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This study evaluated the impact of night frost incidents on the biochemical, physiological, and reproductive functions of the olive varieties Manzanillo, Coratina, Koroneiki, and Picual. Certain cultivars were more suited to moderate cold night stress than others, based on the changes in the performance of the stressed plants, including vegetative growth, tree yield, fruit physical characteristics, and fruit chemical characteristics. Compared to other tested cultivars, the biochemical responses of the plants in terms of photosynthetic pigments, relative water content (RWC), total phenolic compounds, total flavonoid, and antioxidant enzyme accumulation demonstrated that some cultivars could withstand the applied stress. The conclusion that some cultivars responded differently to cold stress than others was supported by the plant phenology. This research could be a game-changer for farmers. By understanding how olive trees adapt to cold snaps, a common stressor in open fields, they can make informed decisions about breeding and choosing the best cultivars, ultimately leading to more resilient crops. The results showed that all tested olive tree cultivars differ significantly regarding cold stress conditions. Coratina and Koroneiki were the most resistant tested cultivars in terms of biochemical, physiological, and reproductive functions, followed in ascending order by Manzanello and Picual.

Abstract The growing demand for energy, driven by population growth and technological advancements, has made ensuring a sufficient and sustainable energy supply a critical challenge for humanity. Renewable energy sources, such as biomass, solar, wind, and hydro, are inexhaustible and environmentally friendly, offering a viable solution to both the energy crisis and the fight against global warming. However, selecting the optimal renewable energy source remains a complex decision-making problem due to the varying characteristics and impacts of these sources. Motivated by the need for more accurate and nuanced decision-making tools in this domain, this paper introduces a novel multicriteria group decision-making (MCGDM) approach based on $$\:(p,q,r)-$$ spherical fuzzy Frank aggregation operators. By integrating Frank t-norm with $$\:(p,q,r)-$$ spherical fuzzy sets, we develop aggregation operators (AOs) that effectively manage membership, neutral, and non-membership degrees through parameters $$\:p$$ , $$\:q$$ , and $$\:r$$ . These AOs provide a more refined framework for decision-making, leading to improved outcomes. We apply this approach to evaluate and identify the superior and optimal renewable energy source using artificial data, demonstrating the advantages of the proposed operators compared to existing methods. This work contributes to the field by offering a robust tool for addressing the energy crisis and advancing sustainable energy solutions.

Heat stress, low mango yields and inconsistent fruit quality are main challenges for growers. Recently, licorice-root extract (LRE) has been utilized to enhance vegetative growth, yield, and tolerance to abiotic stresses in fruit trees. Potassium sorbate (PS) also plays a significant role in various physiological and biochemical processes that are essential for mango growth, quality and abiotic stress tolerance. This work aimed to elucidate the effects of foliar sprays containing LRE and PS on the growth, yield, fruit quality, total chlorophyll content, and antioxidant enzymes of ‘Osteen’ mango trees. The mango trees were sprayed with LRE at 0, 2, 4 and 6 g/L and PS 0, 1, 2, and 3 mM. In mid-May, the mango trees were sprayed with a foliar solution, followed by monthly applications until 1 month before harvest. The results showed that trees with the highest concentration (6 g/L) of LRE exhibited the maximum leaf area, followed by those treated with the highest concentration (3 mM) of PS. Application of LRE and PS to Osteen mango trees significantly enhanced fruit weight, number of fruits per tree, yield (kg/tree), yield increasing%, and reduced number of sun-burned fruits compared to the control. LRE and PS foliar sprays to Osteen mango trees significantly enhanced fruit total soluble solids ˚Brix, TSS/acid ratio, and vitamin C content compared to the control. Meanwhile, total acidity percentage in ‘Osteen’ mango fruits significantly decreased after both LRE and PS foliar sprays. ‘Osteen’ mango trees showed a significant increase in leaf area, total chlorophyll content, total pigments, and leaf carotenoids. Our results suggest that foliar sprays containing LRE and PS significantly improved growth parameters, yield, fruit quality, antioxidant content, and total pigment concentration in ‘Osteen’ mango trees. Moreover, the most effective treatments were 3 mM PS and 6 g/L LRE. LRE and PS foliar spray caused a significant increase in yield percentage by 305.77%, and 232.44%, in the first season, and 242.55%, 232.44% in the second season, respectively.

