Search Results (13,302)

The development of efficient and low-cost adsorbents for dye-contaminated wastewater remains an important challenge in environmental remediation. In this study, an interlayer-expanded polypyrrole/maghnite–Cu²⁺ nanocomposite (PPy/Mag–Cu²⁺) was successfully synthesized through purification of raw maghnite, sodium activation, Cu²⁺ ion exchange, and in situ oxidative polymerization of pyrrole. The obtained hybrid was characterized by X-ray fluorescence, X-ray diffraction, Fourier-transform infrared spectroscopy, UV–Vis spectroscopy, scanning electron microscopy, and cyclic voltammetry. The results confirmed the successful incorporation of Cu²⁺ and polypyrrole while preserving the layered aluminosilicate framework. XRD analysis revealed a progressive increase in basal spacing from 17.49 Å for raw maghnite to 25.95 Å for the final nanocomposite, indicating effective intercalation and formation of an expanded hybrid structure. The adsorption performance of PPy/Mag–Cu²⁺ was evaluated for methylene blue removal under batch conditions. Adsorption was strongly influenced by contact time, pH, and initial dye concentration, with equilibrium reached after approximately 80 min and optimum removal at pH 9. Equilibrium data were best fitted by the Langmuir model, with a maximum monolayer adsorption capacity of 43.66 mg g⁻¹, while kinetic data followed the pseudo-second-order model. These findings demonstrate that PPy/Mag–Cu²⁺ is a promising and cost-effective hybrid adsorbent for cationic dye removal from aqueous media. Full article

Offshore wind energy is an important source of clean energy. Single-post platforms, due to their simple structure and strong stability, can adapt to deep water environments through buoyancy and ballast systems, have small motion responses, and have low construction and maintenance costs. They are suitable for offshore wind energy development in deep-sea areas and help expand the application of offshore wind power. This paper conducts a coupled response analysis of offshore wind turbine foundations and mooring systems, as well as an optimization study on the form and number of mooring lines. Under the premise of considering the safety and economy of floating wind turbines, the mooring lines have been optimally arranged. The study calculates frequency-domain responses, time-domain responses, and mooring line forces under the constraints of the original three-line mooring system. Based on this benchmark, the study further optimizes the mooring forms and numbers for the same platform, analyzing four, six, and eight single mooring lines, as well as three groups of single-line, double-line, and triple-line mooring configurations. Finally, using AQWA software (2024 R1), the responses and mooring line forces of different mooring configurations were calculated, and the preferred mooring arrangement for this stepped single-post platform was determined to be a three-group, three-line system (a total of nine mooring lines). The mooring line tension decreased substantially from the original 3.2 × 10⁶ N to 1.8 × 10⁶ N, while the dynamic response was reduced to one-sixth of its original level. Meanwhile, this study provides strong support for the utilization of offshore wind energy and the construction of offshore wind turbine platforms and mooring systems. Full article

The article provides a comprehensive overview of the role of hydrogen as a key vector in the decarbonization of the global transport sector. The study situates hydrogen within the broader context of energy transition and climate neutrality targets, emphasizing its potential to replace fossil fuels in road, rail, maritime, and aviation applications. The analysis integrates a review of current technological, infrastructural, and policy developments, covering both combustion-based and fuel-cell hydrogen propulsion systems. Quantitative and qualitative data were assessed from international reports, scientific publications, and ongoing industrial projects to evaluate performance, efficiency, safety, and cost parameters such as Levelized Cost of Hydrogen (LCOH) and Total Cost of Ownership (TCO). The results indicate that while hydrogen remains economically challenging, technological progress in electrolysis, fuel cells, and refueling infrastructure significantly improves its competitiveness, particularly in heavy-duty and long-range transport. The paper highlights the critical role of international strategies, including the European Hydrogen Strategy and Fit for 55 package, in driving market adoption and regulatory alignment. The conclusions suggest that by 2050, hydrogen could contribute up to one-quarter of total transport energy demand, positioning it as a cornerstone of sustainable mobility and a bridge toward a fully decarbonized transport ecosystem. Full article

Underwater wireless optical communication (UWOC) for autonomous underwater vehicles is severely compromised by the coupling of oceanic optical turbulence and platform motion. Traditional static statistical models fail to capture the temporal evolution of these stochastic processes, hindering effective real-time beam tracking. This paper proposes a unified dynamic framework and a hybrid intelligent control strategy to address beam misalignment in turbulent environments. First, a physically motivated stochastic differential equation (SDE) model is derived from the Radiative Transfer Equation via diffusion approximation. Validated by an inverse Fokker–Planck approach, this model accurately reconstructs drift fields for diverse channel conditions, serving as a dynamic generator for time-varying fading. Second, to maintain robust link alignment, a hybrid Fuzzy-Reinforcement Learning control strategy is developed. This approach integrates the interpretability of fuzzy logic with the adaptive optimization of Q-learning, incorporating a supervisor mechanism to handle deep fading events. Numerical simulations and hardware-in-the-loop (HIL) experiments demonstrate the system’s efficacy. The proposed controller achieves a median alignment error of 3.64 mm and reduces transient errors by over 80% compared to classical PID controllers during signal recovery. These results confirm that the proposed framework significantly enhances link stability and tracking robustness for AUVs in complex random media. Full article

