Skewness Research Articles

Balanced clustering for student admission school zoning by parameter tuning of constrained k-means

The Indonesian government issued a regulation through the Ministry of Education and Culture, number 51 of 2018, which contains zoning rules to improve the quality of education in school educational institutions. This research aims to compare the performance of the k-means algorithm with the constrained k-means algorithm to model the zoning of each school area based on the shortest distance parameter between the school location and the domicile of prospective students. The study used data from 2248 prospective students and 22 public school locations. The results of testing the k-means algorithm in grouping showed the formation of non-circular patterns in the cluster membership with different numbers of centroid cluster members. In contrast, testing the constrained k-means algorithm showed balanced outcomes in cluster membership with a membership value of 103 for each school as the cluster center. The research findings state that the developed constrained k-means algorithm solves the problem of unbalanced data clustering and overlapping issues in the process of new student admissions. In other words, the constrained k-means algorithm can be a reference for the government in making decisions on new student admissions

Impact of Trade Liberalisation and MSME Classification on Productivity of Indian Manufacturing Firms

This article examines the effects of tariff and non-tariff reductions on firm-level productivity of large as well as Micro, Small and Medium Enterprises (MSMEs) in India’s manufacturing sector for the 1999–2009 period. We calculate input and final goods tariffs, effective rates of protection and non-tariff barriers (NTBs) for broad product groups using information from India’s Exim Policy of 1997–2003 and 2004–2009 to examine this impact. We use a balanced as well as unbalanced firm-level panel data set, while accounting for firm, industry, state and time-specific factors. By using fixed effect models and Heckman’s two-step estimation procedure, we find that trade liberalisation is associated with improved firm-level productivity for large firms but not in the case of MSMEs. We posit that this might be due to the relative disadvantages that Indian MSMEs face which prevent them from benefiting from trade liberalisation. We also find that productivity gains arising from the sourcing of imported inputs have been greater than those arising from increased product competition and that NTB liberalisation has had a greater impact compared to tariff liberalisation on firm-level productivity. Changes in MSME legislation and the classification of firms are also found to have a bearing on firm performance. Our study contributes to the literature by confirming the need to focus specifically on constraints faced by MSMEs if they are to benefit from trade liberalisation. It highlights the importance of liberalising trade in intermediates as well as the role of MSME classification in enabling productivity gains. JEL Codes: L6, D24, L1, F13

Is tourism conducive to green growth? An investigation of the role of tourism-based economic expansion in the nexus of leisure tourism and business tourism

PurposeThe gradual expansion of the tourism sector is raising concerns about whether tourism-based economies are conducive to supporting green growth. Hence, the current study aims to analyze the direct impact of tourism motives on green growth along with the indirect impact of tourism-based economic expansion while controlling for country risk and renewable energy.Design/methodology/approachAn unbalanced panel data for a sample of 21 countries comprising OECD and non-OECD economies are employed for the analysis.FindingsRegression results reveal that leisure tourism (LT) significantly and positively influences CO2 intensity compared to business tourism (BT). Propensity score matching results show that the most traveled tourist destinations contribute more to CO2 intensity than those less traveled. Mediation analysis by employing Baron and Kenny’s three-step regression, Sobel’s test and Monte Carlo test shows that tourism-based economic expansion significantly mediates between the nexus of LT and CO2 intensity.Practical implicationsResults of the study provide useful practical implications for sustainable economy and green growth. It recommends to mitigate the challenges of LT, reducing the negative impact and to harness the potential of BT, enhancing the positive influence, through various policies and practices.Originality/valueThis study is the first to examine the impact of LT and BT on green growth, to explore the role of destination popularity and the mediating role of tourism-based economic expansion in this relationship.