Methods based on probability make use of data sets to recognize patterns, evaluate potential outcomes, and predict various scenarios. These methods are often empirically utilized to analyze data sets, with a particular focus on data sets related to energy. The rapid urbanization in China has led to an increase in the share of residential energy consumption in the overall energy consumption. Given the importance of probability-based methods in the energy sector, this paper introduces the weighted very flexible Weibull (WVF-Weibull) distribution as a novel probability distribution. We provide the point estimators and simulation studies carried out under various parameter settings. Finally, the WVF-Weibull distribution is implemented to examine the energy consumption data collected from the urban regions of China. Through the examination of six statistical tools, it has been established that the WVF-Weibull distribution is the optimal model among various probability distributions for energy consumption data.

The importance of statistical distributions in accurately representing real-world scenarios and aiding in educated decision-making is well recognized. Nonetheless, it is also true that the limitations of these distributions can hinder optimal fitting in certain situations. This awareness has led researchers to seek out improved and more optimal probability distributions. Based on factual motivation, this paper introduces a new probability distribution called the weighted sine generalized inverse Weibull (WSGI-Weibull) distribution. This model emerges from the amalgamation of the generalized inverse Weibull distribution and a sine-inspired probabilistic framework. Certain statistical properties, particularly those based on quantiles, of the newly introduced WSGI-Weibull distribution have been derived. An established estimation method is applied to calculate the point estimators of the WSGI-Weibull distribution, and subsequently, a simulation study is conducted. To highlight the benefits of the WSGI-Weibull distribution, two data sets sourced from the reliability and hydrology sectors are analyzed. The empirical fitting of the WSGI-Weibull distribution is evaluated against specific adversarial distributions, utilizing the two data sets as a basis for comparison. Utilizing specific evaluation tools, it has been noted that the WSGI-Weibull distribution delivers the best and most optimal fit for the reliability and hydrological data sets.

In the examination of real-life situations, the application of probability distributions is often crucial for statistical analysis of the real-life scenarios. Many models based on probability have been utilized in disciplines such as music education, music engineering, and other music-related areas. Hence, acknowledging the significance of probability-based approaches, this article introduces an innovative probability model known as a new generalized Rayleigh distribution. The suggested model is established on merging the generalized Rayleigh distribution with a prominent weighted probabilistic method. The mathematical properties, specifically the quartile-based features of the novel generalized Rayleigh distribution, are investigated. Moreover, we outline the derivation of the point estimators for the unknown parameters of the new model. A thorough simulation study is also conducted to examine the performances of these point estimators. Within the music industry, specifically in music education and music engineering, we examine the practical implications of the new generalized Rayleigh distribution. We observe that it offers a superior fit when contrasted with other distributions. Our findings demonstrate that the new distribution is a inclusion to the group of probability distributions that can be applied in music engineering and other closely connected engineering fields for the statistical analysis of real-life events.

This study investigates the utilization of artificial intelligence (AI) for feature engineering in the education sector, highlighting its potential to enhance individualized learning and improve academic outcomes. The correlation analysis, performed using a correlation matrix of the feature set, indicated that specific pairings of characteristics exhibit a strong association, resulting in the ineffectiveness of conventional models. In order to tackle this issue, we utilized three sophisticated machine learning methodologies: Adaptive Lasso (ALasso), Artificial Neural Networks (ANN), and Support Vector Regression (SVR). The ALasso model discovered several influential characteristics, namely Gender (X5), Education (X1), Hours of Work (X4), and Marital Status (X6), that significantly affect salaries. Subsequently, a comparative evaluation of these methods was conducted using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results demonstrated that SVR outperformed the other techniques, with the most optimal RMSE of 0.595 and MAE of 0.423. These findings emphasize the significance of using data-driven strategies in policymaking and propose further investigation into the use of AI methods in various educational contexts to improve the identification of features and the performance of models.

