Search

Abstract Background The structural abnormality of the heart and its blood vessels at the time of birth is known as congenital heart disease. Every year in Pakistan, sixty thousand children are born with CHD, and 44 in 1000 die before they are a month old. Various studies used different techniques to estimate the risk factors of congenital heart disease, but these techniques suffer from a deficiency of capacity to present human understanding and a deficiency of adequate data. The current study provided an innovative approach by defining the latent variables to handle this issue and building a reasonable model. Method Data used in this study has been collected from mothers and hospital records of the children. The dataset contains information on 3900 children who visited the OPD of the Chaudry Pervaiz Elahi Institute of Cardiology (CPEIC) Multan, Pakistan from October 2021 to September 2022. The latent variables were defined from the data and structural equation modeling was used to model them. Result The results show that there are 53.6% of males have acyanotic CHD and 54.5% have cyanotic CHD. There are 46.4% of females have acyanotic CHD and 45.5% have cyanotic CHD. The children who have no diabetes in the family are 64.0% and children who have diabetes in the family are 36.0% in acyanotic CHD, the children who have no diabetes in the family are 59.7% and children have diabetes in the family are 40.3% in cyanotic CHD. The value of standardized root mean residual is 0.087 is less than 0.089 which shows that the model is a good fit. The value of root mean square error of approximation is 0.113 is less than 0.20 which also shows the good fit of the model. Conclusion It was concluded that the model is a good fit. Also, the latent variables, socioeconomic factors, and environmental factors of mothers during pregnancy have a significant effect in causing cyanotic while poor general health factor increases the risk of Acyanotic congenital heart disease.

Objectives: To ascertain the diversity and patterns observed in the complete blood count within the population of Tehsil Kot-Momin , Sargodha. Study Design: Observational Cross Sectional Study Place of Study: Rai Medical College,Sargodha Materials & Methods: A comprehensive collection of 401 CBC samples was meticoulously acquired from individuals at Medina Medical Complex in Kot-Momin ,Sargodha, subsequent to securing verbal informed consent. Subsequent research activities were conducted at Rai Medical College,Sargodha, post-recipt of sanction from the institutional ethical review committee. The time frame for data accumulation extended from june 2023 through August 2023 and the results were analyzed on SPSS Ver 27. Results: The study aimed to explore gender differences in hematological parameters among a sample of 401 individuals. Key findings indicated significant gender-based variations in hemoglobin (p < 0.001), total RBC count ( p< 0.001), hematocrit (p

Cardiovascular Disease (CVD) is one of the main causes of death in recent years. To overcome the challenges faced during diagnosing CVD at an early stage, deep learning has been used. With advancements in technology, the clinical practice in the health care industry is likely to transform significantly. To predict CVD, we constructed two models: Dense Belief Network (DB-Net) and Deep Vanilla Recurrent Network (DVR-Net). Proximity Weighted Random Affine Shadow sampling balancing technique is used for balancing the highly imbalanced Heart Disease Health Indicator dataset. SHapley Additive exPlanations exhibits each feature’s contribution. It is used to visualize features contribution to the output of DB-Net and DVR-Net in CVD prediction. Furthermore, 10-Fold Cross Validation is performed for evaluating the proposed models performance. Cross-dataset evaluation is also conducted on proposed models to see how well our proposed models generalize on unseen data. Various evaluation measures are used for assessment of models. The proposed DB-Net outperforms all the base models by achieving an accuracy of 91%, F1-score of 91%, precision of 93%, recall of 89%, and execution time of 1883 s on 30 epochs with batch size 32. The DVR-Net beats the state-of-art models with an accuracy of 90%, F1-score of 90%, precision of 90%, recall of 90%, and execution time of 2853 s on 30 epochs with batch size 32.

The study focuses on socioeconomic factors motivating labor to become part of labor unions in Pakistan. Various socioeconomic variables were chosen and tested their impact on employees’ decision of joining union. The sample comprises of the respondents working in Pakistan international Airline, State bank and MEPCO. A total of 214 responses were collected through questionnaire via simple random sampling technique. In order to observe the effect of independent variables i.e. income, age, sex, education, marital status, household size and loan are examined on dependent variable i.e. peer family influence, job environment satisfaction, satisfaction with union and financial benefits associated with union. Employing regression tests, it was analyzed that family peer influence is negatively affected by education, household size, loan taken and by age. The results further indicate that job environment satisfaction is positively affected by age. The study also found that satisfaction with union is positively affected by education whereas it is negatively affected by marital status and household size. This research also concluded that financial benefits associated with union is positively affected by salary, education and age whereas it is negatively affected by marital status, household size and loan taken.

This paper delves into the multifaceted dimensions of capital punishment within Islamic criminal law, examining its application and underlying objectives. The study revolves around two primary objects: the application of capital punishment and the objectives of implementing the death penalty. By exploring these dimensions, this paper seeks to elucidate the intricate interplay between justice, societal well-being, and legal philosophy within Islamic jurisprudence. The first object addresses the legal framework and ethical considerations surrounding the practice of capital punishment. Drawing from both retributive and consequentialist theories, the analysis illuminates the dual nature of Islamic jurisprudence in its pursuit of fairness and societal harmony. The second object investigates the objectives underpinning the death penalty's application. Rooted in the concepts of benefits (maslahah) and justice, Islamic legal thought underscores the balanced amalgamation of individual rights and collective welfare. In conclusion, this paper underscores the significance of collaborative efforts among legal scholars, policymakers, and communities to ensure a just and nuanced application of capital punishment. Through comprehensive engagement, Islamic societies can advance a holistic approach to punishment that aligns with the ethos of justice and societal well-being.

Meiotic sex chromosome inactivation is an essential event in male germ cell development, which is directed by DNA damage response signaling independent of Xist RNA to silence the transcription activity of the sex chromosomes. However, the specific mechanism of establishment and maintenance of meiotic chromosome silencing is still unclear. Here we identify the HSF5 as a testicular specific protein and the expression of which was at the onset of meiosis pachytene stage to round sperm. When the function of the HSF5 was lost, meiosis sex chromosome remodeling and silencing fail, followed by activation of CHK2 checkpoint leads to germ cell apoptosis. Furthermore, we found that SMARCA4 in the linking the HSF5 to MSCI and uncover additional factors with meiotic sex chromosome remodeling. Together, our results demonstrate a requirement for HSF5 activity in spermatogenesis and suggest a role for the mammalian HSF5-SMARCA4 in programmed meiotic sex chromosome remodeling and silencing events that take place during meiosis.

