Search Results (24,700)

To address the limited accuracy of power demand prediction for hybrid electric vehicles under complex and dynamic driving conditions, this paper proposes a hybrid prediction approach based on the cascade correction of Long Short-Term Memory networks and Least Squares Support Vector Machines (LSTM-LSSVM-CC). The proposed method adopts a stage-wise modeling framework that exploits the least-squares optimality of LSSVM for low-frequency steady-state signals and the dynamic compensation capability of LSTM for high-frequency non-stationary residuals, thereby achieving complementary feature representation in the frequency domain. Specifically, an LSSVM is first used to construct a baseline regression model that captures stationary components, followed by an LSTM network that performs deep temporal modeling of the residual sequence to correct nonlinear prediction errors. Extensive experiments conducted on three standard driving cycles—CLTC-P, WLTP, and UDDS—demonstrate that the proposed model consistently outperforms conventional methods including LSSVM, RNN, ELMAN, and Random Forest in multi-step predictions, achieving an average RMSE reduction of 28–52% and maintaining correlation coefficients (R²) between 0.87 and 0.99. Particularly under highly dynamic and abrupt load conditions, the model exhibits superior real-time performance and stability while significantly mitigating cumulative prediction errors. These results demonstrate that the proposed LSTM-LSSVM-CC model achieves robust modeling performance of non-stationary time series while balancing prediction accuracy and computational efficiency, providing an effective technical foundation for hybrid vehicle energy management optimization and offering a transferable theoretical framework for time-series prediction in complex systems. Full article

Modern performance-based bridge design seeks to control damage in specific failure modes in order to balance safety and economy, particularly in high-seismic regions where inelastic and ductile deformation is expected to occur, both in the structure and soil, allowing potential reduction in seismic demand through fuse elements. In short-span bridges, abutments strongly influence longitudinal response, whereas transverse performance depends largely on seismic components such as shear keys and other energy-dissipation devices. Thus, performance assessment requires explicit representation of their hysteretic behavior. This study presents a numerical evaluation of the damping provided by common elements in typical bridge systems, using as reference damage observations from bridges affected by recent interface earthquakes in Mexico. Three-dimensional finite-difference models were developed, and nonlinear response-history analyses were performed to simulate ductile behavior and energy dissipation. The Sig3 hysteretic model available in FLAC^3D was used for abutments and foundation soils, while shear keys were represented as nonlinear springs. The results established a relationship between plastic deformation and energy dissipation, showing that incorporating the hysteretic behavior of both soil and sacrificial structural components enhanced the seismic bridge performance assessment, and led to more reliable and cost-efficient designs when inelastic deformation capacity was explicitly included in the numerical simulations. Full article

This study addresses the increasing need to structure knowledge in the field of circular resource management, with a focus on energy efficiency and sustainable resilience. Previous studies have examined various taxonomies for the circular economy, yet a clear gap remains in understanding how energy efficiency and resilience serve as the main pillars for operational stability. This study is designed as a bibliometric analysis based on a selection of relevant scientific articles. The identified factors were extracted based on their frequency of occurrence in the literature and processed using statistical clustering techniques to group them into coherent categories. The results show that the field is defined by a set of interconnected factors that can be structured into distinct clusters, reflecting key dimensions such as operational performance, environmental impact, and system resilience. Specifically, the analysis demonstrates how energy-related attributes and resilience attributes act as stabilizing factors within closed-loop systems. Based on these findings, this study proposes a structured framework that organizes the identified factors into a clear configuration. This framework provides a reference point for researchers who aim to develop models in this area and for practitioners involved in the design and optimization of circular systems. This study contributes by offering a structured view of the field and by supporting the development of consistent analytical and decision-making approaches grounded in the necessity of balancing resource recovery with system stability. Full article

