primary contact of cement is with water. The feature interaction plot shows that high water content has a negative impact on the recycled aggregate concrete (RAC) splitting-tensile strength, but the increased cement content had a beneficial effect.

Geopolymers might be the superlative alternative to conventional cement because it is produced from aluminosilicate-rich waste sources to eliminate the issues associated with its manufacture and use. Geopolymer composites (GPCs) are gaining popularity, and their research is expanding. However, casting, curing, and testing specimens requires significant effort, price, and time. For research to be efficient, it is essential to apply novel approaches to the said objective. In this study, compressive strength (CS) of GPCs was anticipated using machine learning (ML) approaches, i.e., one single method (support vector machine (SVM)) and two ensembled algorithms (gradient boosting (GB) and extreme gradient boosting (XGB)). All models’ validity and comparability were tested using the coefficient of determination (R2), statistical tests, and k-fold analysis. In addition, a model-independent post hoc approach known as SHapley Additive exPlanations (SHAP) was employed to investigate the impact of input factors on the CS of GPCs. In predicting the CS of GPCs, it was observed that ensembled ML strategies performed better than the single ML technique. The R2 for the SVM, GB, and XGB models were 0.98, 0.97, and 0.93, respectively. The lowered error values of the models, including mean absolute and root mean square errors, further verified the enhanced precision of the ensembled ML approaches. The SHAP analysis revealed a stronger positive correlation between GGBS and GPC′s CS. The effects of NaOH molarity, NaOH, and Na2SiO3 were also observed as more positive. Fly ash and gravel size: 10/20 mm have both beneficial and negative impacts on the GPC′s CS. Raising the concentration of these ingredients enhances the CS, whereas increasing the concentration of GPC reduces it. Gravel size: 4/10 mm has less favorable and more negative effects. ML techniques will benefit the construction sector by offering rapid and cost-efficient solutions for assessing material characteristics.

Sustainable concrete is gaining in popularity as a result of research into waste materials, such as recycled aggregate (RA). This strategy not only protects the environment, but also meets the demand for concrete materials. Using advanced artificial intelligence (AI) approaches, this study anticipates the split tensile strength (STS) of concrete samples incorporating RA. Three machine-learning techniques, artificial neural network (ANN), decision tree (DT), and random forest (RF), were examined for the specified database. The results suggest that the RF model shows high precision compared with the DT and ANN models at predicting the STS of RA-based concrete. The high value of the coefficient of determination and the low error values of the mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) provided significant evidence for the accuracy and precision of the RF model. Furthermore, statistical tests and the k-fold cross-validation technique were used to validate the models. The importance of the input parameters and their contribution levels was also investigated using sensitivity analysis and SHAP analysis.

The low tensile strain capacity and brittle nature of high-strength concrete (HSC) can be improved by incorporating steel fibers into it. Steel fibers’ addition in HSC results in bridging behavior which improves its post-cracking behavior, provides cracks arresting and stresses transfer in concrete. Using machine learning (ML) techniques, concrete properties prediction is an effective solution to conserve construction time and cost. Therefore, sophisticated ML approaches are applied in this study to predict the compressive strength of steel fiber reinforced HSC (SFRHSC). To fulfil this purpose, a standalone ML model called Multiple-Layer Perceptron Neural Network (MLPNN) and ensembled ML algorithms named Bagging and Adaptive Boosting (AdaBoost) were employed in this study. The considered parameters were cement content, fly ash content, slag content, silica fume content, nano-silica content, limestone powder content, sand content, coarse aggregate content, maximum aggregate size, water content, super-plasticizer content, steel fiber content, steel fiber diameter, steel fiber length, and curing time. The application of statistical checks, i.e., root mean square error (RMSE), determination coefficient (R2), and mean absolute error (MAE), was also performed for the assessment of algorithms’ performance. The study demonstrated the suitability of the Bagging technique in the prediction of SFRHSC compressive strength. Compared to other models, the Bagging approach was more accurate as it produced higher, i.e., 0.94, R2, and lower error values. It was revealed from the SHAP analysis that curing time and super-plasticizer content have the most significant influence on the compressive strength of SFRHSC. The outcomes of this study will be beneficial for researchers in civil engineering for the timely and effective evaluation of SFRHSC compressive strength.