Search Results (5,175)

This study applies a deep neural network (DNN) to optimize the 22.5 m pontoon hull form of a small waterplane area twin hull (SWATH) vessel with fin stabilizers, aiming to reduce calm water resistance at a Froude number of 0.8 under even keel conditions. The vessel’s resistance is simplified into three components: pontoon, strut, and fin stabilizer. Four design parameters define the pontoon geometry: fore-body length, aft-body length, fore-body angle, and aft-body angle. Computational fluid dynamics (CFD) simulations using STAR-CCM+ 2302 provide 1400 resistance data points, including fin stabilizer lift and drag forces at varying angles of attack. These are used to train a DNN in MATLAB 2018a with five hidden layers containing six, eight, nine, eight, and seven neurons. K-fold cross-validation ensures model stability and aids in identifying optimal design parameters. The optimized hull has a 7.8 m fore-body, 6.8 m aft-body, 10$°$ fore-body angle, and 35$°$ aft-body angle. It achieves a 2.2% resistance reduction compared to the baseline. The improvement is mainly due to a reduced Munk moment, which lowers the angle of attack needed by the fin stabilizer, thereby reducing drag. The optimized design provides cost-efficient construction and enhanced payload capacity. This study demonstrates the effectiveness of combining CFD and deep learning for hull form optimization. Full article

Sulfur-containing fertilizers increase production costs, which leads to low utilization of this nutrient. Thus, evaluating how the absence of sulfur influences the early development of Urochloa brizantha is essential. Study was conducted in a greenhouse at the Federal University of Rondonópolis in a completely randomized design, with six treatments in a 3 × 2 factorial scheme, and eight replications. Three cultivars of U. brizantha (Marandu, Xaraés and Piatã) were evaluated under two fertilization strategies: with or without sulfur fertilization. Sufur presence increased the number of leaves and forage mass, in which cultivar Xaraés presented the greatest means. Piatã was the cultivar most sensitive to sulfur deficiency at establishment, which reduced forage mass, number of leaves and number of tillers by 42%, 32%, and 45%, respectively. Despite these differences between cultivars, sulfur efficiently increased the forage yield. Sulfur fertilization increased the concentrations of nutrients in the plants without significantly affecting the uptake of nitrogen, phosphorus, potassium, calcium and magnesium. Sulfur omission resulted in increased phosphorus uptake in all grass. In contrast, Marandu grass exhibited the greatest reduction in sulfur uptake. Therefore, the use of sulfur in the fertilization of grasses is recommended, it is important to evaluate the responses of each cultivar to better adjust the fertilization management. Full article

Background: Increasing regulatory pressure for maritime decarbonization (e.g., IMO CII, FuelEU) drives adoption of low-carbon fuels and prompts reassessment of regional ports’ competitiveness. This study aims to evaluate the economic and environmental viability of rerouting deep-sea container services to regional ports in a decarbonizing world. Methods: A scenario-based analysis is used to evaluate total costs and CO₂ emissions across the entire container shipping supply chain, incorporating deep-sea shipping, port operations, feeder services, and inland rail/road transport. The Port of Liverpool serves as the primary case study for rerouting Asia–Europe services from major ports. Results: Analysis indicates Liverpool’s competitiveness improves with shipping lines’ slow steaming, growth in hinterland shipment volume, reductions in the emission factors of alternative low-carbon fuels, and an increased modal shift to rail matching that of competitor ports (e.g., Southampton). A dual-port strategy, rerouting services to call at both Liverpool and Southampton, shows potential for both economic and environmental benefits. Conclusions: The study concludes that rerouting deep-sea services to regional ports can offer cost and emission advantages under specific operational and market conditions. Findings on factors and conditions influencing competitiveness and the dual-port strategy provide insights for shippers, ports, shipping lines, logistics agents, and policymakers navigating maritime decarbonization. Full article

