Search Results (26,035)

Chronic kidney disease is a global public health concern, representing a critical global public health challenge with increasing morbidity and mortality rates. The disease is a long-term condition characterized by the progressive loss of renal function. Early detection of declining kidney health and timely intervention are crucial to slow disease progression and improve prognosis, mitigating complications, including cardiovascular events. Current diagnostic standards are unable to detect early stages of kidney disease, reflecting early signs of glomerular and tubular damage. This creates an urgent need to identify reliable biomarkers for early detection, prognosis and therapeutic monitoring of kidney diseases. Novel biomarkers, including urinary microRNA, exosomal components, proteomic signatures and integrated multi-omics profiles, facilitated by up-to-date technologies offer strong promise for enhancing early diagnosis, risk assessment and monitoring of the disease. We focus on the fundamental biological significance and clinical application of these markers, discussing a critical evaluation of novel methodologies and clinical evidence supporting their potential for earlier and more precise diagnosis. This review summarizes innovative urinary biomarkers and advanced analytical technologies that can provide a more comprehensive and accurate assessment of the kidney status towards early diagnosis, better prognosis and better quality of life for patients with chronic kidney disease. Full article

Accurate large-scale crop mapping is critical for yield prediction, agricultural disaster monitoring, and global food security. Synthetic aperture radar (SAR), with its all-weather imaging capability, plays a vital role in remote sensing based on crop mapping studies. However, although feature-level fusion has been widely explored in remote sensing, existing VV and VH fusion approaches for rice mapping are still predominantly conducted at the data level and fail to adequately integrate their complementary information across the rice growth cycle, so the simplistic fusion methods yield features that are redundant or conflicting at field boundaries and in heterogeneous areas, thereby increasing classification errors. To address these challenges, this study proposes a novel spatiotemporal attention model (SimTA) for feature fusion to improve rice mapping. (1) A VV-VH feature-level fusion scheme is designed, integrated with a Content-Guided Attention (CGA) fusion method which effectively exploits the complementary information of the dual-polarized SAR data for achieving deep spatiotemporal dynamics fusion. (2) A Central Difference Convolution Spatial Extraction Conv (CDCSE Conv) Block is designed, enhancing sensitivity to edge variations in rice fields by combining standard and central difference convolutions. (3) To achieve efficient spatiotemporal feature integration across SAR time series, a Temporal–Spatial Attention (TSA) Block is developed, utilizing large-kernel convolutions for spatial feature extraction and a squeeze-and-excitation mechanism for capturing long-range temporal dependencies of rice time series. Extensive experiments were conducted by comparing SimTA with different models under five fusion schemes. Results demonstrate that feature-level fusion consistently outperforms other schemes, with SimTA achieving the best performance: OA = 91.1%, F1 score = 90.9%, and mIoU = 86.2%. Compared to the baseline Simple Video Prediction (SimVP), SimTA improves F1 score and mIoU by 0.8% and 2.1%, respectively. The CGA enhanced feature-level fusion further boosts SimTA’s performance to OA = 91.5% and F1 = 91.4%. SimTA bridges the gap between existing VV-VH deep fusion schemes and modern spatiotemporal modeling demands, offering a more accurate and generalizable approach for large-scale rice field mapping. Full article

Understanding how volcanic systems evolve over time is a major challenge due to their complex behaviour and constantly changing conditions. This study explores a novel approach to detecting significant changes in multiparametric signals of volcanic unrest by analysing how different types of data, such as ground deformation, gas emissions, temperature, and earthquakes, interact with each other. Focusing on the Solfatara–Pisciarelli volcano system, which is a more active area in the Campi Flegrei Caldera (Southern Italy), we used two advanced methods to identify critical transitions in the system: one to model the nonlinear relationships between variables, and the other to detect key moments when the system’s behaviour shifts. By including time delays between signals (LAG), we found that our model became much more accurate in identifying these changes. In contrast, models that ignored time lags showed higher uncertainty. The results highlight the importance and effectiveness of using integrated multivariate approaches such as Multivariable Fractional Polynomial Analysis (MFPA) and Global Critical Point Analysis (GCPA) to gain deeper insights into the systemic behaviour of the caldera and its temporal evolution within a complex area like the Campi Flegrei over the selected time period. Full article

