Search Results (97,257)

Hemodiafiltration (HDF) is increasingly used because of its enhanced theoretical clearance of diverse uremic toxins, particularly middle molecules and inflammatory cytokines, relative to conventional hemodialysis (HD), yet evidence on its biochemical benefits remains conflicting. Therefore, this meta-analysis was performed to evaluate the effects of HDF versus HD on uremic toxins, inflammation, anemia, and nutritional parameters. A systematic literature search was conducted using PubMed, Scopus, and the Cochrane Central Register of Controlled Trials to identify relevant studies. Only randomized controlled trials (RCTs) were included. Random-effects meta-analyses were performed to evaluate changes in the prespecified outcomes. Twenty-four RCTs involving 6072 dialysis patients were included. Compared with conventional HD, HDF was associated with significant reductions in serum phosphorus (weighted mean difference [WMD] −0.28 mg/dL; 95% CI −0.44 to −0.12) and β2-microglobulin (WMD −4.84 mg/dL; 95% CI −6.13 to −3.54). HDF also significantly reduced serum urea and C-reactive protein (CRP) levels, along with weekly erythropoietin requirements. Serum albumin levels were slightly but significantly lower in the HDF group than in the conventional HD group (WMD –0.06 g/dL; 95% CI −0.10 to −0.01); however, the clinical significance of such a difference remains uncertain. Higher convective volumes were identified as a key determinant of greater reductions in β2-microglobulin and CRP. Compared with conventional HD, HDF demonstrated superior reductions in several surrogate endpoints, including serum phosphorus, urea, β2-microglobulin, CRP, and weekly erythropoietin requirements. Reduced need for phosphate binders and anemia management may lower treatment-related costs. Full article

Metaheuristic algorithms have become essential tools for solving complex, high-dimensional, and constrained optimization problems. This paper introduces an adaptive R implementation of the parameter-free Jaya algorithm, enhanced with methodological innovations for both single-objective and multi-objective settings. The proposed framework integrates adaptive population management, dynamic constraint-handling, diversity-preserving perturbations, and Pareto-based archiving, while retaining Jaya’s parameter-free simplicity. These extensions are further supported by parallel computation and visualization tools, enabling scalable and reproducible applications. Benchmark evaluations on standard test functions demonstrate improved convergence accuracy, solution diversity, and robustness compared to the classical Jaya and other baseline algorithms. To highlight real-world applicability, the method is applied to a renewable energy planning problem, where trade-offs among cost, emissions, and reliability are explored. The results confirm that the adaptive Jaya approach can generate well-distributed Pareto fronts and provide practical decision support for energy system design. The main contributions of this work are threefold: (i) the development of an adaptive multi-objective extension of the Jaya algorithm that preserves its parameter-free philosophy while incorporating diversity preservation, dynamic constraint handling, and Pareto-based selection; (ii) a unified and openly available R implementation that integrates methodological advances with parallel computation and visualization, addressing the lack of transparent and reusable MO-Jaya tools in the existing literature; and (iii) a systematic evaluation on benchmark test functions and a renewable energy planning case study, demonstrating competitive convergence, robust Pareto diversity, and practical decision-making insights compared to established methods. By openly releasing the software in R (≥3.5.0), this work contributes both a methodological advance in multi-objective metaheuristics and a transparent tool for applied optimization in engineering and environmental domains. Full article

Resilience to climate change is a complex concept, especially in metropolitan areas where diverse services and stakeholders interact. Promoting sustainable climate adaptation, a resilience assessment method focused on regional areas and nature-based solutions is presented, along with its open-access, web-based platform, supporting resilience assessment, planning, and monitoring. Floods, droughts, heat or cold waves, windstorms, and forest fires can be assessed. A framework for holistic assessment and other framework, addressing critical infrastructure, are integrated. Four resilience dimensions are assessed: organizational (governance, social aspects, finance); spatial (exposure, impacts, and mapping); functional (service management, interdependencies); and physical (infrastructure robustness, redundancy). Strategic services comprise, e.g., water, waste, and natural areas. Resilience capacities, e.g., to prevent, respond, and recover from disruptions, are also assessed. The paper emphasizes new developments and assessment. Practical step-by-step guidance aligned with assessment purposes is included, aiming to address observed limitations (e.g., fragmented service provision, communication silos, data constraints). Overall results of a Spanish metropolitan area (AMB) and an exploratory application to an Austrian rural case (SLR) are also presented. Following the guidelines, AMB progressed from an essential to a comprehensive assessment. Overall, almost 1/3 of the metrics are advanced or progressing. SLR assessed its resilience capabilities regarding electrical infrastructure. Full article

