Search Results (786)

The discharge of metal-containing effluents into aquatic systems remains a major environmental concern because metal ions can persist in water bodies and accumulate in biological systems, potentially affecting ecosystem and human health. Among these contaminants, Cr(III) is frequently encountered in waste streams generated by industrial activities, making its removal an important objective in water quality management. This study investigated the adsorption behavior of Cr(III) using lignocellulosic biosorbents obtained from tamarind shell (Tamarindus indica) after water, H₂O₂, and HCl pretreatments, with particular emphasis on equilibrium behavior, thermodynamic characteristics, and pretreatment-induced physicochemical modifications. Batch adsorption experiments were conducted to evaluate equilibrium behavior. The highest adsorption capacity (41.6 mg g⁻¹) was obtained with the water-treated biosorbent at 60 °C. The equilibrium data were best represented by the Sips model, suggesting that Cr(III) adsorption occurred on surfaces containing adsorption sites with different energetic characteristics. Thermodynamic analysis revealed that the adsorption process was spontaneous, while the enthalpy changes indicated predominantly endothermic behavior for the pretreated biosorbents. ATR-FTIR, SEM, EDS, and XRD analyses were performed to characterize the biosorbents before and after adsorption. The characterization results indicated that oxygen-containing functional groups, particularly hydroxyl and carbonyl functionalities, were associated with the adsorption process. SEM images showed morphological changes associated with pore occupation, while EDS confirmed chromium adsorption and suggested possible ion-exchange mechanisms. XRD patterns indicated a mainly amorphous structure. The results demonstrated that pretreatment-induced modifications strongly influenced the adsorption performance of tamarind shell. Water pretreatment produced the most favorable adsorption behavior, yielding the highest adsorption capacity among the evaluated biosorbents. The combined interpretation of equilibrium, thermodynamic, and characterization results revealed a close relationship between surface properties and Cr(III) uptake. Full article

The digital transformation of agrifood systems demands an integrated infrastructure to ensure traceability, trust, and intelligent decision-making across complex and heterogeneous value chains. METROFOOD-IT, a large-scale national research infrastructure in food metrology aligned with the ESFRI METROFOOD-RI, addresses these challenges by combining advanced experimental facilities with a comprehensive digital ecosystem. This paper focuses on the IT kernel of METROFOOD-IT and presents an integrated architectural model that brings together four key technological paradigms: data acquisition through Internet of Things (IoT) and laboratory infrastructures, an Open Data Platform for interoperability and sharing, blockchain-based notarization for integrity and provenance, and Artificial Intelligence (AI) for knowledge extraction and decision support. Rather than describing these components in isolation, the paper abstracts from their implementation within the Italian National Recovery and Resilience Plan (NRRP) project METROFOOD-IT to distill a coherent and reusable architectural pattern in which data management, trust enforcement, and intelligent analytics are tightly coupled. Five explicit design principles are identified and articulated: federated data with centralized metadata, selective on-chain anchoring, user-unobtrusive trust infrastructure, explainability as a first-class architectural concern, and machine learning as the backbone of decision-making. Two empirical case studies—one centered on explainable AI for hyperspectral crop nitrogen assessment and the other on IoT-driven sustainable agriculture monitoring secured by distributed ledger technology—serve a dual role: they motivate and shape the architectural pattern, and they exemplify the operational regimes the resulting design supports. A reference deployment on the Ethereum Sepolia public test network, grounded on an IBM Power E1050 and IBM Storage Scale enterprise substrate, provides quantitative evidence for the proposed hybrid on-chain/off-chain pattern with streaming hash-only notarization. The architecture illustrates how research infrastructures can evolve into integrated digital platforms that enable transparent, verifiable, and scalable agrifood systems, and offers a foundation for generalizable design principles in data-intensive and trust-sensitive settings. Full article

