Statistically Research Articles

Numerical Optimization of Variable Blank Holder Force Trajectories in Stamping Process for Multi-Defect Reduction.

An intelligent optimization technology was proposed to mitigate prevalent multi-defects, particularly failure, wrinkling, and springback in sheet metal forming. This method combined deep neural networks (DNNs), genetic algorithms (GAs), and Monte Carlo simulation (MCS), collectively as DNN-GA-MCS. Our primary aim was to determine intricate process parameters while elucidating the intricate relationship between processing methodologies and material properties. To achieve this goal, variable blank holder force (VBHF) trajectories were implemented into five sub-stroke steps, facilitating adjustments to the blank holder force via numerical simulations with an oil pan model. The Forming Limit Diagram (FLD) predicted by machine learning algorithms based on the Generalized Incremental Stress State Dependent Damage (GISSMO) model provided a robust framework for evaluating sheet failure dynamics during the stamping process. Numerical results confirmed significant improvements in formed quality: compared with the average value of training sets, the improvements of 18.89%, 13.59%, and 14.26% are achieved in failure, wrinkling, and springback; in the purposed two-segmented mode VBHF case application, the average value of three defects is improved by 12.62%, and the total summation of VBHF is reduced by 14.07%. Statistical methodologies grounded in material flow analysis were applied, accompanied by the proposal of distinctive optimization strategies for the die structure aimed at enhancing material flow efficiency. In conclusion, our advanced methodology exhibits considerable potential to improve sheet metal forming processes, highlighting its significant effect on defect reduction.

How Many Leave Europe and Why Do They? The Birth of Statistics on Global Migration. Brussels 1853

Abstract The historical study of the production of statistical knowledge concerning global migration has garnered little attention among historians. This is striking in light of the broader and richly explored domains of global migration regulation, knowledge production, and the history of sciences, including statistics. The marginalisation of this area in historical discourse is noteworthy, considering the pivotal role that statistical knowledge plays in understanding and managing migration. An examination of this topic unveils the interplay between migration policy, statistical methodologies and the contexts in which they were collected. Statistical data bears the mark of the context of its creation and incorporates the peculiarities related to the state of administrative technologies and the resources of the states that collected it. Such data also shows the imprint of social representation, that make it difficult to distinguish the ‘other’, the foreigner, the alien and the migrant, as well as the political agendas that justify the establishment of such a costly means of knowledge.

THE IMPACT OF ICT EDUCATION ON GDP EVOLUTION AT LOCAL LEVEL. A ROMANIAN CASE STUDY

Purpose – This article focusses on the impact generated on GDP growth by the number of graduates in the ICT domain of Romanian counties in the period 2014–2020. Research methodology – We have collected the number of ICT graduates, the GDP per inhabitant in EUR per Romanian county, the number of ICT employees, the number of information service activities employees from the National Institute of Statistics (Romania) and Eurostat. Additionally, we consolidate the correlation matrix and regression analysis for previous statistics indicators. Findings – We have revealed a strong correlation (85,77%) between the number of ICT graduates and the GDP per inhabitant in EUR per Romanian county, and very strong correlations between the number of ICT graduates and the number of ICT employees (94,80%) and the number of employees of information service activities (97,38%). Research limitations – The data set from the National Institute of Statistics (Romania) does not contain entries for every county, due to its statistical methodology, and other socioeconomic phenomena which contribute to the development of the ICT field were just enumerated, such as natality, immigration, and emigration. Practical implications – The study revealed that investment in ICT education (number of graduates) contributes to the local prosperity of communities, counties, and, in general, at the national level as GDP growth. Romanian government can utilize the outcomes of this paper to engage additional resources, funds from Ministry of Education, to develop a long-lived learning culture of ICT skills for the entire population. Originality/Value – The article methodologically highlights the most important ICT hubs in Romania, which are correlated with the existence of an ICT educational infrastructure (high schools and universities) and the measure of impact on GDP growth for the number of highly skilled graduates in the ICT domain. The selected national and European indicators contributed to the novelty of this study.

