Search Results (1,105)

Accurate modeling and parameter estimation of photovoltaic (PV) systems are vital for advancing energy sustainability and achieving global decarbonization goals. Reliable PV models enable better integration of solar resources into smart grids, improve system efficiency, and reduce maintenance costs. This aligns with the vision of sustainable energy systems that combine intelligent optimization with environmental responsibility. The recently introduced Black-Winged Kite Algorithm (BWKA) has shown promise by emulating the predatory and migratory behaviors of black-winged kites; however, it still suffers from issues of slow convergence, limited population diversity, and imbalance between exploration and exploitation. To address these limitations, this paper proposes an Improved Black-Winged Kite Algorithm (IBWKA) that integrates two novel strategies: (i) a Soft-Rime Search (SRS) modulation in the attacking phase, which introduces a smoothly decaying nonlinear factor to adaptively balance global exploration and local exploitation, and (ii) a Quadratic Interpolation (QI) refinement mechanism, applied to a subset of elite individuals, that accelerates local search by fitting a parabola through representative candidate solutions and guiding the search toward promising minima. These dual enhancements reinforce both global diversity and local accuracy, preventing premature convergence and improving convergence speed. The effectiveness of the proposed IBWKA in contrast to the standard BWKA is validated through a comprehensive experimental study for accurate parameter identification of PV models, including single-, double-, and three-diode equivalents, using standard datasets (RTC France and STM6_40_36). The findings show that IBWKA delivers higher accuracy and faster convergence than existing methods, with its improvements confirmed through statistical analysis. Compared to BWKA and others, it proves to be more robust, reliable, and consistent. By combining adaptive exploration, strong diversity maintenance, and refined local search, IBWKA emerges as a versatile optimization tool. Full article

Accurate estimation of ship emissions is essential for the effective enforcement of emission control policies in inland waterways. However, existing “bottom-up” models face significant challenges owing to severe data scarcity for inland ships, particularly regarding ship static parameters. This study proposes a novel data fusion and machine learning framework to address this issue. The methodology integrates real-time SO₂ and CO₂ pollutant concentrations on the Nanjing Dashengguan Yangtze River Bridge, Automatic Identification System (AIS) data, and meteorological information. To address the scarcity of design data for inland ships, web scraping was used to extract basic parameters, which were then used to train five machine learning models. Among them, the XGBoost model demonstrated superior performance in predicting the main engine rated power. A refined activity-based emission model combines these predicted parameters, ship operational profiles, and specific emission factors to calculate real-time emission source strengths. Furthermore, the model was validated against field measurements by comparing the calculated and measured emission source strengths from ships, demonstrating high predictive accuracy with R² values of 0.980 for SO₂ and 0.977 for CO₂, and MAPE below 13%. This framework provides a reliable and scalable approach for real-time emission monitoring and supports regulatory enforcement in inland waterways. Full article

