Search Results (179)

Graphene-based nanosheets (GNS), including graphene, graphene oxide (GO), reduced graphene oxide (rGO), and graphene nanoplatelets (GNPs), have attracted increasing attention for developing high-performance and sustainable cementitious composites. Compared with conventional dispersion strategies, graphene-based coating strategies enable the targeted localization of GNS at critical interfacial transition zones (ITZs), thereby maximizing their reinforcing efficiency while mitigating agglomeration issues. This review systematically summarizes recent advances in GNS coating technologies for cementitious composites, including physical adsorption, chemical assembly, electrophoretic deposition, and in situ growth. The effects of GNS coatings on interfacial engineering, mechanical performance, durability enhancement, and smart functionalities are critically discussed. Existing studies indicate that GNS coatings can improve strength, crack resistance, impermeability, and resistance to chloride ingress, freeze–thaw cycles, and other degradation processes mainly through ITZ densification and microstructure refinement. However, these benefits are strongly dependent on the coating method, substrate type, and stability of the graphene–substrate interface in calcium-rich alkaline pore solutions. In particular, physically adsorbed GO coatings may suffer from desorption or Ca²⁺-induced aggregation, chemically assembled coatings require further validation beyond laboratory-scale systems, and electrophoretic deposition is mainly applicable to electrically conductive substrates. In addition, localized conductive networks created by GNS coatings facilitate multifunctional properties such as self-sensing, electromagnetic shielding, and electrothermal performance. From a carbon-neutrality perspective, the improvements in mechanical properties and durability provide opportunities to reduce material consumption, extend service life, and lower life-cycle carbon emissions. Nevertheless, their carbon-neutral contribution should be verified through quantitative life-cycle assessment rather than inferred directly from strength or durability enhancement alone. Finally, the remaining challenges associated with large-scale implementation, long-term stability, cost-effectiveness, and field-scale validation are discussed. Particular attention is given to the fact that most existing evidence is derived from laboratory-scale specimens rather than real structural elements exposed to service environments. Full article

Gliomas remain among the most treatment-resistant malignancies of the central nervous system. Glioblastoma (GBM), the most aggressive adult-type diffuse glioma, is associated with persistently poor survival despite maximal safe resection followed by chemoradiation. Gliomas do not grow in isolation. Work over the past twenty years has dismantled the older tumor-centric view of glioma biology, replacing it with a model in which malignant cells operate within a tumor microenvironment (TME) composed of immune, vascular, stromal, and neural elements that together govern disease behavior. What makes the glioma TME so difficult to treat is not just its composition of immune cells, vasculature, stroma, and neurons, but the fact that these elements are arranged unevenly across the tumor. Different regions harbor different cellular mixtures and signaling environments, and, as a result, different vulnerabilities to therapy. Cytoreduction has not lost its importance, far from it. However, the same surgical window now also serves a different purpose; it lets the surgeon see which tissue is biologically dangerous rather than just visually abnormal, locate the true edge of infiltration, and get therapeutics past a blood–brain barrier (BBB) that has historically locked them out of the brain. This review examines two technology domains, including: (1) optical theranostics (5-aminolevulinic acid fluorescence-guided surgery, fluorescein-guided visualization, Raman spectroscopy, and stimulated Raman histology); and (2) blood–brain barrier disrupting technologies. The direction they collectively point toward is a version of glioma surgery that is guided less by anatomy and more by the biology of the tumor itself. Full article

This paper presents a numerical investigation into a prototype energy harvesting system utilizing airflow around a bluff-body. The system consists of a flexible cantilever beam in a flag configuration with bonded piezoelectric transducers, integrated with a nonlinear triple-well potential established by auxiliary elastic elements. Three distinct bluff-body geometries—triangular, square, and semi-circular—with characteristic heights of 20 mm and 30 mm were analyzed. Aerodynamic excitation parameters were identified using CFD simulations, comparing exact and simplified mathematical representations of the lift force. The system’s dynamical response was evaluated through bifurcation diagrams, Diagrams of Coexisting Solutions (DS), and 3D Poincaré sections for zero and variable initial conditions. The results indicate that the triangular cross-section provides the widest frequency band for high-amplitude inter-well oscillations, maximizing energy harvesting effectiveness. A key innovation of this study is the demonstration that the simplified excitation model provides sufficient accuracy for rapid engineering design while significantly reducing computational overhead. Furthermore, it highlights the practical applicability of a flag-type system integrated with flexible elements to stabilize the beam’s free end. Full article