Role of R&D Investment on Future Performance: Evidence From Malaysian Companies

R&D Investment is an issue that is still interesting in business research today because it constantly explores company development in terms of technology and innovation. Our study intends to investigate the impact of R&D investment on Malaysian company performance in the future. This empirical study uses company financial statement data accessed via the OSIRIS database. The sample is a non-financial company in Malaysia from 2003 to 2019. This study evaluates The hypothesis using unbalanced panel data regression on 460 company years of observation. Our findings indicate that R&D investments have a degrading impact on the performance of Malaysian companies in the future. These findings indicate that companies in developing countries such as Malaysia have yet to give special attention to investing in R&D as the main business strategy.

GDF-15 is a potential candidate biomarker for an elevated risk of cardiotoxicity in breast cancer patients receiving neoadjuvant dual anti-HER2 therapy

ObjectiveGrowth differentiation factor 15 (GDF-15) is a stress-responsive cytokine that regulates myocardial injury, cardiac overloading pressure, and inflammation and is related to the risk of cardiovascular diseases and events. The current study aimed to investigate the correlation of GDF-15 levels with clinical features, biochemical indices, and especially the risk of cardiotoxicity in breast cancer patients receiving neoadjuvant dual anti-HER2 therapy.MethodsA total of 103 HER2-positive breast cancer patients who underwent neoadjuvant dual anti-HER2 therapy (trastuzumab and pertuzumab plus chemotherapy) were included. Serum GDF-15 levels before neoadjuvant treatment were detected by enzyme-linked immunosorbent assay. Cardiotoxicity was evaluated during neoadjuvant therapy by referring to a decline of ≥10 percentage points in the left ventricular ejection fraction from baseline to an absolute level less than 50%.ResultsGDF-15 exhibited a skewed distribution, with a median level of 714 (range: 207–1805) pg/mL. GDF-15 was positively correlated with age (p = 0.037), diabetes (p = 0.036), and the N-terminal pro-brain natriuretic peptide level (p = 0.013) and positively correlated with the total cholesterol level (p = 0.086) and troponin T level (p = 0.082), but these correlations were not statistically significant. A total of 6.8% of patients experienced cardiotoxicity during neoadjuvant therapy. By comparison, the GDF-15 level was greater in patients who experienced cardiotoxicity than in those who did not (p = 0.008). A subsequent receiver operating characteristic curve revealed that GDF-15 predicted cardiotoxicity risk, with an area under the curve of 0.803 (95% CI: 0.664–0.939). After multivariate adjustment, GDF-15 independently predicted a greater risk of cardiotoxicity (p = 0.020).ConclusionGDF-15 is a candidate biomarker for increased risk of cardiotoxicity in breast cancer patients receiving neoadjuvant dual anti-HER2 therapy.

A doubly robust estimator for the Mann Whitney Wilcoxon rank sum test when applied for causal inference in observational studies

ABSTRACT The Mann–Whitney–Wilcoxon rank sum test (MWWRST) is a widely used method for comparing two treatment groups in randomized control trials, particularly when dealing with highly skewed data. However, when applied to observational study data, the MWWRST often yields invalid results for causal inference. To address this limitation, Wu et al. (Causal inference for Mann–Whitney–Wilcoxon rank sum and other nonparametric statistics, Stat. Med. 33 (2014), pp. 1261–1271) introduced an approach that incorporates inverse probability weighting (IPW) into this rank-based statistic to mitigate confounding effects. Subsequently, Mao (On causal estimation using U-statistics, Biometrika 105 (2018), pp. 215–220), Zhang et al. (Estimating Mann Whitney-type causal effects, J. Causal Inference 7 (2019), ARTICLE ID 20180010), and Ai et al. (A Mann–Whitney test of distributional effects in a multivalued treatment, J. Stat. Plan. Inference 209 (2020), pp. 85–100) extended this IPW estimator to develop doubly robust estimators. Nevertheless, each of these approaches has notable limitations. Mao's method imposes stringent assumptions that may not align with real-world study data. Zhang et al.'s (Estimating Mann Whitney-type causal effects, J. Causal Inference 7 (2019), ARTICLE ID 20180010) estimators rely on bootstrap inference, which suffers from computational inefficiency and lacks known asymptotic properties. Meanwhile, Ai et al. (A Mann–Whitney test of distributional effects in a multivalued treatment, J. Stat. Plan. Inference 209 (2020), pp. 85–100) primarily focus on testing the null hypothesis of equal distributions between two groups, which is a more stringent assumption that may not be well-suited to the primary practical application of MWWRST. In this paper, we aim to address these limitations by leveraging functional response models (FRM) to develop doubly robust estimators. We demonstrate the performance of our proposed approach using both simulated and real study data.

