Search Results (1,207)

The large-scale integration of electric vehicles (EVs) presents both challenges and opportunities for power grid stability and renewable energy utilization. Vehicle-to-Grid (V2G) technology enables EVs to serve as mobile energy storage units, facilitating peak shaving and valley filling while promoting the local consumption of photovoltaic and wind power. However, uncertainties in renewable energy generation and EV arrivals complicate the scheduling of bidirectional charging in stations equipped with hybrid energy storage systems. To address this, this paper proposes a multi-stage rolling optimization framework combined with a fuzzy logic-based decision-making method. First, a bidirectional charging scheduling model is established with the objectives of maximizing station revenue and minimizing load fluctuation. Then, an EV charging potential assessment system is designed, evaluating both maximum discharge capacity and charging flexibility. A fuzzy controller is developed to allocate EVs to unidirectional or bidirectional chargers by considering real-time predictions of vehicle arrivals and renewable energy generation. Simulation experiments demonstrate that the proposed method consistently outperforms a greedy scheduling baseline. In large-scale scenarios, it achieves an increase in station revenue, elevates the regional renewable energy consumption rate, and provides an additional equivalent peak-shaving capacity. The proposed approach can effectively coordinate heterogeneous resources under uncertainty, providing a viable scheduling solution for EV-aggregated participation in grid services and enhanced renewable energy integration. Full article

Non-toxic Jatropha curcas cake is a by-product rich in protein that can be used in the food industry. Proteolytic kinetics were used to identify and quantify its antioxidant, antidiabetic, angiotensin-converting enzyme inhibitory, and hypocholesterolemic capacities. J. curcas cake was subjected to two systems of solid-state fermentation (SSF) hydrolysis by Pleurotus ostreatus (FPO) or Ganoderma lucidum (FGL), recording every 6 d until 24 d had passed. The maximum proteolytic capacity in FPO was reached on day 6 of the study, whereas FGL was achieved at 12 d. The FPO and FGL electrophoresis gels revealed the presence of 28 bands corresponding to peptides with molecular weights of less than 10 kDa in both systems analyzed. The highest FRAP values were obtained at 12 d for FPO and at the start of SSF for FGL. The highest antidiabetic capacity of FPO was obtained at 18 d and that of FGL at 24 d. The best antihypertensive activity obtained for FPO and FGL was observed at 6 d. FPO’s highest hypocholesterolemic activity was observed at the start of the SSF, while FGL’s was obtained at 24 d, which is the first report of the hypocholesterolemic activity of J. curcas. It should be noted that fermentation with G. lucidum outperformed fermentation with P. ostreatus, confirming its greater multi-bioactivity. The authors consider this method easy, practical, and generally recognized as safe (GRAS) for obtaining bioactive peptides. Full article

This paper discusses the total replacement of egg white (EW) with faba bean protein concentrate (FPC) in a marshmallow formulation. The physico-chemical and techno-functional characterizations of the ingredients revealed that FPC, with a protein content of 68%, exhibited an interesting foaming capacity (200%) compared to EW, which had comparable foaming stability. The physico-chemical properties of the final products indicated that the FPC marshmallow (FPCM) had a higher density (0.519 g/mL), lower moisture (17.337%), and a water activity within the recommended range for this type of product. The FPCM had the highest hardness and elasticity values but the lowest cohesiveness and adhesiveness. Scanning electron microscopy showed that the FPCM structure is similar to that of the EW marshmallow (EWM). In front-face fluorescence spectroscopy measurements, the FPCM exhibited higher emission intensity for tryptophan with a maximum at 382 nm and vitamin A with a maximum located around 338 nm. FTIR analysis presented higher peaks at 850, 918, and 1034 cm⁻¹ for the EWM compared to the FPCM. In a hedonic evaluation, the majority of descriptors (hardness, odor, and general acceptability) showed similar scores for both formulations. All results demonstrated the success of the total substitution of egg white by FPC in the marshmallow formulation. Full article