Abstract The effects of foliar application of sodium nitroprusside (SNP), magnesium sulfate (MgSO4) and magnesium oxide nanoparticles (MgO-NPs) on the growth, physiology, and gas exchange parameters of two varieties of spinach (Spinacia oleracea L.) under cadmium (Cd) stress were examined. The experiment was arranged in a completely randomized design with 72 pots. Two varieties of S. oleracea (Desi Palak & Lahori Palak) were used. Two concentrations of Cd (0 µM and 150 µM) in the form of cadmium chloride (CdCl2) were used. Two levels of SNP (0 ppm and 100 ppm) and two levels for each form of Mg i.e. MgSO4 and MgO-NPs (0 and 200 ppm) were foliar sprayed on plants in control and Cd stress. Both varieties behaved similarly under Cd stress and caused reductions in growth, physiology, gas exchange, water content parameters and inorganic ion uptake. However, the biochemical parameters like relative membrane permeability (RMP), malondialdehyde (MDA), and hydrogen peroxide (H2O2) contents were increased. However, all foliar spray treatments increased growth, physiological and gas exchange parameters, water content and inorganic ion uptake. However, this reduced the MDA, RMP, and H2O2 contents. Desi Palak showed the more positive results under foliar application of MgO-NPs. However, Lahori palak showed more positive results under the SNP + MgO-NP treatment. It is concluded that foliar application of SNP, MgSO4 and MgO-NPs could be an innovative approach to alleviated the heavy metals (Cd) toxicity in crop plants.

Abstract Globally from abiotic stresses, salt stress is the major stress that limits crop production. One of them is wheat that has been utilized by more than 1/3 of the world population as staple food due to its nutritive value. Biochar is an activated carbon that can ameliorate the negative impacts on plants under saline conditions. The present study was conducted to examine the ameliorative impact of “Biochar application” to Triticum aestivum L. plant grown under salinity stress and evaluated on the basis of various growth, yield, physiological, biochemical attributes. Preliminary experiment was done to select the Triticum aestivum L. varieties with 90% germination rate for further experiment. The selected varieties, FSD08 and PUNJAB-11 of wheat were treated with two levels of sodium chloride (0 mM and 120 mM). Two varieties of wheat included FSD08 and PUNJAB-11 were treated with two levels of sodium chloride (0 mM and 120 mM). To address the impact of salt stress two levels of biochar 0% and 5% was used as exogenous application. A three way completely randomized experimentation was done in 24 pots of two wheat varieties with three replicates. The results demonstrated that salt stress affected growth, physiological attributes, yield and inorganic mineral ions (Ca2+ and K+) in roots and shoots parameters of wheat negatively while biochar overall improved the performance of plant. SOD, CAT, APX and POD activities enhanced during salt stress as the plant self-defense mechanism against salinity to minimize the damaging effect. Salt stress also significantly increased the membrane permeability, and levels of H2O2, MDA, Cl and Na ions. Biochar treatment nullified negative impacts of NaCl and improved the plant growth and yield significantly. Hence, biochar amendment can be suggested as suitable supplement for sustainable crop production under salinization.

Abstract Objective This paper addresses the Graeco-Latin square design (GLSD) under neutrosophic statistics. In this work, we propose a novel approach for analyzing Graeco-Latin square designs using uncertain observations. Method This approach involves the determination of a neutrosophic ANOVA and the determination of the neutrosophic hypotheses and decision rule. Results The performance of the proposed design is evaluated using the numerical examples and simulation study. Conclusion Based on the results observed, it can be concluded that the GLSD under neutrosophic statistics performs better than the GLSD under classical statistics in the presence of uncertainty.

Abstract This study investigates region-specific inflectional morpheme frequencies within the ICNALE Corpus, exploring significant global linguistic intricacies. Through a quantitative, corpus-based approach, it conducts a comprehensive contrastive analysis, leveraging the extensive accessibility of the online ICNALE. Despite inherent limitations in data collection primarily reliant on interviews and written compositions within the ICNALE Corpus, the study offers illuminating insights into how diverse linguistic backgrounds influence the usage of inflectional morphemes in English. The findings bring to light interesting patterns in possessive usage, reflecting variations among ESL learners in acquiring grammatical features. Additionally, it highlights complexities in acquiring comparative and superlative degrees, aligning with challenges encountered by learners in mastering specific inflections. Analyses of plural noun inflections, subject-verb agreement, past tense, past participle, and present participle usage underscore the multifaceted influences of language background, acquisition stages, and instructional emphasis on learners’ morphological patterns. Future inquiries could further investigate the impact of instructional methodologies on inflectional morpheme acquisition, undertake comparative studies across proficiency levels and age groups, explore morphological transfer from learners’ native languages, and deepen comprehension of cognitive processes guiding effective learning strategies. These endeavors hold promise in refining pedagogical methodologies and enriching language learning experiences for diverse learner cohorts.

Abstract Heavy metals stress particularly cadmium contamination is hotspot among researchers and considered highly destructive for both plants and human health. Iron is examined as most crucial element for plant development, but it is available in inadequate amount because they are present in insoluble Fe3+ form in soil. Fe3O4 have been recently found as growth promoting factor in plants. To understand, a sand pot experiment was conducted in completely randomized design (control, cadmium, 20 mg/L Fe3O4 nanoparticles,40 mg/L Fe3O4 nanoparticles, 20 mg/L Fe3O4 nanoparticles + cadmium, 40 mg/L Fe3O4 nanoparticles + cadmium) to study the mitigating role of Fe3O4 nanoparticles on cadmium stress in three Raphanus sativus cultivars namely i.e., MOL SANO, MOL HOL PARI, MOL DAQ WAL. The plant growth, physiological and biochemical parameters i.e.,shoot length, shoot fresh weight, shoot dry weight, root length, root fresh and dry weight, MDA content, soluble protein contents, APX, CAT, POD activities and ion concentrations, membrane permeability, chlorophyll a, chlorophyll b and anthocyanin content, respectively were studied. The results displayed that cadmium stress remarkably reduces all growth, physiological and biochemical parameters for allcultivars under investigation. However, Fe3O4 nanoparticles mitigated the adverse effect of cadmium by improving growth, biochemical and physiological attributes in all radish cultivars. While, 20 mg/L Fe3O4 nanoparticles have been proved to be more useful against cadmium stress. The outcome of present investigation displayed that Fe3O4 nanoparticles can be utilized for mitigating heavy metal stress.