Urban surface subsidence is primarily induced by intensive above-ground and underground construction activities and excessive groundwater extraction. Integrating InSAR techniques for safety monitoring of urban subway infrastructure is therefore of great significance for urban safety and sustainable development. However, single-track high-spatial-resolution SAR imagery is insufficient to achieve full coverage over large urban areas, and direct mosaicking of inter-track InSAR results may introduce systematic biases, thereby compromising the continuity and consistency of deformation fields at the regional scale. To address this issue, this study proposes an inter-track InSAR correction and mosaicking approach based on the mean vertical deformation difference within overlapping areas, aiming to mitigate the overall offset between deformation results derived from different tracks and to construct a spatially continuous urban surface deformation field. Based on the fused deformation results, subsidence characteristics along subway lines and in key urban infrastructures were further analyzed. The main urban area and the eastern and western new districts of Zhengzhou, a national central city in China, were selected as the study area. A total of 16 Radarsat-2 SAR scenes acquired from two tracks during 2022–2024, with a spatial resolution of 3 m, were processed using the SBAS-InSAR technique to retrieve surface deformation. The results indicate that the mean deformation rate difference in the overlapping areas between the two SAR tracks is approximately −5.54 mm/a. After applying the difference-constrained correction, the coefficient of determination (R²) between the mosaicked InSAR results and leveling observations increased to 0.739, while the MAE and RMSE decreased to 4.706 and 5.538 mm, respectively, demonstrating good stability in achieving inter-track consistency and continuous regional deformation representation. Analysis of the corrected InSAR results reveals that, during 2022–2024, areas exhibiting uplift and subsidence trends accounted for 37.6% and 62.4% of the study area, respectively, while the proportions of cumulative subsidence and uplift areas were 66.45% and 33.55%. In the main urban area, surface deformation rates are generally stable and predominantly within ±5 mm/a, whereas subsidence rates in the eastern new district are significantly higher than those in the main urban area and the western new district. Along subway lines, deformation rates are mainly within ±5 mm/a, with relatively larger deformation observed only in localized sections of the eastern segment of Line 1. Further analysis of typical zones along the subway corridors shows that densely built areas in the western part of the main urban area remain relatively stable, while building-concentrated areas in the eastern region exhibit a persistent relative subsidence trend. Overall, the results demonstrate that the proposed inter-track InSAR mosaicking method based on the mean deformation difference in overlapping areas can effectively support subsidence monitoring and spatial pattern identification along urban subway lines and key regions under relative calibration conditions, providing reliable remote sensing information for refined urban management and infrastructure risk assessment. Full article

Atrial fibrillation (AF) is the most prevalent sustained arrhythmia worldwide and is now increasingly regarded as a disease of chronic inflammation and progressive atrial fibrosis. Understanding of molecular mechanisms that mediate the linkage between systemic metabolic dysregulation, inflammation, and structural atrial changes is crucial for informing risk stratification and targeting of prevention strategies. This review provides evidence from 105 studies focusing on the contributions of transforming growth factor-β1 (TGF-β1), tumor necrosis factor-a (TNF-α), interleukin-6 (IL-6), galectin-3, and galectin-1 to cardiac fibrogenesis, atrial fibrosis, and AF pathogenesis. We also link metabolic syndrome to these biomarkers and to atrial remodeling, as well as echocardiographic correlates of fibrosis. TGF-β1 is established as the central profibrotic cytokine and promotes Smad-based fibroblast activation, collagen accumulation, and structural atrial remodeling. Its role is highly potentiated by thrombospondin-1 by turning latent TGF-β1 into its potent form. TNF-α and IL-6 also play an integral role in the inflammatory fibrotic continuum by activating NF-κB and STAT3 signaling, promoting fibroblast proliferation, electrical uncoupling, and extracellular matrix accumulation. Galectin-3 is a potent profibrotic mediator that promotes TGF-β signaling and is a risk factor for negative outcomes, whereas Gal-1 seems to regulate inflammation resolution and may exert context-dependent protective or maladaptive roles. Metabolic syndrome is strongly associated with excessive levels of these biomarkers, chronic low-grade inflammation, oxidative stress, and ventricular and atrial fibrosis. Chronic clinical findings show that metabolic syndrome (MetS) increases AF risk, exacerbates atrial dilatation, and is associated with worse postoperative outcomes. Echocardiographic data are connected to circulating biomarkers and are non-invasive for evaluating atrial remodeling. The evidence to date supports that atrial fibrosis should be considered an end point of systemic inflammation, metabolic dysfunction, and activation of profibrotic molecular pathways. Metabolic syndrome, due to its chronic low-grade inflammatory environment and prolonged levels of metabolic stress, manifests as an important upstream factor of fibrotic remodeling, which continuously promotes the release of cytokines, oxidative stress, and fibroblast activation. Circulating fibrotic biomarkers, in comparison with metabolic syndrome, serve separate yet interdependent pathways that help orchestrate atrial structural remodeling through the simultaneous process but can also provide a long-term indirect measure of ongoing profibrotic activity. The integration of these biomarkers with superior atrial imaging enables a broader understanding of the fibrotic substrate of atrial fibrillation. This combined molecular imaging approach can facilitate risk stratification, refine therapeutic decisions, and facilitate early identification of higher-risk metabolic phenotypes, thus potentially facilitating directed antifibrotic and anti-inflammatory therapy in atrial fibrillation. Full article

