Pattern Map Research Articles

Anatomy of trade disputes: mapping patterns and trends of WTO dispute initiations (1995–2023)

Anatomy of trade disputes: mapping patterns and trends of WTO dispute initiations (1995–2023)

Bicriteria Shapes: Hierarchical Grouping and Aggregation of Polygons with an Efficient Graph-Cut Approach

An important task of pattern recognition and map generalization is to partition a set of disjoint polygons into groups and to aggregate the polygons within each group to a representative output polygon. We introduce a new method for this task called bicriteria shapes. Following a classical approach, we define the output polygons by merging the input polygons with a set of triangles that we select from a conforming Delaunay triangulation of the input polygons’ exterior. The innovation is that we control the selection of triangles with a bicriteria optimization model that is efficiently solved via graph cuts. In particular, we minimize a weighted sum that combines the total area of the output polygons and their total perimeter. In a basic problem, we ask for a single solution that is optimal for a preset parameter value. In a second problem, we ask for a set containing an optimal solution for every possible value of the parameter. We discuss how this set can be approximated with few solutions and show that it is hierarchically nested. Hence, the output is a hierarchical clustering that corresponds to multiple levels of detail. An evaluation with building footprints as input and a comparison with α -shapes that are based on the same underlying triangulation conclude the article. An advantage of bicriteria shapes compared to α -shapes is that the sequence of solutions for decreasing values of the parameter is monotone with respect to the total perimeter of the output polygons, resulting in a monotonically decreasing visual complexity.

Analysis of CO2 bubble growth detachment kinetics in direct methanol fuel cell flow channels

CO2 bubbles flow in the anode flow channel is an important issue in the commercialization process of direct methanol fuel cells (DMFC). A T-channel model is built in COMSOL using the phase field method to investigate the CO2 bubbles flow at the anode side of the DMFC. The factors and mechanisms of single bubble detachment are discussed by analyzing the methanol inlet velocity (Ul), CO2 inlet velocity (Ug), contact angle of the diffusion layer, and the Weber number during bubble detachment. The research findings indicate that increasing the liquid flow rate leads to the generation of smaller bubbles that detach more rapidly due to the increases of drag force (FD) and the shear-life force (FSL) to overcome the surface tension on the bubble. The CO2 inlet velocity can promote the bubble detachment due to the increase in FSL, but also leads to a larger detachment diameter. Compared to hydrophobic surfaces, hydrophilic surfaces are more conducive to bubble detachment and removal. In all case We (Weber number) is significantly less than 0.6, indicating that liquid momentum dominated the bubble detachment process. Once the ratio of the gas momentum to the liquid one is greater than 1, the bubble is hard to detach. The contour map of bubble flow patterns and the bubble detachment diameters distribute with the ratio of Ug/Ul can further indicate that the bubble detachment is connected with the ratio of Ug/Ul closely, which will have guiding significance for the selection of inlet velocity of DMFC.

Disaggregation of climatic variables using a novel stochastic approach and its application in building performance simulation studies

Daily weather data, both observed and synthesized, are available for most locations worldwide but not at the sub-daily temporal scale, which is desirable for building performance analysis. In this context, we introduce a novel stochastic methodology based on pattern mapping for disaggregation of daily data into sub-daily (hourly) time resolution. The methodology has been tested and validated for three weather variables required for energy simulation (temperature, relative humidity and solar radiation). This approach yields better results than existing methodologies for the selected climate variables. Process efficiency is maintained even with the limited input data. Its stochastic nature enables time-invariant pattern mapping to generate future weather files. The annual ‘space conditioning energy’ was simulated to assess building performance accuracy using weather files for the hottest, coldest, average and test reference years (TRY) from disaggregated and observed datasets. Results show that the pattern mapping disaggregation methodology accurately downscales daily to sub-daily data.

