Search Results (2,457)

The article deals with the further development of national innovative subway cars with asynchronous electric drives and energy recovery systems through the introduction of capacitive energy storage. It has been determined that the assessment of the effectiveness of existing concepts for energy processes control of subway cars with asynchronous electric drives and capacitive energy storage under identical specified conditions remains a relevant issue. Five of the most promising concepts for managing energy processes were selected and idealized. Oscillograms of energy flows for the selected concepts are presented. Parameters for evaluating the effectiveness of the selected control concepts are presented. The scientific novelty lies in the development of a procedure for selecting a rational concept for controlling energy processes in subway rolling stock with asynchronous electric drives and CES, based on the application of a unified comparative analysis system using a comprehensive evaluation criterion. A scheme for replacing subway cars with asynchronous electric drives and capacitive energy storage is presented, and a mathematical model of energy flow processes for traction and regenerative braking modes has been developed based on this scheme. Algorithms for controlling energy processes between asynchronous electric drives, capacitive energy storage devices, and contact networks have been developed for each of the selected concepts. The efficiency of each of the five selected concepts for the same specified operating conditions of the subway cars, parameters of the asynchronous traction electric drive and capacitive energy storage device has been investigated using the developed mathematical model and the formulated comprehensive evaluation criterion. It was established that it is possible to save up to 18% of the electricity consumed from the contact network per braking-acceleration cycle under the specified operating conditions, parameters of the subway cars, asynchronous traction electric drive, and capacitive energy storage device. An additional possibility exists to reduce the installed power of the power supply system equipment by up to 33.5% under the specified operating conditions of a subway train with the proposed technical characteristics. It has been determined that the most rational concept for controlling energy processes in subway cars with asynchronous electric drives and capacitive energy storage is the fifth concept, which allows the use of stored energy from regenerative braking in both normal and emergency operation of the subway power supply system. Full article

Vegetated coastal wetlands, especially mangroves, seagrass beds, and salt marshes, are biodiversity-rich ecosystems whose blue carbon outcomes depend on living communities, sediment dynamics, hydrological connectivity, and landscape context. Biodiversity conservation and blue carbon management are often assessed through separate scientific, monitoring, and policy frameworks. This review uses a staged literature search and thematic synthesis to examine biodiversity–blue carbon linkages across the three major vegetated coastal wetland types. It considers how taxonomic, genetic, functional, and habitat diversity influence productivity, sediment stabilization, trophic exchange, carbon stocks, carbon burial, and carbon retention. It also evaluates how climate change, habitat fragmentation, hydrological alteration, pollution, and anthropogenic disturbance weaken these linkages. The synthesis compares representative carbon-stock and burial-rate baselines, examines conservation and restoration synergies and trade-offs, and expands the discussion of seagrass regime shifts. Field surveys, remote sensing, unmanned aerial vehicles, environmental DNA, and AI-enabled data integration are placed within a tiered monitoring framework. The review further develops an operational decision pathway for biodiversity-centered blue carbon management. Persistent blue carbon benefits arise where conservation and restoration maintain native communities, hydrological exchange, sediment stability, habitat complexity, migration space, and long-term stewardship capacity. Full article

