Clustering Error Research Articles

CONTEXTUALIZING ATTITUDES TOWARD MEDICAL AID IN DYING IN A NATIONAL SAMPLE OF HOSPICE CLINICIANS

Abstract Research on the contextual characteristics informing the care that hospice professionals offer patients interested in medical aid in dying (MAID) is lacking. Thus, we aimed to explore MAID attitudes vis-à-vis hospice values and state law. This cross-sectional study recruited a convenience sample of 450 hospice-based physicians, nurses, social workers, and chaplains from national hospice and palliative care membership associations. Controlling for covariates, we fit a partial proportional odds model with clustered standard errors to examine the associations between participants’ professional experience working in a MAID-legal state, orientation toward patient-centered care, and commitment to the hospice philosophy of care, and their MAID attitudes (0 = “oppose,” 1 = “neither support nor oppose,” 2 = “support”). Results indicated that experience working in MAID-legal states (vs. not; OR = 2.42, p = .002) and increased orientation toward patient-centered care (OR = 1.38, p < .001) were significantly associated with higher odds of greater MAID support across thresholds. Conversely, the direction and statistical significance of commitment to the hospice philosophy of care depended on the threshold of the dependent variable (odds of MAID support higher than “oppose”: OR = 0.98, p = .326; odds of MAID support higher than “neither support nor oppose”: OR = 1.05, p = .043). Findings provide the first quantitative evaluation of a predominantly theoretical debate concerning perceptions of MAID’s concordance with hospice values. Noting legal protections for conscientious objection, higher support from clinicians in states where MAID is legal may suggest training opportunities as levers to improve patient access.

Association between military service identified through public data tracing and Alzheimer’s disease neuropathology at autopsy

AbstractBackgroundMilitary veterans face increased risk of Alzheimer’s disease and related dementias (ADRD) compared to the general population, but the association between the military exposome and ADRD pathology is poorly understood. Existing biobanks rarely collect military service information, complicating efforts to characterize military risk factors. Public data tracing methods linking the military exposome to existing neuropathological data have the potential to catalyze research on the mechanisms driving brain health disparities.MethodSubjects were decedents from two Alzheimer’s Disease Research Center brain banks who died between 1986‐2018 and were characterized on neurofibrillary tangle burden (NFT‐B score, n = 1154). Military service was characterized through public data tracing (Figure 1): systematic extractions from public records including obituaries, newspapers, and enlistment records (Figure 2). NFT‐B score, per National Institute of Aging and Alzheimer’s Association guidelines for neuropathologic change, was extracted from the standardized Neuropathology Dataset form or from original autopsy reports. Generalized ordered logistic regression with site‐level clustered standards errors was used to model NFT‐B score, adjusting for age, sex, and year of death.ResultPublic data tracing identified 373 decedents (32% of the sample) with a history of military service. After adjusting for age, sex, and year of death, decedents with a history of military service had a 12% greater odds (OR = 1.12, 95% CI: 1.11–1.13) of a more severe NFT burden compared to those without a history of military service, which was proportional when comparing the odds at each increasing B score level (B score = (1,2, or 3), vs (0), B score = (2,3) vs (0,1) and B score = (3) vs (0,1,2)) (Table 1).ConclusionPublic data tracing offers a promising way to retrospectively add the military exposome into brain tissue biomarkers research. Military service was associated with more severe neurofibrillary tangle pathology, one of the primary neuropathological features of Alzheimer’s disease. Future research should explore the feasibility and validity of public data tracing for more detailed characterization of military service, such as length and branch of service, involvement in specific conflicts, or exposure to environmental toxins, which may elucidate mechanisms underlying its association with ADRD pathology.