Background/Objectives: This secondary and exploratory meta-analysis re-evaluated 30 randomized controlled trials on free and added sugars (FS) detailed in the European Food Safety Authority’s (EFSA) report on the tolerable upper intake level for dietary sugars, focusing on the influence of food source (beverages, foods, or mixed) on cardiometabolic and anthropometric health. Methods: The EFSA’s method of analyzing the relative FS intake (difference between treatment and comparator arms, Δ%E^fs) was used, with further adjustment for the reported intake of all sources of FS and energy. The EFSA’s “high vs. low” random-effects meta-analysis comparing groups with the highest and lowest FS intake was replicated, and additional exploratory dose–response meta-regressions (linear and non-linear) were performed, stratified by food source. Given the secondary and observational nature of the analysis, all source-stratified findings should be interpreted as hypothesis-generating, rather than causal. Results: There were no interactions between Δ%E^fs and food source for any outcome, and within a source there were linearly positive and statistically significant regressions for body weight (mixed), low-density lipoprotein cholesterol (LDL-C, foods), and uric acid (beverages). Across 13 outcomes, Δ%E^fs was positively and linearly related to greater fasting glucose, high-density lipoprotein cholesterol (HDL-C), and LDL-C, and non-linearly to body weight. However, the data were limited in their representation of FS intake at typical population levels, and there were insufficient data to investigate the effect of FS from foods on most anthropometric outcomes. Conclusions: Meta-regressive dose–responses revealed little relationship between Δ%E^fs from specific food sources and health outcomes, but such effects might be masked by confounding factors. Future trials that test realistic intakes of FS across diverse food matrices and account for dietary compensation would help to overcome limitations in the body of evidence. Full article

Pulsed electric field (PEF) extraction studies often report yield gains but less consistently translate them into purification-relevant metrics. This study re-analyzed a locked comparability tier of 56 solvent-matched control (CTR)-PEF contrasts from five leaf-biomass studies to map total phenolic content (TPC) uplift onto first-order downstream duty proxies. The tier was not designed as a field-wide survey, and audit logs plus dependence-aware analyses were used to reduce author bias. Across the 56 pairs, median ΔTPC% was 26.7% (IQR 12.1–35.0), with positive values in 55 of 56 contrasts. The strongest gains were concentrated at low specific energy (Wspec ≤ 0.15 kJ kg⁻¹ treated biomass) and 0–25% ethanol within this dataset. PLRI translated these uplifts into first-order processed-volume scaling at fixed capture criteria, and Pareto screening identified PLRI values of 1.18–1.73, corresponding to approximately 15–42% lower processed volume under the stated assumptions. Marker-level data were limited to two matrices (three paired observations), but SSF values > 1 (1.06–2.89) were consistent with possible composition steering. No new experiments were performed, and ballast, fouling, and downstream performance were not measured; conclusions are therefore limited to the operating envelope and are intended for screening, reporting standardization, and subsequent purification validation. Full article

The Flexible Job Shop Scheduling Problem (FJSP) is central to smart manufacturing, yet standard algorithms often prioritize productivity (makespan) at the expense of cost and reliability. This paper introduces t-MOHHO, a collaborative optimization framework designed to equilibrate machine load, processing costs, and delivery timeliness alongside throughput. By incorporating an adaptive Student’s t-distribution mutation operator and a non-linear energy escape mechanism, t-MOHHO effectively navigates high-dimensional search spaces. Extensive validation on 10 MK benchmark instances reveals that t-MOHHO demonstrates significant advantages over classic HHO, MOPSO, and MOEA/D across most metrics. Notably, in comparison to the state-of-the-art NSGA-III, t-MOHHO executes a clear trade-off: it trades marginal makespan efficiency for substantial reductions in cost and tardiness. Specifically, on the large-scale MK10 instance, t-MOHHO reduces total tardiness by 56.2% and lowers processing costs by 3.4% compared to NSGA-III. These results demonstrate that t-MOHHO can strategically sacrifice maximum speed to secure superior punctuality and cost-efficiency, making it a robust decision-support tool for Just-in-Time (JIT) production environments. Full article

This study reports that titanium nanoparticles can be used as a surface coating to enhance the corrosion resistance of 316 stainless steel. It specifically examines the influence of the deposition temperature (T_dep) on the coating’s structural and morphological properties, including corrosion behavior. TiO₂ nanoparticles were thoughtfully deposited on steel substrates at temperatures of 300, 400, and 500 °C using a horizontal hot-wall tubular reactor. This equipment was expertly engineered at the CIDETEQ laboratory through the metal–organic chemical vapor deposition (MOCVD) concept. Titanium isopropoxide [Ti(OC₃H₇)₄] was used as the precursor for the coating synthesis. Structural analysis was conducted using X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDS), and scanning electron microscopy (SEM). Corrosion performance was evaluated under accelerated conditions in 0.5 M H₂SO₄ using potentiodynamic anodic polarization (AP), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS). The corrosion test indicates that increasing T_dep significantly differentiates the coating morphology and improves corrosion resistance. AP revealed that the pitting potential (E_pit) shifted to more positive values, ranging from +1.4 to +1.5 V. CV voltammograms indicated that coated samples had lower passive current densities (I_p ≈ 10⁴ to 10⁵ A/cm²) than the bare substrate. EIS analysis demonstrated that the coating deposited at 500 °C processed the most favorable electrochemical performance, resisting corrosion for over 28 days. This coating achieved the highest electrical resistance (297 kΩ·cm²) and the lowest capacitance (2.7 μF/cm²), attributed to the formation of a crystalline anatase phase composed of pyramidal-like nanoparticle agglomerates (~40 nm). The dense packing structure effectively blocks charge-transfer pathways, restricting electron and ion transfer. Finally, MOCVD-based chemical surface modification with TiO₂ nanoparticles is considered an innovative method to improve the corrosion resistance of stainless steel, thereby prolonging its durability under accelerated sulfuric acid exposure. Full article

