Statistically Research Articles

ظاهرة التناص مع القرآن والحديث في رواية "القاهرة الجديدة" لنجيب محفوظ: دراسة وصفية تحليلية

The concept of religious intertextuality involves integrating selected religious texts, whether from the Holy Quran or the noble Prophetic Hadith, into the original text of a novel. These texts are carefully chosen to harmonise with the narrative context and serve a cognitive or artistic purpose. It is undeniable that the Holy Quran and the Prophetic Hadith are among the most significant sources from which both ancient and contemporary novelists have drawn inspiration, prominently featuring them in their creative works. Consequently, this research aims to investigate the phenomenon of religious intertextuality in Naguib Mahfouz’s novel “Cairo Modern,” intending to comprehend the reason for its employment within the novel’s texts. The research follows a descriptive-analytical statistical methodology. Among the key findings are that Naguib Mahfouz was significantly influenced by Islamic culture, as evidenced by his extensive incorporation of religious texts from the Holy Quran and the Prophetic Hadith in “Cairo Modern.” This influence extends to shaping the events and narrative structure, enriching the fictional texts to generate new meanings. The results also suggest that creative writing critically depends on such literary vitality derived from the mechanism of interweaving and blending known texts, known as intertextuality. This process proves to be a crucial and dynamic tool for both authors and readers alike.

Analysis of Economic Convergence in the European Union

Abstract The article analysis the economic convergence of member states of the European Union through ten main macroeconomic indicators. Employing a combination of statistical methodologies encompassing, such as descriptive statistics, multiple regression and ANOVA analysis, the research intends to reveal an updated outlook and also the big picture of economic convergence process within the European Union. Its results, analysing economic indicators, show the serious convergence trends between economies of EU-27 member states in general, but particularising, we can observe variations of the progress for each member state. Also, has been remarked that when the GDP per capita has increased in most of the member states, the differences have significatively decreased, these still exist. Moreover, convergence is discernible in inflation rates, unemployment rates, and trade balances. In the same way, the analysis pointed out also the differences that exist between member states, which have to be treated and separated, but together through inclusion policies, social and economic, to have as a final goal, the absolute convergence to be realised through shorter period of time than the actual trend. Premises, likewise, the realities that exist in the present can lead to a deeper and feasible convergence, even if it can offer the growing rhyme in comparing some states with others. In conclusion, the article highlights the fact that is much more than necessary that political strategies of member states individually, and also member states of the European Union to get together regarding aligning to the best economic and social level.

Simultaneous optimization of activation conditions of alperujo (two-phase olive mill waste) using a desirability function: Production of porous carbons for adsorption and energy storage

Valorization of agro-industrial waste is considered an essential step towards a circular economy. The potential reuse of these byproducts in practical applications requires feasibility assessments and optimization of variables in order to be profitable. In the present study, an abundant and problematic residue from the olive oil industry, “alperujo” (two-phase olive mill waste), was used as precursor material for activated carbons. A simultaneous optimization methodology was applied to maximize two response variables: energy storage capacity and lead (Pb) removal efficiency. The simultaneous optimum conditions were 3.66 g of KOH per g of carbonized residue and a temperature of 650 °C for 120 min. The result was a microporous activated carbon with an energy storage capacity of 252 F/g and a lead removal from aqueous solutions of 68.8 %. These values are comparable with those offered by commercial activated carbons, evidencing that porous materials obtained from alperujo using unique activating conditions for both applications present a remarkable performance. The novelty of the present study lies in the application of a useful statistical methodology for simultaneous optimization through the “desirability function”, resulting in a reduction in cost and time. Our results have demonstrated that it is possible to generate activated carbons with the highest performance for two different applications using the same combination of activation variables.

Minimal Common Oncology Data Elements Genomics Pilot Project: Enhancing Oncology Research Through Electronic Health Record Interoperability at Vanderbilt University Medical Center.

The expanding presence of the electronic health record (EHR) underscores the necessity for improved interoperability. To test the interoperability within the field of oncology research, our team at Vanderbilt University Medical Center (VUMC) enabled our Epic-based EHR to be compatible with the Minimal Common Oncology Data Elements (mCODE), which is a Fast Healthcare Interoperability Resources (FHIR)-based consensus data standard created to facilitate the transmission of EHRs for patients with cancer. Our approach used an extract, transform, load tool for converting EHR data from the VUMC Epic Clarity database into mCODE-compatible profiles. We established a sandbox environment on Microsoft Azure for data migration, deployed a FHIR server to handle application programming interface (API) requests, and mapped VUMC data to align with mCODE structures. In addition, we constructed a web application to demonstrate the practical use of mCODE profiles in health care. We developed an end-to-end pipeline that converted EHR data into mCODE-compliant profiles, as well as a web application that visualizes genomic data and provides cancer risk assessments. Despite the complexities of aligning traditional EHR databases with mCODE standards and the limitations of FHIR APIs in supporting advanced statistical methodologies, this project successfully demonstrates the practical integration of mCODE standards into existing health care infrastructures. This study provides a proof of concept for the interoperability of mCODE within a major health care institution's EHR system, highlighting both the potential and the current limitations of FHIR APIs in supporting complex data analysis for oncology research.