Geopolymer cement is being viewed as an environmentally sustainable substitute for Ordinary Portland Cement (OPC) in oilwell cement. The study examines the impact of varying graphene oxide (GO) concentrations on the mechanical properties of the geopolymer cement. The optimal sodium hydroxide molarity, alkaline activator ratio and GO’s concentration were optimized using Response Surface Methodology based on Central Composite Design to obtain maximize compressive strength formulation. The results show that the highest concentration of GO or the highest molarity of sodium hydroxide can yield the highest strength. Besides, the GO concentration of 0.2% by weight of fly ash (bwof) demonstrate a non-settling and time-independent fluid behaviors at the average test temperature. The formulation results in a 22.9% lower filtrate volume compared to the reference design without GO. The study highlights GO as potential sustainable performance enhancer in geopolymer cement for elevated oilwell condition.

AbstractGeopolymer cement (GPC) is a promising oil and gas well cementing alternative to Ordinary Portland Cement (OPC). OPC forms ettringite, causing water absorption and shrinkage. GPC, with lower calcium content, is expected to have lower shrinkage. This study assesses GPC's performance with elastomeric expandable additive (R-additive), up to 25 wt.%. The mixing, rheological test, free water, compressive strength, and linear expansion test were done in accordance with American Petroleum Institute Recommended Practice 10. Results demonstrated that increasing R-additive concentration reduced slurry density. Additionally, rheological properties improved, with plastic viscosity increasing from 48 to 104 cP and the yield point from 3.8 to 12.3 N/m2. The optimal R-additive concentration for ideal rheology was 20 wt.%. All formulations showed zero free water, while compressive strength and linear expansion (l/l0) varied with curing time. The study also confirmed self-healing in geopolymer cement, preventing flow through the cement sheath at varying temperatures. A 25 wt.% R-additive concentration allowed successful pumping at 4.8 barrels per minute with 100 psi pumping pressure. In a nutshell, the inclusion of R-additive between 10 to 25 wt% into geopolymer cement meets the standards of the oil and gas industry and becomes the potential replacement for OPC.

ABSTRACT Background Young patients ≤ 50 years old with multiple myeloma (MM) account for about 10% of cases and are underrepresented in the literature. Methods We explored disease characteristics, treatments, and outcomes following modern therapies of young MM patients using the Canadian Myeloma Research Group (CMRG) database. We included 493 patients ≤ 50 years old diagnosed with MM or plasma cell leukemia without concurrent amyloidosis or POEMS syndrome from January 1, 2010, to July 1, 2022. Results The median age was 46 years old (range: 25.6–50). Most patients fell into the R‐ISS II category (72.7%), and 24.1% had high‐risk cytogenetics. The majority of patients (89.9%) received a proteasome inhibitor‐based first‐line treatment, 92.1% received a stem cell transplant, and 65.6% had maintenance therapy post–autologous stem cell transplant (ASCT). Median follow‐up from initial treatment to patients' last follow‐up was 48.5 (range: 0–155) months. Median progression‐free survival (PFS) was 45.0 months (95% CI: 40.2–50.0). Maintenance therapy post‐ASCT improved median PFS to 52.3 months (95% CI: 43.1–68.2), compared to 23.6 months (95% CI: 20.0–34.8) without maintenance [p

Abstract Balanced lattice designs are vital in numerous fields, especially in experimental design, where controlling variability among experimental units is crucial. In practical experiments, various sources of uncertainty can lead to ambiguous, vague, and imprecise data, complicating the analysis process. To address these indeterminacies, a novel approach using neutrosophic analysis within a balanced lattice design framework is proposed, termed the neutrosophic balanced lattice design (NBLD). This innovative method employs neutrosophic statistics to derive mathematical neutrosophic sums of squares and construct a neutrosophic analysis of variance (NANOVA) table. The effectiveness of the proposed NBLD is demonstrated through a numerical example, showing that it outperforms traditional methods in handling uncertainty.

Abstract The monitoring of the process relies on the average product lifetime, which adheres to the Lindley distribution and employs a truncated life test-based attribute control chart. Control limits for these charts are determined through a repetitive sampling procedure. Two inner and outer control limits are established based on the desired average run length (ARL). Tables are generated for varying ARL values by adjusting shift values while keeping the shape parameter of the Lindley distribution constant. The effectiveness of the control charts is assessed through application to real-time data, and the performance of the proposed chart is evaluated using simulation data. A comparative analysis is conducted with single sampling scheme based on ARL values, and the results are presented and discussed.

Abstract Sugarcane smut is the most damaging disease that is present almost across the globe, causing mild to severe yield losses depending upon the cultivar types, pathogen races and climatic conditions. Cultivation of smut-resistant cultivars is the most feasible and economical option to mitigate its damages. Previous investigations revealed that there is a scarcity of information on early detection and effective strategies to suppress etiological agents of smut disease due to the characteristics overlapping within species complexes. In this study, 104 sugarcane cultivars were screened by artificial inoculation with homogenate of all possible pathogen races of Sporisorium scitamineum during two consecutive growing seasons. The logistic smut growth pattern and the disease intrinsic rate were recorded by disease growth curve. Variable levels of disease incidence i.e., ranging from 0 to 54.10% were observed among these sugarcane cultivars. Besides, pathogen DNA in plant shoots of all the cultivars was successfully amplified by PCR method using smut-specific primers except 26 cultivars which showed an immune reaction in the field trial. Furthermore, the plant germination and tillering of susceptible sugarcane cultivars were greatly influenced by pathogen inoculation. In susceptible cultivars, S. scitamineum caused a significant reduction in setts germination, coupled with profuse tillering, resulting in fewer millable canes. Correlation analysis demonstrated that there was a positive relationship between reduction in setts germination and increase in the number of tillers. The present study would be helpful for the evaluation of smut resistance in a wide range of sugarcane germplasm, especially from the aspects of setts germination and tillers formation, and it also screened out several excellent germplasm for potential application in sugarcane breeding.

Abstract Background Non-conventional yeasts hold significant potential as biorefinery cell factories for microbial bioproduction. Currently, gene editing systems used for these yeasts rely on antibiotic and auxotrophic selection mechanisms. However, the drawbacks of antibiotics, including high costs, environmental concerns, and the dissemination of resistance genes, make them unsuitable for large-scale industrial fermentation. For auxotrophic selection system, the engineered strains harboring auxotrophic marker genes are typically supplemented with complex nutrient-rich components instead of precisely defined synthetic media in large-scale industrial fermentations, thus lack selection pressure to ensure the stability of heterologous metabolic pathways. Therefore, it is a critical to explore alternative selection systems that can be adapted for large-scale industrial fermentation. Results Here, a novel glucose-dependent selection system was developed in a high pullulan-producing non-conventional strain A. melanogenum P16. The system comprised a glucose-deficient chassis cell Δpfk obtained through the knockout of the phosphofructokinase gene (PFK) and a series of chromosomal integration plasmids carrying a selection marker PFK controlled by different strength promoters. Utilizing the green fluorescent protein gene (GFP) as a reporter gene, this system achieved a 100% positive rate of transformation, and the chromosomal integration numbers of GFP showed an inverse relationship with promoter strength, with a customizable copy number ranging from 2 to 54. More importantly, the chromosomal integration numbers of target genes remained stable during successive inoculation and fermentation process, facilitated simply by using glucose as a cost-effective and environmental-friendly selectable molecule to maintain a constant and rigorous screening pressure. Moreover, this glucose-dependent selection system exhibited no significant effect on cell growth and product synthesis, and the glucose-deficient related selectable marker PFK has universal application potential in non-conventional yeasts. Conclusion Here, we have developed a novel glucose-dependent selection system to achieve customizable and stable multilocus chromosomal integration of target genes. Therefore, this study presents a promising new tool for genetic manipulation and strain enhancement in non-conventional yeasts, particularly tailored for industrial fermentation applications.

