Search Results (3,682)

This study analyzes the hydrodynamic performance of a V-type wave dissipation system and amphibious landing equipment under different combined fields using the Reynolds-averaged Navier–Stokes (RANS) method. A three-dimensional numerical wave tank is established to simulate regular waves and validate the performance of an airbag-type floating breakwater. This study evaluates the optimal hydrodynamic performance of a V-type wave dissipation system under various configurations in a wave-only field and subsequently compares the efficacy of the better-performing system across multiple environmental conditions. The results show that the V-type wave dissipation system in the configurations of 30° and 45° angles is more favorable for the flow field and the amphibious landing equipment behind it. Compared to the wave-only condition, the time histories of wave heights under both wave-current and wind-wave conditions present an obvious phase advancement. In the wave-current field, a following current reduces the wave height and shortens the wave period. Conversely, in the wind-wave field, a following wind velocity leads to a certain increase in wave height while exerting minimal impact on the wave period. Compared to the wave-only condition, the peak and trough values of the wave height monitoring points in the combined wind-wave-current field show an increasing trend, with a significant increase in resistance and a shorter resistance period for the amphibious landing equipment behind the V-type wave dissipation system. This study shows that the selected V-type wave dissipation system proves to be more effective in wave-only and wave-current conditions, providing valuable references for the engineering application of this system. Full article

Residential barbecuing is becoming increasingly popular worldwide, especially in cities, where it is not only a leisure activity but also an important social and cultural practice. Consequently, the number of grills and smokers in use continues to grow. This study evaluated the environmental performance of a household wood-pellet barbecue dual-function smoker/grill using a life cycle assessment (LCA) approach. The functional units selected were per cooking time (1 h) and per unit of energy delivered (1 kWh) at different cooking settings on the smoker. The results show that most of the impacts, including global warming potential (GWP) and resource use, originate from the production of the smoker itself, whereas emissions released during combustion, especially NO_x, are the main contributors to impacts such as acidification and smog formation. The GWP per hour of operation ranged from 0.44 to 0.63 kg CO₂ eq. From an operational perspective, cooking at intermediate temperatures (between 110 and 175 °C) generally leads to lower impacts per hour than very low-temperature smoking. When considering entire meals, meat typically accounts for most of the total impact, with the smoker’s contribution comparatively small. Overall, the study provides a useful reference and shows that both equipment design and food choices play a role in barbecue sustainability. Full article

Fuel consumption in heavy-duty off-road machinery depends on a wide range of interacting factors related to the operating environment, the technical characteristics and condition of the machine, and the behaviour, experience and state of the operator. Existing studies typically address only fragments of this relationship, focusing on vehicle parameters, selected environmental factors or individual aspects of driving style. The method proposed in this work provides a general and transferable framework for assessing fuel consumption in any type of machine or vehicle. The Integrated Fuel Consumption Assessment Model (IFCAM) combines environmental, vehicle and human domains into a coherent structured formula that can be used across different operational contexts. The model was developed using continuous short-term measurements and long-term operational data collected during real industrial work. Its universal structure makes it applicable not only to mining equipment, but also to construction machinery and transport vehicles, as well as conventional passenger cars, where it offers a systematic procedure for estimating fuel demand under variable operating conditions. The results demonstrate that integrating multi-domain data improves predictive accuracy and opens new possibilities for analysing operator influence and overall energy efficiency. Full article