Haloxylon ammodendron plantations constitute a dominant vegetation component of the desert–oasis ecotone in the arid and semi-arid regions of northwest China, playing a critical role in maintaining oasis stability and ecological security. However, the effects of converting natural desert ecosystems into plantations on the soil food webs of arthropods remain poorly understood, particularly with respect to how these effects vary across plantation age. To address this knowledge gap, we conducted a field investigation in the desert–oasis ecotone of the middle reaches of the Hexi Corridor, Gansu Province. Using pitfall trapping, we sampled two key arthropod taxa (arachnids and soil mesofauna) from control areas (natural deserts) and H. ammodendron plantations representing different ages (young and old). The results indicated that both young and old plantations were associated with significantly higher abundance and richness of arachnids, soil mesofauna, mites, and springtails compared with natural deserts, with springtail richness exhibiting a further significant increase in old plantations. Arachnid responses to plantation conversion were strongly structured by body size. Medium arachnid abundance increased in both young and old plantations, whereas large arachnid abundance increased only in young plantations and declined in older ones. In contrast, small arachnid abundance exhibited significant increases exclusively in old plantations. In addition, relationships between arachnid, mite and springtail abundance varied with plantation age: the ratio of large arachnids to mites and springtails declined significantly in old plantations relative to young ones, while the corresponding ratio for small arachnids showed an opposite pattern. Variations in soil mesofauna community composition were primarily explained by shrub cover, herbaceous cover, coarse sand proportion, silt-clay content, and soil soluble salt, which together accounted for 48.9% of observed variation. For arachnids, soil mesofauna as a food resource significantly enhanced abundance and richness. Moreover, shrub cover and silt-clay content were also drivers of arachnid community variation, jointly explaining 6.7% of variance. Overall, the establishment of H. ammodendron plantations promoted the diversity of both arachnids and soil mesofauna, but their relationships shifted dynamically with plantation age, leading to a reorganization of detrital food web structure and functioning. Full article

In the context of the energy transition and the increasing deployment of low-carbon gases (hydrogen, biomethane), reliable analytical monitoring is required to support integrity assessment and traceability of gas infrastructures under diverse on-site conditions while limiting analytical costs through standardized sampling and a single analytical system. We developed and validated integrated workflows combining sampling and laboratory analysis for chemical and compound-specific isotope analysis (CSIA) of natural gas and associated gaseous effluents in underground storage. An original quantification approach was implemented, linking sampling pressure to the amount of each compound collected in vials, and coupled with δ¹³C and δ²H measurements of alkanes (C₁–C₃), CO₂ and H₂. Two complementary sampling modes were optimized and compared: conventional high-pressure cylinders and direct collection into vacuum-sealed vials suitable for a broad range of pressures and field conditions. Using reference gas mixtures and operational samples, both approaches showed good reproducibility and isotopic accuracy during laboratory validation and over two years of monitoring. In particular, δ²H determinations for alkanes and H₂ remained robust under low-pressure sampling typical of annular spaces (~1–2 bar), despite gas-composition fluctuations. These validated methodologies provide a flexible basis for routine, standardized monitoring of stored and circulating gases, including emerging low-carbon components. Full article