Ex Ante Construction of Flow Pattern Maps for Pulsating Heat Pipes

A novel methodology is proposed for the development of empirical flow pattern maps for pulsating heat pipes (PHPs), which relies on the concept of virtual superficial velocity of the liquid and vapour phases. The virtual superficial velocity of each phase is defined using solely the design and operational parameters of the pulsating heat pipe, allowing the resulting flow pattern map to serve as a predictive instrument. This contrasts with existing flow pattern maps that necessitate direct measurements of temperatures and/or velocities within one or more channels of the pulsating heat pipe. Specifically, the virtual superficial velocities are derived from the relative significance of the driving forces and the resistances encountered by each phase during flow. The proposed methodology is validated using flow visualisation datasets obtained from two separate experimental campaigns conducted on flat-plate polypropylene pulsating heat pipe prototypes featuring transparent walls and meandering channels with three turns, five turns, seven turns, and eleven turns, respectively. The PHP prototypes were tested for gravity levels ranging between 0 g and 1 g and heat inputs ranging from 5 W to 35 W. The proposed approach enables the identification of empirical boundaries for flow pattern transitions as well as the establishment of an empirical criterion for start-up.

CartoMark: a benchmark dataset for map pattern recognition and map content retrieval with machine intelligence

Maps are fundamental medium to visualize and represent the real word in a simple and philosophical way. The emergence of the big data tide has made a proportion of maps generated from multiple sources, significantly enriching the dimensions and perspectives for understanding the characteristics of the real world. However, a majority of these map datasets remain undiscovered, unacquired and ineffectively used, which arises from the lack of numerous well-labelled benchmark datasets, which are of significance to implement the deep learning techniques into identifying complicated map content. To address this issue, we develop a large-scale benchmark dataset involving well-labelled datasets to employ the state-of-the-art machine intelligence technologies for map text annotation recognition, map scene classification, map super-resolution reconstruction, and map style transferring. Furthermore, these well-labelled datasets would facilitate map feature detection, map pattern recognition and map content retrieval. We hope our efforts would provide well-labelled data resources for advancing the ability to recognize and discover valuable map content.

Big Epidemiology: The Birth, Life, Death, and Resurgence of Diseases on a Global Timescale.

Big Epidemiology represents an innovative framework that extends the interdisciplinary approach of Big History to understand disease patterns, causes, and effects across human history on a global scale. This comprehensive methodology integrates epidemiology, genetics, environmental science, sociology, history, and data science to address contemporary and future public health challenges through a broad historical and societal lens. The foundational research agenda involves mapping the historical occurrence of diseases and their impact on societies over time, utilizing archeological findings, biological data, and historical records. By analyzing skeletal remains, ancient DNA, and artifacts, researchers can trace the origins and spread of diseases, such as Yersinia pestis in the Black Death. Historical documents, including chronicles and medical treatises, provide contextual narratives and quantitative data on past disease outbreaks, societal responses, and disruptions. Modern genetic studies reveal the evolution and migration patterns of pathogens and human adaptations to diseases, offering insights into co-evolutionary dynamics. This integrative approach allows for temporal and spatial mapping of disease patterns, linking them to social upheavals, population changes, and economic transformations. Big Epidemiology also examines the roles of environmental changes and socioeconomic factors in disease emergence and re-emergence, incorporating climate science, urban development, and economic history to inform public health strategies. The framework reviews historical and contemporary policy responses to pandemics, aiming to enhance future global health governance. By addressing ethical, legal, and societal implications, Big Epidemiology seeks to ensure responsible and effective epidemiological research and interventions. This approach aims to profoundly impact how we understand, prevent, and respond to diseases, leveraging historical perspectives to enrich modern scientific inquiry and global public health strategies.

Construction and Characterization of Traditional Village Landscape Cultural Genome Atlases: A Case Study in Xupu County, Hunan, China

The Yuan River Basin in Hunan Province, known for its rich history and cultural heritage, is home to traditional village landscapes characterized by distinct cultural genes and regional features. This study focuses on 21 traditional villages in Xupu County, Huaihua, and develops four types of traditional village landscape cultural genome atlases at three hierarchical levels: individual villages, multiple villages, and a specific research area. These atlases include genetic linkage maps, spatial sequence maps, distribution pattern maps, and geographical pattern maps. Using Yangquepo in Zhumucun Village as a case study, we constructed a genetic linkage map for the landscape cultural genome of a single traditional village, based on the arrangement structure of courtyard layouts. Spatial sequence maps were derived from the overall spatial configuration, while distribution pattern maps and geographical pattern maps for the 21 villages were developed using kernel density analysis in ArcGIS 10.8 software. The results reveal that traditional village landscapes in the region possess a diverse cultural genome, marked by distinct regional characteristics and spatial organization patterns.