Improving the corrosion resistance of magnesium (Mg) alloys is a long-term challenge, especially when cost-effectiveness is taken into account. In this work, Mg-1Bi and Mg-1Sn extruded alloys are prepared, and the effects of cost-effective Bi and Sn on the corrosion behavior of Mg alloys are comparatively studied. The corrosion resistance of the Mg-1Sn alloy (P_H: 2.83 ± 0.19 mm y⁻¹) is better than that of the Mg-1Bi alloy (P_H: 13.75 ± 1.12 mm y⁻¹), being about five times greater. In addition to the relatively low dislocation density in Mg-1Sn alloy, the difference in corrosion resistance is mainly attributed to two aspects of influence brought about by the addition of Sn and Bi. The Mg₂Sn phase introduced by the addition of Sn has a potential difference (PD) of ~30 mV, which is significantly lower than that (~90 mV) of the Mg₃Bi₂ phase introduced by adding Bi, thereby weakening the micro-couple corrosion tendency of the Mg-1Sn alloy. The addition of Bi has little effect on the corrosion film, while the addition of Sn makes the corrosion film on the Mg-1Sn alloy contain SnO₂, improving the compactness of the corrosion film and thereby enhancing the corrosion protection effect. Full article

The increasing demand for sustainable practices in road construction has prompted the search for environmentally friendly and cost-effective materials. This study explores the incorporation of reclaimed asphalt pavement (RAP) and Panasqueira mine waste (greywacke aggregates) as full replacements for virgin aggregates in hot mix asphalt (HMA), aligning with the objectives of UN Sustainable Development Goal 9. Three asphalt mixtures were prepared: a reference mixture (MR) with granite aggregates, and two modified mixtures (M15 and M20) with 15% and 20% RAP, respectively. All mixtures were evaluated through Marshall stability, stiffness modulus, water sensitivity, and wheel tracking tests. The results demonstrated that mixtures containing RAP and mine waste met Portuguese specifications for surface courses. Specifically, the M20 mixture showed the highest stiffness modulus, improved moisture resistance, and the best performance against permanent deformation. These improvements are attributed to the presence of stiff aged binder in RAP and the mechanical characteristics of the greywacke aggregates. Overall, the findings confirm that the combined use of RAP and mining waste provides a technically viable and sustainable alternative for asphalt pavement construction, contributing to resource efficiency and circular economy goals. Full article

We consider the problem of privately updating a message out of K messages from N replicated and non-colluding databases where a user has an outdated version of the message ${\hat{W}}_{\theta }$ of length L bits that differ from the current version $W_{\theta }$ in at most f bits. The user also has a cache containing coded combinations of the K messages (with a pre-specified structure), which are unknown to the N databases (unknown prefetching). The cache Z contains ℓ linear combinations from all K messages in the databases with $r=\frac{\mathcal{l}}{L}$ being the caching ratio. The user needs to retrieve $W_{\theta }$ correctly using a private information retrieval (PIR) scheme without leaking information about the message index $\theta$ to any individual database. Our objective is to jointly design the prefetching (i.e., the structure of said linear combinations) and the PIR strategies to achieve the least download cost. We propose a novel achievable scheme based on syndrome decoding where the cached linear combinations in Z are designed to be bits pertaining to the syndrome of $W_{\theta }$ according to a specific linear block code. We derive a general lower bound on the optimal download cost for $0\le r\le 1$, in addition to achievable upper bounds. The upper and lower bounds match for the cases when r is exceptionally low or high, or when $K=3$ messages for arbitrary r. Such bounds are derived by developing novel cache-aided arbitrary message length PIR schemes. Our results show a significant reduction in the download cost if $f<{\displaystyle \frac{L}{2}}$ when compared with downloading $W_{\theta }$ directly using typical cached-aided PIR approaches. Full article