Gliomas are notoriously difficult to treat owing to their pronounced heterogeneity and highly variable treatment responses. This reality drives the development of precise diagnostic and prognostic methods. This review explores the modern arsenal of bioinformatic tools aimed at refining diagnosis and stratifying glioma patients by different malignancy grades and types. We perform a comparative analysis of software solutions for processing whole-exome sequencing data, analyzing DNA methylation profiles, and interpreting transcriptomic data, highlighting their key advantages and limited applicability in routine clinical practice. Special emphasis is placed on the contribution of bioinformatics to fundamental oncology, as these tools aid in the discovery of new biomarker genes and potential targets for targeted therapy. The ninth section discusses the role of computational models in predicting immunotherapy efficacy. It demonstrates how integrative data analysis—including tumor mutational burden assessment, characterization of the tumor immune microenvironment, and neoantigen identification—can help identify patients who are most likely to respond to immune checkpoint inhibitors and other immunotherapeutic approaches. The obtained data provide compelling justification for including immunotherapy in standard glioma treatment protocols, provided that candidates are accurately selected based on comprehensive bioinformatic analysis. The tools discussed pave the way for transitioning from an empirical to a personalized approach in glioma patient management. However, we also note that this field remains largely in the preclinical research stage and has not yet revolutionized clinical practice. This review is intended for biological scientists and clinicians who find traditional bioinformatic tools difficult to use. Full article

Code coverage-guided unit test generation (CGTG) and large language model-based test generation (LLMTG) are two principal approaches for the generation of unit tests. Each of these approaches has its inherent advantages and drawbacks. Tests generated by CGTG have been shown to exhibit high code coverage and high executability. However, they lack the capacity to comprehend code intent, which results in an inability to identify deviations between code implementation and design intent (i.e., functional defects). Conversely, although LLMTG demonstrates an advantage in terms of code intent analysis, it is generally characterized by low executability and necessitates iterative debugging. In order to enhance the ability of unit test generation to identify functional defects, a novel framework has been proposed, entitled the intent analysis-guided unit test generation and refinement (IGTG&R) model. The IGTG&R model consists of a two-stage process for test generation. In the first stage, we introduce coverage path entropy to enhance CGTG to achieve high executability and code coverage of test cases. The second stage refines the test cases using LLMs to identify functional defects. We quantify and verify the interference of incorrect code implementation on intent analysis through conditional entropy. In order to reduce this interference, the focal method body is excluded from the code context information during intent analysis. Using these two-stage process, IGTG&R achieves a more profound comprehension of the intent of the code and the identification of functional defects. The IGTG&R model has been demonstrated to achieve an identification rate of functional defects ranging from 65% to 89%, with an execution success rate of 100% and a code coverage rate of 75.8%. This indicates that IGTG&R is superior to the CGTG and LLMTG approaches in multiple aspects. Full article

Extracellular poly-γ-glutamic acid (γ-PGA) produced by Bacillus species demonstrates significant antibacterial properties, positioning it as a promising candidate for diverse biomedical and industrial applications. This study focused on molecular identification of Bacillus subtilis using Polymerase Chain Reaction (PCR) and evaluated the initial production of γ-PGA from a novel biological source of Bacillus subtilis. Shake flask fermentation was utilized for γ-PGA production, with three distinct growth media (Tryptic, MRS, and Mineral medium) assessed for their efficiency in polymer yield. Characterization of γ-PGA was conducted through FT-IR, HPLC, and GC-MS analyses. FT-IR spectroscopy confirmed the presence of characteristic functional groups such as carbonyl, amide, and hydroxyl groups. HPLC and GC-MS analyses provided insights into the polymer’s purity and molecular composition, highlighting components like methyl esters, hexanoic acid, and monomethyl esters. Furthermore, the study quantified γ-PGA production during a four-day shake flask fermentation period. These findings contribute significantly to bacterial characterization, optimization of fermentation processes, and the exploration of γ-PGA’s potential as an antibacterial agent. Future research directions include refining purification techniques to enhance γ-PGA’s antibacterial efficacy and expanding its applications across various fields. Full article

