Clustering Effect Research Articles

Application of Hard and Soft Acid‐base Theory to Construct Heterometallic Materials with Metal‐oxo Clusters

AbstractHeterometallic cluster‐based materials offer the potential to incorporate multiple functionalities, leveraging the aggregation effects of clusters and translating this heterogeneity and complexity into unexpected properties that are more than just the sum of their components. However, the rational construction of heterometallic cluster‐based materials remains challenging due to the complexity of metal cation coordination and structural unpredictability. This minireview provides insights into a general synthetic strategy based on Hard and Soft Acids and Bases (HSAB) theory, summarizing its advantages in the designed synthesis of discrete heterometallic clusters (intracluster assembly) and infinite heterometallic cluster‐based materials (intercluster assembly). Furthermore, it emphasizes the potential to exploit the intrinsic properties of mixed components to achieve breakthroughs across a broad range of applications. The insights from this review are expected to drive the progress of heterometallic cluster‐based materials in a controllable and predictable manner.

Investment Priorities for Technological Breakthroughs Based on Scale Effect

The article presents an analytical approach to calculating the value of technological scale effect in regional industry clusters using econometric models. This approach allows for the identification of production zones in Russia with the highest technological potential. The authors propose a method for calculating investments based on the traditional investment multiplier. This method enables rapid technological advancements in specific regional and sectoral clusters within the country. The methodology has been tested on two groups related to the agricultural sector: the main group and the control group, each consisting of seven territorial entities of the Russian Federation. The article also discusses the implementation of a selective investment policy to foster economic growth and enhance the performance of existing advanced industries.

Research on the efficiency and spatiotemporal evolution characteristics of agricultural green development in central cities of the Yangtze River Delta

IntroductionAgricultural green development (AGD) prioritizes sustainable growth by integrating economic, ecological, and social dimensions, aiming to harmonize agricultural economic development with environmental protection and social progress.MethodsThis study integrates the three-stage super-efficiency DEA-SBM model with the BP algorithm, creating an advanced DEA-SBM-BP model to overcome the shortcomings of traditional DEA in evaluation and management processes. The study further applies the Dagum Gini coefficient, kernel density estimation (KDE), and Moran’s index to assess and forecast the efficiency and spatiotemporal evolution patterns of green agricultural development in key cities within the Yangtze River Delta.ResultsThe analysis shows that AGD in the central city of the Yangtze River Delta is overall balanced; however, substantial variations exist among cities within individual provinces. Factors like macroeconomic conditions, workforce quality, and policy support play a crucial role in promoting the efficiency of AGD. Among these, macroeconomic development level has a negative impact, while labor quality and policy support exhibit bidirectional effects. Infrastructure construction, digitalization of agricultural economy, and rural security have become key factors in the green development of modern agriculture. The green advancement of agriculture in the central Yangtze River Delta region typically exhibits a marked clustering effect; however, the local clustering reveals a trend toward dispersed development.DiscussionDespite the emergence of new characteristics in agricultural production in China within the context of high-quality development, differences in resource endowments and economic structures among cities continue to be significant factors contributing to regional imbalances and changes in the agglomeration patterns of agricultural development.

Protocol for the development and piloting of a cluster randomised controlled trial for stress prevention, management, and coping mechanism among police officers in Nigeria.

Policing is a stressful occupation. Police officers are exposed to stressors that are inherent in the organization and operations of law enforcement. Similar to their counterparts around the world, many Nigeria police officers are exposed to high levels of stress, making them vulnerable to stress-related mental health conditions and other noncommunicable diseases. Despite these risks and their consequences to the health and safety of police officers and the larger society, interventions are currently not available to address this challenge in Nigeria. This pilot study aims to assess the burden of stress from a national sample of police officers and test the feasibility of an intervention for stress prevention, management, and coping mechanisms among police officers in Nigeria. This is a three-phase study: (1) a needs assessment and situational analysis using a convergent parallel, mixed-methods approach to determine the prevalence of stress and mental health burden among 1200 police officers in four randomly selected states in four geopolitical zones of the country, (2) the development of a peer-led intervention for stress prevention, management, and coping mechanism which will be tested using a cluster randomized trial among 200 police officers, and (3) the preliminary evaluation of the intervention based on knowledge about stress management and mental disorders and psychological distress and reduction in stress levels. These measures will be obtained at baseline (T0), immediate (T1), and 6-month post-intervention (T2). Feasibility will be determined based on enrollment rate, attendance, and completion of the group sessions. Multivariable linear regression models taking into account clustering effects will be used to estimate between-group differences in outcome measures. Findings from the study will inform policy review and the development of a pragmatic intervention on stress prevention and management among police officers. This will enhance the policing role of officers, thus contributing to the safety of the communities they serve. PACTR Registry: PACTR202310474721238. Registered on 6 October, 2023. https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=25663 . Protocol version number 1.0 (date: 6 October, 2023).