The low-cost Strapdown Inertial Navigation System (SINS)/Global Navigation Satellite System (GNSS) is widely used in autonomous vehicles for positioning and navigation. Initial alignment is a critical stage for SINS operations, and the alignment time and accuracy directly affect the SINS navigation performance. To address the issue that low-cost SINS/GNSS cannot effectively achieve rapid and high-accuracy alignment in complex environments that contain noise and external interference, an adaptive multiple backtracking robust alignment method is proposed. The sliding window that constructs observation and reference vectors is established, which effectively avoids the accumulation of sensor errors during the full integration process. A new observation vector based on the magnitude matching is then constructed to effectively reduce the effect of outliers on the alignment process. An adaptive multiple backtracking method is designed in which the window size can be dynamically adjusted based on the innovation gradient; thus, the alignment time can be significantly shortened. Furthermore, the modified variational Bayesian Kalman filter (VBKF) that accurately adjusts the measurement noise covariance matrix is proposed, and the Expectation–Maximization (EM) algorithm is employed to refine the prior parameter of the predicted error covariance matrix. Simulation and experimental results demonstrate that the proposed method significantly reduces alignment time and improves alignment accuracy. Taking heading error as the critical evaluation indicator, the proposed method achieves rapid alignment within 120 s and maintains a stable error below 1.2° after 80 s, yielding an improvement of over 63% compared to the backtracking-based Kalman filter (BKF) method and over 57% compared to the fuzzy adaptive KF (FAKF) method. Full article

The sugarcane industry plays a crucial economic role worldwide, with sucrose and ethanol as its main products. However, its processing generates large volumes of by-products—such as bagasse, molasses, vinasse, and straw—that contain valuable components for biotechnological valorization. This review integrates approximately 100 original research articles published in JCR-indexed journals between 2015 and 2025, of which over 50% focus specifically on sugarcane-derived agroindustrial residues. The biotechnological approaches discussed include submerged fermentation, solid-state fermentation, enzymatic biocatalysis, and anaerobic digestion, highlighting their potential for the production of biofuels, enzymes, and high-value bioproducts. In addition to identifying current advances, this review addresses key technical challenges such as (i) the need for efficient pretreatment to release fermentable sugars from lignocellulosic biomass; (ii) the compositional variability of by-products like vinasse and molasses; (iii) the generation of metabolic inhibitors—such as furfural and hydroxymethylfurfural—during thermochemical processes; and (iv) the high costs related to inputs like hydrolytic enzymes. Special attention is given to detoxification strategies for inhibitory compounds and to the integration of multifunctional processes to improve overall system efficiency. The final section outlines emerging trends (2024–2025) such as the use of CRISPR-engineered microbial consortia, advanced pretreatments, and immobilization systems to enhance the productivity and sustainability of bioprocesses. In conclusion, the valorization of sugarcane by-products through biotechnology not only contributes to waste reduction but also supports circular economy principles and the development of sustainable production models. Full article

The plant-based oil industry contributes significantly to food waste/by-products in the form of underutilized biomass, including oil pomace, cake/meal, seeds, peels, wastewater, etc. These waste/by-products contain a significant quantity of nutritious and bioactive compounds (phenolics, lignans, flavonoids, dietary fiber, proteins, and essential minerals) with proven health-promoting effects. The utilization of them as natural, cost-effective, and food-grade functional ingredients in novel food formulations holds considerable potential. This review highlights the potential of waste/by-products generated during plant-based oil processing as a promising source of bioactive compounds and covers systematic research, including recent studies focusing on innovative extraction and processing techniques. It also sheds light on their promising potential for valorization as food ingredients, with a focus on specific examples of food fortification. Furthermore, the potential for value creation in the food industry is emphasized, taking into account associated challenges and limitations, as well as future perspectives. Overall, the current information suggests that the valorization of plant-based oil industry waste and by-products for use in the food industry could substantially reduce malnutrition and poverty, generate favorable health outcomes, mitigate environmental concerns, and enhance economic profit in a sustainable way by developing health-promoting, environmentally sustainable food systems. Full article

The growth of urban and industrial areas is accompanied by an increase in vehicle traffic, resulting in rising concentrations of various air pollutants. This is a global issue that causes environmental damage and risks to human health. The dataset presented in this research contains records with measurements of the air pollutants ozone (O₃) and carbon monoxide (CO), as well as meteorological parameters such as temperature (T), relative humidity (RH), and barometric pressure (BP). This dataset was collected using a set of low-cost sensors over a four-month study period (March to June) in 2024. The monitoring of air pollutants and meteorological parameters was conducted in a city with high industrial activity, heavy traffic, and close proximity to a petrochemical refinery plant. The data were subjected to a series of statistical analyses for visualization using plots that allow for the identification of their behavior. Finally, the dataset can be utilized for air quality studies, public health research, and the development of prediction models based on mathematical approaches or artificial intelligence algorithms. Full article