The Philippines experiences intense rainfall but has limited ground-based monitoring infrastructure for flood prediction. Satellite rainfall products provide broad coverage but contain systematic biases that reduce operational usefulness. This study evaluated whether three correction methods—Quantile Mapping (QM), Random Forest (RF), and Hybrid Ensemble—maintain accuracy when applied to future periods with substantially different rainfall characteristics. Using the Cagayan de Oro River Basin in Northern Mindanao as a case study, models were trained on 2019–2020 data and tested on an independent 2021 period exhibiting 120% higher mean rainfall and 33% increased rainy-day frequency. During training, Random Forest and Hybrid Ensemble substantially outperformed Quantile Mapping (R² = 0.71 and 0.76 versus R² = 0.25 for QM). However, when tested under realistic operational constraints using seasonally incomplete calibration data (January–April only), performance rankings reversed completely. Quantile Mapping maintained operational reliability (R² = 0.53, RMSE = 5.23 mm), while Random Forest and Hybrid Ensemble failed dramatically (R² dropping to 0.46 and 0.41, respectively). This demonstrates that training accuracy poorly predicts operational reliability under changing rainfall regimes. Quantile Mapping’s percentile-based correction naturally adapts when rainfall patterns shift without requiring recalibration, while machine learning methods learned magnitude-specific patterns that failed when conditions changed. For flood early warning in data-limited basins with equipment failures and variable rainfall, only Quantile Mapping proved operationally reliable. This has practical implications for disaster risk reduction across the Philippines and similar tropical regions where standard validation approaches may systematically mislead model selection by measuring calibration performance rather than operational transferability. Full article

Accurate spatiotemporal forecasting underpins high-stakes decision making in smart urban systems, from traffic control and energy scheduling to environment monitoring. Yet two persistent gaps limit current models: (i) spatial modules are often biased toward low-pass smoothing and struggle to reconcile slow global trends with sharp local dynamics; and (ii) the graph structure required for forecasting is frequently latent, while learned graphs can be unstable when built from temporally derived node features alone. We propose SpeQNet, a query-enhanced spectral graph filtering framework that jointly strengthens node representations and graph construction while enabling frequency-selective spatial reasoning. SpeQNet injects global spatial context into temporal embeddings via lightweight learnable spatiotemporal queries, learns a task-oriented adaptive adjacency matrix, and refines node features with an enhanced ChebNetII-based spectral filtering block equipped with channel-wise recalibration and nonlinear refinement. Across twelve real-world benchmarks spanning traffic, electricity, solar power, and weather, SpeQNet achieves state-of-the-art performance and delivers consistent gains on large-scale graphs. Beyond accuracy, SpeQNet is interpretable and robust: the learned spectral operators exhibit a consistent band-stop-like frequency shaping behavior, and performance remains stable across a wide range of Chebyshev polynomial orders. These results suggest that query-enhanced spatiotemporal representation learning and adaptive spectral filtering form a complementary and effective foundation for effective spatiotemporal forecasting. Full article

This study introduces the Sustainable Development Goals Achievement Measurement Framework (SDG-AMF), a novel analytical tool used to systematically evaluate the relationships between digitalization and the Sustainable Development Goals (SDGs). Unlike the United Nations (UN) E-Government Development Index (EGDI) and Organization for Economic Co-operation and Development (OECD) Digital Government Indicators (DGIs) frameworks, the proposed SDG-AMF links digitalization indicators to specific SDG outcomes using proxy-based time-series analysis. The SDG-AMF provides a unified, statistically grounded approach that connects digital development with measurable sustainability outcomes. Using direct, high-quality time-series data (2010–2024) from internationally recognized sources, the framework maps key digitalization indicators such as Internet penetration, e-government maturity, research and development (RD) expenditure, gross domestic product (GDP) per capita, and gender participation in information and communication technology (ICT) to the selected SDG targets (SDGs 4, 5, 8, 9, and 16). Through correlation and regression analyses, the study identifies enabling and inhibiting relationships, highlighting Saudi Arabia’s strengths in digital infrastructure and e-government maturity while emphasizing areas for improvement, such as civic participation and RD intensity. Comparative benchmarking with digitally advanced economies underscores Saudi Arabia’s strengths in Internet penetration and e-government maturity, while gaps in RD investment are identified. The SDG-AMF provides policymakers with a replicable roadmap and scalable model to align foundational connectivity and governance reforms with advanced digital transformation, facilitating progress toward achieving Sustainable Development Goals worldwide. This research contributes original methodological insights and equips stakeholders with practical tools to monitor, compare, and accelerate SDG progress in the digital era. Full article