Background: Artificial Intelligence (AI) and Machine Learning (ML) technologies, such as conversational agents, are becoming increasingly essential tools across multiple industries, particularly in healthcare. This paper presents a scoping review (PRISMA-ScR) of conversational agents (CAs) in mobile health coaching systems (MHCS). It examines existing applications of MHCS, focusing on development strategies, usage contexts, impacts on users, benefits, and research gaps, emphasizing the ability of explainable artificial intelligence (XAI) in making health guidance and decision-support recommendations transparent, trustworthy, and interpretable, if properly integrated. This scoping review identifies opportunities to maximize the use of conversational agents, explainable AI, and mobile technologies to make mobile health coaching systems more accessible and trustworthy, as well as further research gaps worth exploring. Objective: This scoping review maps the evidence on CAs and XAI-enabled technologies in MHCS, identifies trust-related design criteria, categorizes reported outcomes, and highlights opportunities for explainable conversational agents (XCA) in a mobile health context, especially in tackling general medical conditions pertinent in underserved settings. Eligibility criteria: Reported eligible resources evaluated, designed, or conceptually analyzed existing CAs, XAI techniques, and MHCS, AI-supported medical dialogue systems, e-coaching systems, and mobile health applications. We considered sources only relevant to healthcare, health coaching, trust, explainability, or patient engagement that were published between 2006 and 2025. Sources of Evidence: Searches were conducted in IEEE Xplore, Google Scholar, Springer, ScienceDirect/Elsevier, ProQuest, and ACM Digital Library, supplemented by targeted web searches and backward citation checks. Charting methods: Data were charted by system type, communication mode, health context, operational mode, technology used, XAI/trust features, degree of automation, study designs and outcome classification. We applied a revised outcome classification: generated desired outcome (GDO) and partially generated desired outcome (P-GDO), and did not generate desired outcome (DN-GDO). Results: A total of 201 resources were collected. Charted studies clustered around CAs in health, MHCS for chronic diseases and stress management, XAI methods such as LIME, SHAP, Prospector, and counterfactual explanations, and trust-related elements such as voice quality, communication style, appearance, social intelligence, privacy, and performance quality. Most health CAs and MHCS addressed chronic diseases, mental health, or behavior change; fewer addressed general medical diagnosis or autonomous mobile-based primary care support. Conclusions: Existing evidence suggests that CAs and MHCSs can support engagement, coaching, education, and selected decision-support tasks, but evidence for safe, autonomous, explainable general practice functionality remains limited. Future work should prioritize clinically supervised XCA designs, core safety assessment, interfaces with transparent explanation, data protection, culturally and linguistically responsive implementation, and future-oriented review in underserved mobile health settings. Full article

The chiral quark soliton model (CQSM) is an effective quark model of baryons maximally taking account of the most important feature of low-energy QCD, i.e., the spontaneous chiral symmetry breaking of the QCD vacuum and the associated appearance of Nambu–Goldstone pions. It shares many common features with the famous Skyrme model in that the baryons are viewed as rotating hedgehog objects in both models. Despite many similarities, it turned out that the CQSM can give more realistic predictions on most baryon observables. Above all, a decisive advantage of the CQSM over the Skyrme-like models is that it can handle non-local quark–quark correlations in baryons, which is absolutely impossible within the framework of effective meson theories. This feature is decisively important for making theoretical predictions on the quark distribution functions inside the nucleon, which are defined as nucleon matrix elements of bilinear quark operators with light-cone separation. In the present paper, we try to elucidate why and how the CQSM can give successful predictions for a variety of types of nucleon quark distribution functions, especially for the flavor asymmetry of the unpolarized and longitudinally polarized sea-quark (anti-quark) distribution functions in the nucleon. Full article

Pancreatic lipase (PL) inhibition is a promising dietary strategy for obesity management. In this study, the inhibitory mechanisms and structural basis of polyphenols extracted from different sugarcane fractions were investigated using in vitro enzyme assays, spectroscopy, and molecular docking analyses. PL inhibitory activity was evaluated using p-nitrophenyl laurate (pNPL) as the substrate, with all assays performed in triplicate and results statistically analyzed. Among the extracts, sugarcane peel polyphenols (SP) exhibited the strongest inhibition, with a half-maximal inhibitory concentration (IC₅₀) of 31.56 mg/mL, significantly lower than that of sugarcane juice polyphenols (SJ, 55.86 mg/mL) and sugarcane bagasse polyphenols (SB, 65.31 mg/mL). Enzyme kinetic analyses revealed a reversible mixed-type inhibition mechanism. In contrast to crude extracts, individual phenolic monomers showed substantially lower IC₅₀ values (0.13–1.33 mg/mL), highlighting the intrinsic dilution. Compositional analysis identified ferulic acid, gallic acid, chlorogenic acid, and schaftoside as key contributors to PL inhibition. Fourier transform infrared (FTIR) and fluorescence spectroscopy demonstrated that polyphenols altered PL secondary structure by modulating α-helix and β-sheet contents and perturbed the microenvironment of tryptophan (Trp) and tyrosine (Tyr) residues. Molecular docking further indicated that these compounds bind within or near the substrate-binding channel via hydrogen bonding and hydrophobic interactions, engaging critical residues including Ser152, His263, and Phe77, and potentially influencing conformational elements involved in active-site accessibility. Collectively, these results suggest that sugarcane, particularly its peel, represents a valuable natural source of PL inhibitors. Despite the relatively high IC₅₀ values of crude extracts, their inhibitory activity arises from multicomponent contributions and supports their potential application as dietary modulators of fat digestion rather than as pharmaceutical lipase inhibitors. Full article