The optimal allocation of Photovoltaic (PV) and wind-based renewable energy sources and Battery Energy Storage System (BESS) capacity is an important issue for efficient operation of a microgrid network (MGN). The impact of the unpredictability of PV and wind generation needs to be smoothed out by coherent allocation of BESS unit to meet out the load demand. To address these issues, this article proposes an efficient Energy Management System (EMS) and Demand Side Management (DSM) approaches for the optimal allocation of PV- and wind-based renewable energy sources and BESS capacity in the MGN. The DSM model helps to modify the peak load demand based on PV and wind generation, available BESS storage, and the utility grid. Based on the Real-Time Market Energy Price (RTMEP) of utility power, the charging/discharging pattern of the BESS and power exchange with the utility grid are scheduled adaptively. On this basis, a Jellyfish Search Algorithm (JSA)-based bi-level optimization model is developed that considers the optimal capacity allocation and power scheduling of PV and wind sources and BESS capacity to satisfy the load demand. The top-level planning model solves the optimal allocation of PV and wind sources intending to reduce the total power loss of the MGN. The proposed JSA-based optimization achieved 24.04% of power loss reduction (from 202.69 kW to 153.95 kW) at peak load conditions through optimal PV- and wind-based DG placement and sizing. The bottom level model explicitly focuses to achieve the optimal operational configuration of MGN through optimal power scheduling of PV, wind, BESS, and the utility grid with DSM-based load proportions with an aim to minimize the operating cost. Simulation results on the IEEE 33-node MGN demonstrate that the 20% DSM strategy attains the maximum operational cost savings of €ct 3196.18 (reduction of 2.80%) over 24 h operation, with a 46.75% peak-hour grid dependency reduction. The statistical analysis over 50 independent runs confirms the sturdiness of the JSA over Particle Swarm Optimization (PSO) and Osprey Optimization Algorithm (OOA) with a standard deviation of only 0.00017 in the fitness function, demonstrating its superior convergence characteristics to solve the proposed optimization problem. Finally, based on the simulation outcome of the considered bi-level optimization problem, it can be concluded that implementation of the proposed JSA-based optimization approach efficiently optimizes the PV- and wind-based resource allocation along with BESS capacity and helps to operate the MGN efficiently with reduced power loss and operating costs. Full article

To improve the accuracy and robustness of lithium-ion battery state of charge (SOC) estimation, this paper proposes a generalized mixture maximum correlation-entropy criterion-based adaptive extended Kalman filter (GMMCC-AEKF) algorithm, addressing the performance degradation of the traditional extended Kalman filter (EKF) under non-Gaussian noise and inaccurate initial conditions. Based on the GMMCC theory, the proposed algorithm introduces an adaptive mechanism and employs two generalized Gaussian kernels to construct a mixed kernel function, thereby formulating the generalized mixture correlation-entropy criterion. This enhances the algorithm’s adaptability to complex non-Gaussian noise. Simultaneously, by incorporating adaptive filtering concepts, the state and measurement covariance matrices are dynamically adjusted to improve stability under varying noise intensities and environmental conditions. Furthermore, the use of statistical linearization and fixed-point iteration techniques effectively improves both the convergence behavior and the accuracy of nonlinear system estimation. To investigate the effectiveness of the suggested method, experiments for SOC estimation were implemented using two lithium-ion cells featuring distinct rated capacities. These tests employed both dynamic stress test (DST) and federal test procedure (FTP) profiles under three representative temperature settings: 40 °C, 25 °C, and 10 °C. The experimental findings prove that when exposed to non-Gaussian noise, the GMMCC-AEKF algorithm consistently outperforms both the traditional EKF and the generalized mixture maximum correlation-entropy-based extended Kalman filter (GMMCC-EKF) under various test conditions. Specifically, under the 25 °C DST profile, GMMCC-AEKF improves estimation accuracy by 86.54% and 10.47% over EKF and GMMCC-EKF, respectively, for the No. 1 battery. Under the FTP profile for the No. 2 battery, it achieves improvements of 55.89% and 28.61%, respectively. Even under extreme temperatures (10 °C, 40 °C), GMMCC-AEKF maintains high accuracy and stable convergence, and the algorithm demonstrates rapid convergence to the true SOC value. In summary, the GMMCC-AEKF confirms excellent estimation accuracy under various temperatures and non-Gaussian noise conditions, contributing a practical approach for accurate SOC estimation in power battery systems. Full article

