Search Results (6,581)

The present study aims to determine the properties of sandwich mycelium-based composite panels (sandwich-MBC-panel) fabricated with a lightweight core of mycelium-based composites (MBCs) of Ganoderma lucidum and Pleurotus ostreatus and veneers of Gmelina arborea and Vochysia guatemalensis wood. Physical and mechanical properties, acoustic capacity, chemical composition (determined by FT-IR), thermal degradation, and inorganic components were evaluated. The results showed that the sandwich-MBC-panel presented a density of 0.27–0.40 g/cm³, an MC between 14.56 and 24.71%, and a water absorption between 43.64 and 61.32%. Regarding mechanical characteristics, the sandwich-MBC-panel with the highest MOR, MOE, and internal bond was that composed of G. lucidum and G. arborea. The treatment with the best tensile force value was the mixture of G. lucidum with O. pyramidale. The sandwich-MBC-panel constructed with balsawood showed the lowest noise reduction coefficient, while the panel composed of G. lucidum and P. ostreatus showed good substrate properties and appropriate carbon and nitrogen content. FT-IR spectroscopy revealed substrate degradation by fungal mycelium formation, and TGA curves showed that the MBC containing G. lucidum presented higher thermal degradation than the panel without G. lucidum, without fungal attack. The main results of this study showed that sandwich MBC panels, in which the MBC is used as a lightweight core and wood veneers are used on the faces, have the potential for use as acoustic panels and could represent a sustainable alternative to panels that are generally fabricated from synthetic materials and of low densities. Full article

Under strict spatial constraints and environmental interference, autonomous navigation of vessels in inland bridge-restricted waterways demands precise coordination between collision avoidance and trajectory tracking. To meet these operational demands, an integrated framework that directly combines spatiotemporal risk assessment with dynamic control execution is developed. Based on a digital traffic model integrating bridge piers and channel boundaries, collision risks are evaluated by combining trajectory-predicted time to safe distance with the velocity obstacle interval. Such a formulation quantifies the actual spatial difficulty of evasion rather than relying solely on temporal urgency. Driven by this continuous assessment, a time-series rolling strategy calculates feasible maneuvering intervals, generating trajectories that comply strictly with inland navigation rules and physical vessel limits. Subsequently, an adaptive model predictive control algorithm executes these commands, implicitly compensating for the localized hydrodynamic disturbances typical of bridge areas. The effectiveness of the architecture is validated through comprehensive simulations covering rule-based encounters and complex multi-vessel scenarios. Quantitative results indicate that under wind and current disturbances, the maximum route tracking deviation is constrained below 53 m, while the minimum encounter distance with target ships is consistently maintained above 51 m. These performance metrics confirm the capacity to execute safe, rule-compliant maneuvers while preserving high navigational precision in confined inland environments. Full article

Reservoir fracability evaluation is critical for tight reservoir hydraulic fracturing optimization. This study introduces a novel physics-based fracability evaluation framework integrating stacking ensemble learning (SEL) and the marginal effect of the conditional expectation (MECE). First, a multidimensional indicator system was established, covering characteristics such as reservoir geomechanics, rock mechanics, and the development of natural fractures. Second, SEL models were developed to predict open flow capacity, and four performance metrics were compared to select the optimal model from 26 SEL candidates. Finally, to quantify the individual contribution of each fracability indicator while eliminating interference from treatment and petrophysical parameters, the MECE approach was adopted, thereby developing a new fracability model that quantitatively describes the reservoir’s ability to achieve greater stimulated reservoir volume (SRV) under similar hydraulic fracturing parameters. The experimental results indicate that the RF+KNN model demonstrates optimal performance in both prediction accuracy and model stability. Comparing the fracability index with microseismic monitoring data, the linear correlation coefficient between the fracability index and SRV reached 92%, validating the reliability of the fracability evaluation model. This framework provides a transferable interpretable tool for selecting reservoir sweet spots and fracturing parameter optimization. Full article