With the continuous growth in the scale of engineering simulation and intelligent manufacturing workflows, more and more problem-solving tasks are migrating to cloud computing platforms to obtain elastic computing power. However, a core operational challenge for cloud platforms lies in the difficulty of stably obtaining high-quality scheduling solutions that are both efficient and free of symmetric redundancy, due to the coupling of multiple constraints, partial resource interchangeability, inconsistent multi-objective evaluation scales, and heterogeneous resource fluctuations. To address this, this paper proposes a Dual-Improved Whale Optimization Algorithm (DI-WOA) accompanied by a modeling framework featuring discrete–continuous divide-and-conquer modeling, a unified monetization mechanism of the objective function, and separation of soft/hard constraints; its iterative trajectory follows an augmented Lagrangian dual-rollback mechanism, while being rooted in a three-layer “discrete gene–real-valued encoding–decoder” structure. Scalability experiments show that as the number of tasks J increases, the DI-WOA ranks optimal or sub-optimal at most scale points, indicating its effectiveness in reducing unified billing costs even under intensified task coupling and resource contention. Ablation experiment results demonstrate that the complete DI-WOA achieves final objective values (OBJ) 8.33%, 5.45%, and 13.31% lower than the baseline, the variant without dual update (w/o dual), and the variant without perturbation (w/o perturb), respectively, significantly enhancing convergence performance and final solution quality on this scheduling model. In robustness experiments, the DI-WOA exhibits the lowest or second-lowest OBJ and soft constraint violation, indicating higher controllability under perturbations. In multi-workload generalization experiments, the DI-WOA achieves the optimal or sub-optimal mean OBJ across all scenarios with H = 3/4, leading the sub-optimal algorithm by up to 13.85%, demonstrating good adaptability to workload variations. A comprehensive analysis of the experimental results reveals that the DI-WOA holds practical significance for stably solving high-quality scheduling problems that are efficient and free of symmetric redundancy in complex and diverse environments. Full article

Handheld laser-induced breakdown spectroscopy (hLIBS) can be considered one of the most recent techniques for rock characterization in situ. Handheld LIBS devices are useful tools for providing “fit for purpose” qualitative and quantitative geochemical data. The analytical performance of hLIBS instruments varies significantly between similar instruments from different manufacturers. This study employed two commercial hLIBS instruments, both making use of noise reduction and multivariate partial-least-squares (PLS) calibration. Model validation was performed using the Leave-One-Out Cross-Validation (LOOCV) method. The Random Forest (RF) and Artificial Neural Network (ANN) algorithms were also employed as complementary approaches to PLS modeling, with the goal of exploring potential nonlinear relationships between spectral intensities and reference analyte concentrations. A comparison was also made with the most basic and commonly used approach, univariate analysis, demonstrating that multivariate methods achieve superior performances. To evaluate the predictive performance and quantification capability of the acquired LIBS spectra, the Pearson’s coefficient (R²) and root-mean-square error (RMSE) were employed in the analysis of 21 diverse certified geochemical reference materials (CRMs). The results achieved suggested that the spectral resolution was the key factor determining the performance of multivariate LIBS calibrations. The PLS model proved to be satisfactory for analyses performed by the higher-spectral-resolution instrument, whereas complementary algorithms were necessary to achieve better results with the lower-spectral-resolution instrument. Full article

With the rapid advancement of Generative Adversarial Networks (GANs), diffusion models, and other deep generative techniques, AI-generated images have achieved unprecedented levels of visual realism, posing severe challenges to the authenticity, security, and credibility of digital content. This paper proposes a novel hybrid transformer model that integrates spatial and frequency domains. It leverages CLIP to extract semantic inconsistencies in the image’s spatial domain while employing wavelet transforms to capture multi-scale frequency anomalies in AI-generated images. After cross-domain feature fusion, global modeling is performed within the Swin-Transformer architecture, enabling robust authenticity detection of AI-generated images. Extensive experiments demonstrate that our detector maintains high accuracy across diverse datasets. Full article

