Search Results (620)

This paper presents a novel fault-tolerant model predictive control (FTMPC) framework for fuel cell hybrid electric vehicles (FCHEVs) used for postal delivery applications. The main contribution of the proposed FTMPC is the adaptive adjustment of the model predictive control cost function weights based on fault severity. The proposed reformulation incorporates fault characterization across the diverse degradation mechanisms while maintaining reliable vehicle operation. The FTMPC approach dynamically adapts cost function weights and system constraints based on the fault severity index. The resulting control strategy provides fault-aware power allocation between the fuel cell and battery while accounting for the specified operating and safety constraints. To isolate the contribution of the proposed health-dependent adaptation mechanism, a controlled ablation study was performed against a structurally identical fixed-MPC controller under the same vehicle model, driving cycle, initial conditions, prediction and control horizons, solver configuration, and fault scenarios. The adaptive FTMPC achieved a 10.6956% reduction in direct hydrogen consumption relative to the fixed-MPC baseline. Because differences in terminal battery state of charge (SoC) can influence comparisons based solely on hydrogen consumption, a charge-corrected hydrogen-equivalent metric was also evaluated; using this more conservative metric, the adaptive FTMPC retained a 2.7276% improvement. The final quadratic programming implementation achieved a 100% successful optimization rate in the validation run with no fallback-controller activation, while the maximum soft-constraint slack remained on the order of 10−9. Additional sensitivity analyses were conducted to evaluate the influence of relevant vehicle and operating conditions on energy consumption and battery utilization. These results provide direct quantitative evidence of the contribution of the proposed fault-adaptive mechanism and demonstrate its numerical feasibility for FCHEV energy management, while the limitations of the present simulation-based validation are explicitly acknowledged. Full article

Alpine streams are increasingly exposed to multiple disturbances, including extreme flood events, expected to become more frequent under climate change, and the expansion of small hydropower plants (SHPs). Understanding the resilience of aquatic communities and the ability of biomonitoring tools to detect disturbance-driven changes is essential for ecological assessment. We conducted an eleven-year monitoring programme (2014–2024) in the Corsaglia Stream (Northwestern Italy), comprising 16 sampling campaigns before, during, and after two flood events and SHP construction. Macroinvertebrate and fish communities were analysed using taxonomic, functional, and temporal beta-diversity metrics. Macroinvertebrates were assessed using the nationally standardised STAR_ICMi biomonitoring index and the recently developed Flow-T index. Macroinvertebrate assemblages showed high resilience, recovering taxonomic richness while maintaining “Good ecological status” despite severe flood-induced collapse. Recovery followed a nestedness-to-turnover trajectory, indicating recolonization from refugia rather than community replacement. Flow-T detected transient functional changes not captured by STAR_ICMi. In contrast, fish communities exhibited persistent structural changes, with reduced abundance, marked shifts in species composition, and slower recovery of native salmonids and European bullhead (Cottus gobio) under combined flood and hydropower impacts. These findings show that integrating taxonomic, functional, and temporal approaches improves ecological assessment beyond single-index biomonitoring approaches such as STAR_ICMi alone. Full article

Salinity is a key driver of microbial community structure and function in salt lake ecosystems, yet how it shapes functional genes involved in carbon (C), nitrogen (N), and phosphorus (P) cycling remains poorly understood. We collected metagenomic samples along a natural salinity gradient in Yuncheng Salt Lake and examined how salinity was associated with microbial taxonomic and functional diversity and with C, N, and P cycling genes. Both diversity metrics decreased significantly with increasing salinity and were positively correlated with each other. The composition and abundance of C, N, and P cycling genes differed significantly among the low-, medium-, and high-salinity groups. In carbon cycling, most carbon fixation genes were more abundant at higher salinity, whereas most carbon degradation genes were less abundant; within carbon fixation, reductive tricarboxylic acid (rTCA) cycle and Calvin cycle gene abundances were higher. In nitrogen cycling, nitrogen mineralization and assimilation genes were significantly more abundant. In phosphorus cycling, transporter and pyrimidine metabolism genes were more abundant, whereas the relative contribution of purine metabolism genes declined. Co-occurrence network analysis revealed dense positive co-occurrence associations among C, N, and P cycling genes, with mer, GLU, and ppk1 as highly connected genes. Mantel tests identified salinity and pH as the primary environmental factors associated with functional gene variation. These results suggest that salinity may regulate C, N, and P cycling genes partly by reshaping microbial community structure in salt lake ecosystems. Full article