Small pelagic fishes are essential for artisanal fisheries and food security in São Tomé and Príncipe, yet biological information required for stock assessment remains scarce. This study examined the population dynamics and life-history traits of Caranx crysos, Euthynnus alletteratus, Hemiramphus balao, and Cheilopogon melanurus using 9052 specimens collected from artisanal landings between 2023 and 2025. C. melanurus (35.2%) and H. balao (34.0%) dominated the sampled catches, followed by C. crysos (18.1%) and E. alletteratus (12.7%). Standardized CPUE series modelled using GAMs revealed significant temporal and seasonal variation in relative abundance, with contrasting species-specific trends. Length–weight relationships revealed negative allometric growth in three of the four species examined (75%), with b values ranging from 2.50 to 3.19, while Fulton’s condition factor differed significantly among species (Kruskal–Wallis χ² = 6702.7, p < 0.001). Sex-ratio analyses showed significant deviations from parity in C. crysos and C. melanurus, whereas E. alletteratus and H. balao remained balanced. Gonadosomatic index and maturity-stage distributions indicated year-round reproductive activity with distinct spawning peaks. Length at first maturity (L50) ranged from 30.2 cm to 38.8 cm among species. Growth parameters estimated from length-frequency data using the von Bertalanffy Growth Function fitted through ELEFAN_GA in TropFishR yielded L∞ values of 43.9–68.4 cm and K values of 0.065–0.336 yr⁻¹. Growth performance index (φ′) ranged from 2.48 to 2.99, corresponding to theoretical longevities of 8.9–46.3 years. Length-based cohort analysis indicated biomass concentration in intermediate size classes and increasing fishing mortality towards larger individuals. Exploitation rates revealed contrasting fishing pressures among species, while sensitivity analyses identified growth and mortality parameters as the main sources of uncertainty. These findings provide the first integrated biological baseline for the assessment and management of small pelagic resources in São Tomé and Príncipe. Full article

Diadromous fishes support culturally important small-scale fisheries in Galicia (NW Spain), but their assessment is constrained by scarce biological and effort data. We evaluated nine data-poor fishery units using first-sale records for Alosa alosa, Anguilla anguilla, Petromyzon marinus and Platichthys flesus from six Galician ports (A Guarda, Arcade, Carril, Ferrol, Vigo and Rianxo) over the period 1997–2026. The workflow combined data cleaning and ecological classification, Mann–Kendall and Theil–Sen trend analyses, actual catch over maximum catch, and catch-only CMSY models with species-specific resilience priors. Results revealed widespread signals of depletion in both anadromous and catadromous groups. Six of the nine units were classified as collapsed or critical by CMSY, three as overexploited, and none as sustainable. The most severe cases were sea lamprey (P. marinus) in A Guarda and Carril (B/B_MSY = 0.10 and 0.11, critical). A. alosa in A Guarda and European eel (A. anguilla) in Arcade, Carril and A Guarda (glass eel) were classified as collapsed (B/B_MSY = 0.35–0.50). Trend analyses detected significant declines in lamprey at A Guarda and Carril and in adult eel at Ferrol, whereas P. flesus in Rianxo showed a significant positive slope but remained under alert status. Overall, three stocks showed increasing trends (although only one was statistically significant), while six showed declining trends (of which only three were significant). These findings showed that public landing time series can support precautionary assessment in data-limited migratory fisheries, and justify adaptive harvest restrictions, intensified monitoring, and routine screening to support recovery and management prioritization. Full article

The digitization of existing building stock often depends on legacy 2D raster floor plans (scanned drawings, PDF exports, or photographs) because structured building information models are frequently unavailable for older properties. Manual measurement and visual inspection of such documents are time consuming and error prone. This paper presents an integrated deep learning pipeline that extracts semantic information from unstructured two-dimensional floor plan images of existing structures and supports preliminary compliance screening via locally deployed large language models. The pipeline employs YOLOv8 for the localization and classification of 18 architectural symbols and furniture items, and a U-Net with a ResNet34 encoder for the semantic segmentation of walls and interior room spaces. To translate pixel-level predictions into physical metrics, we implement an area calculation module based on user-defined reference scale calibration. An LLM evaluation module, deployed locally via Ollama with a retrieval-augmented generation pipeline, interprets extracted room metrics and flags potential non-compliance against referenced residential design guidelines; it is intended for the assessment of existing layouts rather than generative co-design. We expand a core dataset of 101 manually annotated source floor plans to 303 augmented instances using label-aligned geometric transformations, while reporting generalization in terms of the 101 unique source plans. On the held-out validation split (10 source plans), YOLOv8 achieves 92.3% mAP50 versus 87.2% for a Faster R-CNN reference model on the same data split (detection baselines differ in training epochs and pretraining; see Experiments); U-Net achieves 95.71% mIoU, surpassing DeepLabv3+ (93.2%) under matched segmentation training settings. The system is deployed as an interactive web application for legacy building survey and preliminary regulatory review when only two-dimensional documentation is available. Full article