Integrating k-means clustering algorithm for the symbiotic relationship of aesthetic community spatial science

Abstract In modern society, the scientific symbiotic relationship of aesthetic community space is crucial for the development of cities. This article aimed to explore the symbiotic relationship between aesthetic community space and science, and analyze this relationship based on the k-means clustering algorithm. The article analyzed the spatial characteristics of different types of aesthetic communities, as well as the commonalities and differences between aesthetic communities and science, including the complementary relationship between the two. The k-means clustering algorithm is integrated to classify community spatial features, thereby analyzing and optimizing the relationship between the two. The experiment selected a sample of an aesthetic community and divided it into three different types of spatial features: architectural area, landscape greening area, and other activity areas. This article also compared the clustering results of the k-means algorithm with the k-means algorithm based on three-dimensional grid space. The results showed that the k-means algorithm optimized by the principle of three-dimensional grid space had stronger clustering performance. In the experiment, four different types of clustering algorithms and multiple evaluation indicators were also selected for testing. The data showed that in the clustering of spatial features in building areas, the k-means average clustering error score was controlled at around 13–14 under different missing ratios; the minimum average clustering error score of the characteristic data of the landscape greening area was 12.1. The average clustering error score in spatial feature clustering of other activity areas reached a minimum value of 23.5 when the missing ratio was 10%, which was lower than other algorithms. The overall experimental results indicate that the fused k-means clustering algorithm can effectively partition the aesthetic community space and optimize scientific symbiotic relationships.

Music always helps: Associations of music subject choices with academic achievement in secondary education

AbstractThis paper investigates the relationship between music qualification choice and academic performance in secondary education in England at Key Stage 4 (KS4; usually at ages 15 and 16). We analysed data from 2257 pupils at 18 educational settings in a city in the southeast of England. Two regression analyses with clustered errors modelled KS4 music qualification choice and GCSE academic achievement in English, Mathematics and other English Baccalaureate subjects, while controlling for a range of demographic, academic and socio‐economic variables. Choice of music as a subject at KS4 was positively associated with the total volume of KS4 qualifications entered for examination and was also predicted by coming from an affluent neighbourhood. Furthermore, this choice of music at KS4 was associated with greater academic performance on English Baccalaureate subjects above and beyond other significant predictors (gender, language, prior academic achievement, total volume of KS4 qualifications and neighbourhood socio‐economic status; local Cohen's f‐squared = 0.09). These results point to moderate but significant additive effect of studying music at KS4 in relation to performance on core GCSE subjects. We also found that schools with KS4 music qualification choice greater than the national average were higher in overall academic attainment, in the proportion of pupils attending extra‐curricular instrumental lessons, and in our composite measure of school's engagement with a local music education hub. The results are interpreted in light of sociological theories of education in an attempt to better understand the underlying systemic factors affecting youth music engagement.

Revisiting the puzzle of capital structure determinants: an empirical study based on UK firms

PurposeThis study aims to measure impact of several firm-specific factors on alternative measures of leverage. The authors also aim to study impact of the subprime crisis on such associations.Design/methodology/approachThe authors utilized an unbalanced panel data of 973 firm-year observations on 47 UK listed non-financial firms for the years 1990–2019. Book-based and market-based long-term and total leverage measures have been used as explained variables. The explanatory variables are profitability, size, two measures of growth, asset tangibility, non-debt tax shields, firm age and product uniqueness. Fixed effect and random effect models with clustered robust standard errors have been utilized for data analysis. To find the effect of subprime crisis, original dataset was split to create pre-crisis and post-crisis datasets.FindingsThe authors find that profitability significantly reduces leverage while firms having more tangible assets use significantly more debt in capital structure. Firm size and non-debt tax shield have statistically insignificant positive impact on leverage. Having more unique products reduces use of external debt, albeit insignificantly. Growth, when measured as market-to-book ratio, has inconsistent impact, whereas capital expenditure insignificantly reduces leverage. Age is found to be an insignificant predictor of leverage. After the subprime crisis, firms started relying more on internal fund instead of external debt, more particularly short-term debt. Having more collateral is gradually becoming more important for availing external debt.Research limitations/implicationsData limitations restrict generalization of the findings.Originality/valueThis is one of the pioneering attempts to show how subprime crisis altered the theoretical domain of capital structure research in the UK.

Association of state policies modifying Supplemental Nutrition Assistance Program (SNAP) eligibility and cancer stage at diagnosis.