To address slow convergence, local optimum stagnation, and multi-objective imbalance problems for unmanned aerial vehicle (UAV) three-dimensional (3D) path planning in complex obstacle environments, an improved adaptive two-stage pigeon swarm optimization (IPIO) algorithm is proposed. Firstly, a hybrid initialization strategy integrating Latin hypercube sampling and obstacle avoidance constraints is adopted to improve initial population diversity and the quality of feasible solutions. Secondly, in the map compass stage, a linearly decreasing adaptive map factor and population diversity-based dynamic perturbation strategy are introduced to balance global exploration and local exploitation while preventing premature convergence. In the landmark stage, an inverse fitness weighting elite center updating mechanism and linearly decreasing elite quantity strategy are designed to enhance the guidance of high-quality individuals and accelerate convergence. A multi-objective fitness function integrating path length, obstacle avoidance safety, and flight smoothness is constructed, whose weight coefficients (${\omega }_{\mathrm{L}}=0.3$, ${\omega }_{\mathrm{C}}=0.5$, ${\omega }_{\mathrm{S}}=0.2$) are calibrated through parameter-sensitivity analysis and Pareto frontier comparison across six representative weight combinations. Combining ablation validation for each improved module, single-UAV multi-scenario tests, and preliminary multi-UAV trials, these coordinated improvements realize targeted optimization for UAV 3D flight characteristics. Specifically, the preliminary multi-UAV trials involve three UAVs performing independent trajectory planning in shared obstacle environments without explicit inter-UAV collision avoidance constraints, and the reported improvements are based on single-UAV experiments. Finally, comparative experiments are conducted with a standard 100 × 100 × 50 m space, and varying obstacle densities are demonstrated in six diverse 3D test scenarios, where the proposed IPIO achieves an average path length reduction of 12.8% and 15.3% compared to the standard PIO and PSO, respectively. The average fitness improvement is 14.2% over PIO, 16.8% over PSO, 19.5% over GWO, 24.1% over CO, and 38.7% over CS. Key path-quality metrics include a minimum obstacle clearance of 2.37 m, average smoothness cost of 0.34, average convergence time of 0.60 s, and computational cost of O(N*D*MaxIter). Statistical tests confirm that these improvements are significant (p < 0.05) in all tested scenarios. This study presents an efficient and robust algorithm for autonomous three-dimensional path planning of UAVs in complex obstacle environments. Full article

The King Abdulaziz Royal Reserve (KARR), one of Saudi Arabia’s eight royal reserves, displays strong seasonal vegetation turnover typical of arid-zone ecosystems, yet its environmental drivers remain largely unquantified. This study aimed to establish a seasonally resolved, statistically validated baseline of plant community composition in KARR and to quantify the soil and topographic variables associated with this compositional variation. To this end, we surveyed a defined section of KARR across all four seasons of 2023 (763 plots: 189 winter, 213 spring, 188 summer, 173 autumn) using a systematic 10 km sampling grid. A total of 289 vascular plant species from 47 families were recorded, and species richness, Shannon and Simpson diversity, and functional dispersion were quantified per community; the flora was dominated by therophytes (48.4%) and chamaephytes (21.5%) and by Saharo-Arabian phytogeographic affinities. For each season, Ward’s hierarchical clustering (k = 4) identified four plant communities, validated by PERMANOVA (p = 0.001 in every season) and distinct characteristic species. Soils across the study area were predominantly sandy loam, with texture, pH, electrical conductivity, organic matter, and macronutrients quantified per community. Canonical Correspondence Analysis showed that elevation and soil properties (clay, sand, potassium) significantly explained community composition each season (p = 0.001), though explained variance was modest (4–8%); variance partitioning showed a substantial spatial fraction exceeding the pure environmental fraction in every season. Functional dispersion was consistently lowest in one community each season; characteristic-species composition resolved this into two recurring functional types—a woody, disturbance-associated assemblage (indicated by Rhazya stricta) in winter and summer, and a psammophytic, dune-associated assemblage (indicated by Artemisia monosperma and Moltkiopsis ciliata) in spring and autumn—both reflected in Rao’s quadratic entropy (a closely related metric, r = 0.95). These results provide a seasonally resolved, statistically validated baseline of plant community composition and its environmental correlates for KARR, supporting future vegetation monitoring and habitat-based management, and contribute a data point from an under-studied hyper-arid region to the global understanding of dryland vegetation–environment relationships and grazing-driven degradation. Full article