The zoonotic potential of bat coronaviruses, especially HKU5, is a significant issue because of their capacity to utilize human angiotensin-converting enzyme 2 (ACE2) as a receptor for cellular entry. This study offers structural insights into the binding kinetics of HKU5 (Bat Merbecovirus HKU5) receptor-binding domain (RBD) spike protein with human ACE2 through a multiscale computational method. This study employed structural modeling, 300-nanosecond (ns) molecular dynamics (MD) simulations, alanine-scanning mutagenesis, and computational peptide design to investigate ACE2 recognition by the HKU5 RBD and its interactions with peptides. The root mean square deviation (RMSD) investigation of HKU5–ACE2 complexes indicated that HKU5 exhibited greater flexibility than SARS-CoV-2, with RMSD values reaching a maximum of 1.2 nm. Free energy analysis, Molecular Mechanics/Generalized Born Surface Area (MM/GBSA), indicated a more robust binding affinity of HKU5 to ACE2 (ΔG_Total = −21.61 kcal/mol) in contrast to SARS-CoV-2 (ΔG_Total = −5.82 kcal/mol), implying that HKU5 binding with ACE2 had higher efficiency. Additionally, a peptide was designed from the ACE2 interface, resulting in the development of 380 single-site mutants by mutational alterations. The four most promising mutant peptides were selected for 300-nanosecond (ns) MD simulations, subsequently undergoing quantum chemical calculations (DFT) to evaluate their electronic characteristics. MM/GBSA of −37.83 kcal/mol indicated that mutant-1 exhibits the most favorable binding with HKU5, hence potentially inhibiting ACE2 interaction. Mutant-1 formed hydrogen bonds involving Glu74, Ser202, Ser204, and Asn152 residues of HKU5. Finally, QM/MM calculations on the peptide–HKU5 complexes showed the most favorable ΔE_interaction of −170.47 (Hartree) for mutant-1 peptide. These findings offer a thorough comprehension of receptor-binding dynamics and are crucial for evaluating the zoonotic risk associated with HKU5-CoV and guiding the design of receptor-targeted antiviral treatments. Full article

In this study, a strain of green microalga adapted to the extreme environmental conditions of the Tibetan Plateau was isolated from the Lalu Wetland. The isolate was identified and tentatively designated as Micractinium sp. LL-1. Following the inoculation of strain LL-1 into tannery wastewater, the ammonia nitrogen concentration was rapidly reduced, achieving a removal efficiency of 98.7%. The maximum accumulated biomass reached 1641.68 mg/L and 1461.28 mg/L. Integrated transcriptomic and label-free quantitative proteomic approaches were employed to systematically investigate the molecular response mechanisms of LL-1 under tannery wastewater stress. Transcriptomic analysis revealed that differentially expressed genes were enriched in pathways related to cell proliferation, morphogenesis, intracellular transport, protein synthesis, photosynthesis, and redox processes. Proteomic analysis indicated that LL-1 enhances cellular and enzymatic activities, strengthens regulatory capacity, modulates key metabolic pathways, and upregulates stress-responsive proteins. Under tannery wastewater stress, LL-1 exhibits dynamic adaptation involving signal perception and metabolic reconfiguration through the coordinated regulation of multiple pathways. Specifically, ribosomal translation and nucleic acid binding regulate biosynthetic capacity; the redistribution of energy metabolism boosts photosynthetic carbon fixation and ATP generation; and membrane transport coupled with antioxidant mechanisms mitigates stress-induced damage. Collectively, this study provides theoretical insights into microalgal adaptation to complex wastewater environments and offers potential targets for strain improvement and wastewater valorization. Full article

