Search Results (588)

Neural network quantization has become established as a key strategy for transitioning medical imaging models from research environments to clinical devices and resource-constrained edge platforms; however, the available evidence remains fragmented and focused on highly heterogeneous use cases. This study presents a systematic review of 72 studies on quantization applied to medical images, following PRISMA guidelines, with the aim of characterizing the relationship among quantization technique, network architecture, imaging modality, and execution environment, as well as their impact on latency, memory footprint, and clinical deployment. Based on a structured variable matrix, we analyze—through tailored visualizations—usage patterns of Post-Training Quantization (PTQ), Quantization-Aware Training (QAT), mixed precision, and binary/low-bit schemes across frameworks such as PyTorch V 2.6.0, TensorFlow 2.19.0, and TensorFlow Lite, executed on server-class GPUs, edge/embedded devices, and specialized hardware. The results reveal a strong concentration of evidence in PyTorch/TensorFlow pipelines using INT8 or mixed precision on GPUs and edge platforms, contrasted with limited attention to PACS/RIS interoperability, model lifecycle management, energy consumption, cost, and regulatory traceability. We conclude that, although quantization can approximate real-time performance and reduce memory footprint, its clinical adoption remains constrained by integration challenges, model governance requirements, and the maturity of the hardware–software ecosystem. Full article

In achieving sustainable development goals, soils play a key role in environmental protection, natural resources, and food security. Peatlands are particularly important here, as they function at the interface between terrestrial and aquatic ecosystems and store large amounts of organic matter. However, organic soils are highly susceptible to transformation and degradation; therefore, their degradation caused by, among others, drainage properties is a high risk to both the environment and agriculture—it disrupts the ecosystems, causes greenhouse gas emissions, and eutrophicates the hydrosphere. Soil degradation in drained peatlands is associated with the transformation of soil organic matter (SOM), which in organic soils is the dominant component of the solid phase of the soil. The aim of our study was to assess the properties and degree of organic matter transformation in drained temperate peatland soils, with particular emphasis on sequential fractionation of SOM and humic acid properties. Due to the fact that in Poland, as many as 90% of non-forest peat bogs have been drained, we compare the mursh horizons that formed after peat bog drainage with the peat horizons that constitute the parent rock (where anaerobiosis occurs and morphological changes in the soil material are absent due to peat bog drainage). Studies were conducted on 11 soil profiles located in central-eastern Poland. Basic physicochemical soil properties were determined: pH, bulk density, contents of ash, SOM, total carbon (TC), and total nitrogen (TN). Sequential carbon fractionation was used to qualitatively analyze organic matter, which allowed for the identification of labile fractions, lipid fractions, humic substances (fulvic and humic acids), and residual fractions. Humic acids (HAs) were extracted using the Schnitzer method and analyzed for their elemental composition and spectrometric parameters in the VIS range. It was demonstrated that SOM transformation in drained temperate peatland soils was correlated with comprehensive changes in the soil’s physical and chemical properties. Compared to peat horizons, topsoil horizons were characterized by higher ash content and density, lower SOM content, and a lower TC/TN ratio. Qualitative SOM transformation during aerobic SOM transformation after draining the studied peatlands consisted of an increase in the amount of labile fractions and humic substances and a decrease in the lipid and residual fractions. The research results have shown that the HAs properties depended on the depth. HAs from topsoil horizons, compared to peat horizons, were characterized by a lower “degree of maturity,” as reflected by the values of atomic ratios (H/C, O/C) and absorbance coefficients (A_4/6 and ΔlogK). It was found that the share of the distinguished SOM fractions and HAs properties were closely correlated with the physical and chemical properties of the soils. The study demonstrated the usefulness of the sequential carbon fractionation method for assessing the effects of dewatered peat transformation. The obtained results could contribute to the development of good practices ensuring high quality of organic matter and stability of ecosystems, as well as to the development of methods for limiting the mineralization of organic matter (SOM), greenhouse gas emissions, and the loss of organic soils in agricultural areas. Full article