With the integration of high-proportion renewable energy, the operation modes of the power system are becoming increasingly complex and diverse. The typical operation modes selected with manual experience cannot comprehensively represent system operating characteristics. To more accurately analyze system operating characteristics, an analysis method for power system operation modes based on autoencoder clustering is proposed. Compared to other clustering methods, the autoencoder clustering method can adapt to data of different types and structures, extract features and perform clustering in a reduced-dimensional space, and suppress noise in the data to a certain extent. First, multi-dimensional analysis metrics for power system operation modes are proposed. The metrics are used to evaluate system characteristics such as cleanliness, security, flexibility, and adequacy. The evaluation metrics for clustering are designed based on the metrics. Second, an operation mode analysis framework is constructed. The framework uses an autoencoder to extract implicit coupling relationships between system operation variables. The encoded feature vectors are used for clustering, which helps to find the internal similarities of the operation modes. Regulation resources such as pumped hydro storage are also considered in the framework. Finally, the proposed method is tested on the IEEE 39-node system. In the test, the comparison of clustering evaluation metrics and operation mode analysis errors shows that the proposed method has the best clustering performance and operation mode analysis effect compared to other clustering methods. The results prove that the proposed method can effectively extract the inner correlations and coupling relations of high-dimensional operating vectors, form consistent operation mode clusters, select typical operation modes, and accurately assess the characteristics and risks of the power system with high-proportion renewable energy integration. This paper helps to build a stronger power system that can integrate a higher proportion of renewable energy, replace fossil fuel generation, and contribute to a higher level of sustainable development. Full article

Background: Full field electroretinography (ERG) is an essential tool for assessing retinal function and diagnosing retinal diseases. In recent years, mydriasis-free handheld ERG devices have emerged as portable, non-invasive alternatives to traditional ERG systems. Their main application has been in the screening and monitoring of diabetic retinopathy (DR), particularly in settings with limited access to standard ERG equipment and in pediatric populations where conventional testing may be difficult to perform. This review aims to evaluate the current evidence on handheld ERG devices in ocular diseases, with a focus on their reliability, diagnostic accuracy, and inherent limitations. Methods: A review was conducted to identify studies evaluating handheld ERG devices in diverse clinical settings, including retinal diseases, DR, pediatric populations, and conditions such as glaucoma. A comprehensive search of the Pubmed and Embase databases was performed for studies published up to December 2024. Search terms included “mydriasis free ERG”, “handheld ERG”, “portable ERG”, “RETeval”, “healthy subjects”, “retinal diseases”, “diabetic retinopathy”, “glaucoma”, and “pediatric diseases”, as well as relevant MeSH terms and synonyms. Case reports, conference abstracts, non-human studies, and letters were excluded. After screening titles and abstracts, additional studies not meeting the inclusion criteria were excluded. Of 279 records that were initially identified, 55 met the eligibility criteria and were included in the final review. Results were synthesized narratively due to heterogeneity in the study design, populations, and outcomes. Findings were organized thematically according to clinical context. Results: A total of 57 studies were included in the review: 19 conducted in healthy subjects, 13 in diabetic retinopathy, eight in selected retinopathies, eight in glaucoma, and 14 in pediatric cohorts. Five studies overlapped between groups due to shared populations or study designs. No meta-analysis was performed due to heterogeneity in study design and outcome measures; therefore, findings were summarized narratively across disease categories. Handheld ERG devices have been evaluated in healthy subjects, patients with DR, other retinal pathologies, glaucoma and pediatric cohorts. Evidence indicates that these devices provide a rapid, non-invasive assessment of retinal function and are particularly valuable where conventional ERG is difficult to implement and potentially well-suited for screening purposes. They show good sensitivity and reasonable specificity for detecting functional changes, making them suitable for screening purposes. However, limitations exist: reduced performance in detecting early-stage disease and cone dysfunction, risk of false positives, and variability in waveform morphology and amplitude compared with traditional ERG systems. Reproducibility challenges are noted among pediatric patients and individuals with poor fixation or unstable eye movements. These discrepancies highlight the need for establishing robust normative datasets for both healthy subjects and specific disease states. Conclusions: Handheld ERG devices provide a rapid, accessible and user-friendly option for retinal assessment. While not a replacement for conventional ERG, they serve as complementary tools, particularly in early disease and in contexts where standard testing is less feasible. Further research is required to refine testing protocols, improve diagnostic accuracy, and validate their application across a broader spectrum of ocular diseases. Full article