Understanding user behavior on the web is considered essential for personalization, recommendation, and anomaly detection. Centralized analytics approaches raise significant privacy risks and regulatory concerns, particularly when large volumes of interaction data are collected in the cloud. Federated learning offers a decentralized alternative but faces challenges in handling heterogeneous, Non-Independently and Identically Distributed (non-IID) web interaction data. This paper presents FogLearn-Web, a fog computing-based federated learning framework for privacy-preserving web user behavior analytics. The architecture employs hierarchical aggregation in which browser-embedded models train locally, fog nodes perform behavior-aware regional aggregation, and the cloud maintains a global model with formal differential privacy guarantees. A key contribution is the behavioral sketch, a compact representation of local interaction distributions that enables attention-weighted federated averaging without exposing raw data. Experiments on benchmark and real-world datasets show that FogLearn-Web achieves within 2.3% of centralized accuracy while reducing data transmission by 89% and improving convergence under non-IID settings by 34% over standard FedAvg. Full article

The large-scale integration of wind energy introduces increased uncertainty and variability in modern power systems, with direct implications for both system design and operation. In addressing similar aspects, energy storage plays a pivotal role as a key source of system flexibility. However, the design and sizing of storage systems remain challenging, especially under conditions of increased uncertainty. In this context, the present study proposes an alternative methodological framework, based on an inverse sizing pathway, i.e., from operations to design. More specifically, the uncertainty embedded in day-ahead forecasting of residual errors, associated with wind power generation and load demand, is currently exploited as a design-relevant signal, while energy storage is treated explicitly as a risk-hedging mechanism. Forecasting residuals spanning a year of operation are incorporated in the problem through probabilistic modeling, leading to the generation of trajectories that correspond to different risk levels and are managed as design scenarios. Regarding the modeling of uncertainties, the study examines two different strategies, namely a global modeling approach and a k-means clustering strategy. Accordingly, by mapping the interplay between storage capacity, uncertainty levels (or risk tolerance), achieved RES shares and system-level costs, we highlight the role of energy storage as a risk-hedging entity rather than merely a means of energy balancing. Our results to that end demonstrate that the achieved shares of RES exhibit increased sensitivity, even within constrained regions of wind power variation, while storage capacity features distinct zones of hedging value and hedging saturation effects emerging beyond certain storage levels. Moreover, evaluation of the two modeling strategies reflects on their complementary character, with the global modeling approach ensuring continuity and the clustering strategy capturing local asymmetries within different operational regimes. In conclusion, the methodology presented in this study bridges the gap between operational forecasting and long-term system design, offering a risk-aware framework for storage sizing, grounded in actual operational signals rather than relying on stationary historical data and relevant scenarios. Full article

Bacterial stem and root rot (BSRR), caused by Dickeya dadantii, poses a severe threat to global sweetpotato production, yet the genetic architecture underlying resistance remains elusive. To dissect these mechanisms, we conducted a high-resolution genome-wide association study (GWAS) on 135 diverse accessions, integrating two-year field phenotyping with best linear unbiased prediction (BLUP) and 6.8 million single-nucleotide polymorphism (SNP) markers. This approach mapped nine quantitative trait loci (QTLs) exhibiting significant allelic dosage-dependent effects, with the major locus, qBSRR.6.1 was the primary discriminator between resistant and susceptible genotypes. Crucially, transcriptomic profiling within these loci revealed distinct expression patterns: IbTCP5 and IbERF003 (located in qBSRR.5.1 and qBSRR.6.2) were highly expressed in the susceptible cultivar ‘Xinxiang’ but suppressed in the resistant ‘Guangshu87’. Furthermore, BSRR challenge identified IbPUB4, IbKCS5, and IbLig1 as priority candidate genes involved in defense, with expression patterns suggesting roles in ubiquitin-mediated protein turnover, cuticular wax biosynthesis, and DNA repair, respectively. In stark contrast, IbPUB25 was constitutively upregulated in ‘Xinxiang’, potentially acting as a negative regulator of immunity via degradation of target proteins. These findings elucidate the polygenic, dosage-sensitive nature of BSRR resistance and prioritize specific targets for future functional characterization. Pyramiding favorable alleles of positive candidates while silencing potential negative regulators like IbPUB25 offers a promising avenue for developing durable, high-resistance sweetpotato varieties. Full article