Participation in running events has expanded worldwide, consolidating itself as a form of active leisure and a driver of social and tourism engagement. Although runners’ motivations have been extensively studied, perceived inclusivity, understood as motivation derived from the event’s promotion of equitable participation across gender, age and functional ability, has rarely been examined as a distinct motivational dimension within structural models. This study analyses the motivational structure of participants in the Elche Half Marathon (Spain) and assesses the incremental contribution of inclusivity to traditional motivational frameworks. Based on a sample of 1053 valid responses, a two-stage psychometric and segmentation approach was applied. Exploratory and confirmatory factor analyses (EFA and CFA) were conducted to compare a four-factor model (sport-related hedonism, competition, socialization and digital socialization) with an extended five-factor model incorporating inclusivity. Subsequently, cluster analyses were performed using factor scores derived from each model. The results show that the inclusion of inclusivity improves model fit and increases explained variance, while also generating a more differentiated segmentation structure. The extended model revealed six motivational profiles, some of which displayed continuity with the classical solution, while others were reconfigured when inclusivity was introduced. Overall, the findings indicate that inclusivity functions as a complementary and context-dependent motivational dimension that refines the understanding of participation heterogeneity in running events. Rather than replacing traditional motives, inclusivity contributes incremental explanatory value and enhances the identification of motivational profiles, offering relevant insights for the design and management of mass-participation sporting events. Full article

Sepsis remains a leading cause of global mortality, and early management in the emergency department (ED) is a key determinant of clinical outcomes. Among the earliest physiological derangements in sepsis are abnormalities in coagulation, which represent not merely laboratory disturbances but fundamental reflections of dysregulated host response, endothelial injury, and evolving microvascular thrombosis. Sepsis-induced coagulopathy (SIC) and disseminated intravascular coagulation (DIC) form a dynamic continuum that frequently begins before shock is clinically apparent. Despite their prognostic value and pathophysiologic significance, these abnormalities are often underrecognized in the ED, where coagulation tests are still commonly interpreted through the narrow lens of bleeding risk rather than as markers of systemic thromboinflammation. This narrative review synthesizes current understanding of the mechanisms linking sepsis, endothelial dysfunction, and coagulation abnormalities; outlines the distinction between SIC and overt DIC; and highlights why early identification of coagulopathy in the ED is essential. We discuss practical bedside approaches, including recommended laboratory testing, pattern recognition, and application of validated scores such as the SIC and ISTH DIC criteria. System-level strategies, such as embedding coagulation testing into sepsis bundles, automating score calculation, and enhancing communication between the ED and ICU teams, are explored as avenues to improve early detection. Evidence suggests that ED recognition of SIC/DIC may refine risk stratification, guide triage decisions, and identify patients who may benefit from targeted anticoagulant strategies once stabilized. Ultimately, recognizing coagulation disorders in the ED reframes sepsis not solely as a hemodynamic crisis but as a complex, thromboinflammatory syndrome in which early intervention may alter trajectory and improve outcomes. Full article

Automated animal identification is a practical task for reuniting lost pets with their owners, yet current systems often struggle due to limited dataset scale and reliance on unimodal visual cues. This study introduces a multimodal verification framework that enhances visual features with semantic identity priors derived from synthetic textual descriptions. We constructed a massive training corpus of 1.9 million photographs covering 695,091 unique animals to support this investigation. Through systematic ablation studies, we identified SigLIP2-Giant and E5-Small-v2 as the optimal vision and text backbones. We further evaluated fusion strategies ranging from simple concatenation to adaptive gating to determine the best method for integrating these modalities. Our proposed approach utilizes a gated fusion mechanism and achieved a Top-1 accuracy of 84.28% and an Equal Error Rate of 0.0422 on a comprehensive test protocol. These results represent an 11% improvement over leading unimodal baselines and demonstrate that integrating synthesized semantic descriptions significantly refines decision boundaries in large-scale pet re-identification. Full article