Abstract Multiple myeloma remains an incurable cancer mostly affecting older adults and is characterized by a series of remission inductions and relapses. This study aims to evaluate the outcomes in newly diagnosed transplant‐ineligible patients using bortezomib/lenalidomide‐based regimens in the Canadian real world as well as their outcomes in the second line. The Canadian Myeloma Research Group Database (CMRG‐DB) is a national database with input from multiple Canadian Centres with now up to 8000 patients entered. A total of 1980 transplant ineligible patients were identified in the CMRG‐DB between the years of 2007–2021. The four most commonly used induction regimens are bortezomib/melphalan/prednisone (VMP) (23%), cyclophosphamide/bortezomib/dexamethasone (CyBorD) (47%), lenalidomide/dexamethasone (Rd) (24%), and bortezomib/lenalidomide/dexamethasone (VRd) (6%). After a median follow‐up of 30.46 months (0.89–168.42), the median progression‐free survival (mPFS) and median overall survival (mOS) of each cohort are 23.5, 22.9, 34.0 months, and not reached (NR) and 64.1, 51.1, 61.5 months, and NR respectively. At the time of data cut‐off, 1128 patients had gone on to second‐line therapy. The mPFS2 based on first‐line therapy, VMP, CyBorD, Rd, and VRd is 53.3, 48.4, 62.7 months, and NR respectively. The most common second‐line regimens are Rd (47.4%), DRd (12.9%), CyBorD (10.3%), and RVd (8.9%) with a mPFS and a mOS of 17.0, 31.1, 15.4, and 14.0 months and 34.7, NR, 47.6, 33.4 months, respectively. This study represents the real‐world outcomes in newly diagnosed transplant‐ineligible myeloma patients in Canada. The spectra of therapy presented here reflect the regimens still widely used around the world. While this is sure to change with anti‐CD38 monoclonal antibodies now reflecting a new standard of care in frontline therapy, this cohort is reflective of the type of multiple myeloma patient currently experiencing relapse in the real‐world setting.

In mathematical analysis theory of inequalities has considerable influence due to its massive utility in various fields of physical sciences. These are investigated via multiple approaches to acquire more precise and rectified forms of already celebrated consequences. Integral inequalities are investigated to compute the error bounds for quadrature schemes. Among all of them, one is Hermite-Hadamard inequality, which has mighty efficacy. Numerous generalizations have been proposed in the literature based on different novel and innovative procedures. In recent years, Bullen inequality has been very commonly studied inequality. The main objective of our progressive study is to establish a new set of Bullen-type inequalities concerning the Jensen-Mecer inequality. For the completion of the current investigation, we derive a new general Bullen-Mecer equality, which is beneficial to achieve our primary consequences. Furthermore, Considering the Bullen-Mecer equation, we employ the convexity property together with famous Hölder's type and Young's inequalities, bounding, and Lipschitz characteristics of functions to conclude new variants of generalized upper bounds of Bullen inequality. Also, we deliver some applications of outcomes to means, special functions, error bounds, and iterative methods to solve non-linear problems. Lastly, we verify our findings through various simulations. The advantage of the current study is that several results concerning Bullen's inequality can be retrieved from our proposed results and various new results can be achieved by choosing the values for γ and δ. By utilizing the similar technique that we have adopted new iterative schemes can be established from integral inequalities.

Abstract Many issues in real life are riddled with confusion, vagueness, and ambiguity. The Topp-Leone distribution is a significant one-parameter probability distribution in the context of classical probability theory. There is a gap in the literature when it comes to dealing with circumstances involving interval-valued data with a classical Topp-Leone distribution. So, in this connection, neutrosophic Topp-Leone distribution is presented in this paper as an extension of the traditional Topp-Leone distribution. The new neutrosophic distribution takes into account the indeterminacy and crisp form of interval-valued distributions. The suggested distribution's mathematical features were derived, including moments and related measures, quantile function, survival, hazard, inverted hazard functions, and mills ratio. Maximum likelihood is used to estimate model parameters. Finally, a complex dataset is utilized to show the usefulness of the proposed distributions.

Abstract In this paper, we introduce a distance measure on single-valued neutrosophic sets by sine function which is a generalization of intuitionistic fuzzy sine distance measure. The axiom of metric on single-valued neutrosophic sets is verified and shows that the difference of distance measure from unity is a similarity measure. A new methodology for multi-attribute decision-making problems (MADM) is developed for the most common decision by the smallest measure value of the proposed single-valued neutrosophic distance measure. We further apply this distance measure to a multi-attribute decision-making problem (MADM) for student career determination in a neutrosophic environment to find the best career for suitable students. Finally, the comparison is made between the proposed distance measure and the other distance measures for the final decision chosen from the most common decisions of them.

This paper introduces the geometric distribution in the context of neutrosophic statistics. The research outlines the essential properties of this new distribution and introduces novel algorithms for generating imprecise geometric data. The study explores the practical applications of this distribution in the industry, highlighting differences in data generated under deterministic and indeterminate conditions using detailed tables, simulation studies, and real-world applications. The results indicate that the level of uncertainty has a substantial impact on data generation from the geometric distribution. These findings suggest updating classical statistical algorithms to better handle the generation of imprecise data. Therefore, decision-makers should exercise caution when using data from the geometric distribution in uncertain environments.