Hany G Attia,1 Mai Abdelhalim Hamouda,2 Saeed Alasmari,3 Dalia F El-Telbany,4 Zaenah Zuhair Alamri,5 Safa H Qahl,5 Mohammad Y Alfaifi,6 Majid Mohammad Al-Sawahli,7 Sara Abd El Wahed8 1Department of Pharmacognosy, College of Pharmacy, Najran University, Najran, Saudi Arabia; 2Department of Oral Pathology, Faculty of Dentistry, King Salman International University, El-Tur, Egypt; 3Department of Biology, College of Science and Arts, Najran University, Najran 1988, Saudi Arabia; 4Department of Pharmaceutics, Faculty of Pharmacy, Modern University for Technology and Information (MTI), Cairo, Egypt; 5Department of Biological Science, College of Science, University of Jeddah, Jeddah, Saudi Arabia; 6Department of Biology, College of Science, King Khalid University, Abha, Saudi Arabia; 7Department of Pharmaceutics, College of Pharmacy, The Islamic University, Najaf, Iraq; 8Department of Oral Pathology, Faculty of Dentistry, The British University in Egypt, Cairo, EgyptCorrespondence: Majid Mohammad Al-Sawahli, Department of Pharmaceutics, College of Pharmacy, The Islamic University, Najaf, Iraq, Tel +201119925299, Email majidalsawahli@gmail.com Hany G Attia, Department of Pharmacognosy, College of Pharmacy, Najran University, Najran, Saudi Arabia, Tel +966555857605, Email hgattia@nu.edu.saPurpose: : Polyvinyl alcohol-capped silver nanostructures (cAgNSs) were investigated in order to enhance the cytotoxicity, pro-apoptotic, and oxidant patterns of in human laryngeal carcinoma Hep-2 cells by employing a 50 mT electromagnetic field (LEMF) for 30 min.Methods: Wet chemical reduction was used to synthesize the cAgNSs, and after they had been capped with polyvinyl alcohol, they were specifically examined for particle size analysis and structural morphology. To visualize how the silver may attach to the protein targets, a molecular docking study was conducted. Estimation of cytotoxicity, cell cycle progression supported by mRNA expression of three apoptotic-promoting genes and one apoptotic-resisting.Results: Particle size analysis results were a mean particle size of 157.3± 0.5 nm, zeta potential value of − 29.6 mV± 1.5 mV, and polydispersity index of 0.31± 0.05. Significantly reduction of IC50 against Hep-2 cells by around 6-fold was concluded. Also, we obtained suppression of the proliferation of Hep-2 cells, especially in the G0/G1 and S phases. Significant enhanced mRNA expression revealed enhanced induced CASP3, p53, and Beclin-1 mediated pro-apoptosis and induced NF-κB mediated autophagy in Hep-2 cells. Augmented levels of GR, ROS and MDA as oxidative stress biomarkers were also obtained. HE staining of Hep-2 cells exposed to cAgNSs and LEMF confirmed the enhanced apoptotic potential comparatively.Conclusion: By conclusion, the developed nano-sized structures with the aid of extremely-low frequency electromagnetic field were successful to fortify the anti-cancer profile of cAgNSs in Hep-2 cells. Keywords: electromagnetic field, silver nanoparticles, apoptosis, laryngeal carcinoma

Abstract Ovarian cancer is the second most common and lethal gynecologic malignancy. Among natural product-based therapy, the honeybee products, particularly propolis, serve a valuable source contributing directly to human nutrition and health. In the present study, we determined the chemical composition of different types of propolis originating from Egypt, Germany and France using liquid chromatography-tandem mass spectrometry. The compounds identified belong to different metabolite classes, including flavonoids, cinnamic acid, chalcones, terpenoids, phenolic lipids, stilbenes, phenolic compounds, carbohydrates, vitamins, coumarins, polyprenylated benzophenone, benzoic acids, fatty acid methyl ester, and coumaric acid, and their derivatives. The most active extract is from France then Egypt and Germany. Afterwards, we treated the human ovarian cancer cells, OVCAR4, with different concentrations (1–400 μg/mL) of variable propolis types supplemented or not with vitamin D (0.0015–0.15 μg/mL) in order to evaluate the efficacy and the cytotoxic activities of our local P as compared to other types collected from different geographic regions. Importantly, the combinatorial treatment of OVCAR4 cancer cells with propolis and vitamin D in the same concentration ranges resulted in enhanced cell viability inhibition. Furthermore, such co-supplementation with vitamin D inhibits predominately the proliferative activity of cell population with the French propolis type as manifested by Ki67 expression, while it reduces considerably its expression, particularly with the German type, followed by the Egyptian one. Nowadays, scientists are interested by natural products which have risen to the forefront of drug discovery. Chemically characterized propolis showing cell viability inhibition and antiproliferative potential seems a valuable extract for further consideration as anti-carcinogenic agent. Graphical abstract