Silvopastoral systems in Patagonia (Argentina) aim to synergize forest and grassland productivity through thinning interventions in native forests of Antarctic beech (Nothofagus antarctica (G.Forst.) Oerst.), locally known as ñire, modifying ecosystem dynamics. This study aimed to determine how thinning strategies modify microclimatic conditions (air and soil temperatures, precipitation, soil water content) and modulate the intra-annual radial growth patterns in N. antarctica trees within subpolar deciduous forests of Tierra del Fuego, Argentina. We established three treatments: unmanaged mature forest (UF), thinning under crown cover influence (UC), and thinning outside crown cover influence (OC). Microclimate and radial growth were continuously monitored using high-precision dendrometers and associated data loggers during the 2021–2022 and 2023–2024 growing seasons. Data were analyzed using Generalized Linear Mixed Models and Principal Component Analysis. OC treatment consistently exhibited the highest total annual radial growth, averaging 1.44 mm yr⁻¹, which was substantially greater than the observed in both the UC (0.56 mm yr⁻¹) and UF (0.83 mm yr⁻¹) across the two seasons. An advanced growth dynamic, with cambial activity starting approximately five days earlier than in UF and UC, was detected. Air temperature was a primary positive driver of daily growth (GLMM Estimates > 0.029, p < 0.001 for all treatments), while soil water content (SWC) was significantly higher in OC (mean 25.4%) compared to UF (22.3%) and UC (15.9%). These findings showed that OC, characterized by higher soil moisture, likely facilitated the trees’ ability to capitalize on warm temperature days. This accelerates and extends the period of radial growth, offering a direct strategy to enhance productivity in these silvopastoral systems, essential for long-term forest sustainability. Full article

In the rapidly evolving digital economy, the expansion of business-to-business e-commerce ecosystems has compelled traditional industries to integrate into digital supply chains to achieve sustainable development. Industrial e-commerce is no longer limited to online transactions but extends to the digital transformation of backend operations. Drawing upon the perspective of the digital business ecosystem, this study investigates how digital supply chain integration, manifested through digital transformation, impacts energy efficiency. By utilizing a panel fixed effects model and advanced text mining techniques on a dataset of 721 listed firms in the resource-intensive sectors of China spanning from 2011 to 2023, this research constructs a novel index to quantify corporate digital maturity based on semantic analysis. The empirical results demonstrate that digital transformation significantly enhances energy efficiency by facilitating optimized resource allocation and data-driven decision making required by modern digital markets. Mechanism analysis reveals that green innovation functions as a pivotal mediator that bridges the gap between digital investments and environmental performance. Furthermore, this relationship is found to be contingent upon corporate social responsibility strategies, ownership structures, and the scale of the firm. This study contributes to the electronic commerce literature by elucidating how traditional manufacturers can leverage digital technologies and green innovation to navigate the twin transition of digitalization and sustainability, offering theoretical implications for platform governance in industrial sectors. Full article