A prospective observational study examining weight and psychosocial change in adolescent and adult eating disorder inpatients admitted for nutritional rehabilitation using a high-energy re-feeding protocol

BackgroundHigh-energy re-feeding protocols are increasingly utilised for nutritional rehabilitation in adolescents with anorexia nervosa (AN), however, concern persists that adults with AN may be at greater risk of developing complications. In addition, research on psychological outcomes of eating disorder (ED) inpatient treatment programs, and outcomes of high-energy protocols in avoidant restrictive food intake disorder (ARFID) and bulimia nervosa (BN), is limited. This study of an ED inpatient program using a high-energy protocol, compared changes in weight and psychosocial outcomes between adolescents and adults, and identified medical risk factors associated with deviation from the protocol.MethodThis prospective observational study took place in a voluntary ED treatment program in a private hospital. Weight, height, and psychosocial questionnaires (ED Examination-Questionnaire, Depression Anxiety Stress Score, Clinical Impairment Assessment and AN/BN Stage of Change) were collected from consenting adolescents (16–20 years) and adults (> 20 years) on admission and discharge. Medical tolerance to the high-energy protocol was assessed daily. Independent samples t-tests and paired samples t-tests were applied to normally distributed data, and Mann–Whitney U tests and Wilcoxon signed-rank tests to skewed data. P-values < 0.05 were considered significant statistically.ResultsNinety-seven participants were recruited. The majority (n = 91, 94%) were female and most (n = 80, 83%) had AN. Forty-two (43%) were adolescents and 55 (57%) were adults. In participants with AN, weight change (Δ) was significant [median Δ 8.0 (interquartile range (IQR) 4.3) kg]. There was no difference in rate of weight change between adolescents and adults with AN [mean Δ 1.8 (standard deviation (SD) 0.5) kg/week vs. Δ 1.8 (SD 0.6) kg/week; p = 0.841, respectively]. One (1%) participant with AN did not tolerate the high-energy protocol due to oedema. Participants achieved positive change in psychosocial questionnaire scores (p < 0.001) after the the specialist ED program, with no difference between adolescents and adults (p > 0.05).ConclusionsThis voluntary ED treatment program using a high energy re-feeding protocol was effective in achieving positive weight and psychological change for adolescents and adults with minimal adverse events. This indicates that the specialist ED program has both nutritional and psychological benefits.

Genetic variability, correlation and path analysis in the BC2F2 population of groundnut

The present study was carried out in the backcross population of groundnut involving TMV 7 and ICG 15419. Allele-specific primers were used to screen the population for high oleic acid and a total of 11 yield-contributing traits were included in this study. The number of primary and secondary branches had higher estimates of PCV and GCV whereas pod yield per plant had moderate PCV but low GCV. Along with the variability parameters, plant height, number of primary and secondary branches, pod width, 100 pod weight, oleic acid content and linoleic acid content had good estimates of heritability and genetic advance as a percent of the mean, whereas pod yield per plant had moderate and low, heritability and GAM respectively, with a negatively significant skewed distribution. Association analysis exhibited a positive correlation between the number of primary branches, number of secondary branches, pod length and 100 pod weight with pod yield per plant and it was evident that oleic acid was indirectly proportional to linoleic acid content. Hundred pod weights had the highest direct effect on pod yield per plant. Selection based on traits with a better relationship with pod yield per plant and moderate to high estimates of PCV, GCV, heritability and genetic advancement would help in accelerating the groundnut improvement program. High oleic, low linolenic lines of BC2F2 with better pod yield would be forwarded to the next generation.