Traditional evaluations of ecological restoration in mining lands have long been dominated by above ground vegetation metrics, such as coverage and community diversity. Yet in arid and semi-arid mining regions, soil hydrology recovery is key, as soil organic carbon (SOC) and bulk density (BD) are critical factors affecting soil hydrology. However, their thresholds for water holding capacity and infiltration remain unclear. Therefore, this study determined the SOC and BD thresholds and evaluated the hydrological trends across them. The study was in a Loess Plateau coal reclamation area. Five restoration types and one reference forest were selected, and soil samples were collected from the 0–10 cm layer. Redundancy analysis (RDA), partial least squares structural equation modeling (PLS–SEM), and the threshold regression model were used for analysis. Results show the following: (1) Vegetation indirectly controls soil hydrology via soil structure and chemical properties (RDA: 76.04%). BD limited water holding capacity (path coefficient: −0.908), while chemical properties promoted infiltration (path coefficient: 0.397). (2) Soil hydrological recovery exhibits non-linear threshold responses. The SOC thresholds for water holding capacity and infiltration were 9.52 g/kg and 5.93 g/kg, respectively, while the BD thresholds were 0.93 g/cm³ and 1.03 g/cm³. The reclamation of soil hydrological functions in mining lands follows a two-stage mechanism. First, control by carbon accumulation, then follow by structural dominance. During the early stage, soil organic carbon (SOC) buildup quickly boosts hydrological performance by promoting aggregate formation. However, once the system nears its functional threshold, simply adding more carbon gives limited returns. At this stage, breaking through physical constraints becomes vital to overcoming the bottleneck. Above-ground metrics alone cannot capture below-ground ecosystem functional trajectories. Consequently, a dynamic strategy centered on SOC and BD is proposed. Full article

The three-dimensional (3D) documentation of the Similaun mummy (Ötzi the Iceman) and the associated Copper Age equipment and clothing presents unique challenges due to diverse material properties and strict conservation constraints. This study presents a comprehensive digital preservation workflow, primarily utilizing Structure from Motion (SfM) close-range photogrammetry (a method that reconstructs precise 3D geometry from overlapping 2D digital photographs), integrated with Image-Based Modeling (IBM) and Neural Radiance Field (NeRF) algorithms (a machine learning approach that models a complex scene as a continuous volumetric function method). To overcome the non-Lambertian properties of the mummy’s protective ice layer and wet skin (surfaces that reflect light specularly rather than diffusely, creating glares that can disorient standard reconstruction algorithms), a specialized Polarized Light Photography (PLP) strategy was implemented using custom-built hardware. This integration required advanced anatomical segmentation to resolve postural discrepancies caused by taphonomic processes. The resulting web-based application provides the scientific community with a metrically accurate digital twin, featuring interactive tools for cross-sectioning and X-ray visualization. By adopting a Free/Libre and Open-Source Software (FLOSS) ecosystem, this project establishes a sustainable, modular framework for future forensic investigations and diachronic monitoring, ensuring the long-term digital life of one of the world’s most significant archaeological finds. Full article

