Causal modeling offers a principled foundation for uncovering stable, invariant relationships in time-series data, thereby improving robustness and generalization under distribution shifts. Yet its potential is underutilized in spatiotemporal Earth observation, where models often depend on purely correlational features that fail to transfer across heterogeneous domains. We propose RIC-TSC, a regionally-informed causal time-series classification framework that embeds lag-aware causal discovery directly into sequence modeling, enabling both predictive accuracy and scientific interpretability. Using multi-modal satellite and reanalysis data—including Sentinel-1 microwave backscatter, Sentinel-2 and Landsat-8 optical reflectance, and CARRA meteorological variables—we leverage Joint PCMCI+ (J-PCMCI+) to identify region-specific and invariant predictors of supraglacial lake evolution in Greenland. Causal graphs are estimated globally and per basin, with validated predictors and their time lags supplied to lightweight classifiers. On a balanced benchmark of 1000 manually labeled lakes from two contrasting melt seasons (2018–2019), causal models achieve up to 12.59% higher accuracy than correlation-based baselines under out-of-distribution evaluation. These results show that causal discovery is not only a means of feature selection but also a pathway to generalizable and mechanistically grounded models of dynamic Earth surface processes.

Robust causal discovery in time series datasets depends on reliable benchmark datasets with known ground-truth causal relationships.However, such datasets remain scarce, and existing synthetic alternatives often overlook critical temporal properties inherent in real-world data, including nonstationarity driven by trends and seasonality, irregular sampling intervals, and the presence of unobserved confounders.To address these challenges, we introduce TimeGraph, a comprehensive suite of synthetic time-series benchmark datasets that systematically incorporates both linear and nonlinear dependencies while modeling key temporal characteristics such as trends, seasonal effects, and heterogeneous noise patterns.Each dataset is accompanied by a fully specified causal graph featuring varying densities and diverse noise distributions and is provided in two versions: one including unobserved confounders and one without, thereby offering extensive coverage of real-world complexity while preserving methodological neutrality.We further demonstrate the utility of TimeGraph through systematic evaluations of state-of-the-art causal discovery algorithms including PCMCI+, LPCMCI, and FGES across a diverse array of configurations and metrics.Our experiments reveal significant variations in algorithmic performance under realistic temporal conditions, underscoring the need for robust synthetic benchmarks in the fair and transparent assessment of causal discovery methods.The complete TimeGraph suite, including dataset generation scripts, evaluation metrics, and recommended experimental protocols, is freely available to facilitate reproducible research and foster community-driven advancements in time-series causal discovery.Source code is available at https://github.com/hferdous/TimeGraph.

The Greenland Ice Sheet (GrIS) has emerged as a significant contributor to global sea level rise, primarily due to increased meltwater runoff. Supraglacial lakes, which form on the ice sheet surface during the summer months, can impact ice sheet dynamics and mass loss; thus, better understanding these lakes' seasonal evolution and dynamics is an important task. This study presents a computation-ally efficient time series classification approach that uses Gaussian Mixture Models (GMMs) of the Reconstructed Phase Spaces (RPSs) to identify supraglacial lakes based on their seasonal evolution: 1) those that refreeze at the end of the melt season, 2) those that drain during the melt season, and 3) those that become buried, remaining liquid insulated a few meters beneath the surface. Our approach uses time series data from the Sentinel-l and Sentinel-2 satellites, which utilize microwave and visible radiation, respectively. Evaluated on a GrIS-wide dataset, the RPS-GMM model, trained on a single representative sample per class, achieves 85.46% accuracy with Sentinel-l data alone and 89.70% with combined Sentinel-l and Sentinel-2 data. This performance significantly surpasses existing machine learning and deep learning models which require a large training data. The results demonstrate the robustness of the RPS-GMM model in capturing the complex temporal dynamics of supraglaciallakes with minimal training data.

Triangles are an essential structure in complex network analysis representing metrics such as clustering coefficient and transitivity. In this work, we employ the recently introduced “Triangle Centrality” in exploring cohesive sub-networks of interest within the maximum $k -$core of complex networks. Motivated by the recent interest in higher-order connectivity patterns to understand fundamental structures that control behaviour of complex systems, we employ triangles as “higher-order” structural units in complex systems. Each triangle in the network is attributed with a numeric score, the so called $k -$count, to indicate its “influence” calculated based on the number of other triangles it is associated with. We demonstrate our proposed method to perform visual exploration of cohesive sub-networks of interest on a well-studied benchmark social network “Lusseau’s Dolphin Social Network”.

There is growing interest in applying Scrum practices in Global Software Development to leverage the advantages of both. However, the effective use of Scrum practices largely depends on close interactions between project stakeholders. The distribution of project stakeholders in GSD provides significant challenges related to project collaboration processes that may limit the use of Scrum. However, project managers increasingly seek to use the Scrum model in their distributed projects. While there is an emerging body of industrial experience, there are limited empirical studies that discuss Scrum tailoring in GSD. The paper reports a multi-case study that investigates the impact of key project contextual factors on the use of Scrum practices in GSD. This study is relevant to researchers and practitioners who are seeking ways to use Scrum in GSD and improve project effectiveness.

There is growing interest in applying agile practices in Global Software Development (GSD) projects. But project stakeholder distribution in GSD creates a number of challenges that make it difficult to use some agile practices. Moreover, little is known about what the key challenges or risks are, and how GSD project mangers deal with these risks while using agile practices. We conduct a Systematic Literature Review (SLR) following existing guidelines to identify primary papers that discuss the use of Scrum practices in GSD projects. We identify key challenges, due to global project distribution, that restrict the use of Scrum and explore the strategies used by project managers to deal with these challenges. Our findings are consolidated into a conceptual framework and we discuss various elements of this framework. This research is relevant to project managers who are seeking ways to use Scrum in their globally distributed projects.

There is a growing interest in applying agile practices in global software development (GSD) projects. The literature on using Scrum, one of the most popular agile approaches, in distributed development projects has steadily been growing. However, there has not been any effort to systematically select, review, and synthesize the literature on this topic. We have conducted a systematic literature review of the primary studies that report using Scrum practices in GSD projects. Our search strategy identified 366 papers, of which 20 were identified as primary papers relevant to our research. We extracted data from these papers to identify various challenges of using Scrum in GSD. Current strategies to deal with the identified challenges have also been extracted. This paper presents the reviewpsilas findings that are expected to help researchers and practitioners to understand the challenges involved in using Scrum for GSD projects and the strategies available to deal with them.

Despite the apparent significant differences between the fundamental principles of agile and Global Software Development (GSD), there is a growing interest in assessing the viability of using agile practices in GSD. But there has been little research in identifying and understanding the challenges involved in integrating agile and GSD and potential strategies to deal with them. One of the key challenges is coordination of the development work. Study asserts that a good understanding of coordination difficulties can help devise strategies to overcome them. The aim of this PhD study is to explore and understand the use of different coordinating mechanisms to coordinate GSD projects while using agile software development practices.