This study aims to establish a facile, user-friendly, accurate, and repeatable spectrophotometric detection method for Cr(III) ions in trace quantities. The method utilises reagent 1-nitroso-2-naphthol (NNPh) in a sodium dodecyl sulphate (SDS) 1.0 % micellar solution, replacing the old, time-consuming, and expensive solvent extraction method that employs hazardous solvents such as carbon tetrachloride and chloroform. SDS surfactant is used to solubilise indissoluble chelates, making the proposed technique easy, rapid, and environmentally friendly. The established method exhibits an improved linear calibration range, sensitivity, limit of detection, and selectivity compared to earlier spectrophotometric methods employing different complexing agents. The stoichiometric ratio between Cr and NNP was observed to be 1 : 3. The molar absorption coefficient (ε) was determined to be 2.05 ∙ 104 l mol−1 cm−1 at λmax 579 nm. The limits of detection for Cr(III) was measured as 3.49 ng l−1. A calibration linear curve was achieved in the concentration range of 0.5–5.0 μg ml−1. Results obtained showed good comparison with the results obtained by atomic absorption spectrometry. The developed method has been adapted for the analysis of Cr(III) in various alloys, pharmaceutical, environmental, and biological samples.

Abstract In this study, we have presented a novel probabilistic model called the neutrosophic Burr-III distribution, designed for applications in neutrosophic surface analysis. Neutrosophic analysis allows for the incorporation of vague and imprecise information, reflecting the reality that many real-world problems involve ambiguous data. This ability to handle vagueness can lead to more robust and realistic models especially in situation where classical models fall short. We have also explored the neutrosophic Burr-III distribution in order to deal with the ambiguity and vagueness in the data where the classical Burr-III distribution falls short. This distribution offers valuable insights into various reliability properties, moment expressions, order statistics, and entropy measures, making it a versatile tool for analyzing complex data. To assess the practical relevance of our proposed distribution, we applied it to real-world data sets and compared its performance against the classical Burr-III distribution. The findings revealed that the neutrosophic Burr-III distribution outperformed than the classical Burr-III distribution in capturing the underlying data characteristics, highlighting its potential as a superior modeling toolin various fields.

Abstract The paper focuses on the economic design of group chain sampling plans (GChSP) for the Weibull distribution using Bayesian methodology. The GChSP is a technique to accept or reject a product based on a sample from a lot. The study addresses situations where destructive testing is costly and utilizes the Bayesian approach to make informed decisions. The research outlines the methodology of developing GChSP including the stages of construction, performance evaluation, and cost estimation. The study compares the proposed plans with an existing one and demonstrates that the Bayesian approach generally yields lower costs. We will provide tables, figures, and calculations related to various aspects of the proposed plans and their comparison with existing methods.

This paper introduced the neutrosophic uniform distribution and innovative simulation methods to generate random numbers from the neutrosophic uniform distribution and the neutrosophic Weibull distribution. We introduced simulation methods and algorithms designed to handle indeterminacy for both of these distributions. We provided random numbers generated from both distributions across a range of parameter values and degrees of indeterminacy. Furthermore, we conducted a comparative analysis between the classical simulation method in classical statistics and the neutrosophic simulation method. Our findings reveal that the proposed neutrosophic simulation method generates random numbers of smaller magnitudes compared to the classical simulation method under classical statistics. This observation forms the basis of our conclusion.

Industrial sectors, pivotal for the economic prosperity of nations, rely heavily on affordable, reliable, and environmentally friendly energy sources. Industries like iron and steel, oil refineries, and coal-fired power plants, while instrumental to national economies, are also the most significant contributors to waste gases that contain substantial volumes of carbon monoxide (CO). CO can be converted to a highly efficient and carbon free fuel, hydrogen (H2) through a well-known water gas shift reaction. However, the untapped potential of H2 from waste industrial streams is yet to be explored. This is the first article that investigates the potential of H2 production from industrial waste gases. The available resource (i.e., CO) and its H2 production potential are estimated. The article also provides insights into the principal challenges and potential avenues for long-term adoption. The results showed that 249.14 MTPY of CO are available to produce 17.44 MTPY of H2 annually. This suggests a significant potential for H2 production from waste gases to revolutionize industrial waste management and contribute significantly towards Sustainable Development Goals 7, 9, and 13ensuring access to affordable, reliable, sustainable, and modern energy for all and taking decisive climate action, respectively.

Background: The treatment of choice for most esophageal strictures is esophageal dilatation but it has a low success rate. This study was conducted to determine the effect of topical mitomycin C as an adjunct to dilatation therapy in esophageal strictures among children. Methods: This randomized controlled trial was conducted at the Department of Pediatric Gastroenterology, The Children’s Hospital and Institute of Child Health, Multan, Pakistan, from July 2022 to October 2023. Children aged 1-12 years and having esophageal strictures were included. A simple random sampling technique was adopted. Children in the Mitomycin C group received topical mitomycin C (2 mg/20 ml saline) during the endoscopy, whereas the placebo group received normal saline. The counts of dilatation sessions necessary in each group and the proportion of patients who were symptom-free after 12 months of therapy were noted (outcome). IBM-SPSS 26 was used for data analysis with p

Researchers and scientists became interested in the newly developed idea of hybrid nanofluids because of their exceptional thermal conductivities, which resulted in enhanced thermal performance. These fluids have a wide range of uses in the industrial and technical fields. This novel class of fluids is playing a vital role in solar energy, automotive industry, cooling of the machine and manufacturing, heating and ventilation systems, electronic cooling, nuclear systems cooling, biomedical fields, space, ships, and defense systems. In light of the physical significance of the above-mentioned class of fluids, the current investigations are carried to examine the thermal performance of the mixture of cylindrical shaped aluminum oxide Al2O3 and copper Cu nanoparticles suspended in ethylene glycol using third-grade non-Newtonian fluid flow model. The fluid flow is induced by the stretching of the permeable sheet in exponential manner that is embedded in a Dacry-Forchheimer porous medium. The effects of Lorentz force applied normal to the flow and suction impacts have been incorporated in the current proposed model. The solutions of the flow model transformed to ordinary differential equations are computed using MATLAB built-in solver bvp4c. The solutions are presented in graphical representations. Graphical solutions reflect that fluid velocity slows down by growing the volume fractions of the nanoparticles. Increasing values of viscoelastic parameters reflect that there is a reduction in velocity and intensification in profiles of temperature and concentration have been noted. Rising values of cross-diffusion parameter is leading to the reduction in magnitude of velocity, and augmenting behavior of temperature and concentration has been seen. Observations indicate rising third-grade fluid parameter and porosity parameter lead to reduction in velocity. Increasing Schmidt number gives reduced Sherwood number, and magnetic field parameter improves the rate of hear transfer (Nusselt number) and skin friction coefficient for both Al2O3/ethylene glycol nanofluid and Al3O3-Cu/ethylene glycol-based hybrid nanofluid. The suggested model's validity is confirmed through comparison with previously released findings. Additionally, a grid independence test (sensitivity analysis) has been performed to verify the accuracy and verification of the numerical model.