This study examines the relationship between national open educational resource (OER) policies and Sustainable Development Goal 4 (SDG4) outcomes across 187 countries between 2015 and 2024, with particular attention to the moderating role of artificial intelligence (AI) readiness. Despite widespread optimism about digital technologies as catalysts for universal education, systematic evidence linking formal OER policy frameworks to measurable improvements in educational access and completion remains limited. The analysis employs fixed effects and difference-in-differences estimation strategies using an unbalanced panel dataset comprising 435 country-year observations. The research investigates how OER policies associate with primary completion rates and out-of-school rates while testing whether these relationships depend on countries’ technological and institutional capacity for advanced technology deployment. The findings reveal that AI readiness demonstrates consistent positive associations with educational outcomes, with a ten-point increase in the readiness index corresponding to approximately 0.46 percentage point improvements in primary completion rates and 0.31 percentage point reductions in out-of-school rates across fixed effects specifications. The difference-in-differences analysis indicates that OER-adopting countries experienced completion rate increases averaging 0.52 percentage points relative to non-adopting countries in the post-2020 period, though this estimate remains statistically imprecise (p equals 0.440), preventing definitive causal conclusions. Interaction effects between policies and readiness yield consistently positive coefficients across specifications, but these associations similarly fail to achieve conventional significance thresholds given sample size constraints and limited within-country variation. While the directional patterns align with theoretical expectations that policy effectiveness depends on digital capacity, the evidence should be characterized as suggestive rather than conclusive. These findings represent preliminary assessment of policies in early implementation stages. Most frameworks were adopted between 2019 and 2022, providing observation windows of two to five years before data collection ended in 2024. This timeline proves insufficient for educational system transformations to fully materialize in aggregate indicators, as primary education cycles span six to eight years and implementation processes operate gradually through sequential stages of content development, teacher training, and institutional adaptation. The analysis captures policy impacts during formation rather than at equilibrium, establishing baseline patterns that require extended longitudinal observation for definitive evaluation. High-income countries demonstrate interaction coefficients between policies and readiness that approach marginal statistical significance (p less than 0.10), while low-income subsamples show coefficients near zero with wide confidence intervals. These patterns suggest that OER frameworks function as complementary interventions whose effectiveness depends critically on enabling infrastructure including digital connectivity, governance quality, technical workforce capacity, and innovation ecosystems. The results carry important implications for how countries sequence educational technology reforms and how international development organizations design technical assistance programs. The evidence cautions against uniform policy recommendations across diverse contexts, indicating that countries at different stages of digital development require fundamentally different strategies that coordinate policy adoption with foundational capacity building. However, the modest short-term effects and statistical imprecision observed here should not be interpreted as evidence of policy ineffectiveness, but rather as confirmation that immediate transformation is unlikely given implementation complexities and temporal constraints. The study contributes systematic cross-national evidence on aggregate policy associations while highlighting the conditional nature of educational technology effectiveness and establishing the need for continued longitudinal research as policies mature beyond the early implementation phase captured in this analysis. Full article

Mobility-as-a-Service (MaaS) has emerged as a sustainable solution that integrates multiple transport services through digital platforms. Across different cities, MaaS development exhibits variation in terms of economic support, infrastructure capacity, service integration level, and long-term sustainability orientation. The complexity of multistakeholder interactions and functional components in MaaS ecosystems calls for a more comprehensive performance evaluation framework. To address this, this study proposes a holistic four-dimensional indicator system covering economic, infrastructure, integration and sustainability aspects. To address the hybrid uncertainties arising from the heterogeneous information aggregated by the proposed framework, encompassing both quantitative statistics and qualitative expert judgements, a novel rough–fuzzy best–worst method (BWM) and rough–fuzzy data envelopment analysis (DEA) approach is developed. The empirical application to six representative core cities in China reveals that high performance in “Integration” and “Economic” dimensions plays a pivotal role in determining overall MaaS performance, and coordinated enhancement across dimensions is also important. Comparative and sensitivity analyses validate the framework’s robustness, offering policymakers a reliable tool for benchmarking MaaS maturity. Full article

Over the past two decades, Light Detection and Ranging (LiDAR) technology has evolved from early National Aeronautics and Space Administration (NASA)-led airborne laser altimetry into commercially mature systems that now underpin vegetation remote sensing across scales. Continuous advancements in laser engineering, signal processing, and complementary technologies—such as Inertial Measurement Units (IMU) and Global Navigation Satellite Systems (GNSS)—have yielded compact, cost-effective, and highly sophisticated LiDAR sensors. Concurrently, innovations in carrier platforms, including uncrewed aerial systems (UAS), mobile laser scanning (MLS), Simultaneous Localization and Mapping (SLAM) frameworks, have expanded LiDAR’s observational capacity from plot- to global-scale applications in forestry, precision agriculture, ecological monitoring, Above Ground Biomass (AGB) modeling, and wildfire science. This review synthesizes LiDAR’s cross-domain capabilities for the following: (a) quantifying vegetation structure, function, and compositional dynamics; (b) recent sensor developments encompassing ALS discrete-return ${(ALS}_D)$, and ALS full-waveform $\left({ALS}_{FW}\right)$, photon-counting LiDAR (PCL), emerging multispectral LiDAR (MSL), and hyperspectral LiDAR (HSL) systems; and (c) state-of-the-art data processing and fusion workflows integrating optical and radar datasets. The synthesis demonstrates that many LiDAR-derived vegetation metrics are inherently transferable across domains when interpreted within a unified structural framework. The review further highlights the growing role of artificial-intelligence (AI)-driven approaches for segmentation, classification, and multitemporal analysis, enabling scalable assessments of vegetation dynamics at unprecedented spatial and temporal extents. By consolidating historical developments, current methodological advances, and emerging research directions, this review establishes a comprehensive state-of-the-art perspective on LiDAR’s transformative role and future potential in monitoring and modeling Earth’s vegetated ecosystems. Full article