Maritime emergency communication facilities are essential for establishing land-sea connectivity and supporting disaster rescue operations. However, current systems often struggle with slow deployment, link instability, and insufficient coverage. To overcome these limitations, this paper proposes a method utilizing aircraft equipped with communication payloads for rapid network construction in target sea areas, aiming to satisfy the dual demands of quick response and stable transmission. A critical component of this framework is the optimal selection of aircraft bases. Addressing the distinct coverage capabilities of different platforms, we construct a multi-objective optimization model for base location. This model integrates a hierarchical coverage mechanism involving multiple aircraft types and is solved using the proposed Hybrid Adaptive Multi-objective Optimization (HAMO) algorithm. Experimental validation in the Bohai Sea region demonstrates the feasibility and effectiveness of the proposed model. Full article

Background: Iron ore tailings (IOTs) are a mine waste product used as road materials and suffer from a lack of sufficient strength, which should be improved through stabilization. Unconfined compressive strength (UCS) is a crucial parameter for determining the quality and mix design of stabilized soils, which is time-consuming, requires specialized equipment and professional operators, and is not affordable. Methods: In this study, six ensemble learning techniques, five-fold cross-validation, and the Fennec Fox Optimization metaheuristic algorithm were utilized to predict UCS. For this purpose, cement content, curing time, compaction energy, and moisture content were selected as independent variables. Results: The results suggested that XGBoost-FFO was the most accurate model, R² = 0.9505, MAE = 0.257, MSE = 0.118, and RMSE = 338. Two interpretation methods were employed to evaluate the model’s performance, and the results indicated that the most significant parameter was compaction energy. Conclusions: Moreover, to facilitate practical engineering applications, a graphical user interface (GUI) was also designed to predict the UCS of cement-stabilized IOTs. Full article

Background: Solid organ transplantation (SOT) is a life-saving treatment for patients with end-stage diseases and/or organ failure. However, access to healthy organs is often limited by challenges in organ preservation. Furthermore, upon transplantation, ischemia–reperfusion injury (IRI) can lead to increased organ rejection or graft failures. The work presented aims to address both challenges using an innovative nanomedicine platform for simultaneous drug and oxygen delivery. In recent studies, resveratrol (RSV), a natural antioxidant, anti-inflammatory, and reactive oxygen species (ROS) scavenging agent, has been reported to protect against IRI by inhibiting ferroptosis. Here, we report the design, development, and scalable manufacturing of the first-in-class dual-function perfluorocarbon-nanoemulsion (PFC-NE) perfusate for simultaneous oxygen and antioxidant delivery, equipped with a near-infrared fluorescence (NIRF) reporter, longitudinal, non-invasive NIRF imaging of perfusate flow through organs/tissues during machine perfusion. Methods: A Quality-by-Design (QbD)-guided optimization was used to formulate a triphasic PFC-NE with 30% w/v perfluorooctyl bromide (PFOB). Drug-free perfluorocarbon nanoemulsions (DF-NEs) and RSV-loaded nanoemulsions (RSV-NEs) were produced at 250–1000 mL scales using M110S, LM20, and M110P microfluidizers. Colloidal attributes, fluorescence stability, drug loading, and RSV release were evaluated using DLS, NIRF imaging, and HPLC, respectively. PFC-NE oxygen loading and release kinetics were evaluated during perfusion through the BMI OrganBank^® machine with the MEDOS HILITE^® oxygenator and by controlled flow of oxygen. The in vitro antioxidant activity of RSV-NE was measured using the oxygen radical scavenging antioxidant capacity (ORAC) assay. The cytotoxicity and ferroptosis inhibition of RSV-NE were evaluated in RAW 264.7 macrophages. Results: PFC-NE batches maintained a consistent droplet size (90–110 nm) and low polydispersity index (<0.3) across all scales, with high reproducibility and >80% PFOB loading. Both DF-NE and RSV-NE maintained colloidal and fluorescence stability under centrifugation, serum exposure at body temperature, filtration, 3-month storage, and oxygenation. Furthermore, RSV-NE showed high drug loading and sustained release (63.37 ± 2.48% at day 5) compared with the rapid release observed in free RSV solution. In perfusion studies, the oxygenation capacity of PFC-NE consistently exceeded that of University of Wisconsin (UW) solution and demonstrated stable, linear gas responsiveness across flow rates and FiO₂ (fraction of inspired oxygen) inputs. RSV-NE displayed strong antioxidant activity and concentration-dependent inhibition of free radicals. RSV-NE maintained higher cell viability and prevented RAS-selective lethal compound 3 (RSL3)-induced ferroptosis in murine macrophages (macrophage cell line RAW 264.7), compared to the free RSV solution. Morphological and functional protection against RSL3-induced ferroptosis was confirmed microscopically. Conclusions: This study establishes a robust and scalable PFC-NE platform integrating antioxidant and oxygen delivery, along with NIRF-based non-invasive live monitoring of organ perfusion during machine-supported preservation. These combined features position PFC-NE as a promising next-generation acellular perfusate for preventing IRI and improving graft viability during ex vivo machine perfusion. Full article