In recent scenarios, we cannot ignore the importance of artificial intelligence in many fields like medical diagnosis, computer science, economics and agriculture, etc. In 1960, Schweizer and Sklar developed the concept of Schweizer-Sklar (SS) t-norm and t-conorm by incorporating a parameter which is more flexible and helpful in handling complex and unclear data. If we put parameter then the information of the Hamacher and Lukasiewicz t-norms is simply derived. The primary goal of this study is to develop the fundamental Schweizer-Sklar operational laws for bipolar fuzzy numbers. Additionally, we have defined the notion of bipolar fuzzy Schweizer-Sklar power average and bipolar fuzzy Schweizer-Sklar power geometric aggregation operators. Moreover, we have elaborated on the basic characteristics of these developed notions. We have initiated the algorithm to demonstrate the actual use of this work in artificial neural networking. We have also compared our work with some prevailing theories.

This study aimed to assess the cosmetic outcomes of shave excision for the treatment of acquired melanocytic nevi in a Pakistani tertiary care setting. In total, 120 patients were enrolled in this study. All patients underwent shave excision upon being administered with intra-lesional local anesthesia (2% lignocaine with adrenaline). The lesion was shaved with a 15 number Bald Parker blade. Hemostasis was secured by pressure and 20% aluminum chloride solution. The final cosmetic outcomes (excellent, acceptable, or poor) were assessed three months post-shave excision. Statistical analysis was performed using SPSS, v22. Data were evaluated using chi-square tests, and the phi coefficient. A P value of less than 0.05 was considered statistically significant to determine associations of age and gender to cosmetic outcomes. The mean age of included patients was 29.7±7.4 years. There were 30 (25%) male and 90 (75%) female patients. Out of these, 35 (29.2%) patients had excellent outcomes, 75 (62.5%) patients had acceptable and 10 (8.3%) patients had a poor outcome. The associations of gender and age to dermatological outcomes post-excision were significant (P

On December 31st 2019, Chinese Health Authorities in Wuhan city of their Hubei province, diagnosed 29 pneumonia cases of unknown etiology and informed WHO (World Health Organization). The unknown virus resembled SARS (severe acute respiratory syndrome), that occurred in China in November 2002, caused by a novel corona virus spilling over from an animal reservoir and transmitted by respiratory droplets. More than 8000 cases and 774 deaths were caused by SARS and circa US$20 billion coasted for its control.

This paper presents a novel methodology aimed at generating chi-square variates within the framework of neutrosophic statistics. It introduces algorithms designed for the generation of neutrosophic random chi-square variates and illustrates the distribution of these variates across a spectrum of indeterminacy levels. The investigation delves into the influence of indeterminacy on random numbers, revealing a significant impact across various degrees of freedom. Notably, the analysis of random variate tables demonstrates a consistent decrease in neutrosophic random variates as the degree of indeterminacy escalates across all degrees of freedom values. These findings underscore the pronounced effect of uncertainty on chi-square data generation. The proposed algorithm offers a valuable tool for generating data under conditions of uncertainty, particularly in scenarios where capturing real data proves challenging. Furthermore, the data generated through this approach holds utility in goodness-of-fit tests and assessments of variance homogeneity.

Abstract This paper aims to provide a multiple dependent state (MDS) sampling technique for light-emitting diode luminous intensities under indeterminacy by employing time truncated sampling schemes and the Weibull distribution. This indicates that ASN is significantly impacted by the indeterminacy parameter. Furthermore, a comparison is shown between the existing, indeterminate sampling plans and the recommended sample designs. The projected sampling technique is illustrated by calculating the luminous intensities of LEDs using the Weibull distribution. Based on the findings and practical example, we conclude that the suggested strategy needs a smaller sample size than SSP and the current MDS sampling plan.

Abstract A neutrosophic statistic is a random variable and it has a neutrosophic probability distribution. So, in this paper, we introduce the new neutrosophic Birnbaum–Saunders distribution. Some statistical properties are derived, using Mathematica 13.1.1 and R-Studio Software. Two different estimation methods for parameters estimation are introduced for new distribution: maximum likelihood estimation method and Bayesian estimation method. A Monte-Carlo simulation study is used to investigate the behavior of parameters estimates of new distribution, compare the performance of different estimates, and compare between our distribution and the classical version of Birnbaum–Saunders. Finally, study the validity of our new distribution in real life.

Abstract This research designed a distribution-free mixed exponentially weighted moving average-moving average (EWMA-MA) control chart based on signed-rank statistic to effectively identify changes in the process location. The EWMA-MA charting statistic assigns more weight to information obtained from the recent $$w$$ w samples and exponentially decreasing weights to information accumulated from all other past samples. The run-length profile of the proposed chart is obtained by employing Monte Carlo simulation techniques. The effectiveness of the proposed chart is evaluated under symmetrical distributions using a variety of individual and overall performance measures. The analysis of the run-length profile indicates that the proposed chart performs better than the existing control charts discussed in the literature. Additionally, an application from a gas turbine is provided to demonstrate how the proposed chart can be used in practice.

Abstract The Pareto distribution is of paramount importance in actuarial science, wealth distribution, finance, etc. This paper introduces a control chart inspired by Shewhart's methodology, designed for monitoring shifts in the Pareto distribution through a repetitive sampling approach. The chart employs a modified statistic that combines shape and threshold parameters as its plotting statistic. Coefficients for the Shewhart-type Pareto chart are computed for two-phase limits. The performance of the suggested chart is assessed in terms of run length characteristics, assuming a shift in the process mean. Additionally, we conduct an efficiency comparison with existing control charts. The findings suggest that, on average, the proposed Pareto chart demonstrates greater efficiency in promptly detecting changes compared to alternative methods. To illustrate the practical application of our approach, we present an example using revenue data.

Abstract To address information ambiguities, this study suggests using neutrosophic sets as a tactical tool. Three membership functions (called $$T_r, I_n, $$ T r , I n , and $$ F_i$$ F i ) that indicate an object’s degree of truth, indeterminacy, and false membership constitute the neutrosophic set. It becomes clear that the neutrosophic connectivity index (CIN) is an essential tool for solving practical problems, especially those involving traffic network flow. To capture uncertainties, neutrosophic graphs are used to represent knowledge at different membership levels. Two types of $$CIN_s,$$ C I N s , mean CIN and CIN, are investigated within the framework of neutrosophic graphs. In the context of neutrosophic diagrams, certain node types-such as neutrosophic neutral nodes, neutrosophic connectivity reducing nodes (NCRN) , and neutrosophic graph connectivity enhancing nodes (NCEN) , play important roles. We concentrate on two types of networks, specifically traffic network flow, to illustrate the real-world uses of CIN. By comparing results, one can see how junction removal affects network connectivity using metrics like Connectivity Indexes (CIN) and Average Connectivity Indexes (ACIN) . A few nodes in particular, designated by ACIN as Non-Critical Removal Nodes $$ (NCRN_s) $$ ( N C R N s ) , show promise for increases in average connectivity following removal. To fully comprehend traffic network dynamics and make the best decisions, it is crucial to take into account both ACIN and CIN insights. This is because different junctions have different effects on average and overall connectivity metrics.