Soil contamination by heavy metals (HMs) threatens crop productivity, food safety, and ecosystem health, especially in intensively cultivated Mediterranean regions. This study investigated the influence of soil HM contamination on nutrient uptake, photosynthetic traits, and metal bioaccumulation in avocado (Persea americana Mill.) orchards. Twenty orchard sites were sampled, collecting paired soil and mature leaf samples. Soil physicochemical properties and HM concentrations were determined, while leaves were analyzed for macro- and micronutrients, photosynthetic pigments, and metal contents. Bioaccumulation Factors (BAFs) were computed, and multivariate analyses (Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Linear Discriminant Analysis (LDA), and Partial Least Squares Regression (PLSR)) were applied to assess soil–plant relationships, complemented by Monte Carlo simulations to quantify probabilistic contamination risks. Results revealed substantial inter-site variability, with leaf Cd and Pb concentrations reaching 0.92 and 3.54 mg/kg, and BAF values exceeding 1 in several orchards. PLSR models effectively predicted leaf Cd (R² = 0.789) and Pb (R² = 0.772) from soil parameters. Monte Carlo simulations indicated 15–25% exceedance of FAO/WHO safety limits for Cd and Pb. These findings demonstrate that soil metal accumulation substantially alters avocado nutrient balance and photosynthetic efficiency, highlighting the urgent need for site-specific soil monitoring and sustainable remediation strategies in contaminated orchards. Full article

Soils represent the largest reservoir of organic carbon (OC) in terrestrial ecosystems, storing approximately 1500 Gt C. Forest and grassland ecosystems contribute 39% and 34% to global terrestrial carbon stocks, with soils holding about 44% and 89% of forest and grassland carbon, respectively. Land-use changes, such as the conversions between forest and grassland ecosystems, can strongly influence soil carbon accumulation, though the direction and magnitude remain uncertain. Comparative data from paired-plot studies of forest and grassland soils are still limited. In this study, we conducted pairwise comparisons of total OC and total nitrogen (TN) stocks in mature forest and climax grassland soils along a climatic and pedogenic gradient encompassing Retisols, Luvisols, and Chernozems. Relationships between OC and TN stocks (0–10 cm) and soil physicochemical properties—OC and TN contents, bulk density, pH, clay content, and humus fractional composition, as well as biological indicators—the abundance of culturable fungi and bacteria, microbial biomass carbon, potential metabolic activity, and activities of laccase and dehydrogenase, were evaluated. Strong positive correlations were found between OC and TN stocks and OC and TN contents (r = 0.62–0.99), pH (r = 0.79–0.81), clay content (r = 0.70–0.87), and the fraction of humic acids bound with calcium (r = 0.73). OC stocks also correlated strongly with dehydrogenase activity (r = 0.85–0.95). At 0–10 cm depth, OC stocks were higher in grassland soils than in forest soils by factors of 1.6–1.7 in Retisols and 1.4–1.5 in Chernozems. Similarly, TN stocks were 1.6–2.0 times greater in grasslands across all soil types. Community-level physiological profiling revealed higher potential metabolic activity in forest soils compared with grasslands, with the strongest differences in Retisols and Luvisols, while contrasts were attenuated in Chernozems. Overall, the results highlight the fundamental role of organo-mineral interactions and calcium binding in OC stabilization, as well as the likely involvement of dehydrogenase activity in the biogenic formation of calcium carbonates that contribute to this process. Full article