Evidence for clustering in iron-doped strontium titanate

Electron paramagnetic resonance spectra of iron-doped strontium titanate have been studied at room temperature for Fe concentrations between 0.2 and 1.0 mol%. The linewidth of the line associated with the 1/2 ↔ –1/2 transition of Fe3+ changes very little with with Fe concentration and the line intensity goes through a maximum between 0.40 and 0.60 mol%. The results suggest that the solid solution of Fe3+ in SrTiO3 is not random, but exhibits significant clustering effects.

Mapping the trajectories of digital technology innovation in the construction industry: an integrated SPNP and Louvain approach

PurposeDespite the continuous development and application of new digital technologies in the construction industry, there has been little research on digital technology trajectories in the construction industry. The study addresses the issue faced by the construction industry in exploring digital technology trajectories: how to comprehensively identify and analyse digital technology pathways across multiple technology fields in the construction industry.Design/methodology/approachFirstly, the digital technology patent identification and classification method based on text mining is used to identify digital technology patents and construct a digital technology innovation network. Second, the main path of the digital technology innovation network is identified with the help of SPNP. Then, the subpaths of the digital technology innovation network are identified with the help of the Louvain algorithm and SPNP. Finally, starting from the technology nodes where the main path and subpaths intersect, the technological similarity of the paths is analysed to explore the evolutionary characteristics of the technology trajectories. In light of this, the developed method is applied to the global construction industry patent dataset to analyse the trajectories of digital technologies.FindingsThe technological innovation path in the construction industry starts with construction materials and gradually expands to intelligence, automation and digital data processing technology. Equipment and devices with electronic digital data processing capabilities as well as improvements in green building technologies and user experience-enhancing technologies, may be the future of the construction industry. With the increasing demand for green buildings and intelligent buildings, the direction of digital technology innovation in the construction industry is gradually tilted towards these areas. In addition, influenced by geographic and economic factors, there is a spatial clustering effect of digital technology innovation in the construction industry.Research limitations/implicationsFuture research should analyse in depth the performance of different countries and regions in digital technology innovation and explore the root causes, motivations and influencing factors behind it, such as the policy environment, the level of the economy and the investment in research and development. Exploring the reasons affecting digital technology innovation can help formulate more targeted policies and promote cooperation and exchange of digital technology innovation in the global construction industry. Meanwhile, to solve the problems of overly broad IPC categorization and the difficulty of accurately describing cross-field innovations, combining IPC co-occurrence networks with patent citation networks is an effective strategy. This strategy can track technologically interrelated patents and provide more specific contents to know the advantages and challenges of the construction industry in the field of digital technology innovation.Practical implicationsThe study has practical implications for the construction industry. The identification of digital technology innovation trajectories provides valuable insights for industry firms and research institutes. It helps them understand the current and future directions of digital technology in construction, enabling them to stay at the forefront of technological advancements. The findings highlight the importance of focusing on areas such as solar energy utilisation, green energy, intelligence, automation and data applications. This knowledge can guide firms in developing new building materials, incorporating digital information technologies and enhancing user experiences. The study’s results can inform strategic decision-making, technology adoption and innovation management in the construction sector.Social implicationsThe social implications of this study are significant for various stakeholders. The identification of digital technology innovation trajectories in the construction industry highlights the potential benefits for society. The focus on green energy, intelligent buildings and enhanced user experiences aligns with the increasing demand for sustainability, energy efficiency and comfortable living environments. These technological advancements can contribute to reducing environmental impact, improving quality of life and promoting sustainable development. The findings can inform policymakers, urban planners and architects in shaping regulations, designing sustainable cities and creating buildings that prioritize energy efficiency and user well-being. Ultimately, the study’s social implications aim to foster a more sustainable and livable built environment.Originality/valueAn identification method integrated with SPNP and the Louvain algorithm is developed to map digital technology innovation trajectories in the construction industry. This study helps to reveal the trajectories of digital technology innovation, provides new perspectives, insight and ideas for research in related fields and has great potential for applications in practice to promote the innovation and development of the construction industry.