Background/Objectives: Breast cancer (BC) is the most frequently diagnosed malignancy and a leading cause of cancer-related mortality in women worldwide. This disease is highly heterogeneous and dynamic, and chromosomal instability (CIN) plays a key role in the acquisition of these traits by generating genetic diversity that promotes tumor adaptation and influences therapeutic response and prognosis. Although several methods have been developed to quantify CIN, they are not readily applicable to human tumors and are limited in resolution, hindering a comprehensive understanding of intratumoral heterogeneity. In this study, we aimed to quantify CIN levels and clonal heterogeneity (CH) in HER2-positive (HER2+) and triple-negative (TNBC) breast cancer using single-cell RNA sequencing (scRNA-seq) data. Methods: We analyzed publicly available scRNA-seq data from HER2+ and TNBC tumors and non-malignant controls. CIN was scored at single-cell resolution using transcriptomic signatures, clonal heterogeneity was estimated from single-cell diversity metrics, and copy number alterations were inferred computationally. Differential expression and functional enrichment analyses were performed between cells with very low and extreme CIN levels, with key comparisons confirmed at the patient level. Results: Our analyses revealed pronounced intra- and intertumoral heterogeneity, with higher CIN levels in TNBC than in HER2+ and control samples. Genes differentially expressed in cells with extreme CIN values were mainly involved in cell division and related processes, and included candidate biomarkers not previously reported in this context. Our findings suggest a positive but statistically non-significant trend was observed between CIN and CH. Conclusions: Single-cell approaches such as scRNA-seq provide a powerful framework to elucidate CIN-related mechanisms and to identify potential biomarkers of BC aggressiveness and prognosis, supporting their further application in the study of intratumoral heterogeneity. Full article

3D Gaussian splatting (3DGS) has recently emerged as an efficient and scalable method for high-fidelity 3D scene reconstruction, representation, and real-time rendering. In addition to geometric reconstruction, increasing research attention focuses on enriching 3D Gaussian primitives with semantic information, which can be related to an arbitrary application or domain, as well as common knowledge. However, the existing approaches to semantic 3DGS significantly differ in how semantics are represented, learned, and accessed, which makes systematic analysis difficult. This paper provides a review on semantic extensions to 3DGS. We introduce a unified multi-axis taxonomy that enables us to classify the available methods in terms of five complementary categories: semantic vocabulary space, representation form, functional role, knowledge source, and query mechanism. The analysis reveals key design trade-offs related to the flexibility, efficiency, and semantic expressiveness of the methods. Furthermore, we review datasets, benchmarks, and evaluation metrics used in the field, indicating the diversity of approaches and the lack of common evaluation frameworks. Based on this analysis, we also identify open challenges and possible future research directions. The presented survey is relevant to advances in games and immersive technologies, where semantically enriched real-time 3D representations are essential for interactive environments, AR/VR, and intelligent scene understanding. The systematic analysis presented in this survey aims to facilitate a deeper understanding of semantic 3DGS and support the development of more general, efficient, and task-aware 3D scene understanding systems. Full article

Soil salinisation and periodic flooding jointly shape coastal wetland ecosystems, but how coexisting halophytes differ in their rhizosphere bacterial communities under these conditions remains insufficiently understood. Here, we investigated rhizosphere bacterial communities associated with Suaeda glauca Bunge and Tamarix chinensis Lour. in saline–alkaline habitats of the Yellow River Delta, China. Twenty quadrat-level rhizosphere samples were collected across four plant–habitat groups, and near-full-length 16S rRNA gene amplicons were sequenced using Pacific Biosciences single-molecule real-time sequencing. Our analysis revealed that hydrological habitat and plant identity together contributed to differences in rhizosphere bacterial community composition. Across the dataset, 2325 bacterial operational taxonomic units were identified. T. chinensis showed higher Shannon and Gini–Simpson diversity, whereas richness patterns depended on habitat and the metric examined. Meanwhile, exploratory genus-level association networks revealed host- and habitat-dependent differences in node number, network density and average degree. PICRUSt2-based functional prediction suggested contrasting predicted functional response patterns: the S. glauca rhizosphere showed 19 significantly altered predicted pathways between flooded and non-flooded habitats, whereas the T. chinensis rhizosphere showed no significant pathway shifts after multiple-testing correction. These findings suggest that coexisting halophytes are associated with divergent rhizosphere bacterial community patterns under saline–alkaline and flooding-associated habitat conditions. Full article