Abstract Background Reading problems are the most prevalent type of specific learning disability. Reading problems usually result from phonological deficits; however, it is unclear how much other linguistic abilities are impacted as well. Objective To study different language parameters and cognitive skills in a group of Egyptian children with reading problems to better understand the difficulties that need to be considered in rehabilitation programs for those children. Methods A case–control study was conducted on 30 children with reading problems having IQs above 85 and 30 normal control children matched for age and gender (their ages ranged from 7 to 9 years and 11 months). They underwent testing for dyslexia by the Arabic dyslexia assessment test, language evaluation using the Receptive Expressive Arabic Language Scale (REAL scale), and the Stanford-Binet intelligence scale, fifth edition. Results There were highly significant correlations between the severity of dyslexia and total language scores, most of REAL scale subtests. Also, highly significant correlations were detected between the severity of dyslexia and the total IQ, verbal IQ, and verbal working memory subtests. The linear regression model revealed a highly significant association between dyslexia and expressive language scaled scores, total language scaled scores, and a significant association with expressive vocabulary and understanding oral instructions subtests of REAL scale. Additionally, a highly significant association has been detected between dyslexia and the verbal working memory subtest of the Stanford-Binet test. Correlations between total language scores and intellectual abilities (total IQ, verbal IQ, and non-verbal IQ) revealed highly significant positive results. Conclusion Deficits in both receptive and expressive language were observed in children with reading problems. Expressive vocabulary, listening comprehension, and verbal working memory subtests are the most affected language and cognitive skills.

Doubly resolving sets (DRSs) provide a promising approach for source detection. They consist of minimal subsets of nodes with the smallest cardinality, referred to as the double metric dimension (DMD), that can uniquely identify the location of any other node within the network. Utilizing DRSs can improve the accuracy and efficiency of the identification of the origin of a diffusion process. This ability is crucial for early intervention and control in scenarios such as epidemic outbreaks, misinformation spreading in social media, and fault detection in communication networks. In this study, we computed the DMD of flower snarks $ J_{m} $ and quasi-flower snarks $ G_{m} $ by describing their minimal doubly resolving sets (MDRSs). We deduce that the DMD for the flower snarks $ J_{m} $ is finite and depends on the network's order, and the DMD for the quasi-flower snarks $ G_{m} $ is finite and independent of the network's order. Furthermore, our findings offer valuable insights into the structural features of complex networks. This knowledge can offer direction for future studies in network theory and its practical implementations.

Abstract Cancer, a lethal ailment, possesses a multitude of therapeutic alternatives to combat its presence, metal complexes have emerged as significant classes of medicinal compounds, exhibiting considerable biological efficacy, especially as anticancer agents. The utilization of cis-platin in the treatment of various cancer types, including breast cancer, has served as inspiration to devise novel nanostructured metal complexes for breast cancer therapy. Notably, homo- and hetero-octahedral bimetallic complexes of an innovative multifunctional ether ligand (comprising Mn(II), Ni(II), Cu(II), Zn(II), Hg(II), and Ag(I) ions) have been synthesized. To ascertain their structural characteristics, elemental and spectral analyses, encompassing IR, UV–Vis, 1H-NMR, mass and electron spin resonance (ESR) spectra, magnetic moments, molar conductance, thermal analysis, and electron microscopy, were employed. The molar conductance of these complexes in DMF demonstrated a non-electrolytic nature. Nanostructured forms of the complexes were identified through electron microscopic data. At ambient temperature, the ESR spectra of the solid complexes exhibited anisotropic and isotropic variants, indicative of covalent bonding. The ligand and several of its metal complexes were subjected to cytotoxicity testing against breast cancer protein 3S7S and liver cancer protein 4OO6, with the Ag(I) complex (7) evincing the most potent effect, followed by the Cu(II) with ligand (complex (2)), Cis-platin, the ligand itself, and the Cu(II)/Zn(II) complex (8). Molecular docking data unveiled the inhibitory order of several complexes.