Flight experimental analysis of largemulti-propeller airtanker dropping capability

Abstract In the absence of a dropping capability experiment of domestic airtankers, it is necessary to know the actual dropping capability of fixed-wing airtankers for data support to wildfire-fighting, improving fire-fighting efficiency, and reducing the loss of forest and grassland resources. According to the design characteristics of large multi-launch propeller airtankers in China, this experiment shall collect retardant samplesof the filled flame retardant with collection equipment on the flat ground after being dropped by the airtanker in the air. Then, the sample coverage was counted and contour maps of the ground patterns were plotted to compare the dropping coefficients, dropping heights, and dropping speeds of the different dropping modes. At the last, according to the comparison result, the best dropping strategy shall be shown in different requirements. The result shows that when the coverage level is required to be GPC-6, the maximum coverage shall be obtained by all compartments released simultaneously, with a maximum coverage coefficient of 0.16. When the coverage level is required to be GPC-1, it is better to release the retardant from the tank independently with a suitable interval for larger coverage, and the maximum coverage coefficient is 4.49.

Shade watch: Mapping citywide shade dynamics through ray tracing and LiDAR data in Hong Kong's complex 3-D built environment

The importance of urban shade in city planning has been underscored by its various associations with thermal comfort, living environment quality, human health and well-being. However, existing methods for urban shade mapping are often limited to street-level scopes or singular sampling observational times, leaving citywide shade mapping, its spatiotemporal patterns, and controlling factors largely unexplored. To fill these knowledge gaps, this study proposed a novel protocol that enables seamless mapping of spatiotemporal urban shade patterns over the entire city by fusing look-up-table (LUT)-based ray-tracing approach with Google Earth Engine cloud computing technology, as well as high-resolution digital surface model (DSM) derived from LiDAR data. This protocol was applied to Hong Kong, a city characterized by a complex 3-D built environment, and then delved into the spatiotemporal patterns of urban shade and the associated controlling factors. Validation results indicate that the proposed method can accurately quantify urban shade compared to ground-based photo references and high-resolution satellite imagery. The analysis uncovers that: (1) Shade exhibits spatial heterogeneity across different 3-D built environments while maintaining a consistent "U-shape" diurnal shade fraction during the day; (2) Solar geometry, regardless of solar zenith and azimuth angle, is positively associated with aggregated metrics of shade landscape and negatively associated with fragmented metrics. Moreover, solar zenith angles exert stronger controls over the citywide shade landscape patterns; and (3) Persistent shade significantly reduces the sunlight hours (i.e., the accumulated sunshine hours per day) across administrative districts, local climate zones (LCZs), and seasons. Due to shade effects originating from 3-D urban structures, Hong Kong typically experiences an average of 4-8 sunlight hours per day within a one-year cycle, which is substantially lower than the 11-14 hours of natural daylength. This study offers a practical protocol for large-scale urban shade mapping, enhancing our understanding of urban shade, its spatiotemporal dynamics, and benefits in a broader spatiotemporal context. The associated datasets and findings from this study can inform urban planners and policymakers in developing effective strategies to create healthier and sustainable urban environments in Hong Kong.

Recognition of Street Landscape Patterns in Kunming City Based on Intelligent Decision Algorithm and Regional Cultural Expression

The integration of intelligent decision-making algorithms with urban cultural expression is becoming a hot topic in both academic and practical fields for exploring urban street landscapes. Exploring the application strategies of intelligent decision-making algorithms and regional cultural expression in street landscape pattern recognition and innovative design is a key step. The single layout of urban street construction, cultural deficiency, ecological imbalance, and low resident participation seriously constrain the overall quality improvement of the city. To address this dilemma, this study delved into Kunming City and selected the ten “most beautiful streets”, such as Dianchi Road, for research. By using the Analytic Hierarchy Process, a comprehensive evaluation system covering multiple dimensions, such as the street layout, plant landscape, and historical culture, was constructed to analyze the street landscape of Kunming. The research results indicate that the top four roads in terms of weight evaluation scores are Cuihu Ring Road, Jiaochang Middle Road, Qingnian Road, and Beijing Road, with values of 0.2076, 0.1531, 0.1274, and 0.1173. The weight reveals that each street has its unique landscape factors, such as the profound cultural heritage of Cuihu Ring Road and the beautiful plant landscape of Jiaochang Middle Road. Further analysis also reveals the close relationship between various factors in the evaluation model, emphasizing the importance of supplementing material and cultural elements in street landscape design. The significance of this study goes beyond a single analysis of the street landscape in Kunming City. Drawing a regional street landscape pattern map sets an example for other cities to build distinctive, eco-friendly, culturally rich, and highly humanized street spaces, providing reference and inspiration. More importantly, this study promotes the application and development of intelligent decision-making algorithms in the field of urban landscapes. Future research will further optimize algorithms to improve their adaptability and accuracy in complex environments.