Beta transformation of the Exponential-Gaussian distribution with its properties and applications

This study introduces a five-parameter continuous probability model named the Beta-Exponential-Gaussian distribution by extending the three-parameter Exponential-Gaussian distribution with the beta transformation method. The basic properties of the new distribution, including reliability measure, hazard function, survival function, moment, skewness, kurtosis, order statistics, and asymptotic behavior, are established. Using the acceptance-rejection algorithm, simulation studies are conducted. The new model is fitted to the simulated and real data sets, and its performance is reported. The Beta-Exponential-Gaussian distribution is found to be more flexible and has better performance in many aspects. It is suggested that the new distribution would be used in modeling data having skewness and bimodal distribution.

Design and demonstration of a sensing network for full-scale wind pressure measurements on buildings

There is a need for full-scale wind pressure measurements to evaluate the accuracy of peak wind load estimates from wind tunnel measurements, numerical simulations, or building codes. The objective of this study was to design a sensing network for obtaining long-term records of fluctuating wind pressures, and to demonstrate the network’s potential through deployment. A custom data-logger using the BMP388 absolute pressure sensor, which can measure pressure coefficients within ±0.1 at a wind speed of 10 m/s, was designed. The accuracy of the measured statistics was established through comparison with a standard differential sensor in a wind tunnel experiment. A network of 10 motes was deployed on the sloped roof of the 184 m tall Space Needle, obtaining over 1000 h of fluctuating pressure data. The measurements reveal a windward separation region with strong dependency of the approach wind turbulence on the pressure statistics, but positive skewness limits the peak values. The skewness becomes negative near the edge of a second, leeward separation region, resulting in peak factors up to 2.6 times larger than the code-prescribed value of 3.4. The results demonstrate that long-term full-scale measurements provide valuable insight into peak wind pressures and their dependence on approach wind characteristics.

Study on Wind-Induced Dynamic Response and Statistical Parameters of Skeleton Supported Saddle Membrane Structure in Arching and Vertical Direction

Wind tunnel tests and numerical simulations are the mainstream methods to study the wind-induced vibration of structures. However, few articles use statistical parameters to point out the differences and errors of these two research methods in exploring the wind-induced response of membrane structures. The displacement vibration of a saddle membrane structure under the action of wind load is studied by wind tunnel tests and numerical simulation, and statistical parameters (mean, range, skewness, and kurtosis) are introduced to analyze and compare the displacement data. The most unfavorable wind direction angle is 0° (arching direction). The error between experiment and simulation is less than 10%. The probability density curve has a good coincidence degree. Both the test and simulation show a certain skewed distribution, indicating that the wind-induced vibration of the membrane does not obey the Gaussian distribution. The displacement response obtained by the test has good stability, while the simulated displacement response has strong discreteness. The difference between the two research methods is quantitatively given by introducing statistical parameters, which is helpful to improve the shortcomings of wind tunnel tests and numerical simulations.

The impacts of the Ethiopian developmental state model on the competition, efficiency and profitability of banks