A context-based model of collaborative inhibition during memory search.

Contrary to common intuition, a group of people recalling information together remembers less than the same number of individuals recalling alone (i.e., the collaborative inhibition effect). To understand this effect in a free recall task, we build a computational model of collaborative recall in groups, extended from the Context Maintenance and Retrieval (CMR) model, which captures how individuals recall information alone. We propose that in collaborative recall, one not only uses their previous recall as an internal retrieval cue, but one also listens to someone else's recall and uses it as an external retrieval cue. Attending to this cue updates the listener's context to be more similar to the context of someone else's recall. Over an existing dataset of individual and collaborative recall in small and large groups, we show that our model successfully captures the difference in memory performance between individual recall and collaborative recall across different group sizes from 2 to 16, as well as additional recall patterns such as recency effects and semantic clustering effects. Our model further shows that collaborating individuals reach similar areas in the context space, whereby their contexts converge more than the contexts of individuals recalling alone. This convergence constrains their ability to search memories effectively and is negatively associated with recall performance. We discuss the contributions of our modeling results in relation to previous accounts of the collaborative inhibition effect.

Forecasting the constraints on optical selection bias and projection effects of galaxy cluster lensing with multiwavelength data

Forecasting the constraints on optical selection bias and projection effects of galaxy cluster lensing with multiwavelength data

Option Pricing Based on REGARCH Model with High-Frequency Information

As a prevalent tool for hedging risk, the trading volume of options has been growing increasingly in the derivatives market. Precision in the estimation of volatility leads to accurate option pricing. Since volatility is time-varying and has a clustering effect, GARCH class of volatility models is effective in modeling volatility precisely. This paper utilizes the realized EGARCH (REGARCH) model combined with Monte Carlo simulation to investigate the role of high-frequency information in option pricing. The parameter estimates of the REGARCH model are obtained via joint maximum likelihood estimation using observations on returns and realized measure. Applying the model to S&P options market, the empirical results show that the REGARCH model that using high-frequency data is more efficient than the model that only use daily closing prices, including the EGARCH, NGARCH and GJR-GARCH models. This paper demonstrates that incorporating realized measures into volatility models can improve the accuracy of option pricing. The REGARCH model contained more intraday trading information from high-frequency data, can measure the additional risk premiums and specific volatility shocks.

Effect of zinc clustering, micro-texture, and surface oxide constituents on the electrochemical corrosion of Sn-Zn coatings electrodeposited at different current densities

Effect of zinc clustering, micro-texture, and surface oxide constituents on the electrochemical corrosion of Sn-Zn coatings electrodeposited at different current densities

Integrating mental health and psychosocial support into economic inclusion programming for displaced families in Ecuador

BackgroundPoverty is a key social determinant of mental health among forcibly displaced persons. This study aimed to design and pilot test a strategy to integrate existing mental health and economic inclusion interventions for displaced families in Ecuador.MethodsWe conducted a series of qualitative interviews (n = 30), focus groups (n = 6), and workshops (n = 3) to develop a set of strategies for integrating cross-cutting and focused mental health and psychosocial support (MHPSS) strategies into an existing economic inclusion program for displaced families in Quito. We non-randomly assigned two field offices in Quito to (1) integrate cross-cutting strategies focused on improving economic outcomes or (2) integrate both those cross-cutting strategies plus focused MHPSS strategies into an economic inclusion program. We measured site-level implementation outcomes (adoption, appropriateness, acceptability, feasibility, fidelity, reach, retention, usability) and participant-level psychosocial (wellbeing, depressive symptoms, anxiety symptoms, functioning) and economic inclusion outcomes (financial resources, diet diversity, social capital/networks, self-reliance) over six months. We conducted a mixed-methods analysis to explore the acceptability and feasibility of the integration strategies and the ability to evaluate their effects in a future cluster randomized trial.ResultsWe developed a toolkit that included 10 strategies for integrating MHPSS into economic inclusion interventions. Fifty displaced persons participating in an existing economic inclusion program (25 per study condition) were enrolled and 88% remained in the study through the six-month follow-up. Participants and implementers reported that the integration strategy was appropriate, acceptable, feasible, and usable. Implementers, including people without prior experience in delivering mental health services, were able to deliver the intervention with high fidelity. Integration of focused MHPSS intervention components into an economic inclusion program appeared to improve MHPSS outcomes, the strength of social capital and networks, and engagement in economic and other programs.ConclusionsThis study provides preliminary evidence of the acceptability and feasibility of integrating MHPSS into economic inclusion programs for displaced people. We found evidence supporting evaluation methods that can be employed in a future study to definitively test the added value of integrated approaches to mental health and economic wellbeing for displaced persons.