To address the autonomous traversability challenge of an Unmanned Articulated Road Roller (UARR) operating on harsh terrains where low-adhesion slipperiness and geometric irregularities are coupled, and traction capacity is severely limited, this paper proposes a Terrain-Adaptive Maximum-Entropy Policy Optimization (TAMPO). A unified multi-physics simulation platform is constructed, integrating a high-fidelity vehicle dynamics model with a parameterized terrain environment. Considering the prevalence of geometric irregularities in construction sites, a parameterized mud-pit model is established—generalized from a representative case—as a canonical physical model and simulation carrier for this class of traversability problems. Based on this model, a family of training and test scenarios is generated to span a broad range of terrain shapes and adhesion conditions. On this foundation, the TAMPO algorithm is introduced to enhance vehicle traversability on complex terrains. The method comprises the following: (i) a Terrain Interaction-Critical Reward (TICR), which combines dense rewards representing task progress with sparse rewards that encourage terrain exploration, guiding the agent to both climb efficiently and actively seek high-adhesion favorable terrain; and (ii) a context-aware adaptive entropy-regularization mechanism that fuses, in real time, three feedback signals—terrain physical difficulty, task-execution efficacy, and model epistemic uncertainty—to dynamically regulate policy entropy and realize an intelligent, state-dependent exploration–exploitation trade-off in unstructured environments. The performance and generalization ability of TAMPO are evaluated on training, interpolation, and extrapolation sets, using PPO, SAC, and DDPG as baselines. On 90 highly challenging extrapolation scenarios, TAMPO achieves an average success rate (S.R.) of 60.00% and an Average Escape Time (A.E.T.) of 17.56 s, corresponding to improvements of up to 22.22% in S.R. and reductions of up to 5.73 s in A.E.T. over the baseline algorithms, demonstrating superior decision-making performance and robust generalization on coupled slippery and irregular terrains. Full article

Systemic anticancer therapy causes a number of side effects; therefore, local drug release devices may play an important role in this area. In this study, we developed polyurethane-dendrimer foams containing different amounts of third-generation poly (amidoamine) dendrimers (PAMAM G3) to evaluate their ability to encapsulate and release the model anticancer drug doxorubicin (DOX), as well as their biocompatibility and effectiveness against normal and cancer cells in vitro. PU–PAMAM foams containing 10–50 wt% PAMAM G3 were prepared using glycerin-based polyether polyol and castor oil as co-components. Structural and rheological analyses revealed that foams containing up to 20 wt% PAMAM G3 exhibited a well-developed porous structure, while higher dendrimer loadings (≥30 wt%) led to irregular cell shapes, pore coalescence, and thinning of cell walls, and indicated a gradual loss of structural integrity. Rheological creep–recovery measurements confirmed the structural findings: moderate PAMAM G3 incorporation (≤20 wt%) increased both the instantaneous and delayed elastic modulus (E₁ ≈ 130–140 kPa; E₂ ≈ 80 kPa) and enhanced elastic recovery, reflecting improved cross-link density and foam stability. Higher dendrimer contents (30–50 wt%) caused a decline in these parameters and higher viscoelastic compliance, indicating a softer, less stable structure. The DOX loading capacity and encapsulation efficiency increased with PAMAM G3 content, reaching maximum values of 35% and 51% for 30–40 wt% PAMAM G3, respectively. However, the most sustained DOX release profiles were observed for matrices containing 20 wt% PAMAM G3. Analysis of cumulative release and kinetic modeling revealed a transition from diffusion-controlled release at low PAMAM contents to burst-dominated release at higher dendrimer loadings. Importantly, matrices containing 10–20 wt% PAMAM G3 also indicated selective anticancer action against squamous cell carcinoma (SCC-15) compared to non-cancerous human keratinocytes (HaCaT). Moreover, the DOX they released effectively destroyed cancer cells. Overall, PU–PAMAM foams containing 10–20 wt% PAMAM G3 provide the most balanced combination of structural stability, controlled drug release, and cytocompatibility. These materials therefore represent a promising platform as passive carriers in drug delivery systems (DDSs), such as local implants, anticancer patches, or bioactive wound dressings. Full article