The availability of critical spare parts is essential for maintaining the reliability and operational continuity of combined cycle power plants. However, demand for critical spare parts is typically sparse, intermittent, and highly non-linear, which limits the effectiveness of conventional forecasting approaches based on historical averages or expert judgment. Inaccurate demand estimation may lead to excessive inventory, high holding costs, or stock shortages that increase downtime risks. To address these challenges, this study applies ensemble learning methods to improve demand forecasting accuracy for critical spare parts in a combined cycle power plant. Procurement and usage data from 2020 to 2024 were analyzed using a time-series splitting approach, with model performance assessed using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). To avoid bias caused by zero-demand periods, zero actual values were excluded from MAPE calculations. The results show that the tuned XGBoost model consistently performs better than Random Forest by producing lower forecasting errors and more stable predictions under intermittent demand conditions. These findings indicate that ensemble learning can support more effective procurement planning, inventory control, and maintenance decision-making in combined cycle power plant operations. Full article

Introduction: Understanding population structure is essential for the effective management of marine resources. The bluefish (Pomatomus saltatrix) is a widely distributed and commercially important species along the Brazilian coast, yet its population structure in the South–Southeastern region remains unclear. Objectives: This study aimed to assess the body morphological variation and to infer the population structure of P. saltatrix along the South and Southeastern coasts of Brazil using a landmark-based geometric morphometrics method. Methodology: Individuals were collected between August and October 2024 from artisanal fisheries in Rio de Janeiro (n = 46), São Paulo (n = 37), and Santa Catarina (n = 29). A total of 12 landmarks were used to derive a truss network and extract 25 transformed distances (DTs). Data were analyzed using univariate (one-way ANOVA, followed by Tukey’s test if needed, or Kruskal–Wallis followed by Dunn’s test if needed) and multivariate statistics (PERMANOVA and Flexible Discriminant Analysis (FDA)). Results: Significant regional differences were detected for most DTs, except DT3, DT4, DT6, DT9, DT13, DT24, and DT25. PERMANOVA revealed significant differences among all sampling locations, including pairwise comparisons (p < 0.05). FDA achieved an overall reclassification success of 93% of individuals to their original location. Conclusion: Despite the high reclassification accuracy, the results support the existence of a single stock with spatial structuring rather than completely distinct population units, indicating that P. saltatrix in this region is not homogeneous. Full article

This study presents a comprehensive data-limited stock assessment of bonga shad (Ethmalosa fimbriata) and lesser African threadfin (Galeoides decadactylus) in the Central Gulf of Guinea using complementary catch- and abundance-based approaches, including Abundance-based Maximum Sustainable Yield (AMSY), Catch-based Maximum Sustainable Yield ++ (CMSY++), and the Bayesian State-space Schaefer Model (BSM). These models were applied because they are suitable for evaluating stock status in data-limited fisheries using catch and abundance information. While AMSY primarily uses abundance information, CMSY++ integrates catch and productivity priors, whereas BSM incorporates state-space error structures to account for observation uncertainty. Catch time series (1990–2021) were extracted from Food and Agricultural Organization (FAO) FishstatJ accessed in 2023, with catch values for 2022–2023 cautiously extrapolated from recent trends due to the temporary absence of updated official statistics. Standardized and scaled relative abundance indices from Cameroonian and Nigerian EEZ were used to support model estimation and assess the stock status. For Ethmalosa fimbriata, the results from CMSY++ and BSM yielded an MSY estimate of 126 × 10³ t and 95.5 × 10³ t, respectively, while for G. decadactylus, MSY from CMSY++ and BSM were 9.1 × 10³ and 13.4 × 10³, respectively. Stock status indicators suggested the stock was fully exploited based on both AMSY (F/F_MSY = 0.83) and CMSY++ (F/F_MSY = 1.03) and overfished based on BSM (F/F_MSY = 1.77). For G. decadactylus, the analysis based on AMSY suggested an overfished stock state (F/F_MSY = 1.2), while under CMSY++ and BSM, the stock is fully exploited. The log scale CPUE was symmetrical within the expected bounds, and the posterior parameter distributions were constrained, indicating that the model passed the convergence test and had robust parameter estimates. The study recommends maintaining catches within MSY-based reference points as the total allowable catch (TAC) and emphasizes the need for improved data continuity, regional collaboration, and precautionary management for long-term sustainability of fisheries resources in the Central Gulf of Guinea. Full article