Spatiotemporal dynamics and driving factors of ecosystem services value in Lanzhou City, China

Aligned with the imperatives of national ecological civilization construction, the systematic investigation into the intricate interplay between shifts in land utilization and the assessment of ecosystem services plays a pivotal and indispensable role in advancing ecological civilization. This endeavor holds significant implications. It aids in optimizing the ecological landscape at the regional level and fosters harmonious coexistence between humanity and the natural world. The study utilizes land-use remote sensing interpretation data from three time periods (2000, 2010, and 2020) and employs various methodologies, including equivalent factor coefficient correction, sensitivity analysis, and spatial autocorrelation. The objective is to uncover the spatiotemporal dynamics of land-use changes and Ecosystem Service Value (ESV) in Lanzhou City. Furthermore, geographic detectors are applied to explore the driving factors influencing ESV spatial heterogeneity and their interactions. The research findings indicate the following: (1) From 2000 to 2020, grassland and cropland were the predominant land-use types in Lanzhou City, with cropland and urban land experiencing the most active changes. (2) ESV in Lanzhou City increased from 179.37 billion RMB in 2000 to 193.86 billion RMB in 2020, reflecting an ESV total growth rate of 8.07% and a gradual improvement in the ecological environment. Spatially, ESV exhibits a “west high, east low” distribution pattern, with the center shifting towards the northwest and southeast, gradually reducing spatial imbalance. (3) Analysis of ESV spatial autocorrelation reveals that high-high clusters are predominantly found within the Tulu Gou National Forest Park and the Xinglong Mountain National Natural Reserve, while low-low clusters are primarily concentrated in the central urban area of Lanzhou City. Over the period from 2000 to 2020, the spatial clustering effect of ESV within the study area has progressively intensified. 4)NDVI, precipitation, and GDP emerge as pivotal factors influencing spatial differentiation within Lanzhou City, with natural and societal elements exerting interactive effects on ESV spatial disparities. The research results integrate environmental considerations into the decision-making process, offering valuable insights for formulating targeted ecological protection policies in Lanzhou City. This study embodies concrete measures taken by Lanzhou City in practicing China’s concept of “green water and green mountains are golden silver mountains,” providing a theoretical basis for the harmonious and sustainable development of the ecological economy.

Asymmetric Effects and Volatility Clustering in NSE NIFTY 50: A Comparative Analysis of GARCH Models

This study aims to analyze the volatility patterns, Clustering, and asymmetric effects in the NSE NIFTY 50 index. It involves using daily returns data from the NSE NIFTY 50 from 01 Jan 2010 to 31 Dec 2023. Daily closing prices are obtained from the official NSE website, and returns are calculated based on these prices. EGARCH (1, 1), TARCH (1, 1), GARCH (1, 1), GARCH-M (1,1), and models are utilized to predict volatility, capturing volatility clustering and leverage effects. Using both the Akaike and Schwarz criteria, EGARCH (1,1) was demonstrated to be the best model. The findings reveal that Fluctuations in the Indian stock market, particularly shown in the NIFTY index of the NSE were the highest in the year 2020 due to the COVID-19 pandemic. Past NIFTY returns demonstrate a GARCH effect on today’s NIFTY returns. The results of the research suggest that the impact of negative and positive shocks on the stock returns series varies. Volatility is largely caused by negative news compared to positive news. It is suggested that the model exhibits a leverage effect and that different types of information produce various types of shock to the volatility of the Nifty index return. The findings show that investors of NSE, particularly those involved with the NIFTY50, face heightened volatility and increased risk, especially during periods of significant events.