Autonomous traversal flight in unknown 3D environments remains challenging due to mapping bottlenecks and computational latency. Inspired by pigeons navigating cluttered forests through instantaneous visual perception rather than constructing global metric maps, this paper presents a pigeon-inspired depth-reasoning-driven decision framework for agile quadrotor traversal in unmapped spaces without explicit map construction. To ensure feasibility, we leverage a robust state estimation backbone enhanced by deep-learning-based feature matching, providing stable pose feedback under aggressive maneuvers. The core contribution is a pigeon-inspired depth-reasoning framework that translates raw sensory depth data into a hybrid optimization framework, integrating both hard safety constraints and soft geometric smoothness constraints, directly emulating the three avian mechanisms: gap selection via instantaneous depth gradients, path selection that minimizes posture changes, and a safety field driven by the looming effect. By bypassing time-consuming mapping and spatial discretization processes, the framework significantly reduces perception-to-control latency. Finally, validated via simulations and real-world experiments on a resource-constrained quadrotor platform, our map-less approach achieves superior decision frequencies and comparable safety margins to those of state-of-the-art map-based planners. This framework offers a practical, high-frequency solution for autonomous flight where computational resources and environmental knowledge are strictly limited. Full article

Cement production accounts for approximately 8% of global CO₂ emissions, and while calcined clays have attracted growing attention as supplementary cementitious materials, the literature remains fragmented across clay types and performance metrics, with no unified comparative framework examining how mineralogical composition and calcination conditions jointly govern pozzolanic reactivity and downstream performance outcomes. This study addresses that gap through a PRISMA-guided systematic review of 32 peer-reviewed studies, validated by structured expert interviews, and a comparative assessment of five calcined clay categories: metakaolin (MK), limestone-calcined clay blends (LC³), illite-rich clays, montmorillonite (MM)- based clays, and ceramic waste (CW)- derived clays. Findings establish clear performance hierarchies with direct implications for the construction sector. MK at 10–15% cement replacement delivers compressive strength gains of 8–36%, chloride permeability reductions of 61–87%, and sulphate expansion reductions of up to 89%, confirming its suitability for high-performance, chemically aggressive-environment structural concrete. LC³ systems enable 30–50% clinker substitution, yielding an estimated 30–40% embodied CO₂ reduction alongside 6–10% strength gains and 64–90% reductions in chloride migration, representing the most significant decarbonisation opportunity reviewed. Illite-rich clays reduce compressive strength by 6–25%, limiting application to non-structural uses despite moderate durability gains. MM-based clays exhibit highly variable performance, ranging from a 60% strength loss to an 8% gain, with workability penalties of up to a 90% slump reduction, constraining adoption. CW-derived clays achieve 50–69% reductions in chloride diffusion while valorising industrial waste, though strength reductions of 11–20% limit structural applications. Across all clay types, superplasticiser demand increases by 1.5–3.6 times, posing a universal cost and logistics challenge for practitioners in mix design. Full article

Optimal coordination of distance and directional overcurrent relays (DR–DOCR) aims to achieve a fast, selective, and reliable protection scheme for transmission and sub-transmission systems. However, it constitutes a complex, nonlinear, and highly constrained optimization problem. In particular, single-setting DOCR characteristics used in conventional DR-DOCR coordination introduce additional challenges in lowering relay operating times while satisfying the coordination time interval (CTI) constraint. To address this issue, this paper proposes a novel DR-DOCR coordination approach that leverages a two-level DOCR characteristic. The objective is to exploit this characteristic, which partitions the relay curve into primary and backup protection regions in a highly flexible manner, thereby enabling easier avoidance of CTI violations. In addition, an enhanced variant of the red-tailed hawk algorithm, called ERTH, has been newly developed to solve this challenging problem. The proposed method is validated on versions of the 8-bus and 33-kV portion of the 30-bus power networks that have been modified to include renewable energy sources. Results demonstrate that the proposed method achieves total relay operating times of 23.681 s and 70.742 s for the 8-bus and 30-bus power systems, respectively. These values correspond to an 80.4% and 81.2% reduction compared to the conventional coordination scheme optimized by the ERTH algorithm, which yields 120.702 s and 376.757 s, respectively. Moreover, the ERTH algorithm exhibits superior performance in attaining near-global optimal solutions compared to the original RTH and other competitive optimization algorithms. In particular, for the 30-bus system under the conventional coordination scheme, the second-best result after ERTH is obtained by the teaching-learning-based optimization algorithm with a total relay operating time of 415.885 s. This indicates a 9.4% improvement achieved by ERTH (376.757 s) and a significantly higher improvement of 83% (70.742 s) achieved by the proposed strategy integrating ERTH with the two-level DOCR-based coordination scheme. Full article