The increasing demand for next-generation rechargeable batteries that offer high energy density, a long lifespan, high safety, and low cost has led to a need for better electrode materials for lithium-ion batteries. This also involves developing alternative storage systems using common resources such as sodium-ion batteries, beryllium-ion batteries, or magnesium-ion batteries. Tin carbide (SnC) is highly promising as an anode material for lithium, sodium, beryllium, and magnesium ion batteries due to its ability to form nanoclusters like Sn(Li₂)C, Sn(Na₂)C, Sn(Be₂)C, and Sn(Mg₂)C. A detailed study was done using computational methods, including analysis of charge density differences, total density of states, and electron localization function for these hybrid clusters. This research suggests that SnC could be useful in multivalent-ion batteries using Be²⁺ ions because its properties can match or even exceed those of monovalent ions. The study also shows that the maximum capacity, stability energy, and ion movement in these materials can be understood by looking at atomic-level properties like the coordination between host atoms and ions. Recent findings on using tin carbide in these types of batteries and methods to improve their performance have been discussed. Full article

The accelerated development of smart devices and the increased demand for technological services have given rise to new services with great potential for development in the market. Applications for museums are no exception, and more and more institutions are including such solutions in the cultural industry. However, there is still much to be developed, given the difficulties that people with disabilities have in accessing them. In this work were studied the characteristics that the future application (App) of the Helga de Alvear Museum in Cáceres should have so that it can be used satisfactorily by the maximum number of visitors, regardless of their sensory, intellectual, or motor capacity. Kansei Engineering has identified the emotions and sensations that favour the interaction of users with the application and which have been converted into functionalities and design requirements in order to present a graphic proposal and structure for the App. The appearance and functioning of this App are presented visually, supported by an initial theoretical and research part that has helped to identify the rest of the specific objectives. Some specifications to take into account are functional, non-functional, programming, sequence diagrams, and basic interface requirements. This application has two generic and five specific itineraries to solve the disabilities mentioned in this paper, making it accessible to the different groups. The importance of obtaining an equivalence between the essential requirements of the standard and the basic design specifications that should regulate the work process resides not only in having a direct equivalence but also in obtaining guidelines for other designers who want to face extensive regulation and need help to interpret it and be able to make decisions straightforwardly. Full article

Effective mitigation of geotechnical risk and safety management of underground mine requires accurate estimation of rockburst damage potential. The inherent complexity of the rockburst phenomena due to nonlinear, high dimensional, and interdependent nature of the geological factors involved, however, makes predictive modeling a difficult task. The proposed research is based on the use of the K-Nearest Neighbor (KNN) algorithm to predict the risk of rockbursts with the use of microseismic monitoring data. Several key features like the ratio of total maximum principal stress to uniaxial compressive strength, energy capacity of support system, excavation span, geology factor, Richter magnitude of seismic event, distance between rockburst location and microseismic event, and rock density were applied as input parameters to extract critical rockburst precursor activities. In the test stage, the proposed KNN model recorded an accuracy of 75.50%, a precision of 0.913, a recall value of 0.509, and F1 Score of 0.576. The model is reliable with a significant performance indicating its efficacy in practice. The KNN model showed better classification results as compared to recently available models in literature and provided better generalization and interpretability. The model exhibited high prediction in classified low-risk incidents and had strong indicative capabilities towards high-risk situations, attributed to being a useful tool in rockburst hazard measurement. Full article