During the X-ray Diffraction (XRD) identification of the traditional Chinese medicine Os Draconis, the identification model often suffers from limited classification accuracy due to the difficulty in determining optimal parameters. To address this issue, this paper proposes a Hydrodynamic Improved Particle Swarm Optimization (HIIPSO) algorithm for the deep optimization of model parameters. In practical identification scenarios, the high complexity of XRD data poses severe challenges to the convergence speed and global search capability of optimization algorithms. To enhance model performance, this study introduces the interaction mechanism from fluid dynamics into the particle swarm optimization process. Specifically, HIIPSO incorporates a Voronoi neighbor topology to enhance population diversity and spatial distribution rationality. Concurrently, a hydrodynamic interaction mechanism is constructed to simulate the cooperative behavior of particles in a fluid environment, thereby effectively preventing the algorithm from falling into local optima. A theoretical analysis of the computational complexity of the HIIPSO algorithm in the parameter search task for XRD identification models was conducted, confirming that it falls within an ideal range for engineering applications. Statistical analysis of the experimental results demonstrates that, in the parameter optimization task for the Os Draconis identification model, the HIIPSO algorithm significantly outperforms traditional and other baseline algorithms across key metrics, including the optimal value, mean, standard deviation, and median of the objective function. The experimental data indicates that the HIIPSO algorithm can substantially improve the robustness and identification accuracy of the XRD-based Os Draconis identification model, making it an optimal solution for parameter optimization problems in the digital identification of complex mineral-based traditional Chinese medicines. Full article

This study evaluates the unique ecological and floristic identity of the coastal dune vegetation in the Enez delta and adjacent dune coast of Saros Bay (southern Thrace) by comparing it with diverse dune systems across the Anatolian peninsula. A comprehensive data matrix of 97 phytosociological relevés across nine representative coastal dunes spanning the East Mediterranean, Aegean, Marmara, and Black Sea coasts was analyzed. Methodologically, univariate non-parametric approaches (Friedman variance analysis and Durbin–Conover tests) were integrated with multivariate techniques, including Hierarchical Cluster Analysis and Principal Coordinates Analysis (PCoA) based on a Bray–Curtis dissimilarity matrix. Ephedra distachya ssp. monostachya was found to be the most characteristic and differentiating taxa from the clustering. Friedman test results demonstrated highly heterogeneous species abundance across localities (χ² = 27.2, p < 0.001). The multivariate synthesis revealed a profound ecological decoupling for Saros Bay dunes: while macroclimatic filtering forces a powerful functional convergence with arid Mediterranean models dominated by therophyte, strict composition-based metrics isolate Saros Bay coastal dunes into an entirely independent taxonomic clade. PCoA ordination confirmed this distinctiveness (p < 0.05 against six of the eight national localities), with the first two axes explaining 33.58% of the total variation (Axis 1: 19.66%, Axis 2: 13.92%). This isolation is driven by a high density of Irano-Turanian elements and specialized local lineages like Silene kotschyi. Conversely, a sharp latitudinal bio-climatic macro-gradient was mapped, showing a transition toward humid Black Sea systems strictly dictated by macroclimatic filtering rather than biotic competition (p > 0.05). To preserve these specialized niches, designating coastal dunes of Saros Bay as a Priority Conservation Unit and establishing international, transboundary catchment to coast monitoring frameworks are essential. Full article