Genomic profiling of lung adenocarcinoma and squamous carcinoma in Indian patients: Implications for precision oncology.

e20599 Background: Lung cancer, the leading cause of cancer related mortalities, remains a significant global health burden. Despite advances in next generation sequencing technologies, the genomic landscape of non-small cell lung cancer (NSCLC) in the Indian patients remains underexplored. In this study, we aimed to elucidate the genomic landscape of lung adenocarcinoma and squamous carcinoma among Indian patients using targeted sequencing data of over 400 lung cancer patients. Methods: Utilizing targeted sequencing of 50 established cancer-related genes, we conducted a comprehensive analysis of the genomic alteration profiles in 325 lung adenocarcinoma and 81 lung squamous carcinoma samples from Indian patients. Further, we employed robust statistical methodologies to delineate correlations between genomic alterations and clinical characteristics including age, gender, extent of disease, habits and PD-L1 status. Furthermore, our investigation revealed distinctive genomic signatures of NSCLC specific to the Indian population through comparative analyses across various ethnicities. Results: EGFR mutations were detected in 45.8% of lung adenocarcinoma and a notable 18% of lung squamous carcinoma cases among Indian patients, showing significant prevalence among females and non-smokers. Alongside EGFR, lung adenocarcinoma patients exhibited frequent alterations in TP53 (27.4%), ALK (11.4%), and KRAS (10.2%), while lung squamous carcinoma cases predominantly showed mutations in TP53 (40.7%), PIK3CA (17.3%), and CDKN2A (8.6%). Remarkably, 68% of lung adenocarcinoma cases deemed eligible for NCCN-approved targeted therapies. Additionally, it was observed that 60% of PD-L1-negative squamous carcinoma patients harbored TP53 alterations, indicating intriguing therapeutic implications. Overall, we observed alterations in 88% lung adenocarcinoma and 70% lung squamous carcinoma patients. Conclusions: Our study unveiled distinct genomic variations present in both adenocarcinoma and squamous carcinoma patients from India, revealing crucial insights for precision medicine and clinical management of lung cancer. Our findings provide a significant lead for designing clinical trials aimed at evaluating EGFR inhibitors in lung squamous carcinoma patients, potentially expanding therapeutic options for Indian lung cancer patients.

Assessing Timely Migration Trends Through Digital Traces: A Case Study of the UK Before Brexit

Digital trace data presents an opportunity for promptly monitoring shifts in migrant populations. This contribution aims to determine whether the number of European migrants in the United Kingdom (UK) declined between March 2019 and March 2020, using weekly estimates derived from the Facebook Advertising Platform. The collected data is disaggregated according to age, level of education, and country of origin. To examine the fluctuation in the number of migrants, a simple Bayesian trend model is employed, incorporating indicator variables for age, education, and country. The Facebook data indicates a downward trend in the number of European migrants residing in the UK. This result is further confirmed by the data from the Labour Force Survey. Notably, the outcomes reveal that in the run-up to Brexit, the most significant decline occurred among the age group of 20 to 29 years old – the largest migrant group – and the tertiary educated. This analyses could not be implemented with traditional data sources such as the Labour Force Survey, because this level of disaggregation is not provided. However, there are also important limitations associated with digital trace data, such as algorithm changes and representativeness. These limitations need to be addressed by employing sound statistical methodologies. Nevertheless, this research shows the potential of digital trace data in anticipating migration trends at a timely granularity and informing policymakers.

Potential lethality of organochlorine pesticides: Inducing fatality through inflammatory responses in the organism

