New perspectives through data discovery and modeling

Terrestrial ecosystems play a central role in the global carbon cycle and climate mitigation due to their offering of a large carbon sink. More than one‐fifth of the geographical area of India, one of the largest nations on the Earth, is forested, which is highly diverse in vegetation and climate types, offers huge potential for carbon sequestration, but remains vulnerable to climate change. Hence, it is imperative to know the future changes in the terrestrial carbon budget over this region. Gross primary productivity (GPP) represents the carbon uptake by terrestrial ecosystems. The multimodel ensembles of GPP simulated by the sixth phase of the Coupled Model Intercomparison Project (CMIP6) provide a useful means in this regard. In this work, we study the strength and variability of GPP over India in the near‐past and future using these simulations. In future, all the models show an increasing trend in GPP, however, with widely varying trends. The preferred month of carbon uptake differs among the models. A comparison with a satellite biophysical record shows the models underestimated the GPP during the near‐past over India. The carbon uptake in the Eastern Himalaya dominates the Western Himalaya and central Indian regions. Specifically, till 2100, the growth rate of GPP varies from 4.9 to 16.69 gC m−2 y−2, from 2.47 to 18.91 gC m−2 y−2, and from 0.32 to 21.95 gC m−2 y−2 over these three regions, respectively.

Relationship between lake salinity and the climatic gradient in northeastern China and its implications for studying climate change

Earth system models demonstrate large uncertainty in projected changes in terrestrial carbon budgets. The lack of inclusion of adaptive responses of vegetation communities to the environment has been suggested to hamper the ability of modeled vegetation to adequately respond to environmental change. In this study, variation in functional responses of vegetation has been added to an earth system model (ESM) based on ecological principles. The restriction of viable mean trait values of vegetation communities by the environment, called 'habitat filtering', is an important ecological assembly rule and allows for determination of global scale trait-environment relationships. These relationships were applied to model trait variation for different plant functional types (PFTs). For three leaf traits (specific leaf area, maximum carboxylation rate at 25°C, and maximum electron transport rate at 25°C), relationships with multiple environmental drivers, such as precipitation, temperature, radiation, and CO2 , were determined for the PFTs within the Max Planck Institute ESM. With these relationships, spatiotemporal variation in these formerly fixed traits in PFTs was modeled in global change projections (IPCC RCP8.5 scenario). Inclusion of this environment-driven trait variation resulted in a strong reduction of the global carbon sink by at least 33% (2.1 Pg C yr(-1) ) from the 2nd quarter of the 21st century onward compared to the default model with fixed traits. In addition, the mid- and high latitudes became a stronger carbon sink and the tropics a stronger carbon source, caused by trait-induced differences in productivity and relative respirational costs. These results point toward a reduction of the global carbon sink when including a more realistic representation of functional vegetation responses, implying more carbon will stay airborne, which could fuel further climate change.

Biomedical researchers share a common challenge of making complex data understandable and accessible as they seek inherent relationships between attributes in disparate data types. Data discovery in this context is limited by a lack of query systems that efficiently show relationships between individual variables, but without the need to navigate underlying data models. We have addressed this need by developing Harvest, an open-source framework of modular components, and using it for the rapid development and deployment of custom data discovery software applications. Harvest incorporates visualizations of highly dimensional data in a web-based interface that promotes rapid exploration and export of any type of biomedical information, without exposing researchers to underlying data models. We evaluated Harvest with two cases: clinical data from pediatric cardiology and demonstration data from the OpenMRS project. Harvest's architecture and public open-source code offer a set of rapid application development tools to build data discovery applications for domain-specific biomedical data repositories. All resources, including the OpenMRS demonstration, can be found at http://harvest.research.chop.edu

Arctic marine mammals are increasingly affected by the combined pressures of climate change and expanding human activity. Although individual stressors such as sea ice loss, underwater noise, and chemical pollution are well studied, their interactive effects remain poorly understood, complicating ecosystem-based conservation. We systematically screened 1084 peer-reviewed studies from 1984 to 2025 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)) to identify dominant stressor pathways and species-specific vulnerabilities. Six major stressors emerged: sea ice decline, noise pollution, chemical contaminants, industrial expansion, shifts in prey availability, and disease or biotoxins. We synthesize these findings into a stressor-interaction framework classifying combined effects as additive, synergistic, or antagonistic; Antagonistic interactions occur when the combined effect of two stressors is weaker than expected based on their individual impacts. Across taxa, overlapping pressures reduce movement, foraging success, and habitat use, particularly for narwhals, belugas, bowhead whales, and polar bears, whose ice dependence and slow reproduction heighten risk. High-risk combinations, such as contaminant remobilization during nutritional stress or the dual effects of sea-ice loss and industrial noise, highlight the need for dynamic protected areas, strengthened regulatory measures, and co-management approaches incorporating Indigenous knowledge. A clearer understanding of synergistic and cumulative effects is essential as environmental change accelerates.