Agricultural byproducts cellulose-rich (~40%) sugarcane bagasse (SCB) and rice husk (RH) wastes may be used as fiber sources in biomaterials manufacturing. The hybrid biomass fibers are two kinds of fibers that should generate a biocomposite according to the functions and physical, chemical, and mechanical properties of materials. The biocomposite was synthesized using the solvothermal method. The FeCl₃.6H₂O was dissolved in C₂H₃NaO₂ and C₆H₆O₂ and later heated at 60 °C. The SCB and RH fiber (1:1) are added with HDMA into the mixture, then placed in a Teflon stainless steel autoclave at 200 °C for 6 h. The biocomposite was employed as a green adsorbent to treat wastewater through simultaneous adsorption. The biocomposite had 2.637 mmol g⁻¹ of amine groups, which makes smaller magnetic particles and a high surface area of up to 79%. The pseudo-second-order kinetic model followed the Cu(II) and Pb(II) ions adsorption for 4 h (240 min), and the maximum adsorption capacities were 35.042 mg g⁻¹ and 67.127 mg g⁻¹, respectively, at the pH of 5. The biocomposite not only got rid of metal ions, but it also worked well to get rid of dye, total suspended solids (TSSs), and chemical oxygen demand (COD) as pollutants in wastewater. The biocomposite still worked well after being used four times. Full article

The goal of this work was to determine the effect of soil freeze–thaw processes on the formation of water-stable aggregates (WSA) and plant available water (PAW) in soils of different textures, depending on the intensity of tillage: conventional tillage (CT), reduced tillage (RT) and no-tillage (NT). The WSA value (0.4%) and PAW mean (5.5%) in sandy loam were higher than in loam. The average content of WSA and PAW tended to decrease in the following order: air-dry soil > soil with water content at field capacity > soil near full saturation. These results indicate that WSA in soils that are close to full saturation upon freezing will be less stable after thawing and will decrease the PAW. The content of WSA in NT was 9.4% higher than in RT and 14% higher than in CT. The content of PAW in NT was 5.6% higher than in CT and 13.6% higher than in RT. The effects of various physical and chemical properties on PAW as a function of water level during the freeze–thaw process indicate that WSA content acted as a direct factor for PAW. In a temperate climate zone under dry meteorological conditions, NT would have a promising future for soil stability by maintaining higher WSA and PAW. Full article

Manufacturing tolerances inevitably induce cell-to-cell inconsistencies. These inconsistent cells are connected in series and parallel to form battery packs, which will affect the safety and reliability of the battery system. This study presents a novel optimization framework integrating the multi-level physical model with machine learning to improve battery consistency from the manufacturing perspective. The multi-level physical modeling approach is applied to establish the link between the parameter deviations of the calendering process and the battery inconsistency performance. Based on the multi-level physical model, the Monte Carlo method is used to describe parameter deviations and generate datasets of electrochemical properties. The coefficients of variations in battery capacity and resistance are calculated as the consistency evaluation index based on these datasets. The proposed optimization approach applies machine learning to reduce the computational cost of the multi-level physical simulations due to lots of Monte Carlo simulations. Combined with the multi-level physical model and neural network model, the multi-objective particle swarm optimization algorithm is adopted to provide the optimal calendering process parameter deviations by achieving the trade-off between battery consistency performance and manufacturing cost. Results indicate that the battery consistency performance is improved by controlling the precision of the calendering process and manufacturing cost. This approach can effectively give feedback and guidance to the inverse design of the manufacturing process. Full article

Water pollution, caused by selenium contamination, is a significant global issue due to its toxic effects on humans and animals. Selenium occurs in several oxidation states, among which selenite and selenate are the most mobile and bioavailable forms. Traditional water treatment methods are often limited in efficiency, whereas adsorption offers a simple, cost-effective, and efficient solution. Various adsorbents, including metal and mineral oxides, carbon-based materials (activated carbon, graphene oxide), biosorbents, and nanocomposites, have shown high potential for Se removal. Adsorbent modifications—physical, chemical, or composite—significantly enhance adsorption capacity, selectivity, and material stability. Studies have demonstrated that nanomaterials and nanocomposites, such as MnFe₂O₄, PAA-MGO, magnetic MOFs, and magnetite-based biochars, enable rapid removal of Se(IV) and Se(VI) with high adsorption capacities. Se(IV) is primarily adsorbed through innersphere complexation, while Se(VI) forms weaker outer-sphere interactions, explaining differences in removal efficiency. Factors such as pH, the presence of surface hydroxyl and amino groups, surface charge, and competing ions strongly influence the adsorption process. Multivalent ions reduce Se adsorption efficiency, whereas monovalent ions (NO₃⁻ and Cl⁻) have minimal impact. Modified adsorbents, nanomaterials, and nanocomposites provide sustainable and practical solutions for selenium removal from water, combining high efficiency, selectivity, and reusability, making them suitable for real-world water treatment applications. Full article