Google Earth Engine application for mapping and monitoring drought patterns and trends: A case study in Arkansas, USA

Drought is a prolonged dry period that can have severe impacts on the environment, human health, economies, agriculture, and energy resources. It can lead to water shortages, ruin crops, dry out forests, and reduce the availability of food and water for wildlife and livestock. The primary objectives of this study are to: 1) quantify the variability and distributions of drought patterns in Arkansas, United States (US), 2) use remotely sensed indices to investigate the correlation between drought and vegetation cover in the area, and 3) develop a cloud-based framework (user-friendly app) to facilitate the assessment of drought impact in Arkansas over the past decades. A correlation analysis was also performed between the Vegetation Health Index (VHI) and meteorological indices to better understand the impact of meteorological drought on vegetation stress. In addition, Mann-Kendall trend analysis was used to assess trends in meteorological drought indices. The results indicate that drought is most prevalent during March and August months. The results of this study revealed that approximately 31% of the study area fell under the four drought classes (i.e., 1% Extreme drought, 4% Severe drought, 9% moderate drought, and 19% mild drought), with spring and the growing season experiencing moderate drought, particularly in agricultural areas, most notably within the Mississippi Alluvial Valley Plain at both state and county levels. In August, approximately 31% of the study area fell under the Four drought classes (i.e., 1% Extreme drought, 4% Severe drought, 9% moderate drought, and 19% mild drought), with spring and the growing season experiencing moderate drought, particularly in agricultural areas, most notably within the Mississippi Alluvial Valley Plain at both state and county levels. This study provides an essential foundation for policymakers, environmental scientists, and agricultural stakeholders aiming to mitigate drought impacts and safeguard against future climate uncertainties.

Distinct Neuron Types Contribute to Hybrid Auditory Spatial Coding.

Neural decoding is a tool for understanding how activities from a population of neurons inside the brain relate to the outside world and for engineering applications such as brain-machine interfaces. However, neural decoding studies mainly focused on different decoding algorithms rather than different neuron types which could use different coding strategies. In this study, we used two-photon calcium imaging to assess three auditory spatial decoders (space map, opponent channel, and population pattern) in excitatory and inhibitory neurons in the dorsal inferior colliculus of male and female mice. Our findings revealed a clustering of excitatory neurons that prefer similar interaural level difference (ILD), the primary spatial cues in mice, while inhibitory neurons showed random local ILD organization. We found that inhibitory neurons displayed lower decoding variability under the opponent channel decoder, while excitatory neurons achieved higher decoding accuracy under the space map and population pattern decoders. Further analysis revealed that the inhibitory neurons' preference for ILD off the midline and the excitatory neurons' heterogeneous ILD tuning account for their decoding differences. Additionally, we discovered a sharper ILD tuning in the inhibitory neurons. Our computational model, linking this to increased presynaptic inhibitory inputs, was corroborated using monaural and binaural stimuli. Overall, this study provides experimental and computational insight into how excitatory and inhibitory neurons uniquely contribute to the coding of sound locations.

Main spatial-temporal regularities of the state of the chemical composition of groundwater of the oligocene aquifer in the Shaim oil and gas bearing area

The constantly growing need for fresh water in the Shaim oil and gas region necessitates the exploration of new areas and the development of measures to improve the operation of water intakes. To predict the quality of groundwater, which is an operational parameter of the aquifer aquifer, it is needed up-to-date information on the chemical composition of ground-water. The aim of this study to identify main spatial-temporal regularities of the state of the chemical composition of groundwater of the oligocene aquifer in the Shaim oil and gas bearing area, compared to average values from adjacent areas. Research methods include the systematization of laboratory data on water samples, analysis of groundwater chemistry, and mapping of the main spatial-temporal patterns in component concentration changes. The average values of chemical composition indicators in the studied area are similar to those of neighbouring areas, with some indicators exceeding drinking water standards. Similarities are observed in the variability of the chemical composition, spatial patterns of changes in most chemical indicators, and the presence of areas with extreme values. Current data on the chemical composition of the oligocene aquifer ground-water, including key characteristics and spatial-temporal trends presented in distribution maps, can be used in designing groundwater intakes and forecasting long-term groundwater quality.