214 Background: Food insecurity is associated with access to care and is particularly prevalent among adolescents and young adults (AYA) with cancer. The Supplemental Nutrition Assistance Program (SNAP) was designed to minimize food insecurity for low-income households. Under the broad-based categorial eligibility (BBCE) policy, states were given the ability to relax requirements for qualifying for SNAP through increasing the income limit or eliminating the asset test. While most SNAP policy changes under the BBCE policy occurred from 2009-2011, it is unknown whether these policies affect access to care among AYA with cancer. We sought to determine whether state SNAP eligibility policies were associated with stage at diagnosis among AYA patients. Methods: We utilized the Surveillance, Epidemiology, and End Results database to obtain data from 2006-2019 for newly diagnosed patients with cancer ages 18-39 years from 11 states. All cancer types were included with the exception of liquid tumors and other unstaged cancers. Difference-in-differences analyses were utilized to evaluate changes in the percentage of localized and distant stage diagnoses from pre- to post-policy enactment between states that enacted (1) asset test elimination or (2) increased income limit policies. Linear probability models were adjusted for state and year fixed effects, race, ethnicity, sex, metropolitan residence, marital status, county-level income, cancer type, and Medicaid expansion effects. We also accounted for state-level clustering via clustered standard errors. Event study analyses were utilized to assess the plausibility of the parallel trends assumption. Results: A total of 157,380 AYAs with cancer were identified. In adjusted difference-in-differences analyses, there was a 0.67 percentage point (PP) (95% CI = 0.15 to 1.19, P=.012) increase in localized stage cancer diagnoses and a -0.81 PP (95% CI = -1.41 to -0.21, P=.008) decrease in distant stage cancer diagnoses in states with increased income limits for SNAP eligibility relative to other states. There were no statistically significant changes in the percentage of localized (0.87 PP, 95% CI = -1.27 to 3.00, P=.43) or distant (0.05 PP, 95% CI = -1.44 to 1.55, P=.94) stage cancers with policies eliminating the asset test. Among patients with cervical cancer, where screening is recommended in this age group, there were extremely similar patterns. Conclusions: State policies increasing income eligibility limits for the Supplemental Nutrition Assistance Program were associated with a higher percentage of localized stage and a lower percentage of distant stage cancers among adolescents and young adults. These data add to a growing body of literature suggesting that policies that address social determinants of health may improve access to care.

The gender gap in financial distress

ABSTRACT In this study, we investigate the gender gap in personal financial distress within the United States. Our study commences with a rigorous quantification of the gender disparity in financial distress, employing Logit regressions. Additionally, we perform a standard meditation analysis to dissect potential channels of income shocks and financial choices as well as their relative contributions to the gender disparity in financial distress. Leveraging data from the NLSY79 CYA panel, we empirically uncover that women are associated with a higher likelihood of experiencing financial distress than men. Furthermore, we provide evidence that women’s susceptibility to adverse income shocks and tendency towards making suboptimal financial choices are two potential mechanisms, with the latter accounting for a higher proportion in the identified gender gap than the former. Our findings are confirmed by various robustness checks including exploring other financial distress dimensions, accommodating additional personality traits, employing targeted subsample analyses, performing heterogeneity tests, using clustered standard errors at the regional level, and switching to an alternative sample. Our study sheds light on the effect of gender in personal financial fragility and the role of gender gap in economic outcomes.

Living Arrangements and the Speed of Chronic Disease Accumulation among Older Migrants and Natives

Abstract Background Immigrants may experience a faster health decline than natives due to the deleterious impact of migration-related experiences on healthy ageing. Support from living arrangements where family members reside together is known to provide a buffering effect for such health deterioration. This study examines the role of living arrangements in the health of immigrants versus natives, focusing on the rate of chronic disease accumulation to portray the healthy ageing processes. Methods We use data from the Survey of Health, Ageing and Retirement in Europe (SHARE). First, we employ ordinary least square regression models with clustered standard errors in the pooled sample to determine the average number of chronic diseases by migration background. Then, we run immigrant status- and living arrangement-stratified analyses to illustrate the age-related profiles of chronic disease accumulation in immigrants versus natives. Results According to our results, living with a spouse or family has a buffering effect on chronic health deterioration for natives, which disappears among immigrants. This beneficial effect is especially prevalent for native men, explaining the immigrant-native gap in the average number of chronic diseases - observed only in a couple or family households among men. Further, chronic diseases accumulate at different rates with age between immigrants and natives based on their living arrangements. Immigrant men living with a family have a slower chronic disease accumulation than native men, while among women, the accumulation speed is faster in immigrants than in natives. Conclusions Our results indicate that living with a spouse or a family member provides a different amount of protection by the migration background, leading to different healthy ageing processes between immigrants and natives. Therefore, public policies should monitor the health of the ageing migrant population not only as a whole but also by living arrangements. Key messages • Types of living arrangements are associated with age-related profiles of chronic disease accumulation. • Living with a spouse or a family member provides a different amount of protection by the immigration background, leading to different healthy ageing processes between immigrants and natives.