The synergistic effect of Co atomic clusters and single atoms facilitates the fenton-like reaction on A- and R-TiO2

The regulation of single atom (SA) sites is crucial for the promotion of advanced oxidation processes (AOPs). Here A- and R-TiO2 with Co mixed sites (clusters and single atoms, CS) were prepared to degrade sulfamethoxazole (SMX) by activating peroxymonosulfate (PMS). Co CS/A-TiO2+PMS system can completely remove SMX within 14 min under secondary effluent and dark conditions, and its rate constant (0.4926 min-1) was 4.4, 3.2 and 2.5 times higher than that of Co SA/R-TiO2, Co SA/A-TiO2 and Co CS/R-TiO2 systems, respectively. Experiments and calculations verified that the electron configuration of Co SA was optimized by Co clusters, facilitating the adsorption, conversion and desorption of PMS by Co SA, leading to the enhancement of Fenton-like activity. Co CS/A-TiO2 catalytic sponges exhibited exceptional degradation performance in microreactor treatment experiment. This work reveals the synergistic effect between SA and clusters at atomic level and provides an all-purpose strategy (catalytic sponge) for the recovery of non-magnetic catalysts in wastewater treatment.

Synergistic effects of metal single atoms and clusters on graphene-supported electrocatalysts: insights into the mechanism of oxygen reduction reaction

Synergistic effects of metal single atoms and clusters on graphene-supported electrocatalysts: insights into the mechanism of oxygen reduction reaction

Associative analysis of sludge microbiota and wastewater degradation efficacy within swine farm sludge systems

Industrial wastewater management is a significant global challenge. Sludge microbiota from swine farms may play a crucial role in enhancing wastewater treatment processes, thereby reducing water pollution from industrial activities. A deeper understanding of this complex community could lead to innovative approaches for improving wastewater treatment methods. Sludge samples were collected from the anaerobic, sedimentation, and thickening tanks of ten swine farms. The microbiota communities were analyzed using 16S rRNA full-length sequencing on the PacBio platform, with subsequent data analysis conducted on the QIIME2 platform utilizing the SILVA database. Compared to anaerobic and thickening tanks, the sedimentation tanks exhibited a unique profile of sludge microbiota, with higher abundances of the phyla Proteobacteria, Bacteroidota, and Caldatribacteriota. Additionally, sludges from farms already utilized in processing industrial water—specifically farms B, G, and J—contained higher concentrations of bacteria (> 20 ng/μL), indicating the robustness of the bacterial load for practical industrial use. Furthermore, sludge from farms with higher alpha diversity, such as E, G, I, and J, exhibited enriched degradation profiles, including the degradation of aromatic compounds, polymers, industrial compounds, toluene, and vanillin. The farms were categorized based on wastewater ammonia nitrogen degradation levels, revealing a clustering effect of the microbiota from the sedimentation tanks in the Principal Coordinates Analysis (PCoA) plot. A higher relative abundance of the families Rhodocyclaceae, AKYH767, and Comamonadaceae, and a lower abundance of the families Anaerolineaceae and Christensenellaceae, were found in groups with high ammonia nitrogen reduction, suggesting potential targets for bioaugmentation strategies. In conclusion, this study underscores the critical role of microbial abundance, composition, and biodiversity in optimizing wastewater treatment and advocates for comprehensive microbiota analysis to identify suitable sludge for industrial applications.

Media Use in Daily Routines and Young Children’s Social Skills

ABSTRACT Research Findings: The current study examined (1) whether children’s media use during afterschool or bedtime routines, beyond their total media use, contributes uniquely to their peer social skills, and (2) whether joint media engagement serves as a protective factor for any negative associations between media use in daily routines and peer social skills. The study followed 459 children in a Midwestern U.S. state from kindergarten to Grade 1. Teachers reported on children’s peer social skills at both time points. Parents reported frequency of media use in afterschool and bedtime routines, daily media use duration, and joint media engagement. Hierarchical linear modeling was utilized to account for class-level clustering effects. Frequency of media use in afterschool routines negatively predicted children’s peer social skills in first grade, controlling for total media use, peer social skills in kindergarten, and demographics. However, media use in bedtime routines did not significantly predict social skills. Neither of these associations was significantly moderated by parental joint media engagement. Practice or Policy: The current results call for future research focused on effective means to regulate children’s media use in their daily routines to foster positive social development.