The transition from non-renewable to renewable energy sources has emerged as a pressing global issue, driven by concerns over climate change, resource depletion, and the need for sustainable development. This study compares Canada, an energy-rich nation, and Bangladesh, an energy-scarce country, to understand the structural, institutional, and market factors driving their respective renewable energy transitions. Using univariate time-series models (ARIMA, ETS, and Prophet) for energy demand forecasting and extensive literature-based policy evaluation, the paper examines trends in energy production, consumption, and trade from 1990 to 2024. Our analysis indicates that Canada’s vast reserves of both renewable and non-renewable energy sources, its diversified energy portfolio, and carbon-pricing framework support a stable decarbonization pathway, with renewables projected to account for more than 20% of total supply by 2030. However, regional disparities and political resistance from the established energy sector continue to delay transition outcomes. On the other hand, Bangladesh has limited renewable and non-renewable energy sources, with its primary energy resource being natural gas reserves. Consequently, its heavy reliance on imports (over 75% of primary energy) and institutional bottlenecks expose its energy system to commodity-price volatility, undermining energy security and slowing renewable investment. Despite these challenges, targeted solar programs and concessional financing have modestly increased the penetration of renewable energy. The analysis highlights that commodity market fluctuations, technological innovations (such as smart grids and energy storage), and market-based policy instruments critically shape each country’s transition trajectory. A coordinated policy linking market stabilization, innovation investment, and social inclusion is essential for achieving a just and secure low-carbon transition in both countries. Full article

The accelerating global energy transition has substantially increased demand for critical minerals such as copper, nickel, and lithium, positioning mining firms as key actors in the decarbonization of energy systems. However, the expansion of mineral extraction raises important sustainability challenges because mining activities remain highly energy- and carbon-intensive. This study investigates whether green innovation can simultaneously improve environmental performance and financial performance in critical mineral mining firms and examines the moderating role of institutional governance. Using a balanced panel of 35 publicly listed mining companies from Australia, Canada, Chile, Brazil, and Indonesia over the period 2015–2024, the analysis applies fixed-effects panel regressions complemented by dynamic specifications and multiple robustness tests, including alternative variable definitions and System Generalized Method of Moments (GMM) estimation. The results show that green innovation significantly reduces carbon intensity, indicating that environmental investments in renewable energy integration, electrification, and process efficiency contribute to improving emissions performance in mining operations. Green innovation also enhances firm financial performance, although the benefits emerge gradually over time, suggesting delayed financial gains followed by long-term efficiency improvements. Furthermore, governance quality strengthens the positive relationship between green innovation and firm performance, highlighting the importance of institutional environments in shaping the economic returns of sustainability strategies. By providing firm-level evidence across major mineral-producing economies, this study contributes to the literature on critical minerals, environmental finance, and the institutional dimensions of the just energy transition. Full article

With the rapid penetration of renewable energy, grid-connected microgrids have become a cornerstone of low-carbon power systems, while also posing major challenges for coordinated scheduling under coupled economic and environmental goals. The resulting dispatch problem is highly nonlinear and high-dimensional, featuring tight operational constraints and conflicting cost–emission trade-offs that often undermine the efficiency and reliability of conventional optimization methods, thereby limiting overall economic productivity. This paper presents an adaptive economic–environmental dispatch framework for grid-connected microgrids formulated as a multi-objective optimization problem that simultaneously minimizes operating cost and environmental protection cost. To navigate the rugged and constrained search landscape, we develop an enhanced metaheuristic termed the Lévy–Triangular Walk Dung Beetle Optimizer (LTWDBO). The LTWDBO integrates (i) chaotic population initialization to improve diversity and feasibility coverage, (ii) a geometry-inspired triangular walk operator to strengthen local exploitation, and (iii) an adaptive Lévy-flight strategy to boost global exploration, achieving a robust exploration–exploitation balance over the entire optimization process, representing a process innovation in metaheuristic-driven dispatch optimization. The proposed method is validated on a representative grid-connected microgrid comprising photovoltaic generation, wind turbines, micro gas turbines, and battery energy storage. Comparative experiments against representative baselines (DBO, WOA, TDBO, and NSGA-II) demonstrate that the LTWDBO achieves consistently better solution quality. Our LTWDBO attains the lowest optimal objective value of 255,718.34 Yuan, compared with 357,702.68 Yuan (DBO), 347,369.28 Yuan (TDBO), and 3,854,359.36 Yuan (WOA). The LTWDBO also yields the best average objective value of 673,842.24 Yuan, an improvement of over 1,001,813.10 Yuan (DBO). Full article