The integration of IT services is a critical challenge for public organizations that seek to modernize their operational ecosystems and strengthen mission-oriented processes. In the field of fiscal oversight, supreme audit institutions (SAIs) increasingly require systematized and interoperable service architectures to ensure transparency, accountability, and effective public resource control. However, existing literature reveals persistent gaps concerning how service integration models can be deployed and validated within complex government environments. This study describes an enterprise architecture-driven service integration model designed and evaluated within the Office of the General Comptroller of the Republic of Colombia (Contraloría General de la República, CGR). The study tests the hypothesis that an Enterprise Architecture-driven integration model provides the necessary structural coupling to align technical IT performance with the legal requirements of fiscal oversight, which is an alignment that typically does not appear in generic governance frameworks. The methodological approach followed in this study combines an IT service management maturity assessment, process analysis, architecture repository review, and iterative validation sessions with institutional stakeholders. The model integrates ITILv4 (Information Technology Infrastructure Library), TOGAF (The Open Group Architecture Framework), COBIT (Control Objectives for Information and Related Technologies), and ISO20000 into a coherent framework tailored to the operational and regulatory requirements of an SAI. Results show that the proposed model reduces service fragmentation, improves process standardization, strengthens information governance, and enables a unified service catalog aligned with fiscal oversight functions. The empirical validation demonstrates measurable improvements in service delivery, transparency, and organizational responsiveness. The study contributes to the field of applied system innovation by: (i) providing an integration model, which is scientifically grounded and evidence-based, (ii) demonstrating how hybrid governance and architecture frameworks can be adapted to complex public-sector environments, and (iii) offering a replicable approach for SAIs that seek to modernize their technological service ecosystems through enterprise architecture principles. Future research directions are also discussed to provide guidelines to advance integrated governance and digital transformation in oversight institutions. Full article

Intensive greenhouse management profoundly alters soil biogeochemical processes and biotic interactions, distinguishing greenhouse soils from open-field systems. Understanding the drivers of soil fauna assembly is essential for sustaining soil health and productivity. In this study, we examined nematode community drivers in greenhouse melon systems under 2- and 10-year rotations using environmental DNA sequencing. Plant phenology, more than rotation, shaped nematode communities, particularly omnivore predators and bacterivores. This driver was mirrored by a shift in nematode faunal indices from an enriched, bacterial-dominated state at seedling stages to a structured state at maturity. LDA Effect Size and random forest identified key genera (Prismatolaimus, Acrobeloides, and Ceramonema), demonstrating multidimensional drivers of community assembly. Redundancy analysis showed soil organic matter (SOM) and acid phosphatase as major drivers. Mantel tests indicated that the microbial biomass carbon and nitrogen ratio (MBC/MBN) consistently explained community variation (relative abundance: r = 0.229; functional diversity: r = 0.321). Structural equation modeling linked available phosphorus to microbial carbon cycling via cumulative carbon mineralization (CCM, 0.41) and MBC (0.40). SOM increased MBN (0.62) but suppressed Chao1 (−0.76). MBN had the strongest positive effect on Pielou_e (0.49). pH negatively affected functional diversity (−0.33), while nitrate nitrogen (0.35) and CCM (0.32) had positive effects. Our results indicate that MBC and MBN act as microbial bridges linking soil properties to nematode diversity, providing a mechanistic basis for optimizing greenhouse soil management and ecosystem functioning. Full article

Artificial intelligence (AI) has been increasingly embedded within data-driven financial decision-making; however, its effectiveness was found to remain dependent upon the maturity of data governance frameworks. This systematic review was conducted in accordance with PRISMA 2020 guidelines to synthesise evidence from 1155 Scopus-indexed studies published between 2015 and 2025. A mixed-methods design combining corpus analysis, quantile radar regression, and radar visualisation of structural equation modelling (SEM) was employed. Empirical validation was found to demonstrate a robust model fit (CFI = 0.947; RMSEA = 0.041). Governance maturity was confirmed as a mediating construct ($\beta$ = 0.73) linking AI integration ($\beta$ = 0.76) to financial outcomes ($\beta$ = 0.71). The findings were found to indicate that algorithmic capacity alone does not ensure decision quality without transparent, auditable, and ethically grounded governance systems. A quantile-sensitive radar visualisation is advanced in this review, offering conceptual and methodological novelty for explainable, responsible, and data-centric financial analytics. This study is found to contribute to the ongoing discourse on sustainable digital transformation within AI-enabled financial ecosystems. Full article