Direct electrification and hydrogen utilization represent two key pathways for decarbonizing the glass industry, with their effectiveness subject to adequate furnace design and renewable energy availability. This study presents a techno-economic assessment for optimal solar energy integration in a representative 300 t/d oxyfuel container glass furnace with a specific energy consumption of 4.35 GJ/t. A mixed-integer linear programming formulation is developed to evaluate specific melting costs, carbon emissions, and renewable energy self-consumption and self-production rates across three scenarios: direct solar coupling, battery storage, and a hydrogen-based infrastructure. Battery storage achieves the greatest reductions in specific melting costs and emissions, whereas hydrogen integration minimizes electricity export to the grid. By incorporating capital investment considerations, the study quantifies the cost premiums and capacity requirements under varying decarbonization targets. A combination of 30 MW of solar plant and 9 MW of electric boosting enables the realization of around 30% carbon reduction while increasing total costs by 25%. Deeper decarbonization targets require more advanced systems, with batteries emerging as a cost-effective solution. These findings offer critical insights into the economic and environmental trade-offs, as well as the technical constraints associated with renewable energy adoption in the glass industry, providing a foundation for strategic energy and decarbonization planning. Full article

The objective of this work is to evaluate how spent catalyst from fluid catalytic cracking (SCFCC) affects the physical, mechanical and durability properties of fly ash (FA) and blast furnace slag (BFS)-based alkali-activated materials (AAMs). Recycling of SCFCC by integrating it in a AAM matrix offers several advantages: valorization of the material, reducing its disposal in landfills and the landfill cost, and minimizing the environmental impact. Mineralogical, physical and mechanical characterization were carried out. The durability of the specimens was studied by performing acid attack and thermal stability tests. Mass variation, compressive strength and porosity parameters were determined to assess the durability. BFS- and FA-based AAMs have a different chemical composition, which contribute to variations in microstructure and physical and mechanical properties. Acid neutralization capacity was also determined to analyse the acid attack results. Porosity, including the pore size distribution, and the acid neutralization capacity are crucial in explaining the resistance of the AAMs to sulfuric acid attack and thermal degradation. Herein, a novel route was explored, the use of SCFCC to enhance the durability of AAMs under harsh operating conditions since results show that the compositions containing SCFCC showed lower strength decay due to the lower macroporosity proportions in these compositions. Full article

High-precision time–frequency systems are essential for low Earth orbit (LEO) navigation satellites to achieve real-time (RT) centimeter-level positioning services. However, subject to stringent size, power, and cost constraints, LEO satellites are typically equipped with oven-controlled crystal oscillators (OCXOs) as the system clock. The inherent long-term stability of OCXOs leads to rapid clock error accumulation, severely degrading positioning accuracy. To simultaneously balance multi-dimensional requirements such as clock bias accuracy, and frequency stability and phase continuity, this study proposes a linear quadratic Gaussian (LQG) frequency precision steering method that integrates a four-dimensional constraint integrated (FDCI) model and hierarchical weight optimization. An improved system error model is refined to quantify the covariance components (Σ₁₁, Σ₂₂) of the LQG closed-loop control system. Then, based on the FDCI model that explicitly incorporates quantization noise, frequency adjustment, frequency stability, and clock bias variance, a priority-driven collaborative optimization mechanism systematically determines the weight matrices, ensuring a robust tradeoff among multiple performance criteria. Experiments on OCXO payload products, with micro-step actuation, demonstrate that the proposed method reduces the clock error RMS to 0.14 ns and achieves multi-timescale stability enhancement. The short-to-long-term frequency stability reaches 9.38 × 10⁻¹³ at 100 s, and long-term frequency stability is 4.22 × 10⁻¹⁴ at 10,000 s, representing three orders of magnitude enhancement over a free-running OCXO. Compared to conventional PID control (clock bias RMS 0.38 ns) and pure Kalman filtering (stability 6.1 × 10⁻¹³ at 10,000 s), the proposed method reduces clock bias by 37% and improves stability by 93%. The impact of quantization noise on short-term stability (1–40 s) is contained within 13%. The principal novelty arises from the systematic integration of theoretical constraints and performance optimization within a unified framework. This approach comprehensively enhances the time–frequency performance of OCXOs, providing a low-cost, high-precision timing–frequency reference solution for LEO satellites. Full article