The Automatic Identification System (AIS) frequently suffers from data loss and irregular report intervals in real maritime environments, compromising the reliability of downstream navigation, monitoring, and trajectory reconstruction tasks. To address these challenges, we propose BAARTR (Boundary-Aware Adaptive Regression for Kinematically Consistent Vessel Trajectory Reconstruction), a novel kinematically consistent interpolation framework. Operating solely on time, latitude, and longitude inputs, BAARTR explicitly enforces boundary velocities derived from raw AIS data. The framework adaptively selects a velocity-estimation strategy based on the AIS reporting gap: central differencing is applied for short intervals, while a hierarchical cubic velocity regression with a quadratic acceleration constraint is employed for long or irregular gaps to iteratively refine endpoint slopes. These boundary slopes are subsequently incorporated into a clamped quartic interpolation at a 1 s resolution, effectively suppressing overshoots and ensuring velocity continuity across segments. We evaluated BAARTR against Linear, Spline, Hermite, Bezier, Piecewise cubic hermite interpolating polynomial (PCHIP) and Modified akima (Makima) methods using real-world AIS data collected from the Mokpo Port channel, Republic of Korea (2023–2024), across three representative vessels. The experimental results demonstrate that BAARTR achieves superior reconstruction accuracy while maintaining strictly linear time complexity ($\mathit{O}\left(\mathit{N}\right)$). BAARTR consistently achieved the lowest median Root Mean Square Error (RMSE) and the narrowest Interquartile Ranges (IQR), producing visibly smoother and more kinematically plausible paths-especially in high-curvature turns where standard geometric interpolations tend to oscillate. Furthermore, sensitivity analysis shows stable performance with a modest training window (n ≈ 16) and minimal regression iterations (m = 2–3). By reducing reliance on large training datasets, BAARTR offers a lightweight, extensible foundation for post-processing in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic Service (VTS), as well as for accident reconstruction and multi-sensor fusion. Full article

A promising application of smart materials based on natural polymers is the potential to solve problems related to hemostasis in cases of severe bleeding caused by injury or surgery. This can be a life-threatening situation. Cellulose and its modified derivatives represent one of the most promising sources for creating effective hemostatic systems, as well as for various sensing applications related to disease detection, infection diagnosis, chronic condition monitoring, and blood analysis. The aim of this review was to identify key criteria for the efficiency of cellulose-based gels with hemostatic activity. Experimental studies aimed at evaluating new hemostatic devices were analyzed based on international sources using the PRISMA methodology. A total of 111 publications were identified. Following the identification and screening stages, 20 articles were selected for the final qualitative synthesis. The analyzed publications include experimental studies focused on the development and analysis of highly porous cellulose-based scaffolds in the form of aerogels and cryogels. The type and origin of cellulose, as well as the influence of additional components and synthesis conditions on gel formation, were investigated. Three major groups of key criteria that should be considered when developing new cellulose-based highly porous scaffolds with hemostatic functionality were identified: (I) physicochemical and mechanical properties (pore size distribution, compressive strength, and presence of functional groups); (II) in vitro tests (blood clotting index, red blood cell adhesion rate, hemolysis, cytocompatibility, and antibacterial activity); (III) in vivo hemostatic efficiency (hemostasis time and blood loss) in compliance with the 3Rs policy (replacement, reduction, refinement). The prospects for the development of highly porous cellulose-based scaffolds are not only focused on their hemostatic properties, but also on the development of smart platforms. Full article