Listing on environmental, social and governance index and financial distress: does the difference-in-differences matter?

PurposeEnvironmental, social and governance (ESG) issues have become the cornerstone of investment decisions in firms today. With that, publicly traded ESG indices (like the BSE ESG 100 index in India) have come into existence. The existing literature signifies that ESG generates financial implications and induces stability. The current study aims to test whether the firms listed on the ESG index (ESG-sensitive firms) face less financial distress than those not listed on such an index.Design/methodology/approachThe study applies panel data difference-in-differences (DID) regression by considering ESG as an unstaggered treatment to 74 non-financial firms listed on India's Bombay Stock Exchanges (BSE) 100 index. In total, 42 firms are ESG treated as they got listed on the BSE ESG 100 index, formed in 2017. The remaining 32 firms form the control group. The confidence intervals and standard errors are estimated using clustered robust errors and the Donald and Lang method.FindingsListing on the ESG index matters for financial stability; differences in financial distress are significant on financial distress. ESG-sensitive firms face less financial distress than non-ESG firms (or firms not perceived as ESG-sensitive). The results are consistent across two financial distress measures, Altman z-scores for emerged and emerging markets. Thus, the DID in distress status between ESG-sensitive and non-ESG firms matter.Practical implicationsThe study creates vibrant implications for practitioners using ESG to reduce financial distress.Originality/valueThe study is one of its kind to test the treatment effects of ESG on firm value and quantify treatment effects on financial distress.

Environmental regulation and de-zombified governance of heavy polluting enterprises: Quasi-natural experiment of the China's New Environmental Protection Law

Removing backward production capacity and clearing out zombie enterprises is crucial to realize the carbon peaking and carbon neutrality goals. However, as a command-control environmental regulation policy, whether the New Environmental Protection Law (NEPL) can guide heavy-polluting enterprises to achieve green transformation and get rid of the zombified risk remains unknown. Our article applies the difference-in-difference (DID) method to explore the impact of the stricter environmental regulation policies on the de-zombification of heavily polluting enterprises in the context of the implementation of NEPL in China. The results show that the implementation of NEPL significantly reduces the zombified risk of heavy-polluting enterprises. The results were valid by a series of robustness tests of propensity score matching, common trends test, placebos test, conducting double clustered standard errors to industry and province level, eliminating other policy interference, ruling out reverse causation, easing the potential endogeneity problems, and controlling enterprise's internal management level. Furthermore, there is a time lag in NEPL policy, which requires a long-term mechanism. Moreover, the NEPL can promote enterprise de-zombie governance through the mechanism of market competition and market allocation. The effects of the NEPL are heterogeneous due to the nature of enterprise property rights, enterprise scale, industry radiation capacity, regional development level, and internal management capacity. This article provides policy implications for developing countries to promote de-zombified governance of their economic systems and defuse risks of green transition.

Coarse-to-fine segmentation of individual street trees from side-view point clouds