Background: The growing complexity of liquid biopsy in precision oncology demands a structured classification framework that can accommodate its expanding multi-omic scope. As the field has matured from early Tumor Microemboli research—focused on multicellular clusters of circulating tumor cells (CTCs) that drive high-efficiency metastasis—to the broader systemic analysis of the “Tumor Microenvironment” (TME) encompassing malignant and non-malignant components, the need for a hierarchical taxonomy has become evident. Objective: To integrate these diverse data streams into a coherent clinical framework, a multi-tiered classification system is needed. This review proposes a foundational roadmap that formally distinguishes the systemic ecosystem from its physical and functional subsets and highlights their clinical utility in therapeutic decision-making. Proposed Taxonomy: We advocate for the adoption of Circulating Tumor Microenvironment (cTME) as the inclusive term for the systemic environment, encompassing non-cellular factors such as ctDNA, extracellular vesicles, and biophysical attributes. Conversely, physical cellular clusters should be strictly classified as Circulating Tumor Emboli (CTE). Crucially, we define Circulating Tumor-Associated Cells (C-TACs) as the functional cellular subset within the cTME, encompassing single CTCs, CTE, and supporting non-malignant cells like CTECs and CAFs. Clinical Applications: Establishing this distinction allows for the seamless integration of molecular profiling (NGS) and functional assays. We highlight emerging evidence that C-TACs may serve as the primary substrate for Chemo-Response Profiling (CRP), with early proof-of-concept studies reporting high concordance with clinical outcomes that still await independent prospective confirmation. Furthermore, preliminary evidence suggests that identifying these functional units, particularly perioperative CTE, may help predict the efficacy of adjuvant chemotherapy in early-stage malignancies, although this remains to be confirmed in prospective studies. Conclusions: Adopting this unified taxonomy may help advance precision oncology. By recognizing the cTME as the superordinate ecosystem and C-TACs as its functional executors, clinicians may be better positioned to interpret multi-modal liquid biopsy data, providing a conceptual roadmap for integrating these technologies into platforms for personalized cancer management. We emphasize that this framework is intended to be hypothesis-generating and that its clinical applications require prospective validation before routine adoption. Full article

Tropical wetlands are highly sensitive to climatic and anthropogenic disturbances, and their macrophyte communities provide valuable information about environmental conditions and habitat structure. This study evaluated the relationship between aquatic macrophyte richness, community composition, and habitat vulnerability to climate change in aquatic ecosystems of the San Luis rural district, Barrancabermeja municipality (Santander, Colombia). Macrophyte communities were characterized at 47 monitoring sites distributed across six mesohabitats: floodplain depressions, swamp, wetland, artificial ponds, naturalized ponds, and stream riparian zones. A total of 63 species belonging to 30 families and 51 genera were recorded. Contrary to theoretical expectations, correlation analyses showed no significant relationship between macrophyte species richness and habitat vulnerability indices (Spearman ρ = −0.118, p = 0.428; Pearson r = −0.069, p = 0.646). However, species richness differed significantly among mesohabitats (Kruskal–Wallis, p < 0.05), indicating strong spatial heterogeneity in aquatic plant distribution. In addition, multivariate analyses using Principal Component Analysis (PCA) revealed that macrophyte community composition was strongly structured by local anthropogenic activities, including livestock farming, oil palm cultivation, and wastewater inputs. Floodplain depressions and artificial ponds were dominated by disturbance-tolerant and eutrophication-resistant species such as Urochloa plantaginea and Salvinia minima, reflecting higher levels of environmental pressure. These results demonstrate that macrophyte community composition, rather than species richness alone, is a more reliable indicator of habitat conditions and anthropogenic disturbance in tropical wetland systems. Overall, this study highlights that taxonomic richness is not a robust predictor of climate-related vulnerability in highly disturbed wetlands and emphasizes the importance of considering species composition and environmental context when assessing ecosystem conditions. Full article