Abstract Intuitionistic fuzzy sets (IFSs) represent a significant advancement in classical fuzzy set (FS) theory. This study advances IFS theory to generalized intuitionistic fuzzy sets (GIFSBs) and introduces novel operators GIFWAA, GIFWGA, GIFOWAA, and GIFOWGA, tailored for GIFSBs. The primary aim is to enhance decision-making capabilities by introducing aggregation operators within the GIFSB framework that align with preferences for optimal outcomes. The article introduces new operators for GIFSBs characterized by attributes like idempotency, boundedness, monotonicity and commutativity, resulting in aggregated values aligned with GIFNs. A comprehensive analysis of the relationships among these operations is conducted, offering a thorough understanding of their applicability. These operators are practically demonstrated in a multiple-criteria decision-making process for evaluating startup success in the Tech Industry.

Abstract Integration between the analyses of both fieldwork and remote sensing dataset were employed to assess the structural evolution of molasse basins and explore gold mineral deposits in the El Qash area, Central Eastern Desert of Egypt. Sandstone, mudstones, wacke, and pebbly conglomerates make up most of the sediments in the molasse basins. The younger basins (595–575 Ma) are small, less weathered, and mostly Dokhan detritus-filled basins compared to the larger older basins (> 625–605 Ma), which have more weathered sources mainly of arc metavolcanics. The molasse basins appear to have originated in a north–south regional extensional setting, characterized by a prolonged period of left-lateral transtension combined with the formation of NNE-trending synformal folds during an ENE–WSW compression phase. Notably, en-echelon arrays of normal faults, oriented perpendicular to the boundary of the strike-slip shear zone, are a recurring feature in basins formed through transtension. Furthermore, oblique NE-SW compression played a role in generating SW-dipping thrust faults and regional folding with NW–SE orientation, along with the reactivation of transtensional normal faults in a reverse manner. Multispectral satellite imagery data and radar data are used to map the relevant lithological units, structures, and hydrothermal alteration zones. This study concludes that the regions where gold deposits may be explored in connection with prospecting alteration minerals include the area around the ophiolitic assemblages, and the granitic intrusions, in addition to the felsite and the molasse sediments contacts.

Nanofluids consisting of tetra nanoparticles are crucial in bio medical sciences due to their improved thermal transport characteristics. The advanced tailored properties of tera nanoparticles make them useful in several medical interventions, such as hyperthermia treatment, where the targeted tissue can be heated more efficiently, leading to better treatment outcomes. The current study investigates the heat transfer enhancement in a hemodynamic system using tetra nanoparticles. The physical configuration of the blood flow is assumed with in a permeable cylindrical shape stenosed artery. The model incorporates the Carreau model with inclusion of diverse factors such as, exponential space-based heat source, viscous dissipation, infinite shear rate and permeability of surface. Additionally, impact of chemical reaction (autocatalysis) and magnetohydrodynamic (MHD) consequences is also integrated into the system. The framed partial differential equations (PDEs) generated by physical problem are converted into new dimensionless form of an ordinary differential system (ODEs). Bvp4c MATLAB procedure is fetched for numerical investigation. It is observed that, velocity profile of the fluid is reduced due to intensification in inclined magnetic effect, whereas autocatalysis effect promotes the concentration of nanoparticles in blood flow mixture, which increases the temperature field of fluid. Furthermore, augmentation in the values of Wassenberg number increased the elasticity in blood which enables it to deform and stretch more readily in reaction to alterations in flow conditions and hence reduction is seen in overall blood flow rate. The results revealed the significance of these integrated factors for accurate modelling of blood flow passing through a stenosed artery, which is crucial in medical interventions.