Segmenting individual street trees from a street side-view point cloud is the first and key step of obtaining a street tree inventory. Using the classification-segmentation framework for individual tree segmentation makes tree detection simple and accurate, but segmenting overlapping trees is still challenging. To more accurately segment overlapping trees, a coarse-to-fine method for segmenting individual street trees from a side-view point cloud is proposed in this paper. Following the classification-segmentation framework, the tree points are first detected from the side-view street point cloud by a pointwise classifier fused from 13 local geometric features and then trained using random forest (RF). Second, the tree proposals are obtained by density-based spatial clustering of applications with noise (DBSCAN) clustering and detection error filtering. Third, the overlapping tree proposals are recognized by trunk identification, and the single tree proposals are directly output as individual trees. Fourth, the overlapping trees are roughly divided into individual tree proposals through vertical planes. Finally, individual trees with optimized contours are obtained by iteratively using DBSCAN clustering and k-nearest neighbor (k-NN) classification. The side-view point cloud of a 290 m-long urban street containing 77 street trees is captured by a hand-held mobile ZEB Horizon laser scanner. The tree detection attained an F1 score of 0.9916 with a precision of 0.9989 and a recall of 0.9864. For individual tree segmentation, the F1 score was 0.9745 with a precision of 0.9672 and a recall of 0.9819. Compared to two current classification-segmentation methods, the overlapping tree segmentation F1 scores were increased by 0.0914 and 0.0617, respectively. The proposed method can be applied to tree parameter extraction, which is an important urban forest inventory task and is crucial for urban forest management. In our experiment, the root mean squared error (RMSE) of the trunk diameter at breast height (DBH) estimation was 0.8485 cm.

Protected areas are effective on curbing fires in the Amazon

The assessment of whether protected areas (PAs) inhibit environmentally damaging fires is challenged by three sources of bias: (i) non-random site protection, (ii) influence of simultaneous land use and environmental changes, and (iii) unobservable time-invariant fire predictors. These biases were mitigated here with a quasi-experimental approach combining matching, differences-and-differences and the fixed-effects estimator. An extensive pixel-level dataset covering the 6 million km2 of the Amazon region across 18 years was analyzed. The five types of PAs considered differed on governmental level (national or subnational) and protection stringency (permitting either indirect or direct use of resources, or only indigenous inhabitants). Results attested PAs' effectiveness in containing fires on average, although in a degree 1.4 orders of magnitude smaller than in biased estimation. Still, the effect was considerable, with fire likelihood reduced, depending on the fire measure, in 7% or 12% in the average pixel-year. Also, the effect was heterogeneous, being larger in subnational PAs, albeit not with indirect use permitted, whereas national direct use PAs did not avoid fires. Results were robust to clustered standard errors and, in a reasonable degree, to Rosenbaum's hidden bias test. Consequently, expanding subnational PAs and enforcing all PA types could curb fires' environmental damages.

Background initialization in video data using singular value decomposition and robust principal component analysis

Background initialization is used in video processing applications to extract a scene without the foreground scene. In recent times, the issue of background initialization has gained researchers’ attention in different fields such as video surveillance, video segmentation, computational photography, and so on. The initialization of the background is affected due to different complex dissimilarities such as shadow, intermittent movement, illumination, camera jitter, and clutter. To overcome the aforementioned issues, this paper proposes a decomposition using the combination of the Singular Value Decomposition (SVD) and Robust Principal Component Analysis (RPCA) for Singular Spectrum Analysis (SSA) to perform an effective background initialization. The incorporation of RPCA in SVD is used to overcome the issues related to non-Gaussian noise and it also uses an effective structural knowledge of the video input i.e. sparse and low rank matrix which improves the Peak-Signal-to-Noise-Ratio (PSNR) of the background image. The SBI dataset was used to analyze the performances of SSA-SVDRPCA. The SSA-SVDRPCA is analyzed using MultiScale Structural Similarity Index (MSSSIM), Average gray-level error (AGE), Percentage of clustered error pixels (pCEPS), Percentage of error pixels (pEPs), and PSNR. The existing approaches such as Background Initialization Singular Spectrum Analysis (BISSA) and Quaternion-based Dynamic Mode Decomposition (Q-DMD) are used to compare with the SSA-SVDRPCA method. The PSNR of the SSA-SVDRPCA for Board class is 30.39 dB which is higher than the BISSA and Q-DMD.

Nonsmooth Optimization-Based Model and Algorithm for Semisupervised Clustering.