BackgroundOrganochlorine pesticides, with their environmental persistence and bioaccumulation potential, have gained significant attention. This study explores the impact of organochlorine pesticides on mortality and chronic diseases, investigates their link to inflammatory states, and examines the role of anti-inflammatory diets in mitigating adverse reactions to these pesticides. MethodsThis study, with 2,847 participants, used gas chromatography and mass spectrometry to measure organochlorine pesticide exposure in NHANES data. Conventional statistical methodologies, encompassing survival curves, Cox proportional hazards regression, regression analysis, and restricted quadratic spline analysis, were employed to investigate the association between pesticides and mortality, chronic ailments, and inflammation. Furthermore, machine learning techniques, comprising RF, AdaBoost, Extra-Trees, LightGBM, and BPNN, were leveraged to evaluate the impact of pesticides on chronic disease and mortality prognostication. ResultsOrganochlorine pesticides were significantly and positively correlated with increased mortality (p<0.05). Additionally, these pollutants were linked to the incidence of chronic diseases such as chronic kidney disease, diabetes, and hypertension (p< 0.05). Our study, utilizing various machine learning models, also showed a notable increase in the Area Under the Curve when incorporating organochlorine pesticide indicators into the model as opposed to excluding them. Furthermore, strong correlations were observed between serum c-reactive protein (CRP) and CRP to serum albumin ratio (CAR) concentrations with these substances, demonstrating their pro-inflammatory effects at specific concentrations. Interestingly, cutting down on dietary inflammation through changes in diet effectively reduced the risk of death at high organochlorine pesticide exposure levels, but the effect was less noticeable at low to moderate exposure levels. ConclusionsExposure to organochlorine pesticides was linked to a higher risk of mortality, likely due to an increased prevalence of chronic diseases. In this context, inflammation played a crucial role, and adopting an anti-inflammatory diet significantly reduced the mortality risk associated with these pesticides.

Exploring the evolution of machine scheduling through a computational approach

Since 2000, the field of machine scheduling—an integral part of computer science and operations research—has seen significant advancements. This paper explores the dynamic progression of machine scheduling, offering a detailed overview of its past advancements, current practices, and future directions. Anchoring the research in robust data analysis and statistical methodologies, the paper reveals the subtle yet impactful changes that have characterized the field in the last two decades. It examines the prominence of various scheduling problems, identifies leading research journals, and highlights international contributions and collaborations, thereby offering a thorough guide to the machine scheduling ecosystem. The study delves into specific problem characteristics and assesses performance criteria and solution methods to provide an in-depth view of the field's multifaceted nature. Ultimately, this paper captures the essence of machine scheduling's evolution and suggests new paths for exploration. The insights gained contribute significantly to academic discussions and equip practitioners with a comprehensive understanding of the dynamic landscape of machine scheduling.

Influence of Motivation and Work Environment on Employee Performance

This study aims to analyze the impact of motivation and work environment on employee performance. It employs Descriptive and Verification Methods. The data source utilized in this study consists of a questionnaire administered to employees of a fashion firm located in Bandung. The research has a sample size of 35 respondents. The research employs Descriptive and Verification Methods as its statistical methodologies. The initial step involves employing Correlation Data Analysis, Path Analysis, and Coefficient of Determination Analysis, with assistance from the SPSS (Statistical Product and Service Solution) program version 26. After conducting a comprehensive study of the data collected, the Motivation, Work Environment, and Employee Performance at a fashion company in Bandung are categorized as somewhat reasonable. The R Square number indicates that it is 75.5%. Employee Performance is primarily influenced by Motivation and Work Environment, accounting for 75.5% of the overall impact. The remaining 24.5% is attributed to unexplored factors. In addition, Motivation has a lesser impact, accounting for 42.3% of the total influence, in contrast to the Work Environment, which has a total influence of 50.6%. Companies should prioritize creating a conducive work atmosphere while striving to optimize Motivation in a fashion company in Bandung.

Maximizing Organizational Performance: The Synergy of AI and BI

Objective: This paper focuses on ways artificial intelligence and business intelligence technologies can be utilized to improve the efficiency of an organization, this paper aims to propose a framework for organizational use to leverage AI and its performance in organizations. Theoretical Framework: In this topic, the author proposes a framework for integrating AI and BI, which includes the following components: data collection and statistical analysis, machine learning algorithms, visualization, and integration with BI systems. Method: This study will employ a statistical methodology called multiple regression, the analysis helps to examine the relationship between a dependent variable (organizational performance) and multiple independent variables (extent of BI integration, extent of AI integration, and AI and BI integration). Results and Discussion: Combining AI and BI in business processes improves decision-making, increases efficiency, and enhances customer experiences. AI automates complex processes, detects patterns, and predicts based on vast data, enhancing BI systems. AI-powered BI systems give businesses a competitive edge by improving decision-making, performance, and customer engagement. Integrating AI and BI leads to improved organizational performance. Research Implications: This paper stresses the significance of evaluating organizational structure before implementing AI-based systems. It guides infrastructure requirements for successful AI implementation in business, emphasizing security implications when making decisions based on AI-generated data. Originality/Value: Integrating AI and BI can lead to faster and more efficient business processes, resulting in higher performance. AI technology can revolutionize decision-making across industries, including business. Practical recommendations are available on how to use AI to improve decision-making in organizations.

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.