Abstract This paper aimed to develop a coefficient of variation (CV) control chart utilizing the generalized multiple dependent state (GMDS) sampling approach for CV monitoring. We conducted a comprehensive examination of this designed control chart in comparison to existing control charts based on multiple dependent state sampling (MDS) and the Shewhart-type CV control chart, with a focus on average run lengths. The results were then compared to run-rule control charts available in the existing literature. Additionally, we elucidated the implementation of the proposed control chart through concrete examples and a simulation study. The findings clearly demonstrated that the GMDS sampling control chart shows significantly superior accuracy in detecting process shifts when compared to the MDS sampling control chart. As a result, the control chart approach presented in this paper holds significant potential for applications in textile and medical industries, particularly when researchers seek to identify minor to moderate shifts in the CV, contributing to enhanced quality control and process monitoring in these domains.

Abstract The increasing demand for electricity in daily life highlights the need for Smart Cities (SC) to use energy efficiently. Both technical and Non‐Technical Losses (NTL), particularly those resulting from electricity theft, present powerful obstacles; NTL alone can reach billions of dollars. Although Machine Learning (ML) based approaches for NTL detection have been embraced by numerous utilities, there is still a lack of thorough analysis of these methods. Limited research exists on NTL identification evaluation criteria and unbalanced data management in the context of SC. This research compares ML algorithms and data balancing methods to optimize electricity consumption detection. The given research applied the 15 ML techniques of Logistic regression, Bernoulli naive Bayes, Gaussian naive Bayes, K‐Nearest Neighbour, perceptron, passive‐aggressive classifier, quadratic discriminant analysis, SGD classifier, ridge classifier, linear discriminant analysis, decision tree, nearest centroid classifier, multi‐nomial naive Bayes, complement naive Bayes and dummy classifier. While SMOTE, AdaSyn, NRAS, and CCR are considered for data balancing. AUC, F1‐score, and seven relevant performance metrics were used for comparison. We have also implemented SHapely Additive exPlanations (SHAP) for feature importance and model interpretation. Results show varying classifier performance with different balancing methods, emphasizing data preprocessing's role in NTL detection for smart grid security.

Smoking has a serious complicated impact on Cardiovascular Health (CVH), which makes it an easily controlled risk factor for Cardiovascular Disease (CVD). The importance of early diagnosis and treatment is crucial because of CVD, which remains to be the leading cause of morbidity and death across the globe, therefore, smoking behaviour prediction is essential. This research explores the complex relationship between smoking and CVD, and clarifies how smoking negatively impacts CVH. The first step of the analysis is removing the Not-a-Number (NaN) values in the smoking behaviour bio signals data because all NaN values are in the target column ‘smoking’, which could lead to generate biased predictions. Further, robust scaling is applied to standardize the data, where all features are on the same scale. The Random Forest feature selection technique is applied to select the most relevant features for smoking status prediction. The Borderline-Synthetic Minority Oversampling Technique is used to balance the minority class with the majority class, to reduce the risk of overfitting in results. Finally, the cleaned and preprocessed data is passed to the Proposed Blending Model (PBM). In PBM, the Echo State Network and GoogleNet are placed on the base layer, and AlexNet is placed on the meta layer to make a robust smoking status prediction model. In terms of accuracy, precision, recall, and f1-score, the PBM outperforms the baseline techniques with an improvement score of 5%, 6%, 5%, and 7%, respectively. Additionally, the PBM’s results are validated through a 10-fold cross-validation method, which can increase its reliability and durability. Finally, the SHapley Additive exPlanations technique is used to understand the predictions made by the PBM. It can reveal the explainability and interpretability of the PBM. Conclusively, during the thorough simulation process, the results are collected and the ablation study is performed. The simulation results show that the early prediction of hidden smokers can help to reduce the effect of the CVDs. It also makes proactive healthcare strategies possible for better future outcomes and public health.

ABSTRACT Amorphous indium gallium zinc oxide (a-IGZO)-based thin film transistors (TFTs) are increasingly becoming popular because of their potential in futuristic applications, including CMOS technology. Given the demand for CMOS-compatible, ultra-scaled, reliable, and high-performing devices, we fabricate and analyze scaled-channel a-IGZO-TFTs with an optimal double-gate structure, a thin nanosheet-based channel, and an effective high- $\kappa$ dielectric namely HfO2. The reliably reported double gate IGZO nanosheet TFTs (DG-IGZO-NS-TFTs) are tested under positive and negative bias stress at variant temperatures, and the resulting modulations are analyzed critically. The reported DG-IGZO-NSTFTs exhibit a negative side threshold voltage shift ( $\Delta$ Vth) accompanied by an increase in Ion/Ion(0) under negative bias temperature stress (NBTS) at elevated temperatures, which indicates the presence of additional charges. An anomalous negative side shifting of the Vth is observed under positive bias temperature stress (PBTS), where diffused hydrogen atoms are identified as introducing excess n-type carriers into the channel and causing the observed $\Delta$ Vth. The spectroscopic analysis is performed to establish evidence for the assumed mechanisms, and the role of individual gates is investigated in the context of performance variance under temperature-bias stress. Moreover, the partial reversibility of the stress-induced degradation is experimentally established and methodically discussed. Overall, the reported results offer a comprehensive understanding of scaled-channel DG-NS-IGZO-TFTs, which help shape performance-enhancement strategies, control degradation mechanisms, and define appropriate application scenarios.

Abstract This manuscript presents the development of an attribute control chart (ACC) designed to monitor the number of defective items in manufacturing processes. The charts are specifically tailored using time-truncated life test (TTLT) for two lifetime data distributions: the half-normal distribution (HND) and the half-exponential power distribution (HEPD) under a repetitive sampling scheme (RSS). To assess the effectiveness of the proposed control charts, both in-control (IC) and out-of-control (OOC) scenarios are considered by deriving the average run length (ARL). Various factors, including sample sizes, control coefficients, and truncated constants for shifted phases, are taken into account to evaluate the performance of the charts in terms of ARL. The behavior of ARLs is analyzed in the shifted process by introducing shifts in its parameters. The superiority of the HEPD-based chart is highlighted by comparing it with both the HND-based ACC and the ACC based on the Exponential distribution (ED) under TTLT using RSS. The results showcase the superior performance of the proposed HEPD-based chart, indicated by smaller ARL values. Additionally, the benefits of another proposed ACC using HND are compared with the ED-based ACC under RSS, further confirming the effectiveness of the HND-based approach through smaller ARLs Finally, the proposed control charts are evaluated through simulation testing and real-life implementation, emphasizing their practical applicability in real-world manufacturing settings.