Approaching Statistics as a Distinct Subject and Its Impact on Reasoning and Conceptual Errors

Objective: This study aims to determine the level of statistical reasoning and misconceptions expressed by ninth-grade students in two schools. In one of the schools, statistics is taught as a specific subject, while in the other, it is included within the mathematics curriculum. Theoretical Framework: Students are taught statistics throughout their primary education, so by secondary school, they are expected to have developed a good level of statistical reasoning. However, classroom experience suggests this may not be the case. Additionally, this topic often receives less attention in favor of other subjects included in the mathematics curriculum. Method: The methodology adopted for this research includes descriptive and inferential analyses to compare the rate of correct reasoning responses and conceptual errors. Data collection was carried out using the Statistical Reasoning Assessment (SRA) test. Results and Discussion: The results showed different behaviors between the two groups. This suggests that teaching statistics separately from mathematics can influence the learning of this subject. Research Implications: The advantages offered by separating statistics from the rest of mathematics suggest that this approach should be considered in the future teaching of the subject. Originality/Value: This study contributes to the literature by demonstrating that the separation of statistics from mathematics teaching should be considered. The relevance and value of this research are evidenced by the need to improve students' statistical reasoning abilities.

Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables

This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p < 0.0001). This positive trend is coupled with an increase in precipitation by 0.4801 mm/year (p = 0.0016). In contrast, our analysis recorded a slight decrease in temperature (− 0.01011 °C/year, p < 0.05) and a reduction in solar radiation (− 0.27526 W/m2/year, p < 0.05). Notably, cross-wavelet transform analysis underscored significant coherence between NDVI and climatic factors, revealing periods of synchronized fluctuations and distinct lagged relationships. This analysis particularly highlighted precipitation as a primary driver of vegetation growth, illustrating its crucial impact across various Pakistani regions. Moreover, the analysis revealed distinct seasonal patterns, indicating that vegetation health is most responsive during the monsoon season, correlating strongly with peaks in seasonal precipitation. Our investigation has revealed Pakistan's complex association between vegetation health and climatic factors, which varies across different regions. Through cross-wavelet analysis, we have identified distinct coherence and phase relationships that highlight the critical influence of climatic drivers on vegetation patterns. These insights are crucial for developing regional climate adaptation strategies and informing sustainable agricultural and environmental management practices in the face of ongoing climatic changes.

#1549 The effect of sglt2 inhibitors (dapaglifozin) on albuminuria in patients of diabetic nephropathy above standard of care