With advancements in weak magnetic detection technology, the electromagnetic wake signals induced by UUVs in stratified seawater are becoming stable interference sources for detection equipment. This study developed a numerical model combining fluid dynamics and electromagnetism to examine the electromagnetic wake evolution of the UUV system under varying propeller propulsion coefficients, and formation mechanism of the wake electromagnetic field is revealed. The flow field results were validated using PIV and relevant literature. The flow characteristics of the near-field wake are analyzed by visualizing the vortex structure. Additionally, this study investigates the attenuation law of far-field wake using electromagnetic field intensity attenuation curves. The wake’s electromagnetic field frequency characteristics were examined through the normalized amplitude spectrum. Results indicate that the near-field wake vortex structure resembles a propeller’s topological structure. The electric field intensities in the near-field and far-field are approximately on the order of 10⁻⁴ V/m and 10⁻⁵ V/m, respectively, while the magnetic field intensities are around 10⁻¹⁰ V/m and 10⁻¹¹ V/m. The electromagnetic interference spectrum within the wake typically shows high intensity in the low-frequency band. A high-precision magnetometer can detect the electromagnetic field’s intensity and frequency characteristics. It offers theoretical support for developing advanced anti-interference algorithms in engineering practice. Full article

This study develops an integrated vessel-level framework for assessing logistics performance and operational energy efficiency in container shipping. The novelty of the study lies in the development of a unified analytical approach that explicitly integrates logistics indicators with fuel consumption and emissions within a consistent system boundary, including auxiliary engine operation during both sea passages and port stays. The framework is applied to four medium-sized container vessels (6000–7500 TEU; 20-foot equivalent unit) under normalised operating conditions. The results show that higher capacity utilisation and economies of scale significantly improve both cost and energy performance, while emissions intensity varies by more than twofold across vessels. A deterministic sensitivity analysis is applied to evaluate the influence of key operational parameters. The analysis identifies service speed as the dominant driver, followed by vessel loading rate, while port-related parameters—such as auxiliary engine load and port productivity—have a lower yet still measurable influence, reducing emissions by up to 5% under improved conditions. The main contribution of the study is the development of a practical vessel-level benchmarking tool that captures logistics–energy interactions and supports operational decision-making under current regulatory frameworks, including EU ETS, FuelEU Maritime, and the IMO Carbon Intensity Indicator (CII). Full article

Recently, polyhydroxyalkanoates (PHAs) have gained significant attention as a bioactive material for replacing petrochemical plastics. PHAs can be produced by microorganisms growing on sludge substrates. In this study, fish-processing wastewater was investigated as an alternative substrate for PHA production using Bacillus cereus. Wastewater dilution, carbon-to-nitrogen ratio modification, and the addition of fish oil as a lipidic substrate were examined, and bacterial growth and biopolymer production were optimized. First, wastewater was diluted (25–100%) and examined. The 50% dilution treatment was selected, yielding a CDM of 0.426 g/L and a PHA content of 6.69%. In subsequent steps, the effects of wastewater fermentation and bacterial adaptation prior to the main production processes were investigated. According to the results, the 50% and 100% fermented treatments exhibited higher CDM values (0.970–1.022 g/L) compared to the non-fermented treatments. Cultures inoculated with adapted bacteria showed superior performance (CDM: 1.455 g/L, PHA: 0.499 g/L, PHA content: 34.63%) relative to non-adapted treatments. The effect of the carbon-to-nitrogen (C/N) ratio was also optimized by supplementing two carbon sources: glucose and crude fish oil. The optimal treatment T1 (effluent + 0.6 g/L glucose) had a CDM of 1.32 g/L and a PHA content of 0.215 g/L. Treatment 1, which consisted solely of effluent and fish oil, exhibited higher values (CDM: 1.12 g/L, PHA: 0.65 g/L) and was therefore considered the cost-effective treatment. Subsequently, a scale-up process was conducted in a 4 L bioreactor over 300 h under semi-continuous, long-term cultivation. The optimal harvesting time for the biopolymer was achieved during the fourth cycle (180–240 h). The produced biopolymer was characterized using FTIR, NMR, TGA, DSC, SEM, and XRD analyses, confirming the production of a copolymer, specifically poly(3-hydroxybutyrate-co-3-hydroxyvalerate). This study used wastewater from the fish industry for the production of biodegradable polyhydroxyalkanoates. Full article