High-energy methods dominate the development of lipid nanoparticles but often require specialized equipment that increases production costs. Low-energy approaches, particularly those free of organic solvents, offer a promising alternative. This study aimed to obtain nanostructured lipid carriers (NLCs) using a solvent-free, low-energy process combining microemulsification and phase inversion. Cetearyl alcohol and PEG-40 hydrogenated castor oil were selected as the solid lipid and surfactant, respectively; the formulation and process were optimized through a Box–Behnken Design. Incorporation of the ionic surfactant extended colloidal stability, while the poloxamer in the aqueous phase enhanced steric stabilization. Resveratrol was efficiently encapsulated (E.E. = 98%), contributing to reduced particle size (291 nm), improved homogeneity (PDI = 0.25), and positive surface charge (+43 mV). Scale-up yielded stable particles carrying resveratrol with a mean size of 507 nm, PDI = 0.24, and ZP = +52 mV. The optimized formulation remained stable for 90 days at 8 °C. In vitro release demonstrated a sustained and controlled release profile, with significantly lower resveratrol release compared to the free compound. Thermal analysis confirmed drug incorporation within the lipid matrix, while transmission electron microscopy (TEM) revealed spherical particles (~200 nm) and SAXS indicated a nanostructure of ~50 nm. Overall, this study demonstrates that solvent-free, low-energy processing can produce stable and scalable NLC formulations, successfully encapsulating resveratrol with favorable physicochemical properties and controlled release behavior. These findings highlight a simple, cost-effective strategy for developing lipid-based nanocarriers with potential applications in drug delivery. Full article

Liquid coolant jets are commonly used to remove excess heat from workpieces during grinding. There is a pressing need to reduce energy waste that contributes to environmental heat pollution and to limit the spread of oil-based coolants and mist formation. As a liquid jet issues from a nozzle and enters the surrounding air, surface instabilities develop, causing the jet to break into droplets. This breakup diminishes the jet’s ability to deliver maximum momentum to the workpiece and grinding wheel in grinding operations, thereby reducing cooling efficiency. The presence of moving ambient air near the workpiece and rotating grinding wheel further complicates cooling. First, the study investigates jet breakups in stationary air, predicting breakup lengths with reasonable agreement to experiments at varying jet velocities using the Reynolds Averaged Navier–Stokes (RANS) method equipped with Shear Stress Transport (SST) k-ω model of turbulence. The coolant jet breakup length for a jet normal to the grinding wheel is different from that for a free jet and affected by the proximity of grinding wheel to nozzle that was not evaluated in prior studies. Simulations were performed using Ansys Fluent software 2023R1, with careful tuning of numerical schemes and selection of breakup criteria. The results include analysis of jet breakup phenomena in presence of rotating grinding wheel and workpieces, determination of breakup lengths across a range of Weber numbers, and effects of nozzle design. Full article

This paper presents a waveguide energy-selective device operating in the Ku band. By utilizing the nonlinear characteristics of PIN diodes, the device can autonomously switch its operating state according to the power level of incident signals inside the waveguide, achieving an adaptive transmission response. Concurrently, through a dual-layer structural design and optimized inter-layer coupling, it enables the device to deliver broadband-protective performance within the Ku band. To validate its feasibility, the device was designed and implemented based on the waveguide WR62. The results indicate that during the transmission of a −10 dBm signal, the device exhibits insertion loss fluctuating around 1 dB within the 13–17 GHz band, whereas under 45 dBm signal incidence, the shielding effectiveness exceeds 10 dB across this frequency range. The device can be integrated into waveguides to provide adaptive high-power protection, thus demonstrating significant application potential in the field of electromagnetic protection for sensitive electronic equipment. Full article