Introduction: The discharge of treated wastewater into coastal and marine environments represents a continuous source of pollutants, including pharmaceuticals and plastic additives with endocrine-disrupting (ED) potential. These compounds are of increasing concern for the European Union due to their capacity to interfere with hormonal systems and their inclusion in current environmental monitoring priorities. ED compounds may induce sublethal effects in aquatic organisms, particularly in vertebrates, where endocrine pathways are highly conserved. In this context, the use of Cyprinodon variegatus, a euryhaline fish species, provides a suitable model to assess potential risks in marine ecosystems. Despite advances in wastewater treatment technologies, the persistence of biologically active substances in treated effluents remains a concern. Objective: This study aims to evaluate whether treated effluent water still contains compounds with endocrine-disrupting activity and to assess their potential effects on marine organisms. Methodology: Larvae of C. variegatus from a laboratory stock maintained at ECIMAT (University of Vigo), one of the few available stocks of this species in Europe, were exposed for 48 h to environmentally relevant dilutions (1:10, 1:30, and 1:100) of wastewater treatment plant effluent collected after UV disinfection as the final treatment step. Pools of 10 larvae were used for each condition. Sublethal effects were assessed through gene expression analysis using quantitative PCR (qPCR), targeting biomarkers involved in endocrine regulation. Two housekeeping genes (tbp and hprt) were used for normalization. Estrogenic responses were evaluated through vtgab and zp2, while androgenic responses were assessed using 17hsd and 11hsd. Results: Preliminary results indicate significant alterations in estrogen-related gene expression, particularly in vitellogenin (vtgab) and zona pellucida (zp2), highlighting the activation of estrogenic pathways and supporting the presence of endocrine-disrupting activity in treated effluent water. Conclusions: This study highlights the relevance of assessing endocrine disrupting activity in treated effluents and supports the use of molecular biomarkers as sensitive tools for evaluating their potential impact on marine ecosystems, contributing to the improvement of wastewater monitoring and management strategies. Full article

Introduction: Brown trout (Salmo trutta L.) is a salmonid fish considered a genetically and geographically highly structured species. The species is catalogued as vulnerable in the Spanish Red List since 1992. Based on morphological differentiation, some authors have proposed splitting Salmo trutta into many taxa and in recent years, several binomials involving morphologically differentiated Western Mediterranean brown trout morphs have regained relevance as distinct species and are now assessed in the IUCN Red List. However, Spanish legislation still considers a single species, S. trutta, inhabiting Atlantic and Mediterranean rivers. Analyses of mitochondrial DNA variation across the species’ native range have revealed numerous matrilineal lineages and helped disentangle the phylogenetic relationships and biogeographic history of brown trout. Four largely distant evolutionary lineages defined by sequencing the mitochondrial control region (Adriatic, AD, Mediterranean, ME, Atlantic, AT and Duero, DU) are native among the Iberian brown trout populations. Stocking activities have resulted in introgressive hybridization of these native populations with non-native northern Atlantic stocks throughout the Iberian rivers and compromise the evolutionary singularities of brown trout in the Iberian Peninsula. Methodology: In this study, we compare complete mitogenomes from Iberian brown trout specimens with those from other regions, using both newly sequenced samples and whole mitogenomes from the GenBank database and estimate their divergence times relative to those observed among species of other Salmoninae genera, such as Oncorhynchus and Salvelinus, to assess patterns of species-specific divergence among Iberian brown trout populations. Results: The evolutionary relationships of the specimens using their mitogenomes fitted with previous analyses using partial mitochondrial sequences and confirmed the strong differentiation among the Iberian lineages. All the branches involving distant Iberian mitogenomes are sister branches with taxa historically described under different binomials (such as Salmo macrostigma, S. multipunctatus, S. pallaryi). Conclusions: The results provide evidence supporting the existence of potentially distinct and undescribed native Salmo species in the Iberian rivers. Full article