Air-to-air heat pumps are a key technology for improving energy efficiency and reducing carbon emissions in residential buildings, yet their optimal control remains challenging under real-world conditions. This study evaluates the performance of a hybrid physical–LSTM model for controlling an air-to-air heat pump in a residential building in Zadar, Croatia. The hybrid framework integrates a first-order energy balance model of the building envelope with LSTM-based temperature correction using adaptive weighting. The physical model was calibrated and validated against 52,128 real IoT measurements collected during the 2024/2025 heating season, achieving high accuracy (RMSE ≈ 0.076 °C). Rolling one-day and continuous multi-day closed-loop simulations (up to 15 days) show that the hybrid model yields slightly lower RMSE in long-term runs compared to the pure physical model. However, this apparent statistical improvement is accompanied by systematic underestimation of indoor temperature and significantly higher simulated energy consumption. The results indicate that the observed effect originates from an implicit virtual heat flux introduced by the LSTM correction, which affects thermodynamic consistency in closed-loop operation. The findings highlight that short-term error metrics such as RMSE alone are insufficient for evaluating hybrid models intended for model predictive control (MPC). The main contribution of this study is the explicit demonstration and quantification of an implicit virtual heat flux generated by the LSTM correction in closed-loop multi-day operation, which leads to misleading statistical improvements while causing significant thermodynamic inconsistency and energy overconsumption. In 15-day continuous simulations the hybrid model (ω = 0.05–0.10) caused an indoor temperature underestimation of 1.25–1.31 °C and increased simulated electricity consumption by more than 300% (316 kWh vs. 72 kWh) compared to the physical model. These results have direct implications for the development of reliable digital twins and model predictive control strategies in residential HVAC systems. Full article

Sea ice classification is of fundamental importance for polar monitoring and global climate research. Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a frontier technology in polar remote sensing due to its high spatiotemporal resolution and cost-effectiveness. Based on BeiDou System Reflectometry (BDS-R) data acquired from the Fengyun-3E (FY-3E) satellite, this study introduces a classification approach that integrates multi-dimensional sea ice information. A comprehensive feature set was constructed by integrating the Spectral Entropy (SE) of the Normalized Integrated Delay Waveform (NIDW) First-order Differential Curve to characterize the oscillatory complexity of the trailing edge power decay process as a scattering dynamic property, the Root Mean Square height (RMS) to characterize the attenuation magnitude of scattering intensity arising from surface roughness and related factors as a scattering intensity attenuation property, and salinity (S) and L-band brightness temperature (TB) data from SMOS to describe dielectric and radiative properties. These novel features are combined with traditional GNSS-R features. After selecting the optimal feature set via an ablation study, the features were used to train a Random Forest (RF) classifier for sea ice classification. Validated against Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea ice type products, the proposed method yielded an overall accuracy of 93.86% and a Kappa coefficient of 0.8061. The integration of multi-dimensional features notably improved the identification of Multi-Year Ice (MYI), achieving a Recall of 85.11% and an F1-score of 84.43%. These results indicate that the proposed multi-dimensional feature set provides an effective solution for GNSS-R-based sea ice classification. Full article

Recent underwater and remote sensing surveys identified, located, and documented the wreck of a Liberty-class cargo ship, SS Samuel J. Tilden, which sank during the German raid of Bari in December 1943. The use of remote sensing technologies (MBES, ROVs) and the photogrammetric acquisition for the creation of 3D models were central for a comprehensive analysis of the wreck site. The analysis of remote sensing and photogrammetric data indicates a well-preserved wreck, as previously noted in avocational underwater surveys, and a complex maritime landscape. By applying remote sensing and non-invasive technologies to conflict archaeology remains, this paper provides a basis for future studies on World War II wrecks in Italy. Full article

Infrared small-target segmentation (IRSTS) is crucial for a wide range of applications, including maritime search-and-rescue operations and intelligent traffic surveillance. However, current deep learning methods struggle with dynamic scale variations in infrared small targets, resulting in false detections and missed detections, alongside inadequate core localization accuracy. To address these challenges, we propose an infrared small-target segmentation framework founded on morphological attention and an energy core loss function, IRSTS_Unet. Specifically, we design a Dynamic Shape-adaptive Deformable Attention Module (DSDAM), which achieves parameterized feature extraction via “initial localization–offset deformation–precise sampling”. This approach enables the network to differentially focus on target cores and background cues to suppress clutter. To improve the efficiency of multi-scale feature aggregation, we embed the DSDAM within both the feature extraction and cross-layer fusion stages. Furthermore, we formulate a Core Energy-aware Core-Priority loss (CECP-Loss) function that incorporates the energy prior distribution of small targets, effectively counteracting the “core dilution” phenomenon endemic to conventional loss functions. Through extensive experiments on multiple public datasets, we demonstrate that IRSTS_U-Net outperforms state-of-the-art approaches in terms of both detection accuracy and robustness. Full article