Abstract Background and Aims Albuminuria is the hallmark of diabetic nephropathy (DN) and progression of albuminuria denotes declining renal functions culminating in End Stage Renal Disease. Intensive blood glucose control (HbA1c &amp;lt;7%), intensive blood pressure control (&amp;lt;130/90 mmHg), blockade of Renin Angiotensin Aldosterone System (RAAS) by Angiotensin converting enzyme inhibitors (ACE-i) and Angiotensin receptor blockers (ARBs) constitute the standard line of care for treatment of DN with proteinuria. Sodium Glucose cotransporter 2 inhibitors (SGLT2i) are drugs are recently introduced for proteinuria reduction with no data available in this part of the world. Hence, I, observed the antiproteinuric effect of SGLT2i in patients who have type2 diabetes mellitus (T2DM) with albuminuria and who are already on maximum doses of ACE-I or ARBs. My study population was on dapaglifozin due to easy availability and low cost. Method it is an observational study including patients who attended the out-patient clinic of Nephrology, at Medica Hospital, Kolkata between July 2022 to February 2023. Inclusion criteria: Exclusion criteria: Quantitative estimation of urine albumin was performed using the VITROS Chemistry Products mALB Reagent along with the VITROS Chemistry Products Calibrator Kit 24 on the VITROS 5,1 FS Chemistry System and the VITROS 5600 Integrated System. Blood sugar was measured by glucose oxidase method. eGFR by CKD EPI equation. Statistical Methodology Continuous variables were presented with mean and standard deviation and tested for normal distribution by the Kolmogorov-Smirnov test. Categorical variables were summarized using frequency and percentages. For testing of related samples, Wilcoxon sign ranked test is used. Statistical analyses were performed using the SPSS software with version 26.0. Results A total of 100 patients were included in the study, of which 3 patients were lost to follow up and 2 patients discontinued the study at 3 months due to fall in eGFR below 25 ml/min/1.73 m2 and 4 patients discontinued after 3 months because of fungal genital infections. After Dapaglifozin administration, a significant reduction in Urine Albumin-creatinine ratio (UACR) was observed, by a percentage decrease of 37.3% (p&amp;lt;0.001) at 3 months and of 70.2% (p&amp;lt;0.001) at 6 months from baseline. Similarly, there was an acute reduction in eGFR from baseline. A percentage decrease by 5.16% (p&amp;lt;0.001) was observed at 3 months &amp; a decrease by 5.69% (p&amp;lt;0.001) at 6 months from baseline. Conclusion Conventional approaches reduce but have not been able to stop disease progression in Diabetic Kidney Disease (DKD). SGLT2i can protect against the onset of DKD, slow disease progression independently. They have an antiproteinuric effect in patients with diabetic nephropathy which was sustained throughout the follow-up period in my study. Studies have also shown that SGLT2i reduce major adverse cardiovascular events in patients with T2DM and established cardiovascular disease. With the new results of EMPA-KIDNEY trial, SGLT2i are being used at even lower eGFR (&amp;lt;25 ml/min/1.73 m2 till on hemodialysis) and such vast health benefits, invention of SGLT2i are is a boon to the mankind.

Transforming data with the arcsine distribution for random walks

This article delves into a pioneering methodology for optimizing the analysis of random walk data by implementing the arcsine distribution. The application of the arcsine distribution serves as an imperceptible yet potent solution, mitigating asymmetry and introducing bounds while effectively modeling the nuanced characteristics intrinsic to random walk patterns. Through a meticulous exploration of the mathematical foundations and practical applications of this distribution, this study discreetly advances statistical methodologies for handling random walk data. The article illuminates the theoretical underpinnings, subtle advantages, and pragmatic implications of arcsine distribution utilization, showcasing its imperceptible yet impactful role in capturing and reshaping random walk dynamics. Through a meticulous exploration of the mathematical foundations and practical applications of this distribution, this study discreetly advances statistical methodologies for handling random walk data, particularly in the context of financial modeling.

Characterization of EaD students participating in a Research Project

Mediated by information and communication technologies in which there is no physical contact between teachers and students, Distance Education has been established in recent years in the national scenario. This article aims to discuss the characteristics and participation of the students of EaD in a Project of Scientific Initiation (SI) offered by the University of North of Paraná to the students of the courses of Social Service, Pedagogy and Physical Education in more than 400 poles EaD of the institution. This SI project consists of a General Questionnaire to analyze the profile of students from the 24 Brazilian states plus the Federal District. The analysis of the data was done through the descriptive statistical methodology.

Layout Characteristics and Design Principles of Atrium Integrated Space

With the development of China's cruise tourism industry, it is necessary to study the design and construction of cruise ships, especially the central atrium, which is an important part of public area design and represents the quality and characteristics of the entire ship's design. However, compared with Europe, China's research foundation for large-scale cruise ship design is still lacking. Therefore, studying the spatial layout characteristics of the central atrium is of great significance for improving China's cruise industry. This article focuses on the design principles and characteristics of integrated space layout in the central atrium of cruise ships. By applying basic theories from architecture, shipbuilding engineering, aesthetics, international maritime safety conventions and technology to analyzing representative central atria from 25 typical domestic and foreign cruise brands through data collection and research methods including mathematical statistics methodology, clustering methodology and cases research methodology. Through comparison and generalization of the cases, the study eventually summarizes six overall principles and four structural principles for designing integrated space in the central atrium of a cruise ship. The six overall principles include safety, rationality, applicability, artistry, coordination and technique-economics. The four structural principles include clear space circulation, natural spatial connection, theme coordination &amp; unification and prominent key levels. This study provides insights into designing integrated space in the central atriums of cruise ships that hopefully can be applied in improving China's cruise design work while also supporting guidance for future related fields. Keywords: central atrium of cruise ship, integrated space, design principles, functional layout, decoration style