Spatial distribution and migration characteristics of heavy metals at an abandoned industrial site in the Southwest of China

The rapid acceleration of global industrialization has rendered heavy metal contamination at abandoned industrial sites a severe challenge, particularly in geologically complex and fragile karst regions of Southwest China, posing significant threats to ecosystems and public health. However, existing research lacks a comprehensive understanding of the spatial distribution and migration mechanisms of heavy metals in this region. In this study, 523 soil samples and 30 groundwater samples were collected, and the pollution levels were systematically assessed using the Geo-Accumulation Index, Single Pollution Index, and Nemerow Integrated Pollution Index. Horizontal and vertical spatial heterogeneity was explored through Moran's I and Voronoi polygon analysis. Furthermore, 3D geological modeling and groundwater flow simulations were employed to investigate the influence of hydrogeological conditions on contaminant migration. The results indicate elevated concentrations of Cd, Hg, Pb, and As in the surface layer, with concentrations initially decreasing and then increasing with depth, likely due to the presence of discontinuous clay layers. Moran's I revealed significant clustering effects at depths of 0.2 meters and 4 meters, while Voronoi analysis confirmed vertical heterogeneity. This study provides a scientific basis for pollution assessment and targeted remediation in karst regions.

Determinants of animal source food consumption among children aged 6–23 months in sub-Saharan Africa: multilevel mixed effect model

A diversified diet in early childhood that comprises plant and animal source foods contributes to better health outcomes throughout the life course. However, inadequate infant and young child-feeding practices are serious public health problems in Africa. Evidence on the magnitude and determinants of inadequate animal source food consumption (ASF) in sub-Saharan Africa (SSA) is not yet well established. Hence, this study aimed to estimate the pooled magnitude and determinants of ASF consumption in SSA. A total weighted sample of 92,309 children aged 6–23 months from the Demographic and Health Survey dataset of 35 SSA countries was used for this study. A multilevel mixed-effect model was fitted. The intra-class correlation coefficient (ICC) and likelihood ratio (LR) tests were used to assess the presence of the clustering effect. The model comparison was made using deviance information criteria (DIC). A p-value of p < 0.05 was used to declare statistical significance. The pooled magnitude of ASF consumption among children aged 6–23 months in SSA was 23.7% (95% CI: 23.4, 23.9). Across the SSA regions, the Southern Africa region had the highest consumption of ASF (51.35%), and the lowest was from the Eastern Africa region (21.41%). Younger children aged 6–9 months (AOR = 3.39; 95% CI: 3.14, 3.65), mothers who had no formal education (AOR = 1.46; 95% CI: 1.37, 1.56), children from mothers with no media exposure (AOR = 1.17; 95% CI: 1.11, 1.24), children from the poorest households (AOR = 2.59; 95% CI: 2.36, 2.84), rural residence (AOR = 1.20; 95% CI: 1.12, 1.28), living in low-income countries (AOR = 2.43; 95% CI: 2.03, 2.90), and children from the East Africa region (AOR = 1.24; 95% CI: 1.14, 1.35) were among the significant predictors for not consuming ASF. In this study, ASF consumption was markedly low. Almost three-fourths of children aged 6–23 months in SSA did not consume ASF in the previous day. Age of the child, ANC visits, maternal educational status, maternal marital status, media exposure, and household wealth index were significant individual-level predictors of ASF consumption, whereas the place of residence, country’s income level, and regions of SSA were found to be significant community-level predictors of ASF consumption. Thus, the study highlighted that policymakers, governments, and their partners should demonstrate rigorous efforts to improve dietary diversification with due consideration to the identified predictors of ASF.

Uncovering hidden alignments in two-dimensional point fields

The problem of mapping hidden alignments of points in data sets of two-dimensional points is of significant interest in many geoscience disciplines. In this paper, we revisit this issue and provide a new algorithm, insights, and results. The statistical significance of alignments is assessed by using percentile confidence intervals estimated by a Monte Carlo procedure in which important issues, such as the shape of the geometric support and the possible non-homogeneity of the point density (i.e., clustering effects), have been considered. The procedure is not limited to the simplest case of occurrence and the chance of triads (alignments of three points in a plane) but has been extended to k-ads with k arbitrarily large. The important issue of scale, when searching for point alignments, has also been taken into account. Case studies using synthetic and real data sets are provided to illustrate the methodology and the claims.