Agrivoltaics (AV) has emerged as an integrated land-use innovation capable of simultaneously addressing food, energy, and water challenges, yet its systemic implications for farming system sustainability remain insufficiently synthesized. This review adopts a farming system dynamics perspective to examine how AV systems reorganize biophysical, ecological, and socio-economic interactions across agroecosystems. Drawing upon agroecological principles, pathways of sustainable intensification and ecological intensification, and resource-loop strategies in circular economy, we identify the key elements and cause-and-effect relationships that shape AV system performance. Evidence indicates that the co-location of photovoltaics (PV) structures and crop cultivation generates new system properties, altered light distribution, moderated microclimates, redistributed soil moisture, and diversified production functions that influence productivity, resource-use efficiency, ecological services, and farm resilience. Using causal loop analysis, we conceptualize four central feedback dynamics: (i) PV–crop trade-offs and spatial-sharing relationships; (ii) microclimate modifications and crop physiological responses; (iii) ecological performance and landscape-level interactions; and (iv) circularity loops connecting resource conservation, renewable-energy substitution, soil processes, and material flows. This feedback collectively determines eco-efficiency outcomes, including enhanced land-equivalent productivity, improved water-use efficiency, strengthened regulating services, and reductions in external energy dependence. At the farming-system scale, AV diversifies income streams and stabilizes yields under climatic variability, whereas at the landscape scale, it fosters multifunctionality by supporting regenerative resource flows and ecological resilience. Building on these insights, we propose an integrated framework that links agroecological elements with dynamic feedback structures to guide context-specific AV design, management, and governance. This system-oriented synthesis provides a foundation for future research and policy efforts aimed at optimizing AV as a circular, resilient, and sustainable farming system innovation. Full article

Protein turnover and extracellular proteolysis continuously generate diverse peptide fragments within biological systems, yet the metabolic and pharmacological implications of these peptides remain incompletely understood. Among these transporters, members of the solute carrier family 15 (SLC15), including peptide transporter 1 (PEPT1/SLC15A1) and peptide transporter 2 (PEPT2/SLC15A2), mediate the proton-coupled uptake of dipeptides, tripeptides, and structurally related compounds across cellular membranes. While these transporters have been extensively studied in the context of intestinal peptide absorption and drug delivery, their potential roles in cancer biology remain incompletely understood. Tumor microenvironments are characterized by extensive proteolysis and dynamic metabolic remodeling, processes that can generate diverse peptide fragments derived from extracellular matrix proteins and intracellular protein turnover. These peptides may accumulate locally and potentially serve as substrates for cellular peptide transport systems. Once internalized through peptide transporters, dipeptides are typically hydrolyzed into free amino acids that can support biosynthetic pathways, energy metabolism, and cellular growth. In addition to their potential metabolic roles, certain endogenous dipeptides have also been reported to influence cellular signaling pathways and redox homeostasis. The broad substrate specificity of peptide transporters has also attracted significant interest in pharmacology because numerous clinically used drugs exploit these transport systems for efficient cellular uptake. This property raises the possibility that peptide transporters may be utilized for transporter-mediated drug delivery strategies, including the development of peptide-modified prodrugs or dipeptide–drug conjugates. In this review, we summarize the molecular characteristics and physiological functions of dipeptide transport systems with a particular focus on the SLC15 transporter family. We then discuss emerging evidence linking peptide transporters to tumor metabolism and the tumor microenvironment. Finally, we highlight current progress and future perspectives in exploiting peptide transport systems for transporter-mediated drug delivery and therapeutic targeting in cancer. Full article