Abstract The conventional Z-test for correlation, grounded in classical statistics, is typically employed in situations devoid of vague information. However, real-world data often comes with inherent uncertainty, necessitating an adaptation of the Z-test using neutrosophic statistics. This paper introduces a modified Z-test for correlation designed to explore correlations in the presence of imprecise data. We will present the simulation to check the effect of the measure of indeterminacy on the evolution of type-I error and the power of the test. The application of this modification is illustrated through an examination of heartbeat and temperature data. Upon analyzing the heartbeat and temperature data, it is determined that, in the face of indeterminacy, the correlation between heartbeat and temperature emerges as significant. This highlights the importance of accounting for imprecise data when investigating relationships between variables.

Handwriting is one of the basic skills crucial in communication and early childhood development. It plays a major role in the psychomotor and cognitive growth of children. Therefore, it is required to devise interesting and modern ways to teach this skill. A handwriting tutoring system designed to teach Urdu transcription engagingly and efficiently can help to solve this problem. Essential to these teaching systems is the quality evaluation module that analyzes and evaluates the quality of transcription based on legibility and writing process and provides feedback to the writer. This feedback can not only help the young students to improve and identify their mistakes but can also help to repair the handwriting skills of any individual wanting to improve their transcription abilities. The existing handwriting evaluation methods are either designed for Arabic writing style, or those designed for Urdu need more comprehensive feedback on the quality of handwriting. Therefore, it is the need of time to develop a technology-assisted educational system for teaching Urdu handwriting that evaluates and provides intelligent feedback to the learners. In this work, we have proposed an innovative model for evaluating different aspects of Urdu handwriting and providing qualitative and quantitative feedback. The system takes on real-time writing samples, and its transcription profiler module then individually evaluates six aspects of handwriting, including legibility, shape formation, the direction of writing, proportion with the baseline, count, and order of strokes. Based on the evaluation, the feedback generator module provides overall feedback and detects mistakes made while writing. A machine learning-based hybrid architecture is constructed that combines spatio-temporal and structural features to do a quality analysis of each aspect of the transcript. The contribution of this work is twofold: first, to develop a standard for evaluating the quality of Urdu handwriting or any cursive writing script, and second, to create an architecture that relies on machine learning-based models to provide detailed and intelligent feedback while learning handwriting. The system’s performance is verified by comparing it with the test data evaluated by real teachers. It achieved an average of 97% accuracy among all character classes in all handwriting aspects. The results show that the system is effective in identifying and correcting mistakes, thus helping individuals seeking to improve their handwriting skills.

Recently, a-IGZO has advanced toward the next-generation electronics system because of its compatibility with complementary metal oxide semiconductor (CMOS) and back-end-of-line (BOEL) based systems. A systematic electrical characterization of a-IGZO TFT related to reliability issues, such as positive bias temperature stress (PBTS) and negative bias temperature stress (NBTS), would entitle its integration into novel electronics systems. Unexpectedly, PBTS is characterized by the transition of positive Vth shift to negative Vth shift (ΔVth, the positive shift followed by the stress and temperature activated negative shift). This transition is attributed to charge trapping/trap-site generations and hydrogen migration to the active layer. The ΔVth shift mechanism depends on the temperature and voltage stress. On the other hand, a negative ΔVth shift has been observed during the NBTS operation and could be attributed to the hole trapping at the interface of GI/IGZO. An effective suppression of the gate leakage current has also been observed during reliability tests. Simulation results reveal a pronounced potential at the edges of source and drain regions, and considered the origin of hydrogen migration into the IGZO layer. Thermal image results also reveal the strong temperature/potential distribution at the edges of the source/drain regions, indorsing the simulation results.

A trustworthy cloud services provider makes sure that data backups, disaster recovery, and compliance with industry standards are all met, allowing businesses to concentrate on their core competencies and innovation while leaving the complexity of IT infrastructure management to the professionals. The selection of the finest cloud services provider is a multi-attribute decision-making problem and thus, in this article, we investigate the selection of the finest cloud services provider by employing a multi-attribute decision-making technique using the invention of hesitant bipolar complex fuzzy set. The hesitant bipolar complex fuzzy sets deal not only with the data involving complex and hesitant information but also the data of dual aspects that are both positive and negative because a lot of real-life situation contains information that has hesitancy, $2^{nd}$ dimension, and a dual aspect simultaneously. Furthermore, in this manuscript, using the structure of a hesitant bipolar complex fuzzy set we initiate numerous aggregation operators. Under the environment of the proposed methodology, we invent different aggregation operators such as hesitant bipolar complex fuzzy Dombi weighted averaging, hesitant bipolar complex fuzzy Dombi ordered weighted averaging, hesitant bipolar complex fuzzy Dombi hybrid averaging, hesitant bipolar complex fuzzy Dombi weighted geometric, hesitant bipolar complex fuzzy Dombi ordered weighted geometric and hesitant bipolar complex fuzzy Dombi hybrid geometric operators. All these operators are used to solve different situations at different times. When any decision-maker wants to solve hesitation under a complex environment with $2^{nd}$ dimensions then these operators are very useful. Without proposed operators, we cannot handle the above situations because no doubt different tools for different situations are available in the market but there is no tool available for solving hesitant bipolar complex fuzzy information. Afterward, we devise a technique of multi-attribute decision-making for tackling genuine life decision-making dilemmas under the setting of hesitant bipolar complex fuzzy sets. We investigate a case study associated with cloud service providers by employing the invented technique of decision-making. In the end, we compare our newly suggested work with a few other current theories to show the effectiveness of our work.

Abstract In this article, an attribute control chart is proposed when the lifetime of a product follows a Weibull distribution in two-stage sampling, which is based on the number of failures from a truncated life test. The coefficients of the proposed double sampling attribute control chart and the test duration are determined so that the average run length when the process is in control is close to the target value. An overview is reported on how double sampling np control charts work. Tables reporting the out-of-control average run lengths are given for various shift parameters. A case study is given to illustrate the proposed control chart for industrial use. A comparison of two-stage and single-stage sampling of failure of products is discussed.