A variety of highly potent derivatives of 2-pyrazoline-5-one, including chromene-3-carbohydrazide 2, 2-thioxothiazole-5-carbohydrazide 3, 2-oxopyridine-3-carbonitriles (4–6), 3-aryl-2-cyanoacrylohydrazide (7, 8), and 3-amino-5-arylpyrazole-4-carbohydrazide (9, 10), were synthesized as pesticide agents using acetohydrazide 1, a versatile and easily obtainable compound. The compounds structures were entirely ascertained utilizing different spectroscopic methods, including Fourier-transform infrared (FTIR), nuclear magnetic resonance spectroscopy, and analysis of elements. In the laboratory, we assessed the impact of nine 2-pyrazoline-5-one derivatives on the fourth larval stage of the cotton leaf worm (Spodoptera littorals), Monacha obstructa, and Tetranychus urticae adults. We used abamectin as the reference compound. The LC50 values of compound 3 for S. littorals, M. obstructa, and T. urticae are 0.90, 0.70, and 0.52 mg a.i./L, respectively. The LC50 values for the same compound for S. littorals, M. obstructa, and T. urticae are 0.80, 0.60, and 0.45 mg a.i./L, respectively. Therefore, it is a remarkably potent compound. Compound 6 exhibited lower levels of danger, as indicated by LC50 values of 1.94, 1.90, and 1.83 mg a.i./L against the three tested pests. Ten days after treatment, all three pests were moderately toxic to the remaining 2-pyrazolin-5-one derivatives, 1, 2, 4, 5, and 7–10. Conversely, the mortality rate of the tested compounds increased when the treated individuals were exposed to high concentrations. These 2-pyrazolin-5-one derivatives are suggested as suitable alternatives and foundational structures for developing novel insecticides.

The focus of this article lies on the notion of the edge version of doubly resolving sets (EVDRSs) in circulant networks. EVDRSs refer to unique edge subsets that are necessary for identifying individual edges in a network and distinguishing them based on their edge distances to the elements of the EVDRS. The main objectives were to define the minimal size of EVDRSs for circulant networks $ C_n(1, 2) $ and to investigate their basic properties. The systematic research helped to achieve a new understanding of the existence, construction, and characterization of EVDRSs in circulant networks $ C_n(1, 2) $. It is established that the EVDRSs in the circulant network $ C_n(1, 2) $ are finite and are bounded by the order of the network. Among the numerous implications of these findings are those that refer to the design and optimization of distributed sensor networks, improving communication and network protocols, as well as tracking the spread of infectious diseases and epidemics over social networks. The application of the identified methodology helps improve the process of network optimization which contributes to the development of more effective and robust circulant-based structures.

Abstract The concept of interval-valued intuitionistic fuzzy sets is intellectually stimulating and holds significant utility in the representation and analysis of real-world problems. The development of similarity measures within the class of interval-valued intuitionistic fuzzy sets possesses significant importance across various academic disciplines, particularly in the fields of decision-making and pattern recognition. The utilization of similarity measures is of utmost importance in the decision-making process when implementing interval-valued intuitionistic fuzzy sets. This is due to its inherent capability to quantitatively assess the level of resemblance or similarity between two interval-valued intuitionistic fuzzy sets. In this article, the drawbacks of the existing similarity measures in the context of an interval-valued intuitionistic fuzzy environment are addressed, and a novel similarity measure is presented. Many fundamental properties of this new interval-valued intuitionistic fuzzy similarity measure are also established, and the effectiveness of this similarity measure is illustrated by presenting a useful example. Moreover, a comparison is given to demonstrate the validity of the newly proposed similarity measure within the existing knowledge of similarity measures in the interval-valued intuitionistic fuzzy environment. In addition, an algorithm is designed to solve multi-criteria decision making problems by means of the proposed measure in the interval-valued intuitionistic fuzzy setting. Furthermore, this newly defined similarity measure is successfully applied to select an optimal renewable energy source to reduce energy crises. Finally, we conduct a comparative study to showcase the authenticity of the recently defined technique within the existing knowledge of similarity measures in the interval-valued intuitionistic fuzzy environment.