The use of renewable energy sources instead of conventional ones, together with the development of efficient electricity storage solutions, represents a central objective of the transition to sustainable and resilient energy systems. In this context, two main development directions are the integration of hydrogen in the energy chain (Power-to-Gas) and the use of batteries, each with specific advantages and disadvantages, compared to internal combustion engines. The purpose of this work was to evaluate the dynamic response time of a hydrogen fuel cell model powered by green hydrogen, under conditions of sudden and instantaneous power demand, for its integration into wind-based renewable energy systems. Experimental research was carried out on an autonomous installation designed to operate continuously for an unlimited duration, simulating the integration of hydrogen produced from wind sources. The novelty consists of the application of an instrumental method for automatic measurement of the response time of a proton exchange membrane hydrogen fuel cell, based on the automatic acquisition and processing of measured electrical signals. The response time of the fuel cell was compared with that of an internal combustion engine based on the classic Carnot cycle, using a dedicated oscilloscope. The load connection time, the current and voltage variation as a function of time were recorded simultaneously. The results show that the response time of the fuel cell is relatively short (approximately 0.3 ms), much lower than that of the internal combustion engine (0.7 s), being of the order of about 2333 times smaller. In conclusion, the hydrogen fuel cell can be effectively integrated into renewable energy systems for the role of an uninterruptible power supply, with an exceptionally fast dynamic response, suitable for applications in regulating and supporting wind-powered networks. Full article

There is a risk of wire breakage and falls when constructing high-voltage transmission lines. If this occurs, it seriously endangers the safety of crossing objects. As key structures commonly used in power construction to protect crossing facilities from wire breakage, the scientific design and accurate calculation of the safety margins for suspension-crossing frames are particularly important. However, the existing energy transfer mathematical model for impact-bearing cables after conductor fracture cannot accurately describe the physical process, and the value of the fixed break impact coefficient (e.g., 2.89 for the double circuit) adopted in the design specification is not sufficiently accurate. Thus, there is a large deviation in the bearing cable safety factor, which can cause the safety margin to be either too large or insufficient, in turn seriously affecting the safe and efficient completion of cross-line construction. To this end, in this study, we first constructed a mathematical model of impact energy conversion based on the law of conservation of energy; then, we proposed an accurate method for calculating the safety factor of the bearing cable. To verify the method’s accuracy, a full-scale true wire breakage impact test was conducted. The results show that the error between the impact coefficient calculated by this method and the test result is only 6.7%, significantly better than the 38.3% error, found when the traditional design specification is used to fix the value. This method is applied to a 220 kV crossing project case. The analysis shows that, to meet the same safety requirements, the model recommends the use of Φ12 Dyneema rope, while the traditional method requires Φ16 Dyneema rope; simultaneously, for the Φ18 Dyneema rope, the maximum allowable span calculated by this method is 450 m, which is greater than the 400 m calculated using the traditional method. Thus, this method can calculate a more accurate impact coefficient based on actual working conditions, thereby significantly optimizing the selection of load-bearing cables and increasing the upper limit of span design while ensuring construction safety. Overall, the research conclusions provide important theoretical and technical support for optimizing the design and safety check of the suspension-crossing frame. Full article

Phase change material (PCM)-based latent heat storage (LHS) systems help address the mismatch between renewable energy supply and thermal demand. However, their practical implementation is constrained by the strongly nonlinear and multiphysics nature of phase change, which makes high-fidelity simulations and real-time applications computationally expensive. This review examines mathematical reduced-order modeling (ROM) as an effective strategy to overcome this limitation by combining physics-based simplifications, projection methods, interpolation techniques, and data-driven models for PCM-based LHS systems. While physical simplifications (such as dimensional reduction and effective property approximations) represent an important first layer of model reduction, the primary focus of this work is on the mathematical ROM methodologies that operate on the governing equations after such physical simplifications have been applied. The review covers approaches including two-temperature non-equilibrium and analytical thermal-resistance models, Proper Orthogonal Decomposition (POD), CFD-derived look-up tables, kriging and ε-NTU grey/black-box metamodels, and machine-learning methods such as artificial neural networks and gradient-boosted regressors trained from CFD data. These ROM techniques have been applied to packed beds, PCM-integrated heat exchangers, finned enclosures, triplex-tube systems, and solar thermal components, achieving speed-ups from tens to over 80,000 times faster than full CFD simulations while maintaining prediction errors typically below 5% or within sub-Kelvin temperature deviations. A critical comparative analysis exposes the fundamental trade-off between interpretability, data dependence, and computational efficiency, leading to a practical decision-making framework that guides method selection for specific applications such as design optimization, real-time control, and system-level simulation. Remaining challenges—including accurate representation of phase change nonlinearity, moving phase boundaries, multi-timescale dynamics, generalization across geometries, experimental validation, and integration into industrial workflows—motivate a structured roadmap for future hybrid physics–machine learning developments, standardized validation protocols, and pathways toward industrial deployment. Full article