Data mining aims to develop a new generation of tools to intelligently assist humans in analyzing mountains of data. Over the past few years, great progress has been made in both research and applications of data mining. Data mining systems have helped many businesses by exposing previously unknown patterns in their databases, which were used to improve profits, enhance customer services, and ultimately achieve a competitive advantage. In this paper, we present our unique data mining system DM-II (Data Mining Integration and Interestingness). DM-II is a PC-based system working in Windows 95/98/NT environment. Apart from the normal components of a data mining system, DM-II has a number of unique and advanced sub-systems. These sub-systems have been applied in many real-life applications, including education applications, insurance application, accident application, disease application, drug screening application, image classification, etc. Here, we focus on discussing the following sub-systems: CBA: CBA originally stands for Classification-Based on Associations [lo]. It has now been extended with a number of other advanced features. In all, CBA has the following capabilities: Building classifiers using association rules: Traditionally, association rule mining and classifier building are regarded as two different data mining tasks. CBA unifies the two tasks. It is able to ,generate association rules and builds a classifier using a special subset of the association rules. What is significant is that CBA, in general, produces more accurate classifiers compared to the state-ofthe-art classification system C4.5 [ 161. It also helps to solve some outstanding problems with the existing classification systems. Pruning and summarizing the discovered associations: One major problem with association

This research aims to find out descriptively about: the needs of management information systems in the Master Program of Education Management Graduate School, State University of Jakarta (UNJ), planning of management information system model that needed, the development of management information system model, and testing of management information system model. The method used is research and development. Data collected by observation, interview, documentation study, and audio-visual material. The research findings inform that in the Master program study of Education Management, data management and information currently been carried out using information systems as processing and storage of data and information. Needs that exist in the course include the need for tools, software, and human resources. Needs in the form of equipment such as computers, networks, and other support tools have been prepared. Software requirements include information system software. While human resources need such as the provision of administrative and IT staff that competent for the job. Planning includes the design objectives, aspects of education management, procedures, and design models. The design model consists of design of data flowchart and structure menu that illustrates the systems and sub-systems of the information system that designed. Development of the model is done by developing a few things like the development of graphic design, engineering source code, local webserver installation, and design layout. Development is based on the needs analysis and planning. Tests carried out in three stages. The first phase was conducted to test the way of supporting components on the local computer system. The second phase was conducted to test the way of the system designed after being embedded on a local webserver. The third phase is done to test the way of the overall information system.

This paper exposes a methodological approach that intends to lead later to an interactive braking system simulation model. Our Component Based Modeling (CBM) method begins by the product definition and the analysis of significant moments to determine the actions of the driver and the interactions produced between the components of the braking system, the driver and the environment. All our approach is based on the concept of product lifecycle. Development, qualification and updating of the automotive braking system model are going to be done thanks to instrumentation with sensors and micro-sensors. These elements have the advantage of being easily integrable and non intrusive.

The correlation and synthesis of a variety of two- and three-dimensional data is necessary to conceptualize and characterize ground-water flow systems in regional or site-specific areas. This paper presents an integrated, multidisciplinary, step-wise approach for conceptualizing, characterizing, and numerically simulating ground-water flow systems using Geographic Information Systems (GIS). The method presented involves seven iterative steps that can be accomplished manually or by using GIS: 1) Data gathering and preparation; 2) Field (on-site) conceptualization; 3) Surface characterization; 4) Subsurface characterization; 5) Hydrogeologic model characterization; 6) Hydrologic system characterization; and 7) Numerical model simulation. GIS may be used for: 1) data management, analysis, and visualization; 2) the integration of diverse data sources; 3) rapid development, visualization, and testing of alternative hydrogeologic and hydrologic system models; 4) the incorporation of contouring, attribute tables, and statistical analysis during data analysis and model development; and 5) the integration of basic and interpreted data bases with numerical modeling for purposes of model design, parameter discretization, statistical evaluation, and sensitivity analysis.

Beyond big fish: The case for more detailed representations of top predators in marine ecosystem models