Urban patterns and the geography of street robberies in the city of São Paulo, Brazil

The geography of urban crime has often been associated with the city's geography. In this study we analyze the geography of street robbery and its relation to the various urban patterns in the city of São Paulo. First, we analyze how different variables influence the spatial distribution of street robbery, using a combination of geospatial and statistical methodologies. Then, we analyze how these different variables behave over the city of São Paulo according to eight types of urban patterns. Finally, we interpret the resulting body of evidence considering existing criminological theory. Our results suggest that the geography of street robbery in São Paulo is a result of two main drivers: (1) accessibility and population flow, tied to key roadways in the city, and (2) social issues, tied to high murder rates and, in a smaller degree, low income. Within the city's geography, these two factors tend to not align spatially and are sometimes diametrically opposite. As we found, the geography of street robbery in São Paulo is a middle of the way between these two drivers, albeit closer to the social issues driver. We also discuss some policy implications and possibilities for future research.

The Dynamics of Fire Activity in the Brazilian Pantanal: A Log-Gaussian Cox Process-Based Structural Decomposition

We present a novel statistical methodology for analyzing shifts in spatio-temporal fire occurrence patterns within the Brazilian Pantanal, utilizing remote sensing data. Our approach employs a Log-Gaussian Cox Process to model the spatiotemporal dynamics of fire occurrence, deconstructing the intensity function into components of trend, seasonality, cycle, covariates, and time-varying spatial effects components. The results indicate a negative correlation between rainfall and fire intensity, with lower precipitation associated with heightened fire intensity. Forest formations exhibit a positive effect on fire intensity, whereas agricultural land use shows no significant impact. Savannas and grasslands, typical fire-dependent ecosystems, demonstrate a positive relationship with fire intensity. Human-induced fires, often used for agricultural purposes, contribute to an increase in both fire frequency and intensity, particularly in grassland areas. Trend analysis reveals fluctuating fire activity over time, with notable peaks in 2018–2021.

Enhancing the National Cancer Database content using natural language processing and electronic health record data

BackgroundThe prevalence of missing data in the National Cancer Database (NCDB) has marked implications on clinical care and research. The objective of this study was to enhance the NCDB by decreasing rates of missingness and adding new variables using automated statistical methodology. MethodsOne health system’s NCDB data from 2011–2021 was linked to electronic health record (EHR). Variables with frequent missingness and new clinically significant variables not yet included in the NCDB including patient Eastern Cooperative Oncology Group (ECOG) score, specific chemotherapy regimen, American Society of Anesthesiologists Physical Status Classification (ASA class), and discrete surgical procedure were identified in structured and unstructured EHR data. After automated incorporation of structured data from EHR, a natural language processing tool incorporating rule-based algorithms was designed to further extract variables from unstructured notes. Rates of missingness were compared between the original NCDB and the enhanced dataset, and example multivariable models were run to assess for altered model performance with reduced missingness and the addition of new clinically significant variables (chemotherapy regimen). ResultsA total of 6050 patients with NCDB records were linked to their EHR data. Prior to enhancement, rates of missingness for key variables ranged from 2.0% to 5.3%. Following dataset enhancement, missingness was significantly reduced, with relative missingness being reduced between 31.9% to 68.0%. Of the new variables added, 1367 (22.6%) of 6050 patients gained ECOG score, and 1099 (57.8%) of 1901 who received chemotherapy gained their chemotherapy regimen. Of 2989 who underwent surgery, 979 (32.8%) gained their procedure name and 621 (20.8%) gained ASA class. Comparison of the multivariable models demonstrated significant differences between the original NCDB and the enhanced dataset. Specifically, when replacing the binary predictor for chemotherapy in the original NCDB data with discrete regimens, the effect of ethnicity diminished, and the effect of radiation became significant. DiscussionWe applied statistical methodology to reduce rates of missingness in existing variables and add new variables to enrich the NCDB. While further refinement is needed to decrease missingness in new variables, this automated methodology can replace or augment manual chart review and improve the ability of to use the NCDB to study unanswered questions leading to clinical advancements in oncology.