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), equipped with the Advanced Topographic Laser Altimeter System (ATLAS), is capable of acquiring large-scale terrain and forest structural information through photon-counting LiDAR. However, photon point clouds exhibit significant noise variability due to diurnal changes and variations in beam intensity, which undermines the accuracy and stability of terrain and canopy height retrievals in forested regions. To address the limited adaptability of existing methods under daytime/nighttime and strong/weak beam conditions, this study proposes a multi-stage processing framework integrating photon denoising, classification, and quasi-full-waveform reconstruction. First, local statistical features combined with adaptive parameter optimization were employed, applying Gaussian and exponential fitting to denoise daytime strong and weak beams and enhance the signal-to-noise ratio (SNR). Subsequently, an improved random sample consensus (RANSAC) algorithm was introduced to remove residual noise and classify photons under both diurnal and beam-intensity variations. Finally, a radial basis function (RBF) interpolation was used to reconstruct quasi-full-waveform curves for terrain and canopy heights. Compared with the ATL08 product (terrain root mean square error (RMSE): 2.65 m for daytime strong beams and 5.77 m for daytime weak beams), the proposed method reduced RMSE by 0.53 m and 1.30 m, respectively, demonstrating enhanced stability and robustness under low-SNR conditions. For canopy height estimation, all beam types showed high consistency with airborne LiDAR measurements, with the highest correlation achieved for nighttime strong beams (R = 0.90), accompanied by the lowest RMSE (4.82 m) and mean absolute error (MAE = 2.97 m). In comparison, ATL08 canopy height errors for nighttime strong beams were higher (RMSE = 5.67 m; MAE = 4.16 m). Notably, significant improvements were observed for weak beams relative to ATL08. These results indicate that the proposed framework effectively denoises and classifies photon point clouds under diverse daytime/nighttime and strong/weak beam conditions, providing a robust methodological reference for high-precision terrain and forest canopy height estimation in forested regions. Full article

Background: Local specific biomarkers for MASLD risk stratification are urgently needed in Argentina. Aim: The aim of the study was to characterize the interaction of gut microbiome signatures and genetic and clinical risk factors for MASLD in patients with diabetes from different regions of Argentina. Materials and Methods: We recruited 214 patients with diabetes from different regions of Argentina. Anthropometric, clinical, and lifestyle data were obtained from all participants, who also underwent abdominal ultrasound for MASLD diagnosis and oral swabbing. The PNPLA3 gene was amplified by PCR from the swabs, and the rs738409 genotype was determined via bidirectional sequencing. To profile the MASLD-associated microbiome, stool was collected from 170 participants. V4 16S rRNA gene sequencing was performed, and reads were analyzed using QIIME2 2024.10.1. R Studio 2023.05.1 was used for statistical analyses. Results: MASLD prevalence was 77.9%, with similar rates of occurrence in all regions represented. FIB-4 scores < 1.3 and > 2.67 were detected in 55.3% and 7.4% of patients, respectively. Half of the diabetic patients had the PNPLA3 GG genotype, with the highest rates occurring in patients from Northwestern Argentina (64.9%; p = 0.02 vs. Buenos Aires). The PNPLA3 GG genotype was an independent risk factor for FIB-4 score (p = 0.0008) and a protective factor against glycated hemoglobin (p = 0.004), fasting plasma glucose (p = 0.008), and cholesterol levels (p = 0.02). Marked regional differences were observed in microbiota diversity and composition in Argentina. After adjusting for geographical region, Negativibacillus genus was exclusively detected in diabetic patients with MASLD and GG carriers. The Catenibacterium genus was related to FIB-4 > 2.67. Short-chain fatty acid-producing bacteria were linked to the absence of MASLD. Conclusions: Although some geographical regions of Argentina were not represented in this study and these results therefore cannot be generalized to the country as a whole, these specific signatures could be useful as biomarkers for MASLD risk stratification in Argentines with diabetes. Full article