With the rapid intensification of industrial and agricultural activities, water contamination by heavy metal ions has emerged as a critical global challenge, gravely imperiling ecosystem stability and public health. Among the various remediation technologies, adsorption has been widely adopted due to its high efficiency, low-cost water treatment, and simplicity of operation. However, conventional inorganic or synthetic adsorbents often exhibit poor degradability and pose a risk of secondary contamination, substantially limiting their sustainable application. Consequently, the development of environmentally benign and renewable adsorbent materials has become a central research focus in this field. Recently, cellulose-based composite hydrogels, derived from renewable resources and characterized by excellent eco-friendliness and highly tunable three-dimensional porous structures, have attracted considerable attention as promising green adsorption materials. These hydrogels demonstrate outstanding performance in the efficient sequestration of heavy metal contaminants from aqueous environments. This review systematically summarizes recent advances in cellulose-based composite hydrogels for heavy metal removal, to elucidate the structure–performance relationships linking material fabrication strategies, structural modulation, and adsorption efficiency. First, we outline the principal construction approaches, including physical crosslinking, chemical modification, and supramolecular self-assembly, and comprehensively analyze how different synthesis routes regulate pore architecture, mechanical properties, and the distribution of surface functional groups. Second, the underlying adsorption mechanisms, primarily coordination complexation, electrostatic interactions, and ion exchange, are discussed in detail. Finally, recent studies on the adsorption of cationic heavy metals (e.g., Pb(II), Cu(II), and Cd(II)) and anionic oxyanions (e.g., As(III) and Cr(VI)) are critically reviewed, with particular emphasis on the relationships between selective adsorption performance, material design principles, and specific recognition mechanisms. Overall, this review provides a theoretical foundation and practical guidance for the design and development of next-generation water treatment materials with high adsorption capacity, excellent selectivity, non-toxicity, and strong environmental compatibility, followed by future research recommendations. Full article

In sediment-laden irrigation channels, sediment deposition upstream of hydraulic measuring structures can degrade hydraulic performance, reduce measurement reliability, and increase maintenance demand. To clarify the effects of structural optimization on sediment transport and siltation resistance, physical model experiments were conducted on an airfoil weir-orifice facility under different discharges, structural angles, and sediment concentrations. The analysis focused on sediment deposition patterns, longitudinal water surface profiles, sediment concentration, suspended sediment transport rate, cross-sectional velocity distribution, vertical velocity gradient, and Froude number. The results showed that the optimized configuration produced a flatter and more uniform upstream bed morphology, and the average deposition thickness decreased from 4.83 cm to 4.31 cm, corresponding to a reduction of 10.58%. Under all tested conditions, the optimized configuration reduced upstream backwater, increased local flow velocity, and shifted the hydraulic jump closer to the facility outlet. Sediment concentration and suspended sediment transport rate were consistently higher after optimization, indicating enhanced sediment carrying capacity. In addition, the optimized configuration increased the vertical velocity gradient and Froude number, while all cases remained within the subcritical-flow regime. These findings demonstrate that structural optimization can simultaneously improve hydraulic regulation and siltation resistance, and provide an experimental basis for the application of streamlined hydraulic measuring structures in sediment-laden irrigation channels. Full article