Abstract While gilts and sows are regularly vaccinated against the porcine parvovirus (PPV), little is known on the presence of antibodies in vaccinated sows nor the decline of maternally derived antibodies (MDA) in their offspring. On twelve farms serum samples were taken from 180 gilts and sows vaccinated at least twice with one of three different commercial PPV vaccines. On nine farms, additional 270 serum samples were collected from growing pigs of three different age categories. All 450 samples were examined for PPV antibodies (Abs) by ELISA and haemagglutination inhibition (HI) assay. In total, 65% of all gilts vaccinated twice with either vaccine 1 or vaccine 3 were seronegative by HI assay. In each farm, there were at least three animals with high Ab titres (≥ 1:1280) indicating the presence of PPV in all twelve study farms. However, PPV DNA could not be detected in collected faecal samples. While low to moderately high Ab titres (1:10–1:640) were measured in 98% of twelve-weeks-old pigs, ELISA was only positive in 30% of the same pigs. Though, the statement on the duration of MDA may depend on the applied test, we could confirm an exponential decay of MDA. In addition, we could demonstrate that applied serological tools are insufficient for the confirmation of successful vaccination.

Objectives: Dengue emerged as a significant health threat in endemic regions in recent years. However, inconsistent diagnostic accuracy in sequential dengue infections necessitate improved testing methods to ensure effective management of dengue cases. Here, we evaluated a portable, rapid, and sensitive molecular assay—reverse transcriptase recombinase polymerase amplification assay (RT-RAA)—utilizing a mobile suitcase laboratory to detect infections in suspected dengue cases in Bangladesh. Methods: A total of 364 suspected patients with dengue were enrolled in the study. Dengue cases were confirmed by a positive result from any of the four tests: non-structural protein 1 (NS1) rapid diagnostic test (RDT), immunoglobulin (Ig) M RDT, quantitative reverse transcriptive-polymerase chain reaction (RT-PCR), and RT-RAA assay. IgG RDT was performed to differentiate between primary and secondary dengue infections. Results: Of 364 suspected cases, 320 were confirmed dengue cases, with 55.94% classified as primary and 44.06% as secondary infections. Laboratory results showed comparable positivity rates between RT-RAA (78.8%) and NS1 RDT (77.1%) in primary dengue, followed by quantitative RT-PCR (57.5%) and IgM RDT (12.8%). RT-RAA demonstrated superior positivity rates in secondary dengue (76.6%), surpassing RT-PCR (60.3%), NS1 RDT (27%), and IgM RDT (24.8%). Combining RT-RAA with NS1 RDT detected infections in 89.95% primary and 81.56% secondary dengue. Conclusions: The findings suggest that complementing RT-RAA with NS1 RDT could significantly improve dengue detection rate, particularly, for secondary infections.

COVID-19 is a novel virus that has spread globally, and governments around the world often implement different strategies to prevent its spread. In the literature, several COVID-19 models have been studied with the bilinear incident rate. In this study, the S1V1E1I1Q1R1 (susceptible-vaccinated-exposed-infective-quarantined-recovered) COVID-19 model is proposed. To investigate how the disease spreads in the population, an algorithm is used. The efficacy of the algorithm is used to calculate the disease-free equilibrium point. A next generation matrix technique is used to find R0. Furthermore, to check the effect of parameters on the basic reproduction number (R0), the sensitivity analysis is conducted. Numerical simulation displays that the disease spreads in the population by increasing the value of the contact rate β while the disease spread in the population reduces by increasing the value of the vaccination rate θ, quarantine rate ϕ, and recovery rate γ. Different optimal control strategies, such as social distance and quick isolation, are also implemented.

To address the shortcomings of the cubic intuitionistic fuzzy sets (CIFSs) for the entitlement of multi-argument approximate function, the cubic intuitionistic fuzzy hypersoft set (Ω-set) is an emerging study area. This type of setting associates the sub-parametric tuples with the collection of CIFSs. Categorizing the evaluation of parameters into their corresponding sub-parametric values based on non-overlapping sets has significance in decision making and optimization related situations. Some operations of Ω-set are proposed in this study, along with certain practical features. We provide the complement, P-order, and R-order subsets, P-union (∪P), R-union (∪R), P-intersection (∩P) and R-intersection (∩R) of Ω-sets. The internal cubic intuitionistic fuzzy hypersoft set (ΩI-set) and the external cubic intuitionistic fuzzy hypersoft set (ΩE-set) are also proposed in this paper, which will aid researchers in applying this new theory to other areas of study. We show a few examples in this context and look into some more aspects of ∪P, ∪R, ∩P and ∩R of ΩI-sets and ΩE-sets. Arguments for a few significant theorems about ΩI-sets and ΩE-sets are also presented. Lastly, an algorithm is presented that assists decision-makers in evaluating appropriate solar panels to establish solar plants. The proposed algorithm uses the idea of ∪P and ∪R for two Ω-sets constructed based on expert opinions of decision makers.