Accurate model aggregation is pivotal for the efficient dispatch and control of massive distributed photovoltaic (PV) and energy storage (ES) resources. However, the lack of unified standards across equipment manufacturers results in inconsistent data formats and resolutions. Furthermore, external disturbances like noise and packet loss exacerbate the problem. The resulting data are massive, multi-source, and heterogeneous, which poses severe challenges to building effective aggregation models. To address these issues, this paper proposes a hierarchical aggregation method based on multi-source multi-scale data fusion. First, a Multi-source Multi-scale Decision Table (Ms-MsDT) model is constructed to establish a unified framework for the flexible storage and representation of heterogeneous PV-ES data. Subsequently, a two-stage fusion framework is developed, combining Information Gain (IG) for global coarse screening and Scale-based Trees (SbT) for local fine-grained selection. This approach achieves adaptive scale optimization, effectively balancing data volume reduction with high-fidelity feature preservation. Finally, a hierarchical aggregation mechanism is introduced, employing the Analytic Hierarchy Process (AHP) and a weight-guided improved K-Means algorithm to perform targeted clustering tailored to the specific control requirements of different voltage levels. Validation on an IEEE-33 node system demonstrates that the proposed method significantly improves data approximation precision and clustering compactness compared to conventional approaches. Full article

The inner spacer module, which profoundly affects the final performance of a device, is a critical component in GAA NSFET (Gate-all-around Nanosheet Field Effect Transistor) manufacturing and necessitates systematic optimization and fundamental innovation. This work aims to develop an advanced SiGe etching process with high selectivity, uniformity and low damage to achieve an ideal inner spacer structure for logic GAA NSFETs. For three distinct dry etching technologies, ICP (Inductively Coupled Plasma Technology), RPS (Remote Plasma Source) and Gas Etching, we evaluated their potential and comparative advantages for inner spacer cavity etching under the same experimental conditions. The experimental results demonstrated that Gas Etching technology possesses the uniquely high selectivity of the SiGe sacrificial layer, making it the most suitable approach for inner spacer cavity etching to reduce Si nanosheet damage. Based on the results, in the stacked structures, the SiGe/Si selectivity ratio exhibited in Gas Etching is ~9 times higher than ICP and ~2 times higher than RPS. Through systematic optimization of pre-clean conditions, temperature and chamber pressure control, we successfully achieved a remarkable performance target of cavity etching: the average SiGe/Si etching selectivity is ~56, the inner spacer shape index is 0.92 and the local etching distance variation is only 0.65 nm across different layers. These findings provide valuable guidance for equipment selection in highly selective SiGe etching and offer critical insights into key process module development for GAA NSFETs. Full article

With the continuous and rapid growth of global photovoltaic (PV) installed capacity, the fluctuation, intermittence, and randomness of its output aggravate the inertia loss of traditional power systems, which poses severe challenges to grid voltage stability, frequency regulation, and safe operation of equipment. Stability control of PV power stations has become a necessary aspect of technical support for the construction of new power systems (NPSs). In this paper, a technical analysis framework of stability control of photovoltaic power stations is systematically constructed. First, the core stability problems of photovoltaic systems are sorted out. Then, a technical review of the three control levels, namely the equipment, system, and grid, is carried out. At the same time, the application potential of emerging technologies such as data-driven and AI control, digital twin predictive control, and advanced grid-forming (GFM) inverters is described. Based on existing reviews, this paper proposes an equipment–system–grid hierarchical analysis framework and explicitly integrates emerging technologies with classical methods. This framework provides references for the selection, engineering deployment, and future research directions of stability control technologies for photovoltaic power plants, while also offering technical support for the safe and efficient operation of high-penetration renewable energy power grids. Full article