A sustainable strategy was developed to valorize rabbit manure waste by synthesizing a porous quaternary Ni-Co-Zn-Fe layered double hydroxide/biochar nanocomposite (QL-RMB) for the efficient removal of Mn(VII) in the form of permanganate (MnO₄⁻) from aqueous solutions. The QL-RMB adsorbent exhibited a well-developed mesoporous structure with uniformly dispersed nanoparticles, achieving 73% MnO₄⁻ removal within 60 min under optimized conditions (pH 3.0; dosage 0.5 g L⁻¹). Adsorption followed pseudo-second-order kinetics and was best described by the Freundlich isotherm model (R² > 0.98), yielding a maximum Langmuir adsorption capacity (q_max) of 45.13 mg g⁻¹. Statistical physics modeling confirmed a multi-ionic, vertically oriented adsorption configuration, while thermodynamic analysis demonstrated that the process was spontaneous and exothermic, governed by electrostatic attraction, anion exchange, and surface complexation. The QL-RMB composite exhibited excellent MnO₄⁻ selectivity in the presence of competing ions (selectivity coefficients: 24.96 for Fe³⁺, 31.59 for Ni²⁺, 23.56 for Zn²⁺) and retained significant removal efficiency (73.96%) after five regeneration cycles. In a circular economy approach, the Mn (VII)-spent adsorbent (QL-RMB/Mn) was valorized as an electrocatalyst for urea electro-oxidation, achieving a current density of ~127.19 mA cm⁻² for pristine QL-RMB, which increased to ~217.07 mA cm⁻² after Mn(VII) adsorption (QL-RMB/Mn) in 1 M KOH/1 M urea. Batch scale-up studies revealed an efficiency of 42.55 g or 95% MnO₄⁻ removal from 50 L water, with a low estimated production cost of 0.0602 USD g⁻¹. Environmental sustainability was confirmed by the National Environmental Methods Index (NEMI), modified Green Analytical Procedure Index (Mo-GAPI), Eco-scale (score: 77), and Analytical GREEness (AGREE) assessment frameworks. Full article

Sarcopenia is increasingly recognized as a key extracardiac manifestation of heart failure (HF), contributing to functional impairment, reduced quality of life, and adverse clinical outcomes. Characterized by progressive loss of skeletal muscle mass, strength, and physical performance, it affects more than half of hospitalized HF patients. It is independently associated with increased mortality and reduced exercise capacity. The pathophysiology of sarcopenia in HF is multifactorial and closely linked to metabolic and nutritional disturbances. Chronic inflammation, neurohormonal activation, oxidative stress, endothelial dysfunction, and anabolic resistance contribute to muscle catabolism and impaired protein synthesis. These alterations are further exacerbated by inadequate dietary protein intake and micronutrient deficiencies, promoting progressive muscle wasting and functional decline. Sarcopenia may also represent an early and potentially modifiable stage in the continuum toward cardiac cachexia. This narrative review provides a comprehensive synthesis of current evidence on the epidemiology, pathophysiological mechanisms, and management of sarcopenia in HF, with particular emphasis on nutritional and metabolic determinants. Emerging data support a multimodal therapeutic approach integrating exercise training with targeted nutritional strategies, including adequate protein intake, essential amino acid supplementation, and correction of micronutrient deficiencies. However, evidence from large, well-designed trials remains limited. In summary, improved recognition and integrated management of sarcopenia in HF are essential. Future research should focus on the development of effective, nutrition-centered therapeutic strategies. Full article

In complex marine environments, achieving low-cost, highly reliable, and continuous navigation is crucial for the intelligent and autonomous operation of unmanned surface vehicles (USVs). Currently, the integrated Global Navigation Satellite System and Strapdown Inertial Navigation System (GNSS/SINS) serves as the primary navigation architecture for USVs. While the cost of high-performance GNSS receivers has steadily decreased, high-precision SINS remains prohibitively expensive. Consequently, micro-electromechanical system (MEMS)-based SINS has emerged as a preferred alternative due to its favorable balance of cost, power consumption, and size. However, significant inertial sensor errors make it difficult to maintain high-precision positioning during GNSS outages. To address this limitation, the single-axis rotational inertial navigation system (SRINS) has been introduced. Nevertheless, constrained by the single-axis mechanical structure and complex sea state disturbances, the system still struggles to effectively modulate random errors and azimuth gyroscope drift, rendering it insufficient for highly demanding navigation tasks. To overcome these bottlenecks, this article systematically reviews four core technologies: (1) Comprehensive denoising and temperature drift compensation techniques for MEMS gyroscopes; (2) rapid moving-base initial alignment models under high sea state disturbances; (3) fast online calibration methods for azimuth gyroscope drift; and (4) adaptive and robust GNSS/SINS integration architectures capable of accommodating high-dynamic conditions and non-Gaussian interference. Finally, this article discusses the engineering conflict between deploying high-precision algorithms and the limited onboard computational capacity of USVs. It concludes by highlighting a highly promising navigation paradigm for future research: the integration of factor graph optimization with physics-informed deep learning. Full article