The construction sector’s transition to carbon neutrality requires innovative strategies to address the performance and cost challenges of advanced building designs, such as passive-oriented nearly zero-energy buildings. This study proposes an artificial intelligence-based multi-objective optimization framework to reduce both energy consumption and construction costs for residential building envelopes in Chengdu’s hot summer and cold winter climate. The framework uses the NSGA-II genetic algorithm within DesignBuilder to explore trade-offs between energy efficiency and economic cost. Key design parameters (wall insulation thickness, roof insulation thickness, and window glazing type) are optimized to obtain a Pareto-optimal front. A subsequent global incremental cost analysis of the non-dominated solutions identifies the optimal balance where significant energy savings are achieved before diminishing returns set in. The research results show that by combining the NSGA-II algorithm with the global incremental cost method in the Chengdu area, the parameters of the enclosure structure can be systematically optimized, and the optimal balance point between energy conservation and cost can be effectively identified. Based on this, an “energy-saving optimal—trade-off optimal—cost optimal” template set design path based on dual objectives of energy consumption and cost can be obtained, which is applicable to different demand-oriented engineering scenarios. This research provides a quantifiable decision-making basis for the design of buildings with passive design strategies that achieve near-zero energy consumption in hot summer and cold winter regions, helping to achieve the coordinated optimization of energy efficiency goals and economic feasibility, and promoting the reliable promotion and application of near-zero energy buildings. Full article

Ports play a pivotal role in global trade but are also associated with significant environmental and social challenges. Despite growing research on green ports, existing studies remain fragmented, with limited integration between technological, environmental, and governance perspectives within the blue economy framework. This review examines the transition from green port initiatives toward integrated blue-economy-oriented port systems by synthesizing recent advances in sustainable maritime infrastructure, smart port technologies, renewable energy integration, and policy frameworks. The analysis reveals three major findings. First, ports are increasingly evolving into energy-integrated hubs, with leading examples adopting shore power systems, renewable energy microgrids, and hydrogen-based infrastructure, thereby contributing to emissions reductions. Second, digitalization through artificial intelligence, IoT, and data-driven logistics significantly enhances operational efficiency, reduces energy consumption, and improves real-time decision-making. Third, effective governance frameworks that combine regulatory measures and incentive-based instruments are critical to accelerating sustainability transitions while ensuring economic competitiveness. In addition, the review highlights the growing integration of biodiversity conservation, marine pollution mitigation, and community engagement into port management strategies, reflecting a shift toward ecosystem-based approaches. Overall, the findings demonstrate that ports are transitioning from conventional logistics hubs into integrated socio-technical systems that enable low-carbon maritime transport while supporting inclusive and resilient coastal development. Full article

This study presents a representation-centric evaluation of audio foundation models for fine-grained musical instrument analysis, focusing on cymbal classification. A confound-aware comparison of CLAP and MERT embeddings is conducted to examine how each latent space supports recoverability of acoustically and semantically relevant information. To support this analysis, the study introduces a representation-centric, confound-aware multi-stage evaluation framework that separates exploratory geometry, leakage-safe probing, and supporting unsupervised clustering evidence. The methodology is applied to a challenging cymbal dataset characterized by hierarchical labels, class imbalance, and subtle acoustic variation. Results reveal a target-dependent profile of representational strengths rather than a single overall winner. CLAP exhibits stronger variance concentration and more label-consistent local neighborhood organization, and it outperforms MERT on fine-grained, strike-related targets. MERT, however, retains a small but consistent advantage on higher-level cymbal-type classification. Unsupervised analyses show that these advantages reflect local neighborhood structure, not strong global cluster formation, and confound diagnostics indicate that size-related information remains largely type-mediated. Overall, the findings underscore the importance of structured, multi-stage evaluation for disentangling embedding geometry, recoverability, and confound effects while demonstrating the complementary strengths of AFMs in complex audio classification settings. Full article