Kazakhstan’s growing potato industry underscores the need to develop and apply digital solutions that boost grading efficiency. A comparison between two traditional deep neural network architectures used to classify color images of potatoes from Kazakhstan is discussed in the paper. Ten representative varieties of Kazakhstani potatoes were selected as objects of study: Alians, Alians mini, Astana, Astana mini, Edem, Edem mini, Nerli, Nerli mini, Zhanaisan, and Zhanaisan mini. Two convolutional neural network (CNN) models, SqueezeNet and GoogLeNet, were refined via transfer learning employing three optimization approaches. Then, they were used to classify the potato images. A comparison of the two neural networks’ classification performance was conducted using common evaluation criteria—accuracy, precision, F1 score, and recall—alongside a confusion matrix to highlight misclassified samples. The comparative analysis demonstrated that both CNN architectures—SqueezeNet and GoogLeNet—achieve high classification accuracy for Kazakhstani potato varieties, with the best performance on Astana and Zhanaisan (>97%). The study confirms the applicability of lightweight CNNs for digital varietal identification and automated quality assessment of seed potatoes under controlled imaging conditions. The developed approach is the first comparative CNN-based varietal identification of Kazakhstani potato tubers using transfer learning and contributes to the digitalization of potato breeding, and provides a baseline for future real-time sorting systems using deep learning. Full article

Background/Objectives: Mild cognitive impairment (MCI) affects multiple functional and cognitive domains, rendering it challenging to diagnose. Brief mental status exams are insensitive while detailed neuropsychological testing is time-consuming and presents accessibility issues. By contrast, the Oculo-Cognitive Addition Test (OCAT) is a rapid, objective tool that measures oculometric features during mental addition tasks under one minute. This study aims to develop artificial intelligence (AI)-derived predictive models using OCAT eye movement and time-based features for the early detection of those at risk for MCI, requiring more thorough assessment. Methods: The OCAT with integrated eye tracking was completed by 250 patients at the Mayo Clinic Arizona Department of Neurology. Raw gaze data analysis yielded time-related and eye movement features. Random Forest and univariate decision trees were the feature selection methods used to identify predictors of Dementia Rating Scale (DRS) outcomes. Logistic regression (LR) and K-nearest neighbors (KNN) supervised models were trained to classify PMCI using three feature sets: time-only, eye-only, and combined. Results: LR models achieved the highest performance using the combined time and eye movement features, with an accuracy of 0.97, recall of 0.91, and an AUPRC of 0.95. The eye-only and time-only LR models also performed well (accuracy = 0.93), though with slightly lower F1-scores (0.87 and 0.86, respectively). Overall, models leveraging both time and eye movement features consistently outperformed those using individual feature sets. Conclusions: Machine learning models trained on OCAT-derived features can reliably predict DRS outcomes (PASS/FAIL), offering a promising approach for early MCI identification. With further refinement, OCAT has the potential to serve as a practical and scalable cognitive screening tool, suitable for use in clinics, at the bedside, or in remote and resource-limited settings. Full article

The quantity, size, and distribution of non-metallic inclusions in High-Strength Low-Alloy (HSLA) steel critically influence its service performance. Conventional detection methods often fail to adequately characterize extreme inclusion distributions in large-section components. This study developed an integrated full-process inclusion analysis system combining high-precision motion control, parallel optical imaging, and laser spectral analysis technologies to achieve rapid and automated identification and compositional analysis of inclusions in meter-scale samples. Through systematic investigation across the industrial process chain—from a dia. 740 mm consumable electrode to a dia. 810 mm electroslag remelting (ESR) ingot and finally to a dia. 400 mm forged billet—key process-specific insights were obtained. The results revealed the effective removal of Type D (globular oxides) inclusions during ESR, with their counts reducing from over 8000 in the electrode to approximately 4000–7000 in the ingot. Concurrently, the mechanism underlying the pronounced enrichment of Type C (silicates) in the ingot tail was elucidated, showing a nearly fourfold increase to 1767 compared to the ingot head, attributed to terminal solidification segregation and flotation dynamics. Subsequent forging further demonstrated exceptional refinement and dispersion of all inclusion types. The billet tail achieved exceptionally high purity, with counts of all inclusion types dropping to extremely low levels (e.g., Types A, B, and C were nearly eliminated), representing a reduction of approximately one order of magnitude. Based on these findings, enhanced process strategies were proposed, including shallow molten pool control, slag system optimization, and multi-dimensional quality monitoring. An intelligent analysis framework integrating a YOLOv11 detection model with spectral feedback was also established. This work provides crucial process knowledge and technological support for achieving the quality control objective of “known and controllable defects” in HSLA steel. Full article