Integration of Complex Mathematical Approaches and Statistical Methodologies in Machine Learning for Design of Robust Prediction Models

The disease known as breast cancer continues to be one of the most common and potentially fatal diseases that affect women all over the world. In order to provide effective therapy and more favorable outcomes for patients, early detection and correct diagnosis are absolutely necessary. This research explores the integration of operational research (OR) methodologies and advanced statistical methods within machine learning frameworks to enhance the prediction accuracy of breast cancer outcomes. By harnessing the power of nonlinear optimization and statistical techniques, including regression analysis and probability distribution models, we aim to refine the predictive capabilities of existing algorithms. The research employs a comprehensive dataset derived from clinical trials and patient records, analyzed through a series of machine learning models that incorporate elements of combinatorial optimization, decision analysis, and stochastic modeling. Key performance metrics, such as accuracy, sensitivity, and specificity, are evaluated against standard benchmarks to determine the efficacy of the integrated approaches. Preliminary results indicate that incorporating OR and statistical methods significantly improves model robustness and predictive accuracy. The study not only demonstrates the potential of applied mathematics in medical diagnostics but also provides a framework for future research in enhancing machine learning models for health outcomes prediction through mathematical innovations. This investigation contributes to the field of mathematical oncology by demonstrating how applied nonlinear analysis can bridge the gap between theoretical mathematical approaches and practical clinical applications, offering new pathways for early and more accurate detection of breast cancer.

Comparative Studies on English Translation of the Four Main Characters’ Titles in “Xiyouji”

The study aims to compare Anthony C. Yu’s and W. J. F. Jenner’s translations of the four main characters’ titles in “Xiyouji.” By categorizing the main characters’ titles using a statistical methodology and determining what the characters’ personalities are embodied in the main titles, the study analyzes which translation is more appropriate from the perspective of cultural communication. The most frequently used titles for “孙悟空(Sun Wukong)” are “(孙)行者(Pilgrim/Moneky)” and “(齐天)大圣(Great Sage),” accounting for 48% and 14% of his 119 titles. For “唐玄奘(Tang Xuanzang),” the predominant titles are “师父(Mater)” and “三藏(Sanzang/Tripitaka),” making up 40% and 25% of his 17 titles. “猪悟能(Zhu Wuneng)” is most commonly called “(猪)八戒(Eight Rules/Pig)” and “呆子(Idiot),” constituting 73% and 18 % of his 11 titles. As for “沙悟净(Sha Wujing),” the prevalent titles are “沙和尚(Sha Monk)” and “悟净 (Wujing),” encompassing 90% and 7% of his seven titles. Whether Sun Wukong’s justice, braveness and supernatural ability and his role as the crisis solver of the pilgrimage, Tang Sanzang’s wisdom, leadership and enlightenment and role as the leader, Zhu Wuneng’s transgressive nature and role as the rule breaker or Sha Wujing’s devoutness and role as the follower, their titles reflect their personalities and positions in the narrative. By comparing Yu’s and Jenner’s translations of these titles, the paper observes that Yu’s translation conveys the implied meanings. In contrast, Jenner’s translation is straightforward and simplified. Yu’s translation is more beneficial for the target language readers to understand Chinese Culture.