PurposeThis study examines the impacts of the Ethiopian developmental state model on the competition, efficiency and profitability of banks.Design/methodology/approachThe competition, efficiency and profitability of the Ethiopian bank are measured using Panzar Rose, data envelopment analysis and financial ratio. Fixed-effect panel regression methods are applied to test the direction and strength of association between the Ethiopian developmental state model and the competition, efficiency and profitability of the country's banks while controlling bank-specific market structure and macroeconomic factors.FindingsThe Ethiopian developmental state model embeds the state-directed financial system, which affects the banking industry using a range of credit allocation instruments. Of which, directed credit schemes, interest rate control and the lack of financial freedom reduce the competition and efficiency of banks. The National Bank of Ethiopia (NBE) advances to the government and the sale of Treasury bills to a captive market enhances banking competition while negatively affecting banking efficiency. Interest rate control and the lack of financial freedom lower banking profitability. Unexpectedly, directed credit schemes improve banking profitability.Research limitations/implicationsAs with any study, this one has limitations. The intra-period comparison of efficiency is based on balanced data. Future studies can use methods that can measure the efficiency of banks using unbalanced data. The computation of the yearly H-statistic is constrained by the small sample size. The use of high-frequency data for measuring competition can provide us with better insights into banking competition in Ethiopia. Furthermore, there are a number of methods for measuring banking competition, efficiency and profitability with different assumptions. Approaching the subject of this study by applying different methods will offer different insights.Practical implicationsThe contributions of this study to practice are at two levels. First, at the policy level, it enhances our understanding of the impacts of developmental state model policies, as implemented in Ethiopia, on the banking industry and therefore provides suggestions to policymakers to reform the sector's policies. Second, it offers input to the management of banks regarding the factors that impact the industry.Originality/valueThe banking industry is often studied in the context of financial liberalisation. The originality of this study lies in investigating how the competition, efficiency and profitability of banks are affected when operating in the context of significant state interventions in the industry.

An intelligent assessment method of criminal psychological attribution based on unbalance data

Criminal cases often exhibit imbalance and cannot be extended by data augmentation when classified into attribution types. To solve the problem of unbalance data in offenders’ attribution classification, this paper proposes a criminal psychological attribution assessment model by an improved Balanced TF-Distinguishing IDF method (B-TF-dIDF) and constructed a hybrid network with attention method to fuse numerical and text features for improving the accuracy. First, as a statistical method, B-TF-dIDF is presented to reduce the impact of class-imbalance for extraction of numerical features, in which a balanced element is added to reduce the effects of incorrect type keywords on classification, and a distinguishing element is added to discriminate the types of keywords. Then, an improved hybrid network model composed of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) is constructed to balance the influence of different lengths of text samples for extracting the semantic features of criminal texts. For evaluating different feature weights by their importance, Spatial Attention is used to improve CNN in the feature maps. Moreover, the self-attention is also performed to re-evaluate the mixed features. Finally, the softmax classifier provides a scientific basis for developing a hierarchical treatment mechanism further. Additionally, we build a criminal data set with labels from real cases for testing. The experiment proved that the proposed model is better than other related methods in various evaluation indicators, including the micro and macro scopes. Moreover, the F1 of minority samples has increased by 6%–8%, indicating that the proposed method can reduce the impact of class-imbalance.

Measuring income inequality via percentile relativities

AbstractThe adage “the rich are getting richer” refers to increasingly skewed and heavily-tailed income distributions. For such distributions, the mean is not the best measure of the center, but the classical indices of income inequality, including the celebrated Gini index, are mean based. In view of this, it has been proposed in the literature to incorporate the median into the definition of the Gini index. In the present paper we make a further step in this direction and, to acknowledge the possibility of differing viewpoints, investigate three median-based indices of inequality. These indices overcome past limitations, such as: (1) they do not rely on the mean as the center of, or a reference point for, income distributions, which are skewed, and are getting even more heavily skewed; (2) they are suitable for populations of any degree of tail heaviness, and income distributions are becoming increasingly such; and (3) they are unchanged by, and even discourage, transfers among the rich persons, but they encourage transfers from the rich to the poor, as well as among the poor to alleviate their hardship. We study these indices analytically and numerically using various income distribution models. Real-world applications are showcased using capital incomes from 2001 and 2018 surveys from fifteen European countries.

Dimensionality assessment in ordinal data: a comparison between parallel analysis and exploratory graph analysis.