This study explores the influence of thermal radiation and heat source on magnetized flow micropolar nanofluid through a Stretchable Sheet in the presence of mobile microbes. The presence of microorganisms adds a biological dimension to the problem, affecting fluid dynamics and heat transfer, which are central to this investigation. To motivate the problem, the flow is induced by a Darcy porous exponentially stretching sheet with generalized boundary conditions. A regression analysis is conducted to analyze the interplay of various parameters on the flow and heat transfer characteristics. Additionally, similarity transformations are implemented to transform the set of partial differential equations (PDEs) into a set of ordinary differential equations (ODEs). The resulting equations are solved numerically using the shooting approach via the bvp4c platform in Matlab. The regression model quantifies the impact of each parameter and their interactions on the flow field, temperature distribution, and Darcy effects. Furthermore, the impact of involved parameters on flow, energy, volumetric concentration, density of microbes, and micro-rotations distribution are analyzed and discussed through graphs, literature, and tables. The findings of this research are crucial for various engineering, industrial, and pharmaceutical applications, including biofilm formation, geothermal energy extraction, and wastewater treatment processes.

Multiple-Attribute Group Decision-Making (MAGDM) is a significant area of research in decision-making, and its principles and methodologies are widely implemented. A Pythagorean Fuzzy Set (PFS) is an extension of an Intuitionistic Fuzzy Set (IFS) that is highly valuable for representing uncertain information in real-world scenarios. The 2-Tuple Linguistic Pythagorean Fuzzy Number (2TLPFN) is a specific type of Pythagorean Fuzzy Number (PFN) that can be used to represent uncertainty in real-world decision making through the use of 2-Tuple Linguistic Terms (2TLTs). This paper focuses on the examination of Multiple Attribute Group Decision Making (MAGDM) using 2TLPFNs. Dombi's t-norm and t-conorm operations were commonly referred to as Dombi operations, which might have been greater degree of applicability if offered in a new form of flexibility within the general parameter. In this research, we implement Dombi operations to construct some 2-Tuple Linguistic Pythagorean Fuzzy (2TLPF) Dombi Aggregation operators. These operators include the 2TLPF Dombi Weighted Averaging (2TLPFDWA) operator, 2TLPF Dombi Ordered Weighted Averaging (2TLPFDOWA) operator, 2TLPF Dombi Weighted Geometric (2TLPFDWG) operator, and 2TLPF Dombi Ordered Weighted Geometric (2TLPFDOWA) operator. An analysis is conducted to examine the unique characteristics of these suggested operators. Subsequently, we leveraged the proposed operators to develop a model aimed at tackling the MAGDM problems in the 2TLPF environment. Eventually, a suitable instance has been demonstrated to validate the formation of the model as well as exhibit its implementation and resilience.

The sensible selection of celestial objects for observation by the James Web Space Telescope (JWST) is pivotal for the precise decision-making (DM) process, aligning with scientific priorities and instrument capabilities to maximize valuable data acquisition to address key astronomical questions within the constraints of limited observation time. Aggregation operators are valuable models for condensing and summarizing a finite set of data of imprecise nature. Utilization of these operators is critical when addressing multi-attribute decision-making (MCDM) challenges. The complex spherical fuzzy (CSF) framework effectively captures and represents the uncertainty that arises in a DM problem with more precision. This paper presents two novel aggregation operators, namely the complex spherical fuzzy Yager weighted averaging (CSFYWA) operator and the complex spherical fuzzy Yager weighted geometric (CSFYWG) operator. Many fundamental structural properties of these operators are delineated, and thereby an improved score function is suggested that addresses the limitations of the existing score function within the CSF system. The newly defined operators are applied to formulate an algorithm for MADM problems to tackle the challenges of ambiguous data in the selection process. Moreover, these strategies are effectively applied to handle the MADM problem of selecting the optimal astronomical object for space observation within the CSF context. Additionally, a comparative analysis is also performed to demonstrate the validity and superiority of the proposed techniques compared to the existing strategies.