In this research, we designed a universal graph model. We targeted this work on improving graph data organization representation, flexible formation of graph substructures for complex system modeling and generic processing of data organization. We form coherently composed graph modeling features including inheritance hierarchy of basic graph types, data participating graphs of multiple aspects, hierarchically composed and connected graphs, and property-based graph models based on existing graph models. Design cases are used to illustrate its achievement of our design goal. Keywords—graph theory; graph model, multi-aspect graph; hierarchical graph I. INTRODUCTION Computer modeling plays a key role to apply computer's capability in various fields of applications. Effective support of computer modeling should consist of data representation capability, data access and navigation capability, and generic operations for forming more general and reusable generic algorithms. As computer applications keep growing complex in wider areas, we need enhanced computer models capable of supporting these applications. Graph data models are one of the most extensively used discrete mathematical models in computer modeling of various applications from VLSI circuit design, computer system design and modeling, network modeling, etc. Traditional graph libraries such as LEDA(1), Boost Graph Library(2), LINK(3), and GraphStream(4) focuses on single- level directed and undirected graph modeling. Lee's work(5) utilized decorator and visitor patterns to add new vertex data and attach processing during graph traversal. Pizzonia's (6) employed static object-oriented extension of graph property classes associated with corresponding graph operations. Bertault(7) employed an extensible graph property class hierarchy and dynamic property checking. Directed and undirected hypergraphs are not supported in these approaches. Hyperedges in hypergraphs can be represented as vertices in ordinary graphs. Hypergraphs cannot be treated as super types of directed and undirected graphs to support generic algorithms with ordinary graphs. On data organization point of view, these approaches provide various graph organization types but only as single- level specialized graph type. Each of them should be utilized distinctly. Once these graph models need to be combined in a complex data model, designers should add artificial data structures to join them together. Still, such combined data model or its selected subparts cannot be viewed and utilized as if in a single graph model. This requires designers to add more application-specific gluing data structures, processing codes of distinct graph substructures and their gluing logic, and semantically similar but redundant non-generic codes. It thus hinders effective development and reuse of computer models for various complex applications. From our literature survey, we did not find any previous work providing combinational features for flexibly composing graph structures. Our design goal lies in three improvement directions, representation of complex system data organization, flexible formation of graph substructures for processing, and generic processing of data organization as well as individual data. Our main approach is to form coherently composed graph modeling features including inheritance hierarchy of basic graph types, vertex data participating in graphs of multiple aspects, hierarchically composed and connected graphs, and property-based graph models based on existing graph models. It forms a universal graph model of combinations of complex graph-oriented data organizations, flexibly forms various virtual graph substructures for complex system modeling and processing, and support generic processing capability at the graph structure levels and at the leaf data levels. In section 2, we describe refined design goals and the approach of our work. In the following sections, we in sequence introduce planned composed graph modeling features of our universal graph model, including generic accesses among basic graph models, joining multiple graphs into a multi-aspect graph model, generic structuring of graph models, and generic accesses of property-based graph models. In section 7, we introduce its software design and a design case. The last section concludes this research.

The average age of sexual maturity and first pregnancy are important criteria in evaluating the status of a population. However, the estimation procedure for these statistics should be sensitive to the different distributions of age-specific reproductive data and should also incorporate a variance estimate to facilitate comparisons between populations. The described procedure lends itself well to age-specific data and should be useful in discerning differences between populations of [Formula: see text] or more, if a sample of at least 25 is available for each age-class. Key words: reproduction, marine mammals, sexual maturity

Spaces, places and possibilities: A participatory approach for developing and using integrated models for community planning

As with many industries, digital disruption will play a major role in shaping agriculture over the coming years as decisions become increasingly data driven. A significant proportion of this data will come from on-farm sensors that are becoming easier to source and deploy. While access to sensors is becoming increasingly cost effective, accessing and integrating the data they provide is still a major issue for many, due to the use of different standards for describing and sharing the data. The Soil sensing - new technology for tracking soil water availability, managing risk and improving management decisions project has developed a distributed system that addresses the technical challenge of federating disparate data sources through the use of a software mediation layer and a semantically enabled metadata harvest, search and discovery tool. These web services, the O&M Translator and the Data Brokering Layer, allow a unified and federated view of the data, enabling integrated search and discovery and provide access through a SOS compliant API, delivering the data to client using the O&M data model and a TimeseriesML representation. The resulting Data Stream Integrator is already being tested in applications such as SoilWaterApp.

The development of an Electronic Health Record (EHR) system requires a well-defined data set and a robust data model to support complex clinical workflows, interoperability, and data quality. Many existing EHR implementations lack standardized and integrated data models, resulting in redundancy, semantic inconsistency, and limited support for secondary data use. This study aims to develop a comprehensive EHR data model based on a Minimum Data Set (MDS) that systematically represents medical service processes across emergency, outpatient, inpatient, and surgical care. The proposed approach involves the design of a conceptual, logical, and physical data model. Business processes were first analysed to identify relationships among clinical and administrative data classes, which were then transformed into a conceptual data model. The logical data model was developed through database normalization and schema definition, while the physical data model was constructed to represent the actual database structure for implementation. The results demonstrate a structured EHR data model comprising 21 interrelated data classes that reflect real-world medical service workflows. The conceptual data model establishes clear entity relationships, supporting data consistency and interoperability. The logical data model provides a normalized SQL-based schema that minimizes redundancy and enhances data integrity, while the physical data model enables efficient data storage and access. A prototype EHR system was implemented to evaluate the feasibility of the proposed data model, showing stable performance, usability, and alignment with functional requirements. This study concludes that a standardized EHR data model derived from an MDS can effectively support comprehensive clinical documentation, interoperability, and future scalability. The proposed model provides a strong foundation for developing national or institutional EHR systems and can serve as a reference for further EHR data standardization efforts.