In the social sciences, accurately identifying the dimensionality of measurement scales is crucial for understanding latent constructs such as anxiety, happiness, and self-efficacy. This study presents a rigorous comparison between Parallel Analysis (PA) and Exploratory Graph Analysis (EGA) for assessing the dimensionality of scales, particularly focusing on ordinal data. Through an extensive simulation study, we evaluated the effectiveness of these methods under various conditions, including varying sample size, number of factors and their association, patterns of loading magnitudes, and symmetrical or skewed item distributions with assumed underlying normality or non-normality. Results show that the performance of each method varies across different scenarios, depending on the context. EGA consistently outperforms PA in correctly identifying the number of factors, particularly in complex scenarios characterized by more than a single factor, high inter-factor correlations and low to medium primary loadings. However, for datasets with simpler and stronger factor structures, specifically those with a single factor, high primary loadings, low cross-loadings, and low to moderate interfactor correlations, PA is suggested as the method of choice. Skewed item distributions with assumed underlying normality or non-normality were found to noticeably impact the performance of both methods, particularly in complex scenarios. The results provide valuable insights for researchers utilizing these methods in scale development and validation, ensuring that measurement instruments accurately reflect theoretical constructs.

Machine Learning Classification Algorithms for Traffic Stops—A Comparative Study

The application of machine learning algorithms across various fields is gaining momentum, and the results increasingly emphasize the need for further testing and implementation. This is driven by the potential to streamline and expedite numerous processes. In this paper, we have employed five algorithms: KNN, Decision Tree, Random Forest, Logistic Regression, and Naive Bayes, and these algorithms have been tested in three large datasets. On average, their performance ranges from a minimum of 80% to a maximum of 90%. Data preprocessing has been completed, and concurrently, we have implemented the SMOTE algorithm to address the challenge of unbalanced data in this research. Simultaneously, the Naïve Bayes algorithm yields the most favorable results of Accuracy, Precision, Recall, and F1 Score, for the “is_arrested” class. Furthermore, to assess the performance of each algorithm, we employed metrics including Accuracy, Precision, Recall, and F1 Score. These metrics allowed us to decide which algorithm achieved the most effective classification.

Seismic Domain Identification Algorithm Using Fuzzy Logic Methods With Combined Geological and Geomorphological Data for the Case of Sakhalin Island

An algorithm for identifying seismic generation zones or “seismic domains” using fuzzy logic has been developed and tested on the island of Sakhalin. Initial data were obtained from diagrams of the distribution of “weak” zones, relief elevation distribution skewness, and magnitude of recent area deformation for one year. These data were processed using a γ-operator in fuzzy logic with γ = 0.9, which allowed us to identify areas with high seismic activity. The areas where these active areas intersect with zones with increased compressive stress values, as determined by computer modeling, were considered to be seismic zones. It was shown that, if there are not enough source materials available, it is possible to exclude information about the recent deformation field from consideration and use an assumed grid of active faults for computer modeling. This approach may be useful when analyzing areas that have not been studied well.

Relationships between Anthropogenic Activities and Distribution of Medium to Large Mammal Species Assemblage in Madi Wildlife Corridor: Implication for Biodiversity Conservation in Uganda