Knowledge‐Based Deep Learning to Predict Vegetation Carbon, Nitrogen and Phosphorus Densities in China’s Shrublands

AbstractAccurate estimations of carbon (C), nitrogen (N), and phosphorus (P) densities in shrublands are pivotal for assessing terrestrial ecosystem carbon sequestration. Combining in‐situ investigations and machine learning facilitates large‐scale patterns mapping, however, which often overlooks underlying ecological regulations. Here we utilize data from 1,122 survey plots across China's shrublands and develop a novel knowledge‐based deep learning framework that integrates a structural equation model (SEM) to elucidate mechanisms and construct an artificial neural network (ANN) based on these causal relationships. Results show that biomass allocation to different organs follows allometric regulations and that N and P concentrations maintain a degree of stoichiometric homeostasis following biological stoichiometry theory. This insight guides the construction of our ANN, which outperforms both SEM and other prevalent machine learning methods. By leveraging ecological theories to inform model construction, our framework not only enhances prediction accuracy and explainability but also provides a methodological blueprint for ecological research.

The EL-BIOS Earth Observation Data Cube for Supporting Biodiversity Monitoring in Greece

In recent years, the need to protect and conserve biodiversity has become more critical than ever before, as a prerequisite for both sustainable development and the very survival of the human species. This has made it a priority for the scientific community to develop technological solutions that provide data and information for monitoring, directly or indirectly, biodiversity and the drivers of change. A new era of satellite earth observation upgrades the potential of Remote Sensing (RS) to support, at relatively low cost, but with high accuracy the extraction of information over large areas, at regular intervals, and over extended periods of time. Also, the recent development of the Earth Observation Data Cubes (EODC) framework facilitates EO data management and information extraction, enabling the mapping and monitoring of temporal and spatial patterns on the Earth’s surface. This submission presents the ELBIOS EODC, specifically developed to support the biodiversity management and conservation over Greece. Based on the Open Data Cube (ODC) framework, it exploits multi-spectral optical Copernicus Sentinel-2 data and provides a series of Satellite Earth Observation (SEO) biodiversity products and spectral indices nationwide.

RGB Mapping: A Dynamic Approach for Flow Pattern Identification and Classification

Thermal-hydraulic modeling and simulation are heavily dependent on the knowledge of flow regimes, which sort multiphase flow patterns into classifications with static boundaries that display characteristic features of flow patterns and phase distributions. Flow regime maps heretofore have been primarily developed mainly for vertical or horizontal pipe orientations by capturing images of flows at varying conditions and assigning them to different flow regimes based on visualization studies. As such, this method is inherently subjective. Furthermore, classifying two-phase flows into regimes with static boundaries is not physical, as two-phase flows undergo gradual changes as they transition from one regime to another. Therefore, such a static approach is not only unphysical but also inaccurate, resulting in the erroneous analysis of two-phase flows. Additionally, subjective observations at such flow conditions are prone to disagreement and inconsistencies in labeling. These difficulties are exacerbated in inclined two-phase flows. In this study, a novel two-phase flow pattern map employing the dynamic red, green, and blue (RGB) color model is presented. The model contains the embedded information of three normalized time-averaged statistics from signals obtained by a dual-ring impedance meter. A database of impedance meter signals from a 25.4-mm-inner-diameter adiabatic air-water inclinable test facility is used to compute statistics for each experimental flow condition. Multiple statistical measures are evaluated, and a principal component analysis is performed for feature selection. By mapping each parameter to intensities of red, green, and blue, a dynamic RGB flow pattern map can be produced in which groups of similar colors are representative of various flow regimes and the dynamic transition characteristics between the flow regimes are characterized physically. Therefore, this new method makes viable the observation of how flow patterns gradually change with respect to angle, as well as the fuzzification or quantification of the uncertainty surrounding experimental transition boundaries.

