The challenge of decentralized wastewater management in rural areas necessitates robust, low-cost technologies for greywater treatment. A study was conducted with the objective of analyzing the efficiency of four Horizontal Subsurface Flow Constructed Wetlands (HSFCW) in series for household greywater treatment using the macrophyte Typha angustifolia and a pumice substrate. The methodology involved five monitoring events over a 120-day period, with sampling at the inlet and outlet of each HSFCW. Field parameters, including pH (8.12) and water temperature (24ºC), as well as the final turbidity (8.2 NTU), were within permissible ranges. An increase in electrical conductivity was observed, reaching 1.982 mS/cm. Contaminant removal percentages were 97.52% for oils and greases, 96.66% for BOD5, 93.21% for COD, 98.11% for nitrates, 97.63% for total phosphorus, and 46.21% for TSS. The implementation of the HSFCW with Typha angustifolia and pumice substrate proved to be an efficient alternative for treating greywater, exhibiting a high potential for sustainable and decentralized solutions. This validated performance makes it suitable for future integration with real-time monitoring and smart management systems.

Data and data sharing foster information systems that can create value for society, individuals, businesses and organizations. Data sharing and usage require establishing an appropriate data ecosystem where solid and effective data governance and management are in place to deal with associated risks like data being biased, personal, sensitive and stigmatizing, to name a few. Data lineage is a necessary means for data governance and management. In this contribution, we revisit the objectives of data lineage and investigate how it can be deployed in cross organizational settings. Specifically, we provide an overview of the objectives to which contemporary data lineage can contribute, revise the existing definition(s) of data lineage and adapt it to cross organizational settings, and propose architectural models for data lineage deployment across loosely coupled semi-autonomous organizations. Our revised definition of data lineage conceptualizes the physical distribution of data related objects as well as the semantical distribution of the related concepts that relate or apply to those data objects. The proposed architecture for data lineage related metadata management relies on the existing organizational structures, which, therefore, can scale up organically as seen in the case of federated identity management among academia.

There is an increasing concern regarding the water efficiency of turfgrass in cities and parks. This paper aims to test the performance of different warm-season grass cultivars that could replace the more demanding cold-season ones. A control C3 mix (Festuca arundinacea Schreb., Poa pratensis L., and Lolium perenne L.), two C4 mixes (Cynodon dactylon × Cynodon transvaalensis, cultivar' Texoma (OKC1876)' and Zoysia matrella cultivar 'Zorro'), and a C3-C4 mix (Cynodon dactylon × Cynodon transvaalensis, cultivar 'Texoma (OKC1876)' overseeded with L. perenne and F. arundinacea) have been cultivated in individual plots on the experimental grounds of the Universidad Politécnica de Madrid, Spain. At various dates throughout the spring, pictures of the plots have been taken and analysed, measuring green intensity through three different methods (Green Index, Excess of Green, Canopeo) and their homogeneity. Among these three methods used to measure green intensity, the green index method has been able to detect a significant difference between species for a more extended period than the others throughout the spring green-up. After the control mix, the overseeded C3-C4 mix has been the earliest to reach its peak green value, and Cynodon dactylon × Cynodon transvaalensis, cultivar 'Texoma (OKC1876)', has been the most homogeneous one after green-up. The findings in this paper will aid in the selection of turfgrass species for future urban green spaces that are less water-demanding.

This paper examines how process automation can support decision-making in municipal healthcare services, using a medium-sized Norwegian municipality as a case study. Grounded in Socio-Technical Systems (STS) theory, the study explores how digital tools, organizational routines, and professional discretion interact in allocating health and care services. Using qualitative methods, including document analysis, job shadowing, and a structured employee survey, the study identifies a casework process marked by repetitive tasks, fragmented systems, and limited interoperability. Caseworkers express cautious optimism toward Robotic Process Automation (RPA) and Artificial Intelligence (AI), recognizing their potential to streamline administrative routines while voicing concerns about ethics, privacy, and the erosion of human judgment. The findings highlight the importance of maintaining professional discretion and transparency as municipalities adopt automation. RPA and AI are most effective when designed to complement rather than replace human decision-makers. The paper concludes with practical recommendations emphasizing co-design with end users, investment in digital literacy, and the critical assessment of tasks suitable for automation.