Using a nonconvex nonsmooth optimization approach, we introduce a model for semisupervised clustering (SSC) with pairwise constraints. In this model, the objective function is represented as a sum of three terms: the first term reflects the clustering error for unlabeled data points, the second term expresses the error for data points with must-link (ML) constraints, and the third term represents the error for data points with cannot-link (CL) constraints. This function is nonconvex and nonsmooth. To find its optimal solutions, we introduce an adaptive SSC (A-SSC) algorithm. This algorithm is based on the combination of the nonsmooth optimization method and an incremental approach, which involves the auxiliary SSC problem. The algorithm constructs clusters incrementally starting from one cluster and gradually adding one cluster center at each iteration. The solutions to the auxiliary SSC problem are utilized as starting points for solving the nonconvex SSC problem. The discrete gradient method (DGM) of nonsmooth optimization is applied to solve the underlying nonsmooth optimization problems. This method does not require subgradient evaluations and uses only function values. The performance of the A-SSC algorithm is evaluated and compared with four benchmarking SSC algorithms on one synthetic and 12 real-world datasets. Results demonstrate that the proposed algorithm outperforms the other four algorithms in identifying compact and well-separated clusters while satisfying most constraints.

Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern California

Abstract. Environmental streamflow management can improve the ecological health of streams by returning modified flows to more natural conditions. The Ecological Limits of Hydrologic Alteration (ELOHA) framework for developing regional environmental flow criteria has been implemented to reverse hydromodification across the heterogenous region of coastal southern California (So. CA) by focusing on two elements of the flow regime: streamflow permanence and flashiness. Within ELOHA, classification groups streams by hydrologic and geomorphic similarity to stratify flow–ecology relationships. Analogous grouping techniques are used by hydrologic modelers to facilitate streamflow prediction in ungaged basins (PUB) through regionalization. Most watersheds, including those needed for stream classification and environmental flow development, are ungaged. Furthermore, So. CA is a highly heterogeneous region spanning gradients of urbanization and flow permanence, which presents a challenge for regionalizing ungaged basins. In this study, we develop a novel classification technique for PUB modeling that uses an inductive approach to group perennial, intermittent, and ephemeral regional streams by modeled hydrologic similarity followed by deductively determining class membership with hydrologic model errors and watershed metrics. As a new type of classification, this hydrologic-model-based classification (HMC) prioritizes modeling accuracy, which in turn provides a means to improve model predictions in ungaged basins while complementing traditional classifications and improving environmental flow management. HMC is developed by calibrating a regional catalog of process-based rainfall–runoff models, quantifying the hydrologic reciprocity of calibrated parameters that would be unknown in ungaged basins and grouping sites according to hydrologic and physical similarity. HMC was applied to 25 USGS streamflow gages in the “South Coast” region of California and was compared to other hybrid PUB approaches combining inductive and deductive classification. Using an average cluster error metric, results show that HMC provided the most hydrologically similar groups according to calibrated parameter reciprocity. Hydrologic-model-based classification is relatively complex and time-consuming to implement, but it shows potential for simplifying ungaged basin management. This study demonstrates the benefits of thorough stream classification using multiple approaches and suggests that hydrologic-model-based classification has advantages for PUB and building the hydrologic foundation for environmental flow management.

Does my boss’s boss matter? Examining the effects of teleworking and manager quality on employee job satisfaction during the COVID-19 pandemic

PurposeThis study explicitly explores the moderating role of management quality, at multiple organizational levels, in the relationship between telework and job satisfaction.Design/methodology/approachThis study employs fixed effects regression with clustered robust standard errors at the departmental level to account for the multilevel nature of the data.FindingsThe results of fixed effects analyses suggest that when the quality of one's direct supervisor and the quality of their supervisor's manager is high, the relationship between job satisfaction and telework frequency becomes stronger and positive.Originality/valueThis research illuminates the crucial moderating role of management quality at multiple organizational levels in the relationship between telework and employee job satisfaction during an unprecedented workforce shock.