Remote Sensing Mapping and Analysis of Spatiotemporal Patterns of Land Use and Cover Change in the Helong Region of the Loess Plateau Region (1986–2020)

Land use and cover change (LUCC) is directly linked to the sustainability of ecosystems and the long-term well-being of human society. The Helong Region in the Loess Plateau has become one of the areas most severely affected by soil and water erosion in China due to its unique geographical location and ecological environment. The long-term construction of terraces and orchards is one of the important measures for this region to combat soil erosion. Despite the important role that terraces and orchards play in this region, current studies on their extraction and understanding remain limited. For this reason, this study designed a land use classification system, including terraces and orchards, to reveal the patterns of LUCC and the effectiveness of ecological restoration projects in the area. Based on this system, this study utilized the Random Forest classification algorithm to create an annual land use and cover (LUC) dataset for the Helong Region that covers eight periods from 1986 to 2020, with a spatial resolution of 30 m. The validation results showed that the maps achieved an average overall accuracy of 87.54% and an average Kappa coefficient of 76.94%. This demonstrates the feasibility of the proposed design and land coverage mapping method in the study area. This study found that, from 1986 to 2020, there was a continuous increase in forest and grassland areas, a significant reduction in cropland and bare land areas, and a notable rise in impervious surface areas. We emphasized that the continuous growth of terraces and orchards was an important LUCC trend in the region. This growth was primarily attributed to the conversion of grasslands, croplands, and forests. This transformation not only reduced soil erosion but also enhanced economic efficiency. The products and insights provided in this study help us better understand the complexities of ecological recovery and land management.

Machine Learning Reveals the Diversity of Human 3D Chromatin Contact Patterns.

Understanding variation in chromatin contact patterns across diverse humans is critical for interpreting noncoding variants and their effects on gene expression and phenotypes. However, experimental determination of chromatin contact patterns across large samples is prohibitively expensive. To overcome this challenge, we develop and validate a machine learning method to quantify the variation in 3D chromatin contacts at 2 kilobase resolution from genome sequence alone. We apply this approach to thousands of human genomes from the 1000 Genomes Project and the inferred hominin ancestral genome. While patterns of 3D contact divergence genome wide are qualitatively similar to patterns of sequence divergence, we find substantial differences in 3D divergence and sequence divergence in local 1 megabase genomic windows. In particular, we identify 392 windows with significantly greater 3D divergence than expected from sequence. Moreover, for 31% of genomic windows, a single individual has a rare divergent 3D contact map pattern. Using in silico mutagenesis, we find that most single nucleotide sequence changes do not result in changes to 3D chromatin contacts. However, in windows with substantial 3D divergence just one or a few variants can lead to divergent 3D chromatin contacts without the individuals carrying those variants having high sequence divergence. In summary, inferring 3D chromatin contact maps across human populations reveals variable contact patterns. We anticipate that these genetically diverse maps of 3D chromatin contact will provide a reference for future work on the function and evolution of 3D chromatin contact variation across human populations.

Antibacterial and Cell-Viability of Hydroxyapatite Derived from Waste Marine Shell for Biomedical Applications

The present study discusses on the functional groups, structural characterization and thermal behaviour of Crassostrea angulata shell used for the synthesis of a biocompatible hydroxyapatite material. The hydroxyapatite was synthesized from C. angulata shell through precipitation method and characterized using XRD, FTIR, FE-SEM, EDX mapping, HR-TEM and SAED pattern. The formation of hydroxyapatite was confirmed from the high intense X-ray diffraction lines and by the presence of chemical groups such as PO43–, OH– and CO32–. The structural changes during the formation of hydroxyapatite from the thermal decomposition of C. angulata shell were studied through XRD and TG-DTA. The morphology study by FE-SEM indicates that the synthesized hydroxyapatite materials were in the form of clusters of rod-like particles. The SAED patterns and XRD spectral lines confirmed the crystalline nature of the synthesized hydroxyapatite. The antibacterial activity and cell viability assay of prepared hydroxyapatite were studied to assess its suitability in various biomedical applications. The antibacterial activity of hydroxyapatite was assessed against Gram-negative (Escherichia coli) and Gram-positive (Staphylococcus saprophyticus) bacterial strains. The cytocompatibility of hydroxyapatite was assessed through an MTT assay using osteoblast cells (MG-63 cell). Thus, the prepared hydroxyapatite shows good antibacterial action and cell viability nature and it can be applied in various biomedical applications.