Biological invasions and freshwater biodiversity loss are two of the most pressing global conservation challenges yet their interaction during the earliest stages of invasion remains poorly understood. Iberian freshwaters rank among Europe’s most biodiverse ecosystems, harbouring a remarkable assemblage of endemic fish species. This irreplaceable heritage is increasingly threatened by non-native piscivorous predators, to which endemic species often lack innate antipredator responses. The invasive European perch (Perca fluviatilis) was first detected in the Meimoa reservoir, within the Malcata Natural Reserve (Central Portugal), in 2023, and has since expanded exponentially in abundance while dispersing into adjacent stream networks. This emerging invasion provided a unique opportunity to assess the predation impacts of a novel piscivorous predator during the early stages of establishment and dispersion, across both lentic and lotic habitats. From 2022 to 2025, European perch were sampled in the invaded reservoir using gillnetting and in connected streams with electrofishing. Diet was assessed through stomach content analysis, and prey composition was analyzed in relation to site, season, year and ontogeny. European perch exhibited a clear ontogenetic diet shift as expected, from zooplankton and invertebrates to crayfish and fish, with minor variation in prey composition between systems. In the Meimoa reservoir, body size was the strongest driver of diet composition (PERMANOVA: R² = 0.134, p < 0.001), with 50% of the stomachs from individuals above 35 cm containing fish, with the Iberian nase, Pseudochondrostoma polylepis, being the dominant prey. Diet composition remained stable across years (R² = 0.007; p = 0.188), despite a 74% decline in nase catch per unit effort (CPUE) between 2022 and 2025. In streams, despite the absence of large perch, piscivory was recorded earlier and encompassing a broader range of native taxa. The sustained predation pressure on P. polylepis, a formerly dominant and culturally significant species, despite its steep population decline, suggests that European perch holds the potential to locally deplete native fish stocks. The advance of this predator into lotic habitats demands urgent conservation action, as it may critically threaten the long-term persistence of one of Portugal’s most vulnerable freshwater taxonomic groups. Full article

Intelligent sensors and Internet of Medical Things (IoMT) platforms are rapidly changing smart healthcare by enabling continuous capture of physiological, behavioral, and clinical events outside conventional hospital settings. Yet the value of connected sensing depends on more than signal acquisition alone. A practical remote-monitoring ecosystem must also convert sensor alerts, clinician-facing summaries, and historical electronic clinical records (ECRs) into ranked evidence that supports care decisions. This study reframes a large-AI clinical retrieval model as the intelligence layer of an edge–cloud IoMT architecture. The proposed framework combines Transformer-Based Sequence (TBS) encoding, BioBERT-driven representation learning, explicit retrieval, and domain-guided re-ranking to connect sensor-originated narratives, patient records, and clinician queries. The empirical evaluation is conducted on Medical Information Mart for Intensive Care III (MIMIC-III) and i2b2, two de-identified clinical text benchmarks that approximate the documentation layer of real-world remote patient monitoring. Compared with strong baselines, including DeepBio, UniT2T, Web4IR, A2A-API, CoLTiD, VLRG, ColBERT, DeepSDH, BiRex, and DL4BTM, the proposed model achieves the best overall performance, reaching F1/Pre/NDCG scores of 0.8399/0.8338/0.5235 on MIMIC-III and 0.8090/0.8100/0.5129 on i2b2. Ablation experiments confirm the importance of exploratory data adaptation, critical feature modeling, critical token learning, cross-disciplinary supervision, and data-driven regularization. Parameter sensitivity analysis shows stable behavior for beta values greater than or equal to 1, with the strongest results at beta = 5. The study concludes that large-AI retrieval can strengthen the clinical interpretation layer required for IoMT-enabled remote monitoring, while future work should validate the approach on live multimodal sensor streams and privacy-preserving deployments. Full article

Industrial and domestic wastewaters, nonpoint pollution sources, and climate change affect stream ecosystems, water quantity, and quality. Within the scope of this study, the water quality of Nilüfer Stream was evaluated using the Water Quality Index (WQI), One-Way ANOVA, the Kruskal–Wallis Test, and Principal Component Analysis (PCA). In the study, 4686 water quality data from seven sampling stations between 2008 and 2024 were used. WQI results showed a distinct decrease in water quality from the upstream to the downstream of the Stream. Average WQI values for the stations were found to be between 140.83 and 487.83. The lowest WQI value was found at Station 1 and the highest WQI value was found at Station 7. According to WQI, the ranking of the stations by magnitude was St7 > St4 > St5 > St6 > St2 > St3 > St1. A statistically significant difference was observed between the stations in terms of WQI, ANOVA, and Kruskal–Wallis Test (p < 0.05), and water quality was found to be seasonally diverse. Generally, at stations (except for two stations), the seasonal WQI values ranked by magnitude were autumn > summer > winter > spring. The PCA showed that relationships among parameters originating from industrial wastewater associated with the textile, automotive, and metal industries were stronger (component loadings > 0.75), whereas the groups identified in the upstream basin indicated domestic pollution and agricultural pollution from fertilizers and pesticides. PCA conducted between meteorological parameters and the WQI values of the stations showed that climate change could be effective at only two stations. It was determined that the region located before the wastewater treatment plant (St4) was associated with precipitation, humidity, and evaporation, while the downstream region (St7) was related to wind speed. It was observed that water quality was more influenced by industrial, urban, and agricultural pollution sources than by climate change. Full article