This study proposes a novel dual-role weighting framework for dry port location selection, bridging the gap between commercial logistics efficiency and strategic disaster resilience. Designed to establish a new theoretical evaluation paradigm, the research utilizes the Fuzzy Rough SWARA (FR-SWARA) method and a 12-person expert panel to weigh a comprehensive set of 31 criteria under high-dimensional uncertainty. The findings reveal a decisive hierarchical shift, where spatial and infrastructure-related dimensions significantly outweigh traditional cost considerations. This empirical evidence substantiates the transition from ‘just-in-time’ to ‘Just-in-Case’ logistics architectures. Ultimately, the study reconceptualizes the dry port as a ‘strategic stabilizer’—a critical infrastructure node that absorbs systemic shocks and maintains supply chain continuity during diverse crises, including natural disasters and the COVID-19 pandemic. The proposed weighting framework offers a robust theoretical roadmap for policy and managerial decision-making in volatile geographies. Full article

Croatia hosts one of the most intensive recreational boating activities in the Mediterranean, with over 134,600 registered vessels along 5835 km of Adriatic coastline. This paper presents an AI-driven simulation framework for evaluating electrification pathways for the Croatian recreational vessel fleet. A key contribution is the explicit treatment of the AIS data gap: recreational vessels in Croatia are not required to carry AIS transponders, so synthetic operational profiles calibrated from manufacturer specifications and verified economic data are used instead. Six machine learning architectures are compared for vessel energy demand forecasting, with a proposed Transformer-based model achieving the best simulated performance. Fleet-weighted Monte Carlo simulation across three electrification scenarios suggests that an AI-optimised hybrid configuration can, subject to use intensity, reduce per-vessel CO₂ emissions by up to 56.8% relative to conventional engines. Techno-economic analysis shows payback periods ranging from over 15 years for low-use private owners to 7–9 years for charter operators, supporting targeted incentive design. The framework is intended to be transferable to other Mediterranean coastal regions facing comparable data and operational constraints. Full article

To improve ship AIS trajectory prediction under pronounced spatiotemporal coupling and dynamic maneuvering conditions, this study proposes a Spatio-Temporal-Hybrid-Multi-head Attention model (STHA) integrating multiscale convolution, bidirectional long short-term memory, and multi-head attention. Historical AIS data from the Zhoushan waters in 2024 were preprocessed through screening, cleaning, outlier removal, resampling, and cubic spline interpolation to construct trajectory samples. Comparative experiments were conducted against BP, BiLSTM, and BiGRU using MAPE, RMSE, and R² as evaluation metrics. The results show that STHA achieves the best overall predictive performance, more accurately follows trajectory variations across different vessel types, and exhibits better robustness in scenarios involving turning and speed changes. These findings indicate that the proposed model is effective for high-precision ship trajectory prediction and can provide useful support for subsequent collision risk assessment and navigation safety assistance. Full article

Background: Multi-center resting-state functional magnetic resonance imaging (rs-fMRI) is important for neurodevelopmental disorder diagnosis, but cross-site differences in repetition time (TR) can cause temporal feature misalignment. In addition, blood-oxygen-level-dependent (BOLD) signals are non-stationary, so disease-related information may be distributed across multiple time scales. Existing methods usually do not explicitly model physical sampling intervals or coordinate temporal and spectral information across scales, which may limit cross-site generalization in heterogeneous multi-center settings. Methods: We propose Spec-RWKV, a spectrum-guided linear recurrent framework for multi-site rs-fMRI diagnosis. It includes three components: PrismTimeMix, which models temporal dynamics using decay rates derived from physical half-lives and converts them adaptively across TRs; a TR-adaptive continuous wavelet transform, which aligns spectral representations across sites by adjusting frequency coverage; and spectrum-guided adaptive temporal aggregation, which uses spectral context to weight temporal features. Results: On ABIDE-I and ADHD-200, Spec-RWKV achieved AUCs of 75.86% and 76.31%, respectively. Under leave-one-site-out validation, it achieved the best mean AUC on ABIDE-I and the best mean accuracy and AUC on ADHD-200. Conclusions: Spec-RWKV explicitly models sampling-rate differences and multi-scale spectral structure, with results supporting strong cross-site generalizability. Full article