Introduction: Climate change is reducing freshwater availability worldwide, making it essential to understand how freshwater inflow influences estuarine ecosystem functioning and marine fisheries productivity. In the Gulf of Cádiz (SW Spain), one of the most important fishing areas in Spain, the Guadalquivir Estuary serves as a key nursery habitat for commercially important fish and crustacean species. Objective: The aim of this study is to evaluate the effects of droughts and floods on estuarine functioning and coastal fisheries. Methodology: We analyzed 25 years of monthly data (1997–2022) from the Guadalquivir Long-Term Ecological Research Program (GUADALQUIVIR-LTER), using time-series analyses and dynamic structural equation modelling. Environmental variables, zooplankton and mysid biomass, and juvenile biomass of anchovy, sardine, and meagre were examined to assess trophic relationships and recruitment dynamics. Results: Our findings show that positive North Atlantic Oscillation (NAO) phases, associated with drought conditions in southern Europe, reduced freshwater inflow from the Alcalá del Río Dam into the estuary. Freshwater input increased organic matter and turbidity, which positively affected the mysid Rhopalophthalmus tartessicus, an important prey species for anchovy recruits. The mysid Mesopodopsis slabberi showed the strongest positive effect on anchovy recruitment (0.39). Although turbidity initially had a negative effect on M. slabberi, a significant positive effect appeared after monthly lag = 4. Conclusions: These findings demonstrate that spring freshwater inflow is essential for maintaining estuarine productivity, enhancing mysid abundance, and supporting anchovy recruitment, ultimately benefiting adult fish stocks after juveniles migrate from the estuary to coastal waters. Full article

In the context of an increasingly complex and dynamic digital ecosystem, accurately assessing the value of Specialized, Refined, Differentiated, and Innovative (SRDI) enterprises is crucial for making effective decisions. Traditional valuation methods struggle to effectively address issues such as the high R&D expenditures and significant operational risks associated with these enterprises. This study proposes an interpretable intelligent decision-support framework for valuing SRDI enterprises listed on the Beijing Stock Exchange (BSE), constructing a multidimensional indicator system that encompasses solvency, profitability, and R&D capabilities. Feature importance screening using the XGBoost algorithm was conducted to identify key indicators as input variables for a backpropagation (BP) neural network. Concurrently, the Particle Swarm Optimization (PSO) algorithm was applied to the neural network to optimize initial weights and thresholds, thereby modeling nonlinear valuation relationships. Empirical analysis of 770 SRDI firms listed on the Beijing Stock Exchange from 2020 to 2024 indicates that the XGBoost-PSO-BPNN model achieved a coefficient of determination of 0.8083 on the test set, outperforming traditional linear models and benchmark models such as single-tree models. SHAP explainability analysis further reveals that current asset turnover, return on assets, and equity concentration are the primary value drivers. This study employs various clustering methods to further classify enterprises into three categories and proposes recommendations for differentiated regulatory policies, providing intelligent decision support for enterprises operating within complex digital ecosystems. Full article