Remote patient monitoring (RPM) is widely framed as a foundational technology for the next generation of chronic-disease care. Specific applications—pacemaker follow-up, hypertension cohorts, structured heart-failure programmes, post-surgical biosensor protocols, and virtual wards—now generate measurable clinical and economic value. Yet a decade of evaluations and implementation studies suggests that the surrounding ecosystem has matured unevenly: working applications coexist with persistent cross-cutting fragility. In this Perspective we argue that four structural gaps continue to constrain RPM’s promise at scale: (i) economic models that do not credibly compensate the asynchronous clinical work that RPM generates; (ii) ambiguous frameworks for professional liability and accountability for continuous data streams, intensified by artificial-intelligence (AI)-mediated decision support; (iii) privacy, equity, and benefit-sharing arrangements that do not yet make patients unambiguous net beneficiaries—a gap visible across very different health systems internationally; and (iv) engagement and adherence dynamics that determine whether programmes deliver value at all, but are still treated as secondary outcomes. The COVID-19 emergency briefly suspended much of the friction in this ecosystem and produced a useful natural experiment: what scaled rapidly under emergency conditions, and what subsequently atrophied, illuminates which gaps are technical, which are economic, and which are institutional. We close with a six-point research and policy agenda intended to move RPM from localised successes to a trustworthy, generalisable standard of care. Full article

Mountain stream ecosystems are often considered among the least disturbed freshwater environments; however, increasing land-use pressures may affect their ecological integrity even under apparently high-water quality conditions. This study aimed to assess the relative influence of landscape, physicochemical, and hydromorphological factors on benthic macroinvertebrate communities in three sub-catchments (Ambroz, Jerte, and Tiétar) of the Sierra de Gredos (Central Spain). A total of 33 sampling sites were surveyed, and macroinvertebrate assemblages were analyzed in relation to environmental variables using partial Redundancy Analysis (pRDA) and variance partitioning. All sites were classified as having “Excellent” ecological status based on the Iberian Biological Monitoring Working Party (IBMWP) index. However, multivariate analyses revealed clear spatial patterns and responses to environmental gradients. Results indicated that catchment-scale landscape characteristics defined the pool of potential colonizers, while local physicochemical and hydromorphological conditions acted as secondary filters structuring macroinvertebrate assemblages. Landscape variables explained the largest fraction of variance in community structure (30.6%), followed by physicochemical parameters (29.0%) and hydromorphological indices (24.9%), with a significant shared component (16.5%) indicating interactions among drivers. Agricultural land use, particularly in the Jerte sub-catchment, was associated with shifts in community composition, favoring tolerant taxa such as Diptera, while sub-catchments dominated by natural vegetation supported higher richness of sensitive groups, including Ephemeroptera and Plecoptera. These findings highlight the importance of multi-scale processes in structuring mountain stream communities and reveal limitations of traditional biotic indices in detecting early ecological changes. The results support the integration of catchment-scale variables into ecological assessment frameworks and emphasize the need for preventive, basin-scale management strategies to maintain ecological integrity under increasing anthropogenic pressure. Full article

Knee Osteoarthritis (KOA) is a leading contributor to global physical disability, where delayed diagnosis often results in irreversible joint damage and socio-economic cost. Early diagnosis remains challenging due to subtle radiographic biomarkers and limited access to specialized expertise, particularly in distributed healthcare settings. Within the evolving landscape of the Future Internet, characterized by Internet of Medical Things (IoMT), edge–cloud computing, and intelligent digital health infrastructures, there is an increasing demand for scalable, low-latency, and explainable AI-driven diagnostic solutions. In this work, we propose a Dual-Stream Wavelet Fusion Network (DS-WFN) alongside a distributed edge-cloud architectural roadmap tailored for deployment in distributed and edge-enabled healthcare ecosystems. The framework integrates a spatial morphological stream with a spectral wavelet stream, augmented by an Adaptive Wavelet Selection Mechanism (AWSM). The AWSM dynamically selects optimal frequency bases (Haar, Symlet, Daubechies) to preserve fine-grained diagnostic features typically lost in conventional CNN architectures. An Adaptive Spatial Alignment (ASA) module further ensures efficient fusion of heterogeneous representations, enabling robust feature integration across computational nodes. Experimental results across a five-fold patient-isolated cross-validation protocol demonstrate that the DS-WFN achieves a mean classification accuracy of 76.3% (95% CI: 71.6–80.8%) and a macro-averaged F1-score of 0.747 (95% CI: 0.697–0.795), consistently outperforming single-stream baselines while preventing patient-level data leakage. Furthermore, Grad-CAM visualizations provide interpretable outputs aligned with clinical diagnostic criteria, supporting trustworthy AI integration into digital healthcare workflows. Furthermore, we disclose a methodological framework for edge-based implementation, highlighting how localized inference ensures data sovereignty and real-time clinical support. By combining multiscale signal processing with deep learning under a Future Internet paradigm, this work contributes a scalable, explainable, and edge-ready diagnostic framework for early KOA detection, enabling intelligent, connected, and resource-efficient healthcare services. Full article