Impact of ICT Access on Total Factor Productivity(TFP) in Asian Economies

Link between Information and Communication Technology (ICT) and Total Factor Productivity (TFP) has been explored in detail. Particularly in the industrialized world, the productivity conundrum has received much attention. This study aims to shed light on this connection within the context of the productivity riddle by empirically evaluating the effect of ICT Access on Total Factor Productivity in Nine Developing Economies in Asia from 2000- 2018. The empirical research is based on a PCA representing the ICT Access and other control variables that can potentially impact growth of Total Factor Productivity (TFP). Study employed FE model with robust clustered standard errors. Outcomes designate that ICT Access, Investment (I), and, R&D impact the TFP growth of sample countries. The study contributes to the ICT and TFP relationship by dwelling deep into the component of ICT. The policy implications drawn from this research exercise point to the fact that ICT’s total impact on TFP can only be realized by increasing access to ICT thereby reducing the digital divide and making ICT accessible for all in a given economy

Determination of the number of clusters on images from space optic-electronic observation systems using the k-means algorithm

The object of research is the process of clustering images from space optical-electronic surveillance systems. The main hypothesis of the study assumed that experimental studies would make it possible to determine the number of clusters on images from space optical-electronic surveillance systems when using the k-means algorithm. The method of clustering images from space optical-electronic surveillance systems using the k-means algorithm, unlike the known ones, implies: – splitting the source image into Red-Green-Blue brightness channels; – determination of the Euclidean distance between pixels; – distribution of the entire set of image pixels into clusters; – recalculation of "centers" of each subset; – reassignment of new "centers" of each cluster; – minimization of the total intracluster variance. Experimental studies were conducted on the clustering of the original image using the k-means method at different values of k. It was established that with an increase in the value of k, the visual quality of clustering improves, and it is possible to visually determine a larger number of clusters in the images. To determine the number of clusters, the sum of clustering errors of type 1 and 2 at different values of k was evaluated. It was established that when the value of k increases, the sum of errors of the 1st and 2nd kind initially decreases exponentially. A further increase in the value of k does not lead to a significant decrease in errors of the 1st and 2nd kind. It was established that for a typical image from the space optical-electronic observation system, the value of k in the clustering method based on the k-means algorithm should be equal to 4. At the same time, the sum of errors of the 1st and 2nd kind is 31.3 %. Further research is directed to the development of clustering methods that reduce the sum of errors of the 1st and 2nd kind

K-Means Module Division Method of FDM3D Printer-Based Function–Behavior–Structure Mapping

Product performance, function, cost, and the level of module generalization are all significantly influenced by product modular design, but different goods require different division indicators and techniques. The purpose of this study is to provide a set of appropriate modular division techniques for FDM 3D printers. This research offers an ecologically friendly module division index and uses module clustering as the module division principle in accordance with the current industrial development trend and the fundamental requirements of FDM 3D printer consumers in the current market. The K-means algorithm is used to use the Jaccard similarity coefficient as the metric of similarity of the DSM clustering process to realize the module division of the FDM 3D printer after studying the function–behavior–structure mapping model of the 3D printer. Additionally, the elbow method–cluster error variance and average contour coefficient evaluation systems were built, respectively, in order to verify the viability of the FDM 3D printer module division method and obtain the best module division results. By analyzing these two systems, it was discovered that when the FDM 3D printer was divided into three modules, the in-cluster error variance diagram obviously had an inflection point, and the average profile coefficient and other modular approaches that need to be adjusted to their respective goods can use this division method as a theoretical foundation and point of reference.

The political budget cycles in emerging and developing countries

PurposeThis paper examines the political budget cycles in emerging and developing countries using a sample of 91 countries from 1992 to 2019.Design/methodology/approachThis paper employs a pooled ordinary least squares (OLS) model with clustered standard errors at the country level. To address endogeneity issues, the authors also employ a two-step system generalized methods of moments model.FindingsThe authors find clear evidence of political budget cycles in emerging and developing countries. The authors consistently find that incumbents increase total government spending, particularly in economic affairs, public services and social welfare, in the year before an election and the election year. In contrast, they contract spending in the year after an election.Research limitations/implicationsPolicymakers should be aware of the political budget cycles during election years. Promoting control of corruption and democracy helps to alleviate the effects of the political budget cycles in emerging and developing countries.Originality/valueThe authors are among the first to explore the political budget cycles in emerging and developing countries by focusing on the total government spending and its main compositions, including expenditures on economic affairs, public services and social welfare. Besides, the authors also explore the conditioning effects of control of corruption, political ideology and democracy.