Corporate sustainability and the reliability of ESG reporting have gained relevance in the evaluation of listed companies, particularly in emerging capital markets, where reporting practices are still in their early stages of development. The purpose of this study is to analyze the relationship between the quality of ESG reporting, the risk of greenwashing estimated using a proxy derived from reported information, and the market value of companies listed on the Bucharest Stock Exchange. The research employs a mixed-methods design, involving content analysis of annual reports, sustainability reports, and sustainability statements for 25 companies over the 2020–2024 period. The scores corresponding to the Environmental, Social, and Governance dimensions, as well as the proxy for greenwashing risk, were developed using an ordinal scoring grid, which was validated through inter-rater assessment. During the course of the study, the empirical relationships were tested using pooled OLS specifications on short panel data, incorporating the natural logarithm of market capitalization, financial controls, year effects, and sector dummy variables. The results highlight the presence of an association between the quality of ESG reporting and market value, particularly for environmental and social dimensions, while the greenwashing risk proxy exhibits a limited statistical influence. The study contributes to the literature on ESG reporting in emerging markets and highlights the need for a cautious interpretation of indicators constructed based on corporate disclosures. Full article

The European Union’s Common Agricultural Policy promotes extensive farming to achieve sustainability goals, yet dairy production in the Baltic states and Poland has continued to intensify, particularly after the abolition of milk quotas in 2015. This study assesses the technical efficiency of intensive and extensive dairy farms in Lithuania, Latvia, Estonia, and Poland over the period 2015–2022, using Data Envelopment Analysis (DEA) combined with a meta-frontier framework that explicitly accounts for technological heterogeneity across production systems. Farms are classified as intensive or extensive based on stocking density relative to forage area, applying the threshold of one livestock unit per hectare. Results show that in all Baltic countries intensive farms exhibit higher meta-frontier technical efficiency than extensive farms, with the gap increasing over time, especially in Lithuania. Technology Gap Ratio results indicate convergence between production systems in Estonia and Latvia, while in Lithuania intensive farms became technologically closer to the national frontier after 2020. In contrast, Poland shows a different pattern: intensive farms operated closer to the meta-frontier but achieved lower efficiency, suggesting managerial constraints. Regression analysis confirmed that production intensity is a positive and statistically significant determinant of meta-frontier technical efficiency in all Baltic countries. These findings suggest that current economic conditions favour intensification and that extensification policies can only be effective if they adequately compensate for the efficiency disadvantage faced by extensive farms. Full article

Most remote sensing extraction studies utilizing vegetation indices typically classify and extract land cover features based on the phenological characteristics of the study area or rely on a single vegetation index. When attempting to extract multiple land cover types simultaneously, classification accuracy often declines significantly because a single vegetation index is unsuitable for all features. While some recent studies employ deep learning and neural networks for classification and extraction, their complex mechanisms and “black-box effect” hinder clear explanations for accuracy outcomes. In response to the issues outlined above, this paper proposes a simpler and more intuitive method for the hierarchical extraction of typical land cover features. This approach analyzes the difficulty of separating these features based on spectral reflectance data to determine the following extraction order: first water bodies, followed by reed, then Suaeda salsa, and finally tidal flat. Furthermore, by selecting appropriate parameters and substituting vegetation indices for bands that perform better, high extraction accuracy is achieved. The classification and interpretation results were validated using a combination of field survey data and Google imagery, together with a validation sample. Accuracy assessments using overall accuracy and Kappa coefficient demonstrate the following optimal results for the hierarchical approach: NDWI for water, S2REP for reeds, and MSAVI for Suaeda salsa. Overall accuracy reached 98.5% with a Kappa coefficient of 0.9796, validating the effectiveness of this spectral-feature-based hierarchical extraction method using diverse vegetation indices. Using a hierarchical extraction approach to classify typical land cover features in the study area from 2020 to 2025, accuracy rates exceeded 98% in all cases. Based on these classification results, the INVEST model was employed to simulate carbon stock trends in the Liaohe Estuary region over the past five years. The study found that, although factors such as tides and the date of image acquisition had a certain impact on the study area compared with the problems caused by historical development, the ecological environment in the study area is gradually stabilizing at the present stage. Full article