Multivariate statistical analysis of bioavailability of heavy metals and mineral characterization in selected species of coastal flora

This study presents a thorough investigation into the concentration of heavy metals and mineral composition within four distinct coastal flora species: Cyperus conglomeratus, Halopyrum mucronatum, Sericostem pauciflorum, and Salvadora persica. Employing rigorous statistical methodologies such as Pearson coefficient correlation, principal component analysis (PCA), analysis of variance (ANOVA), and interclass correlation (ICC), we aimed to elucidate the bioavailability of heavy metals, minerals, and relevant physical characteristics. The analysis focused on essential elements including copper (Cu), iron (Fe), manganese (Mn), zinc (Zn), magnesium (Mg2+), calcium (Ca2+), sodium (Na+), potassium (K+), and chloride (Cl–), all of which are known to play pivotal roles in the ecological dynamics of coastal ecosystems. Through PCA, we discerned distinctive patterns within PC1 to PC4, collectively explaining an impressive 99.65% of the variance observed in heavy metal composition across the studied flora species. These results underscore the profound influence of environmental factors on the mineral composition of coastal flora, offering critical insights into the ecological processes shaping these vital ecosystems. Furthermore, significant correlations among mineral contents in H. mucronatum; K+ with content of Na+ (r = 0.989) and Mg2+ (r = 0.984); as revealed by ICC analyses, contributed to a nuanced understanding of variations in electrical conductivity (EC), pH levels, and ash content among the diverse coastal flora species. By shedding light on heavy metal and mineral dynamics in coastal flora, this study not only advances our scientific understanding but also provides a foundation for the development of targeted environmental monitoring and management strategies aimed at promoting the ecological sustainability and resilience of coastal ecosystems in the face of ongoing environmental challenges.

Development of Electronic Test Instruments Through Vinesa to Improve Statistical Reasoning Ability on Tendency Central Materials

This study aims to assess the feasibility of developing an e-test instrument for statistics courses on central tendency material so that student learning outcomes can increase. The analysis used in this research is quantitative and descriptive. This research method uses 20 students. Methods of data collection using tests, observations, interviews, and questionnaires The procedures carried out in developing the test instrument are: 1) compiling test specifications; 2) writing test questions; 3) reviewing the item items; 4) conducting test trials; 5) analyzing the item items; 6) revising the test; 7) compiling the test; 8) carrying out the test; and 9) interpreting the test results. The ADDIE model is used in this development research. The results of the feasibility of developing electronic test instruments were stated to be valid with: a) the results of validating the material were very feasible (96%); b) the results of validating learning media were very feasible (94%). If c) the results of validating learning designs were very feasible (94%), d) the results of individual trials were very feasible (81%), and e) the results of small group trials were very feasible (82%), then the development of electronic test instruments is very feasible for use in statistical learning.

The Role of Emotional Intelligence in Leadership Effectiveness and Organisational Behavior

Objectives: The primary objective of this study is to investigate the significance of emotional intelligence (EI) in relation to leadership efficacy and organizational conduct. Specifically, the study aims to explore the constituent elements of emotional intelligence, their influence on leadership behavior, and their correlation with organizational behavior. Additionally, the study seeks to examine the impact of emotional intelligence on fostering a favorable organizational climate, improving communication, facilitating decision-making, resolving conflicts, and promoting employee engagement. Methods: This study employs a quantitative research methodology, utilizing a self-administered survey instrument. The sample consists of 60 participants holding managerial positions in various organizations. Statistical methodologies such as explanatory diagrams and correlation analysis are used to analyze the data and investigate the relationships between emotional intelligence, managerial competence, organizational behavior, and employee satisfaction. Results: The results of the study highlight a positive association between emotional intelligence and managerial competence, organizational behavior, and employee satisfaction. The findings suggest that higher levels of emotional intelligence are correlated with more effective leadership, improved organizational climate, better communication, enhanced decision-making, conflict resolution, and higher levels of employee engagement. Conclusion: In conclusion, this study emphasizes the importance of emotional intelligence in leadership effectiveness and organizational behavior. The findings underscore the positive impact of emotional intelligence on various aspects of organizational conduct and employee satisfaction. Additionally, the study suggests avenues for future research, including longitudinal inquiries, intercultural variances, exploring intervening and moderating factors, developing interventions, and conducting comparative assessments.