Urban parks can act as refuges for biodiversity, but their ecological value depends on the structure and composition of the vegetation they contain. This study evaluated the relationship between vegetation attributes and bird diversity in urban parks of metropolitan Arequipa, Peru, while considering daily variation across three time periods. Vegetation and bird communities were jointly characterized in 12 urban parks using point counts, alpha-diversity metrics, mixed models, multivariate ordinations, taxonomic beta diversity, and functional diversity metrics. We recorded 21 bird species and 17,591 individuals in 291 surveys. Bird richness responded mainly to integrated gradients of park size, tree-shrub structure, and shrub-herbaceous vegetation cover, whereas Shannon diversity increased with the total number of plant families and declined toward the afternoon. Bird composition was associated with vegetation gradients, and taxonomic beta diversity was high and dominated by species turnover. Floristically dissimilar parks also supported more differentiated bird communities. Abundance-weighted functional indices responded to the shrub-herbaceous gradient. These findings indicate that vegetation structure and floristic quality are key to enhancing the ecological value of urban parks in arid cities. Full article

Psychosocial working conditions are important determinants of employee health, work ability, and organizational sustainability, yet existing instruments often assess job demands, job resources, psychosocial risks, or well-being outcomes separately. This study examined the structural validity of the Multidimensional Psychosocial Work Experience Scale for Employed Persons (MPWES), an instrument designed to assess a multidimensional occupational psychosocial profile comprising workplace conditions and demands, psychosocial resources, adverse exposures, health-related experiences, and subjective functioning. A cross-sectional validation study was conducted among 1631 employees from the pharmaceutical, energy, healthcare, and administrative sectors in Latvia. Confirmatory factor analysis with the robust WLSMV estimator was used to test the predefined measurement model. The refined eight-factor model demonstrated acceptable fit in the calibration subsample (N = 400; CFI = 0.930, TLI = 0.922, RMSEA = 0.066, SRMR = 0.078) and was replicated in the held-out validation subsample (N = 1231; CFI = 0.911, TLI = 0.901, RMSEA = 0.068, SRMR = 0.067). Reliability indices were acceptable for several domains, although some factors showed modest or borderline values. Discriminant validity was supported, with all HTMT values below 0.85, and measurement invariance testing supported configural, metric, scalar, and strict invariance across samples. The findings provide initial evidence of the MPWES’s structural validity and measurement stability in the present sample. However, further studies using external validity criteria, longitudinal designs, and culturally diverse samples are needed before the scale can be considered fully validated for broader occupational and public health applications. Full article

The global plastic pollution crisis demands a fundamental re-evaluation of materials systems beyond incremental improvements to fossil fuel-based polymers. Bioplastics, polymers derived from renewable biological feedstocks, biodegradable under defined conditions, or both, offer a chemically diverse and rapidly evolving platform for transitioning toward circular materials economies in which the value of carbon, energy, and material is retained across multiple use cycles. This review provides a comprehensive and critically organized account of the bioplastics field, spanning three generations of feedstock development from food crops through lignocellulosic residues to algae and waste streams; primary production pathways including microbial fermentation, ring-opening polymerization, and biosynthesis; forming processes from extrusion and injection molding to additive manufacturing; and the mechanical, thermal, and barrier properties that determine application fitness. Particular emphasis is placed on life cycle assessment, which reveals that bioplastics’ climate benefits are conditional on feedstock choice, land-use management, energy source at manufacturing, and end-of-life pathway, and that burden-shifting from greenhouse gas emissions to land use, water consumption, and eutrophication is a systematic risk requiring integrated LCA evaluation rather than single-metric optimization. The review further examines end-of-life recycling, composting, and biodegradation pathways; market applications across packaging, agriculture, automotive, biomedical, and electronics sectors; and the growing role of artificial intelligence and machine learning in accelerating materials design, process optimization, and lifecycle data management. Critical barriers to scale, such as cost premiums of 20–75% over conventional plastics, inadequate composting infrastructure, recycling stream contamination, regulatory fragmentation, and consumer labeling confusion, are systematically analyzed alongside mitigation strategies. The review concludes with a forward-looking discussion of emerging feedstocks, smart and functional bioplastics, and the policy and infrastructure investments required to translate the environmental promise of bio-based polymers into realized circular economy impact. Full article