Amid rapid digital transformation and shifting consumption models, the tailoring industry faces a dual challenge: preserving its artisanal essence while adapting to the expectations of an increasingly digital-oriented clientele. This study introduces a methodological framework for designing buyer personas suited to the contemporary artisanal tailoring ecosystem, offering a structured approach to understanding modern consumer behavior within hybrid physical–digital environments. Using a mixed-methods design and Sastrería Jorge Ospina (Caicedonia, Colombia) as a case study, 378 online surveys—117 from current clients and 261 from potential clients—were analyzed using descriptive and inferential statistical techniques (Pearson’s χ², p < 0.05). Managerial priorities were concurrently assessed using a multi-criteria decision-making model (TOPSIS) with entropy-based weighting. The analysis identified two consumer archetypes: (1) the Classic Segment—mature clients motivated by tradition, loyalty, and reliability, who value tangible elegance and experiential craftsmanship; and (2) the Digital Segment—young consumers driven by aesthetic trends, convenience, and immediacy, who prioritize online interaction and personalized digital consumption. TOPSIS results highlighted older men (Cᵢ = 1.000) and young women (Cᵢ = 0.870) as the most strategically valuable customer groups. These findings redefine the post-digital tailoring consumer as a hybrid entity guided by artisanal value, hyper-personalization, and digital engagement. Full article

The electric power grid constitutes a foundational pillar of modern critical infrastructure (CI), underpinning societal functionality and global economic stability. Yet, the increasing convergence of Information Technology (IT) and Operational Technology (OT), particularly through the integration of Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS), has amplified the sector’s exposure to sophisticated cyber threats. This study conducts a comparative analysis of five major cyber incidents targeting electric power systems: the 2015 and 2016 Ukrainian power grid disruptions, the 2022 Industroyer2 event, the 2010 Stuxnet attack, and the 2012 Shamoon incident. Each case is examined with respect to its objectives, methodologies, operational impacts, and mitigation efforts. Building on these analyses, the research evaluates the extent to which such attacks could have been prevented or mitigated through the systematic adoption of leading cybersecurity maturity frameworks. The NIST Cybersecurity Framework (CSF) 2.0, the ENISA NIS2 Directive Risk Management Measures, the U.S. Department of Energy’s Cybersecurity Capability Maturity Model (C2M2), and the Cybersecurity Risk Foundation (CRF) Maturity Model alongside complementary technical standards such as NIST SP 800-82 and IEC 62443 have been thoroughly examined. The findings suggest that a proactive, layered defense architecture grounded in the principles of these frameworks could have significantly reduced both the likelihood and the operational impact of the reviewed incidents. Moreover, the paper identifies critical gaps in the existing maturity models, particularly in their ability to capture hybrid, cross-domain, and human-centric threat dynamics. The study concludes by proposing directions for evolving from compliance-driven to resilience-oriented cybersecurity ecosystems, offering actionable recommendations for policymakers and power system operators to strengthen the cyber-physical resilience of electric generation and distribution infrastructures worldwide. Full article

Living Labs have become key instruments for fostering sustainable and user-driven innovation, yet their conceptual ambiguity and fragmented evaluation practices limit their effectiveness. This paper synthesises academic and policy literature to develop a comprehensive qualitative framework for assessing Living Labs across nine dimensions: governance, user engagement, methods, infrastructure, outputs, scalability, sustainability, equity, and learning. The framework integrates a temporal perspective to capture short-, medium-, and long-term impacts. Exploring the INNOFEIT Energy Living Lab in Skopje, North Macedonia, through the lens of this framework demonstrates how a university-based Living Lab can function as both an experimental ecosystem and a policy instrument supporting the digital and green transitions. The findings reveal strong methodological and infrastructural maturity but highlight the need for deeper co-creation, broader stakeholder inclusion, and longitudinal evaluation. The proposed framework offers a practical tool for improving comparability, reflexivity, and institutional learning across diverse Living Lab contexts, ultimately strengthening their role in sustainable innovation governance. Full article