The contamination of aquatic environments, including treated and drinking water, by microplastics poses a significant threat to ecosystems and human health. Current detection methods often rely on slow laboratory-based tests and offline analysis, which do not support real-time monitoring. This paper presents a novel focusing and concentrating device designed to enhance the real-time detection of microplastics in flowing water. The device utilizes an electrokinetic manipulation mechanism to focus microplastics toward the center of the water flow inside a pipe or fluid channel. A set of 3D rectangular electrodes, with dimensions of 5 mm × 2.5 mm × 1 mm, are arranged circumferentially and longitudinally along the inner perimeter of the fluid channel to generate an intense, non-uniform electric field. Simulation results indicate that microplastics near the channel wall experience a repulsive force on the order of ${10}^{-16}$ to ${10}^{-10}$ N toward the channel center. The applied signal amplitude and the physical properties of the microplastics strongly influence this repulsive force. The trajectories and output concentration of microplastics are investigated under varied conditions. A Voltage of approximately 25 V and a flow rate of 0.05 m/s are found to be ideal for concentrating microplastics into a narrow particle stream, enabling more efficient downstream detection and analysis. Pre-concentrating microplastics in fluid channels prior to sensing is expected to increase sensor sensitivity and improve selectivity. Full article

Predicting high-resolution Normalized Difference Vegetation Index (NDVI) in mountainous ecosystems is challenging due to topographic complexity and climate heterogeneity. Existing methods often struggle to balance fine-grained spatial patterns with multi-scale meteorological drivers. This paper introduces the Hierarchical Multi-Modal Fusion Network (HMMFN), which employs a conditioned reconstruction strategy to decouple spatial learning from environmental forcing. The architecture utilizes a dual-stream encoder to process NDVI imagery and meteorological data in parallel. A Transformer module captures long-term temporal dependencies, while a multi-level fusion decoder integrates climate semantics with local vegetation details. The model is optimized using a hybrid loss function that combines Mean Squared Error and Structural Similarity Index Measure to ensure both numerical precision and spatial fidelity. Evaluated in the Liupan Mountains, HMMFN consistently outperforms baseline models across multiple lead times. For prediction horizons ranging from one to five months, the model maintains high accuracy with $R^2$ values between 0.9123 (1-month horizon) and 0.8195 (5-month horizon), achieving a 10.1% and 3.6% reduction in RMSE compared to the optimal baseline model, respectively. These results demonstrate that HMMFN effectively preserves fine-scale spatial structures while maintaining accurate temporal trends across various time steps. Full article

This article presents the results of a field study conducted in 2022 on groundwater outflows located at the edge of the Kashubian Lake District and the Reda-Łeba Proglacial Stream Valley in northern Poland. The recharge of numerous springs was found to occur from the first aquifer, locally supported by a deeper aquifer connected to the first one near the bowl of Lubowidzkie Lake. Groundwater drainage occurs by gravity. It is relatively abundant for young glacial areas and averages 82 dm³·s⁻¹, making the springs capable of acting as a drinking water reservoir. This assessment is based on major ions and nutrients only; microbiological and trace-organic/metal analyses are required before any drinking-water designation. Spring water is important in the lake’s supply, accounting for 18.0% of the total inflow to the basin. The hydrochemical characteristics of these waters keep the lake in ecological balance. The waters from the springs are characterized by little variation in chemical composition, with the Ca-HCO₃ hydrochemical type. They represent young infiltration waters associated with direct recharge from precipitation (the average age of the water is 60 years). Currently, low nitrate and chloride suggest limited agricultural and urban influence, but phosphate levels and observed human activities warrant caution. Forest management is gradually developing in its catchment, which may result in a reduction of the spring yield and a deterioration of their quality in the future. This may result in a disturbance of the hydrological balance of structures hydraulically connected to spring recharge and to groundwater inflow (river, lake). Although the springs studied are local hydrological phenomena, their functioning and the need for protection are closely linked to global challenges in the field of sustainable development. This primarily concerns the protection of groundwater-dependent ecosystems and, more broadly, water security and increased resilience to climate change. Full article