Explainable machine learning models for defects detection in industrial processes

Machine learning algorithms in non-linear pattern recognition for defect detection in manufacturing processes are increasingly prevalent in the context of Industry 4.0. This scenario enables factories to develop methodologies for monitoring and controlling the quality of their products, obtaining better operational efficiency indicators, and offering increasingly competitive products in the market. Explainable Artificial Intelligence can facilitate understanding how the models make decisions and assist in tracking anomaly points. This paper proposes an explainability framework for machine learning models in defect detection. This work applied the multinational industry’s approach to the tire manufacturing process. Models such as random forest, gradient boosting decision tree, light gradient boosting machine, logistic regression, support vector machine, and multi-layer perceptron were considered to evaluate the performance of tires in compliance with production standards. Statistical methodologies had to be adopted to deal with the small number of samples training the machine learning models. The random forest and logistic regression models achieved the best performances with an accuracy of 92% and 96%, respectively. The Local Interpretable Model-Agnostic Explanations and SHapley Additive exPlanations methods were used to identify the relevant process variables. The approach presents the global and local interpretations of the results, allowing for a deeper understanding of how process variables discriminate the fault. The proposed framework determines reference values for each selected process variable for tire manufacturing in compliance with quality standards. This approach provides a relevant tool for quality control management in tire manufacturing industries.

Powder-bed 3D printing by selective activation of magnesium phosphate cement: Determining significant processing parameters and their effect on mechanical strength

The present work addresses powder bed binder jetting additive manufacturing by selective magnesium phosphate cement activation. Despite the potential of this technology to aid the digitalization of the construction industry, the effect of processing parameters on the mechanical performance of printed materials has not yet been studied to generate a guideline for the further development of the technology. Statistical methodologies were used to screen the effect of four printing process parameters (printing speed, layer thickness, raster angle, and build direction on flexural and compressive strength). As the exploited technology works with constant fluid pressure, the physical interpretation of the effect of each factor can be considered taking into account the interactions between the binder materials in the powder bed. Analysis of variance (ANOVA) indicated that printing speed and layer thickness significantly affect mechanical performances. Furthermore, the layout of samples for the printing process is preferable to be parallel the printhead movement. An anisotropic behavior was observed, and the samples subjected to compressive forces parallel to the layer plane possessed lower strength values. This effect can be interpreted as a result of a weak area of low density in between layers, leading to a pronounced delamination under compression.Even though the strength of the printed material is not suitable for a structural concrete, it can be marginally improved by design of experiment and optimized for non-structural applications, such as for porous artificial stone. Design of experiment coupled with ANOVA methods can be used in the future to support the development of novel material mixtures, thus expanding the fields of application of this novel additive manufacturing technology.

A New Modification of the Weibull Distribution: Model, Theory, and Analyzing Engineering Data Sets

Symmetrical as well as asymmetrical statistical models play a prominent role in describing and predicting the real-world phenomena of nature. Among other fields, these models are very useful for modeling data in the sector of civil engineering. Due to the applicability of the statistical models in civil engineering and other related sectors, this paper offers a statistical methodology to improve the distributional flexibility of traditional models. The suggested method/approach is called the extended-X family of distributions. The proposed method has the ability to generate symmetrical and asymmetrical probability distributions. Based on the extended-X family approach, an updated version of the Weibull model, namely, the extended Weibull model, is studied. The proposed model is very flexible and has the ability to capture the symmetrical and asymmetrical shapes of its density function. For the extended-X method, the estimation of parameters, a simulation study, and some mathematical properties are derived. Finally, the practical illustration/usefulness of the suggested model is shown by analyzing two data sets taken from the field of engineering. Both data sets represent the fracture toughness of alumina (Al2O3).