Accurate diagnosis of gastrointestinal (GI) diseases typically requires invasive procedures or imaging studies that pose the risk of various post-procedural complications or involve radiation exposure. Bowel sounds (BSs), though typically described during a GI-focused physical exam, are highly inaccurate and variable, with low clinical value in diagnosis. Interpretation of the acoustic characteristics of BSs, i.e., using a phonoenterogram (PEG), may aid in diagnosing various GI conditions non-invasively. Use of artificial intelligence (AI) and improvements in computational analysis can enhance the use of PEGs in different GI diseases and lead to a non-invasive, cost-effective diagnostic modality that has not been explored before. The purpose of this work was to develop an automated AI model, You Only Listen Once (YOLO), to detect prominent bowel sounds that can enable real-time analysis for future GI disease detection and diagnosis. A total of 110 2-minute PEGs sampled at 44.1 kHz were recorded using the Eko DUO^® stethoscope from eight healthy volunteers at two locations, namely, left upper quadrant (LUQ) and right lower quadrant (RLQ) after IRB approval. The datasets were annotated by trained physicians, categorizing BSs as prominent or obscure using version 1.7 of Label Studio Software^®. Each BS recording was split up into 375 ms segments with 200 ms overlap for real-time BS detection. Each segment was binned based on whether it contained a prominent BS, resulting in a dataset of 36,149 non-prominent segments and 6435 prominent segments. Our dataset was divided into training, validation, and test sets (60/20/20% split). A 1D-CNN augmented transformer was trained to classify these segments via the input of Mel-frequency cepstral coefficients. The developed AI model achieved area under the receiver operating curve (ROC) of 0.92, accuracy of 86.6%, precision of 86.85%, and recall of 86.08%. This shows that the 1D-CNN augmented transformer with Mel-frequency cepstral coefficients achieved creditable performance metrics, signifying the YOLO model’s capability to classify prominent bowel sounds that can be further analyzed for various GI diseases. This proof-of-concept study in healthy volunteers demonstrates that automated BS detection can pave the way for developing more intuitive and efficient AI-PEG devices that can be trained and utilized to diagnose various GI conditions. To ensure the robustness and generalizability of these findings, further investigations encompassing a broader cohort, inclusive of both healthy and disease states are needed. Full article

The maritime sector’s transition to sustainable energy is critical for achieving global carbon neutrality, with container terminals representing a key focus due to their high energy consumption and emissions. This study explores the potential of hydrogen energy as a decarbonization solution for port operations, using the Chuanshan Port Area of Ningbo Zhoushan Port (CPANZP) as a case study. Through a comprehensive analysis of hydrogen production, storage, refueling, and consumption technologies, we demonstrate the feasibility and benefits of integrating hydrogen systems into port infrastructure. Our findings highlight the successful deployment of a hybrid “wind-solar-hydrogen-storage” energy system at CPANZP, which achieves 49.67% renewable energy contribution and an annual reduction of 22,000 tons in carbon emissions. Key advancements include alkaline water electrolysis with 64.48% efficiency, multi-tier hydrogen storage systems, and fuel cell applications for vehicles and power generation. Despite these achievements, challenges such as high production costs, infrastructure scalability, and data integration gaps persist. The study underscores the importance of policy support, technological innovation, and international collaboration to overcome these barriers and accelerate the adoption of hydrogen energy in ports worldwide. This research provides actionable insights for port operators and policymakers aiming to balance operational efficiency with sustainability goals. Full article