In future active distribution networks with high penetrations of renewable energy, flexible loads are expected to play an increasingly important role as reserve resources to support the sustainable and reliable operation of power grids. Accurate evaluation of flexible load reserve capacity is therefore essential for reliable reserve scheduling. Existing research mainly focuses on the operational characteristics and physical constraints of flexible loads, while insufficiently accounting for user response willingness and the uncertainty of user decision-making behavior, which may lead to biased reserve capacity assessments and impair the sustainability of reserve supply in actual grid operation. To address this issue, this paper proposes a results-oriented reserve capacity evaluation method for flexible loads that explicitly incorporates user response willingness. Specifically, a fuzzy logic system is developed to quantitatively characterize the response willingness of electric vehicle (EV) and air-conditioning (AC) users under multiple influencing factors. Then, a probabilistic modeling approach for user decision-making behavior is established using the theory of planned behavior, enabling explicit representation of behavioral uncertainty. Furthermore, a comprehensive reserve capacity evaluation framework for flexible loads is constructed by integrating user willingness states, sustainable response duration, and operational power constraints. Finally, the case studies demonstrate that the proposed method can effectively improve the objectivity of flexible load reserve capacity assessments while maintaining high user participation willingness, thus supporting the long-term sustainable application of flexible loads as grid reserve resources. Full article

False data injection (FDI) cyberattacks pose a growing threat to modern power distribution systems in smart cities by manipulating state-estimation processes and provoking covert voltage violations that traditional defense mechanisms fail to detect. Recent industry data indicate that coordinated FDI attacks can distort measurement sets by as little as 3–7%, yet trigger voltage deviations exceeding 10% in vulnerable feeders, resulting in operational instability, unnecessary load curtailments, and elevated outage risk. To address these challenges, this paper proposes a quantum-accelerated digital twin (QDT) framework that integrates quantum optimization algorithms with a high-fidelity digital twin (DT) of the distribution system to detect, localize, and remediate FDI-induced cyberattacks in real time. The rationale behind the approach lies in the superior combinatorial search capability of quantum solvers, which accelerates the identification of falsified measurement vectors and optimal corrective control actions compared with classical methods. In addition, the framework introduces an innovative Bitcoin-mining-oriented virtual energy storage (BMOVES) mechanism that treats mining facilities as dynamically controllable, fast-response electrical loads within smart city demand–response programs. By modulating mining power consumption with sub-second granularity, the proposed BMOVES resource provides up to 18–45% flexible capacity during attack scenarios, enabling voltage restoration without relying on conventional energy storage assets. The unified QDT + BMOVES architecture is validated using the 136-bus Brazilian distribution system, a realistic benchmark for cyber–physical resilience studies. Simulation results demonstrate over 99% FDI detection accuracy, up to an 82% reduction in peak voltage violations, and restoration of operational limits 11 times faster than state-of-the-art classical methods. These findings highlight the transformative potential of integrating quantum computing, digital twins, and nontraditional flexible assets to enhance cyber-resilient power infrastructure in future smart cities. Full article

The photocatalytic activity of TiO₂ can be increased by incorporating it into a composite with an electron-trapping co-catalyst. MXene can perform this task as an electron-conducting material. In addition to trapping electrons, it also affects the defects in TiO₂ near the interface. To screen for the best photocatalytic performance, three types of composites were prepared: by physical mixing, chemical deposition, and ALD. During characterization, the structural, optical, and photoelectrochemical properties were determined. Photocatalytic activity was examined in suspension (phenol conversion) and on a layer (gas phase ethanol conversion). It was found that the composite containing the lowest proportion of cocatalyst (1 wt.%) had the highest photocatalytic activity. According to the results of photocatalytic activity measured in suspension, the physical mixtures were proven to be more effective than neat TiO₂, with the composites converting approximately the total amount of phenol in ~40 min, while TiO₂ required ~80–90 min to do so under the same conditions. Thus, the electron-trapping role of MXene is clearly demonstrated in suspension applications, which is also confirmed by other characterization methods (photoluminescence, photocurrent density). TiO₂ performed best during ethanol conversion, as it has the highest ethanol adsorption capacity (33.41%). During ethanol conversion tests, the MXene electron-trapping property was most effectively demonstrated in composites formed using the ALD method. Full article