Semi-enclosed coastal systems are highly dynamic environments where benthic organisms are exposed to strong hydrographic gradients and increasing anthropogenic pressures. This study assessed the habitat suitability of the pearl oyster Pinctada radiata in two contrasting Mediterranean gulfs of Central Greece, the Maliakos and the South Evoikos, by integrating Copernicus Earth Observation (EO) products with an Artificial Intelligence (AI) modeling framework. Environmental variables, including sea surface temperature, salinity, chlorophyll-a concentration, current velocity, and dissolved oxygen, were derived from satellite and marine datasets and used to train a multi-algorithm ensemble combining Maximum Entropy (MaxEnt), Extreme Gradient Boosting (XGBoost), and a Convolutional Neural Network (CNN). The ensemble model showed strong predictive skill (AUC = 0.94; TSS = 0.80) and identified temperature, dissolved oxygen, and substrate type as the main drivers of habitat suitability. Spatial projections indicated that roughly two-thirds of the study area currently supports favorable conditions for P. radiata, particularly in shallow, low-energy, mesotrophic zones. Under a simulated +2 °C warming scenario, highly suitable habitats declined by about 20%, highlighting the species’ sensitivity to future thermal stress and subsequent oxygen depletion, demonstrating the value of EO-driven AI approaches for anticipating ecological change in vulnerable coastal systems.
Rural and regional development is often framed as an economic or service-delivery challenge, whereas water is treated as infrastructure or compliance. That separation is analytically convenient but operationally false. Hydrologic regime reality and water quality dynamics are non-negotiable physical constraints that quietly determine what rural communities can credibly promise, finance, permit, and defend over time. At the same time, many rural water systems and watershed programs operate within institutional arrangements that were not designed for slow hydrologic lags, cross-boundary pollutant legacies, or the legitimacy demands created by uneven exposure to risk. This perspective, therefore, suggests that rural development should be recentered on water governance: the coupled system of hydrologic processes, water-quality legacies, and organizational capabilities that together produce reliability, safety, and trust. Recent primary research is synthesized showing that (1) legacy nutrients and ecosystem memory create multi-decade time lags that can invalidate short political or funding cycles, (2) rural and small system compliance and exposure burdens remain structurally unequal, and (3) adaptive governance capacity depends on institutional fit, partnerships, and policy and planning choices that are themselves socially patterned. A practical agenda for scholars and practitioners is proposed: build hydrologic legitimacy by aligning project claims with hydrologic time, making governance fit explicit across scales, and treating organizational change capacity as core water and rural development infrastructure. The resulting framework provides decision-makers with operational guidance for aligning development claims, governance structures, and investments with hydrologic constraints that ultimately determine long-term feasibility and trust. Rather than presenting new empirical results, this Perspective synthesizes evidence from hydrology, water quality, governance, and organizational change to conceptually reframe rural and regional development around hydrologic legitimacy as a governing constraint.
In this work, fully biobased polybiurets (PBUs) were prepared from the polymerizations of biuret, a green and environmentally friendly chemical derived from urea, with 1,10-decanediamine and 1,6-hexanediamine. No solvent and no catalyst is needed in such polymerizations. Both biuret and urea functions can be observed in the obtained products. The PBUs possess higher glass transition temperature than the corresponding polyureas (~40 °C higher). The strength at break achieves as high as 77 MPa. The mechanical and thermal properties of the PBUs can be feasibly tuned by altering the proportions of the two diamines. It is provided in this work a new strategy in the construction of biobased polymers with high performance.
Traditional electronic Kanban (eKanban) systems depend on manual scans and offer only discrete material visibility, limiting responsiveness and automation in lean manufacturing environments. These operational bottlenecks are magnified in high-mix contexts, where delayed replenishment signals degrade flow stability, increase work-in-progress, and hinder sustainable material handling. Furthermore, vendor-specific systems lack interoperability for scalable automation, constraining the development of intelligent manufacturing solutions. This work investigates whether zone-based replenishment automation can be enabled through real-time locating systems (RTLS) using open interoperability standards, addressing a gap in empirical validation of such approaches. A middleware architecture was developed that integrates ultra-wideband (UWB) positioning, an Omlox-compliant location middleware (DeepHub), and a cloud-based eKanban system to replace manual triggers with geofence-driven order creation. The novelty of this study lies in demonstrating a fully automated Kanban signaling loop built on the open Omlox standard, providing vendor-independent RTLS interoperability and eliminating human intervention in replenishment signaling. This contributes new knowledge on how continuous location data can be converted into actionable replenishment events in a standards-based, modular manner, enabling more intelligent and autonomous material-flow control. A controlled proof-of-concept experiment simulating shop-floor conditions showed that the system achieved a 100% detection success rate, zero duplicate orders, and an average trigger-to-action latency of 2.7 s, while automatically recovering from authentication and WebSocket failures. These results provide the first empirical evidence that Omlox-compliant RTLS middleware can reliably support zone-based eKanban automation. The findings have direct implications for intelligent and sustainable manufacturing by demonstrating a scalable pathway toward interoperable, real-time material-flow systems that reduce manual intervention, avoid unnecessary handling, and lower work-in-progress. More broadly, the work addresses the current lack of empirical validation of open-standard RTLS integration within lean and sustainable production environments.
Functional mitral regurgitation (FMR) is a prevalent valvular disorder driven by adverse remodeling of the left ventricle and/or left atrium. This review synthesizes the contemporary evidence on multimodality imaging and its role in mechanism-specific evaluation and management of FMR, with particular emphasis on distinguishing ventricular FMR (VFMR) from atrial FMR (AFMR). FMR is mechanistically heterogeneous, requiring precise phenotyping to guide therapy. A mechanism-based framework differentiating VFMR, driven by left ventricular dilation and leaflet tethering, from AFMR, driven by left atrial and annular enlargement with preserved ventricular function, is central to contemporary management. Echocardiography remains the cornerstone for real-time assessment of MR severity, hemodynamics, and valve–ventricle interactions. Cardiac magnetic resonance (CMR) provides the gold standard for volumetric quantification and myocardial tissue characterization, enabling improved risk stratification by assessing ventricular remodeling and fibrosis. Computed tomography (CT) offers high-resolution anatomic phenotyping and is essential for procedural planning, particularly for transcatheter edge-to-edge repair (TEER) and transcatheter mitral valve replacement (TMVR). Integration of multimodality imaging supports individualized selection between guideline-directed medical therapy alone, TEER, surgical intervention, or TMVR, based on the dominant mechanism and myocardial substrate. The discordant outcomes of landmark trials such as MITRA-FR and COAPT have underscored the importance of precision in patient selection, highlighting the controversial but clinically relevant proportionate/disproportionate FMR framework and the extent of myocardial fibrosis as key modifiers of treatment response. Emerging advances in advanced imaging and artificial intelligence hold promise for automated phenotyping, improved reproducibility, and earlier identification of patients most likely to benefit from intervention, ultimately enabling a more personalized, mechanism-driven approach to improving outcomes in FMR.