Deep learning methods have been extensively used for drug–drug interaction (DDI) prediction, aiding the development of effective and safe combination therapies. Most studies focus on either the internal molecular structure or external contextual information of individual drugs to improve feature diversity and validity. However, the latent similarities between drug pairs, which are essential for accurate predictions, have largely been overlooked. Therefore, we propose an interpretable predictive approach for graph embedding called PINGE, which relies solely on the interaction network of drugs. Specifically, we constrain the joint features of drug pairs to their interactions, allowing those with similar types to achieve cosine similarity. This similarity in direction helps the joint features converge to the same class during prediction. Additionally, each known drug can link to multiple others, enhancing its diversity. Extensive experiments demonstrate that PINGE outperforms current advanced prediction methods on both KEGG and Drugbank datasets, achieving improvements of 0.7% and 2.4% in ACC while providing network structure-based explanations for predictions. Furthermore, PINGE surpasses advanced baselines by 1% and 1.1% in AUC on the human drug–target dataset and HuRI protein–protein interaction dataset, showcasing excellent versatility. Full article

This study develops a practical framework for forecasting long-term drought conditions in Karaman Province, a semi-arid region of Turkey, where accurate climate information is vital for water planning and agriculture. Since the area has limited rainfall records and strong year-to-year fluctuations, traditional modeling approaches often fall short. To better capture local conditions, drought intensity was defined using a simple monthly wetness anomaly measure based directly on precipitation; here, positive values indicate wetter months and negative values indicate drier ones. This makes the method suitable for regions where detailed hydrological data are scarce. Rainfall observations from 1965 to 2011 were expanded using a combination of kernel density estimation and Cholesky-based correlation reconstruction. These steps preserved the main statistical and temporal patterns of the original data while increasing sample diversity. The enriched dataset was then used to train artificial neural networks to predict both precipitation and drought intensity. The models reached R² values of 0.76 and 0.72, with mean absolute errors of 12.8 mm and 28.4%, which represents an improvement of roughly 10–15% over traditional statistical methods. They were also able to capture the seasonal and year-to-year variability that strongly affects drought conditions in the region. To understand what drives the predictions, the model was examined with LIME, which consistently highlighted lagged rainfall and seasonal indicators as the most influential inputs. A walk-forward validation approach was also used to mimic real forecasting conditions and demonstrated that the model remains stable when projecting into the future. Overall, the proposed framework offers a reliable and practical basis for early-warning efforts and drought-management strategies in semi-arid regions like Karaman. Full article

Forests deliver multiple essential ecosystem functions, and most natural forests occur in highly heterogeneous environments and span different developmental stages. Despite this complexity, the relative influences of biotic and environmental drivers on ecosystem multifunctionality (EMF) remain insufficiently understood across temporal and spatial scales. Here, we surveyed forests along elevational (1800–3500 m) and successional (early to late) gradients on the eastern Tibetan Plateau, quantify how climate, soil properties, and forest attributes (diversity, stand structure, and functional traits) regulate EMF. EMF was constructed from eight indicators representing nutrient cycling, plant productivity, and water conservation. Further, we assessed variation in biodiversity effects, including selection and complementarity effects. We found that soil moisture, functional diversity, and the coefficient of variation in stand diameter exert significant positive effects on EMF, whereas species richness—the most commonly used diversity metric—shows no significant effect. Mean annual temperature and soil bulk density, by contrast, have significant negative effects. The strengths of both selection and complementarity effects vary along elevational and successional gradients, with complementarity effects becoming markedly stronger at higher elevations. Overall, our findings reveal the mechanisms through which climate, soil properties, and forest attributes jointly regulate EMF, underscoring the pivotal roles of plant functional diversity and structural heterogeneity in sustaining the multifunctionality of subalpine forests. Results provide a robust empirical foundation for improving natural forest EMF and restoration management. Full article