Background/Objectives: To determine the optimal reconstruction parameters for accurate visualization of peripheral in-stent restenosis using photon-counting detector CT (PCD-CT), and to evaluate its potential advantages over energy-integrated detector CT (EID-CT). Methods: Endovascular peripheral stents with varying degrees of in-stent restenosis were scanned in a custom-made phantom using EID-CT (Somatom Force) and PCD-CT (Naeotom Alpha) under clinical acquisition protocols. EID-CT images were reconstructed with Bv40 and Bv59 kernels at 512 matrices. PCD-CT data were acquired in standard-resolution (SR) and ultra-high-resolution (UHR) modes. In both modes, images were reconstructed with multiple kernels (Bv40, Bv56 and Bv72) and matrix sizes (512 and 1024 matrix). In SR mode, additional virtual monoenergetic images (40–100 keV) were generated, while UHR mode included only polychromatic reconstructions. Quantitative image quality (noise, contrast, contrast-to-noise ratio [CNR]) was measured, and two blinded readers performed qualitative assessments of restenosis visualization. Results: PCD-CT with SR mode at VMI 40 keV achieved the highest image contrast and CNR, significantly outperforming EID-CT and PCD-CT_UHR under matched conditions (all p < 0.05). The sharper reconstruction kernel further enhanced the image contrast and improved subjective visualization despite increased image noise. Both readers ranked PCD-CT_{SR-Bv72-40keV} at 1024 matrix highest for detecting all degrees of restenosis, with excellent inter-reader agreement (ρ > 0.80). Conclusions: PCD-CT in SR mode at VMI 40 keV, specifically using the Bv72 kernel with a 1024 matrix, optimizes the visualization of peripheral in-stent restenosis. Compared to EID-CT, PCD-CT provides superior image quality and detectability of restenosis. Full article

Background/Objectives: The most abundant flavonoid, quercetin, which is mostly found as glycosides, is widely distributed in plants. Quercetin is rapidly metabolized, having a short half-life in the blood circulation, and forms its conjugates by undergoing ring cleavage of the benzopyranone ring system. Despite its fast clearance in the body, quercetin was demonstrated to have clinically anti-inflammatory, cardioprotective, antidiabetic, and anti-obesity activities. This study aimed to determine whether quercetin itself or its metabolites are responsible for these activities. Methods: We performed molecular docking of 27 metabolites, including quercetin itself, against ten inflammation-related proteins in silico. We then conducted microscale thermophoresis (MST) of selected metabolites towards the NLRP3 inflammasome. Results: Overall, Phase II metabolites yielded better binding energies compared to the metabolites formed by degradation. MST results revealed that isorhamnetin, the 4-O-methylated metabolite of quercetin, gave the best results, with a binding affinity (K_D value) of 16.12 ± 5.16 µM, even better than quercetin itself, which has a binding affinity of 44.84 ± 4.21 µM. Glucuronide metabolites of quercetin (isorhamnetin 3-O-glucuronide, quercetin 7-O-glucuronide, and quercetin 3-O-glucuronide) were found to bind to the inflammasome protein with low binding affinities, whereas small degradation products (hippuric acid and 3,4-dihydroxytoluene) did not bind at all. Conclusions: These results suggest that Phase II metabolites, specifically isorhamnetin, may contribute more significantly to the biological activity of quercetin than the parent compound, however, degradation products appear inactive. Full article

With the constant development of new polymer chemistry technologies, it is necessary to find modern synthetic pathways for the synthesis of polymers bearing numerous applicable characteristics, in an easy, efficient and environmentally friendly way. One such possibility is to present the use of metathesis type reactions and more specifically ring-opening metathesis polymerisation (ROMP), which provides the opportunity to produce linear unsaturated functionalised polymeric chains in a ‘living’ yet controlled manner with the use of ruthenium-based carbene (Ru=CHR) Grubbs’ catalysts (initiators: G1, G2, G3). In order to achieve satisfying results and obtain full conversion of the monomers, sterically hindered molecules are preferred, because the process of opening the ring results in simultaneous release of the energy that propagates the whole process. The incorporation of silicon-based substituents (such as silyl ethers) into the norbornene matrix can provide higher thermal stability of polymers, leading to the creation of flame-retardant materials. Other applications include gas separation membranes or biomedicine, upon further modification. This paper focusses on the development and optimisation of the synthetic method of previously not reported exo-norbornene silyl ethers along with their metathesis polymerisation to achieve linear unsaturated polymers with high isolation yields. Full article