Riparian ecosystems provide numerous services that are critical to integrated, sustainable water management. Their ecological functions face various threats, however, including the construction of impervious surfaces that alter watershed hydrology. The understanding of risks and the design of adequate solutions to the threats posed by impervious cover requires assessment throughout entire watersheds. Yet few assessments have considered parcel-scale changes over larger extents, particularly using readily available public data. Seeking to better characterize recent patterns and to understand how characterizations differ with alternative spatial resolutions and assumptions, we assessed statewide change in impervious land cover within riparian areas in Washington State, USA. Leveraging open data from a public decision-support application, we generated estimates based on high-resolution (1 m) change detections for 2011 to 2017, intersected with riparian areas defined from the current management guidance. As an illustrative contrast, we constructed estimates based on the 2011 to 2016 change in a national dataset of 30 m resolution land cover within a fixed buffer on a coarser stream network. Complementing these depictions of change, we also estimated the 2021 standing impervious area using an independent 1 m land cover layer within the management-based riparian extent for the western portion of the state. The “best available” high-resolution estimate of change indicated that riparian and floodplain impervious cover increased by hundreds of hectares a year statewide during the early and middle 2010s. New impervious cover was more prevalent within reaches associated with urban growth areas (UGAs) and in portions of the assessed extent used by highly valued Pacific salmon. The coarser contrasting approach yielded a similar overall magnitude of change, but this served to clarify methodological sources of uncertainty rather than to confirm accuracy. Notably, in addition to capturing larger blocks of impervious increase, high-resolution data revealed many individual changes that were smaller than a single 30 m × 30 m pixel. In 2021, standing impervious cover was also concentrated in UGA-associated reaches, which contained 43.5% of the impervious area despite being 5.2% of the assessed extent. Much of the observed change within the assessed extent was likely outside of the local riparian regulatory jurisdiction at the time, but the patterns revealed by high-resolution monitoring data underscore the importance of continuing to strengthen riparian protections to maintain ecosystem function. Full article

Wearable devices increasingly capture biosignals such as electrocardiograms, photoplethysmograms, inertial signals, and electrodermal activity during daily life, enabling earlier detection and continuous monitoring outside the clinic. Real-time edge machine learning can convert these streams into timely, privacy-preserving inference by placing computation on a wearable (device-only) or a paired phone, with intermittent cloud assist used selectively for dashboards, summarisation, and lifecycle management. Clinical adoption remains uneven because free-living data are noisy, labels are often delayed, and device ecosystems evolve over time. This narrative review organises the literature as an end-to-end deployment pathway: sensing and artefact management, streaming windowing and multimodal alignment, and model families suited to on-device inference. We compare classical feature-based pipelines with learned representations, including compact CNN/TCN and recurrent and efficient attention-based models, and discuss when self-supervised pretraining and distillation are most useful in low-label settings. We then synthesise deployment engineering levers (quantisation, pruning, and distillation) and benchmarking requirements, emphasising runtime constraints that determine feasibility: latency per update, peak RAM, energy per inference, duty cycle, and thermal behaviour. Applications are grouped across cardiovascular monitoring, blood pressure and haemodynamics, sleep and respiration, and movement and stress, with explicit attention to false-alert burden, adherence, and workflow integration. To support translation, we provide a validation ladder and a reliability toolkit covering calibration, uncertainty-aware thresholds and deferral, drift monitoring triggers, and safe update governance. The novelty of this review is a deployment-oriented synthesis that ties modelling choices to edge tiers and resource budgets and provides reusable reporting templates, including an edge-cost card and comparative tables spanning modalities, models, deployment levers, applications, and reliability requirements. Full article