Wildlife corridors between protected areas increase connectivity by integrating populations into single demographic units, thereby increasing gene flows within populations and thus probability of survival. This study assessed the relationship between anthropogenic activities and mammal species assemblage within and around Madi wildlife corridor. The study adopted recce walks along a zig-zag line transect and straight line transect methods of game tracking. Global Position System (GPS) was used to map locations of direct mammal sightings, vocalizations, tracks, dung/droppings, diggings, carcasses, and skeletal remains of mammal species. Similarly, anthropogenic activities such as hunting using bow and arrows, rifles, trapping, tree cutting, charcoal burning, bush burning, cultivation, and settlements that seemed to influence assemblage of the mammals in the area of study were mapped. The result showed that the Madi wildlife corridor still contained diverse mammal species including but not limited to; the African Elephants (Loxodonta Africana sp.), Buffaloes (Syncerus caffer) Leopards (Panthera pardus), Spotted Hyena (Crocuta Crocuta), Northern Giraffe (Giraffa Camelopardalis), Uganda kob (Kobus kob thomasi), Hartebeest (Alcelaphus buselaphus) and Reedbuck (Redunca arundinum), which were comparable to that of the adjacent Murchison Falls National Park (MFNP). The relationship between anthropogenic activities and species assemblage showed a negatively skewed distribution of some of the large mammal species specifically Elephants, Giraffes, and Hippopotamus. The study recommends gazettement of a wildlife corridor between MFNP and East Madi Wildlife Reserve to promote wildlife connectivity between two adjacent ecosystem-protected areas in northern Uganda. As a long-term strategy for wildlife conservation, it is crucial to undertake a systematic assessment and prioritization and demarcation of wildlife corridors and development of a comprehensive action plan for securing them. We further recommend development of a national Wildlife Corridor Regulations to provide a framework for the sustainable conservation of biodiversity

The Specific Application and Meaning Expression of Folk Art in Urban Landscape Design Under the Optimized Interactive Genetic Algorithm

With the rapid and stable growth of China's economy and the rapid development of urban construction, urban complexes have sprung up in major cities. The lag in urban ecological environmental protection, maintenance and construction has brought great pressure to the needs of today's urban residents in terms of life, safety, and sense of belonging. In the major cities with many high-rise buildings in contemporary China, land resources are becoming more and more scarce, and the urban ecological environment is in urgent need of recycling, and due to the blind imitation of Western culture and design mode and the neglect of my country's traditional regional culture, the urban landscape lacks interaction, resonance, and sense of belonging with citizens, and the phenomenon of landscape similarity emerges in various cities, focusing on the landscape space of urban complexes. There are also these problems. Urban residents urgently need a third space that can adjust their physical, mental, and spiritual needs. How to design an urban complex landscape that meets the aesthetic needs and humanistic needs of contemporary cities and has regional characteristics has become the first important task of my research. Folk art is an artistic treasure created by the working people in their production and life. Folk art is the embodiment of cultural regionality and the foundation of national culture. It awakens people's awareness of the importance of local culture, awakens people's sense of belonging, and is closer to the local public life. Today, the living soil and social and humanistic environment of folk art are in the process of urbanization in our country, and there is a trend of gradual disappearance of lifestyle changes. How to make the contemporary urban complex landscape an organic soil for the survival, expression, application, and development of folk art is an important task in contemporary urban landscape design. Based on optimization, related concepts such as symbols, folk art symbols, urban complexes, urban complex landscape design, etc. have been sorted out. The relevant experimental results show that the construction land accuracy of the logistic regression model based on genetic algorithm has increased from 78.0% to 85.3%. kappa increased from 74.5% to 81.2%. Research shows that the logistic regression parameter optimization method based on genetic algorithm has better simulation effect than the conventional logistic regression method and is more suitable for the situation of unbalanced data distribution and many data features in the simulation of large-scale urban land dynamic changes.

Rank-based regression with bootstrapping and the least square regression for analysis of small sample interrupted time-series data: simulation studies and application to illegal organ transplants

For interrupted time series with small samples and outliers, the standard least squares regression for estimating intervention effects can be biased when the assumption of normality is violated. This article proposes a rank-based regression method combined with bootstrapping to address outliers and non-normality. The proposed method minimizes the Jaeckel dispersion function based on ranked residuals, offering robustness against outliers, skewed error distributions, and correlated errors. Simulation studies and a real-world example compare the performance of the proposed method with the least squares method under various conditions. Both produce unbiased estimates for the intervention effects under normality. However, with non-normal errors, outliers, and correlated errors, the rank-based bootstrapping method outperforms least squares producing narrower confidence intervals, higher efficiency, lower type I error rates, and greater power. Application to the Istanbul declaration on illegal organ activities confirms the improved robustness and precision of the rank-based method with bootstrapping over least squares.