This study assessed the effect of slicer-made interlocking joints on 3D printed sandwich panels manufactured through fused filament fabrication (FFF) in terms of flexural properties and energy absorption. Composites were prepared with thermoplastic polyurethane (TPU) as the core material and polyamide (PA), polylactic acid (PLA), polyethylene terephthalate (PET) as skin materials for each of the three composites, respectively. In order to assess the implications of internal geometry, 3D printing was done on five infill topologies (Cross-3D, Grid, Gyroid, Line and Honeycomb) at 20% density. All samples had 20% core density and underwent three point bending testing for flexural testing. It was noted that the Grid and Gyroid cores had the best performance in terms of maximum load capacity based on stretch-dominated behavior while Cross-3D and Honeycomb had lower strengths but stable moments during the bending process. Since Cross-3D topology offered the lowest deflection, it was selected for further experiments with slicer added interlocks at the face–core interface. This study revealed the most notable improvements as gains of up to 15% in peak load, 48% in maximum deflection, and 51% in energy absorption compared with the non-interlocked designs. The PET/TPU interlocked demonstrated the best performance in terms of the energy absorption (2.45 J/mm³) and peak load (272.6 N). In contrast, the PA/TPU interlocked exhibited the best flexibility and ductility with a mid-span deformation of 21.34 mm. These results confirm that slicer-generated interlocking interfaces lead to better load capacity and energy dissipation, providing a lightweight, damage-tolerant design approach for additively manufactured sandwich beams. Full article

The transition toward intelligent and sustainable power systems requires practical schemes to integrate industrial demand flexibility into short-term operation, particularly in emerging electricity markets. This paper proposes an integrated framework that combines data-driven flexibility characterisation with a bi-level optimisation model for an industrial demand-side aggregator participating in the short-term balancing market. Flexibility is identified from AMI data and process information of large consumers, yielding around 2 MW of interruptible load and 3 MW of reducible load over a 24 h horizon. At the upper level, the aggregator maximises its profit by submitting flexibility offers; at the lower level, the system operator minimises balancing costs by co-optimising thermal generation and activated flexibility. The problem is formulated as a mixed-integer linear programming model and is evaluated on a real subtransmission and distribution network of a local utility in Ecuador, with ex-post power flow validation in DIgSILENT PowerFactory. Numerical results show that, despite the limited flexible capacity, the aggregator reduces the maximum energy price from USD/MWh 172.32 to 139.59 (about 19%), generating a daily revenue of USD 2475.15. From a network perspective, demand flexibility eliminates undervoltage at the most critical bus (from 0.93 to 1.03 p.u.) without creating overvoltages, while line loadings remain below 50% in all cases and total daily technical losses decrease from 89.46 to 89.10 MWh (about 0.4%). These results highlight both the potential and current limitations of industrial demand flexibility in short-term markets. Full article

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical for ensuring the safe operation of equipment, optimizing industrial cost management, and promoting the sustainable development of the renewable energy sector. Although various deep learning-based approaches for RUL prediction have been proposed, their performance is highly dependent on the availability of large training datasets. As a result, these methods generally achieve satisfactory accuracy only when sufficient training samples are available. To address this limitation, this study proposes a novel hybrid strategy that combines a parameter-optimized signal decomposition algorithm with an enhanced neural network architecture, aiming to improve RUL prediction reliability under small-sample conditions. Specifically, we develop a lithium-ion battery capacity prediction method that integrates the Triangle Topology Aggregation Optimizer (TTAO), Variational Mode Decomposition (VMD), and a Bidirectional Long Short-Term Memory (BiLSTM) network. First, the TTAO algorithm is used to optimize the number of modes and the quadratic penalty factor in VMD, enabling the decomposition of battery capacity data into multiple intrinsic mode functions (IMFs) while minimizing the impact of phenomena such as capacity regeneration. Key features highly correlated with battery life are then extracted as inputs for prediction. Subsequently, a BiLSTM network is employed to capture subtle variations in the capacity degradation process and to predict capacity based on the decomposed sequences. The prediction results are effectively integrated, and comprehensive experiments are conducted on the NASA and CALCE lithium-ion battery aging datasets. The results show that the proposed TTAO-VMD-BiLSTM model exhibits a small number of parameters, low memory consumption, high prediction accuracy, and fast convergence. The root mean square error (RMSE) does not exceed 0.8%, and the maximum mean absolute error (MAE) is less than 0.5%. Full article