As a core tenet of Green Urbanism, fostering social sustainability through restorative urban environments is essential for enhancing the psychological resilience of active urban generations. While urban parks are recognized as critical blue-green infrastructure, the micro-mechanisms through which their morphological configurations influence perceived restoration remain insufficiently understood. The aim of this study is to investigate how specific landscape element types and proportions in urban parks modulate the visual behavior and psychological restorative outcomes of young urban populations through a multimodal experimental approach. This study employs a novel assessment framework, integrating VR-based eye-tracking and physiological monitoring (HRV, EDA, EEG), with a sample of 77 young adults (aged 18–30) to investigate how landscape element types and proportions modulate visual behavior and restorative outcomes. The findings indicate that landscape components drive restoration through divergent visual cognitive pathways: natural elements promote recovery by fostering sustained visual engagement and exploratory saccades, whereas artificial elements function as cognitive stressors that fragment visual continuity. Mediation analysis further reveals a “quality-over-quantity” effect, demonstrating that restorative efficacy is governed by specific morphological configurations rather than mere green coverage. We identify critical restorative thresholds where the systematic reduction in artificial visibility, combined with the strategic prioritization of multi-layered vegetation and optimized sky openness, significantly maximizes restorative fascination and physiological relaxation. These evidence-based design strategies offer a precise toolkit for sustainable urban renewal, allowing urban planners to optimize the restorative quality of public spaces. By aligning micro-scale visual perception with macro-scale social sustainability goals, this research contributes to the development of resilient and health-promoting cities under the principles of Green Urbanism. Full article

P2/O3-type Ni/Mn-based layered oxides are regarded as promising cathode materials for sodium-ion batteries (SIBs) because of their high energy density. However, their practical application is limited by low initial Coulombic efficiency, sluggish Na⁺ kinetics, transition-metal dissolution/migration and irreversible phase transitions during cycling. Herein, a controlled P2 phase was achieved through elemental ratio regulation, enabling systematic synthesis of a series of Na_xNi_0.4Co_0.1Mn_0.5O₂(x-NCMO) materials with tailored P2/O3 ratios. The optimized composition (x = 0.8), containing 16.6% P2 and 83.4% O3 phases, achieves an optimal phase equilibrium, thereby maximizing the synergistic coupling between the two layered polymorphs. This biphasic architecture demonstrates significantly enhanced Na⁺ transport kinetics and exceptional electrochemical performance, high initial capacity of 168.65 mAh g⁻¹ and excellent rate performance, maintaining 84.88 mAh g⁻¹ at 10 C, outperforming most reported P2/O3 biphasic cathodes. Structural analysis and electrochemical analysis reveal that elemental ratio regulation modulates the TM–O electronic structure, promotes electronic transport, and accelerates Na⁺ migration. These effects collectively reduce polarization, stabilize the structure, and thereby improve rate capability and long-term cycling capacity retention. This work provides an effective design strategy for designing high-performance layered oxide cathodes with improved structural and interfacial stability. Full article

This study addresses the critical need for sustainable energy by optimizing diaphragm-type piezoelectric elements for efficient waste vibration energy harvesting. Traditional experimental optimization of complex, non-linear design parameters including applied load, tapper diameter, and support structures is often resource-intensive and time-consuming. To overcome these limitations, we developed a novel hybrid machine learning framework that seamlessly integrates an Artificial Neural Network (ANN) with a Modified Grey Wolf Optimization (mGWO) algorithm. The ANN was rigorously trained on experimental data using Bayesian Regularization, establishing itself as a robust and high-fidelity surrogate model capable of accurately predicting voltage output based on diverse input parameters, evidenced by an R-value close to 1. This predictive model subsequently served as the fitness function for the mGWO algorithm, which incorporated a non-linear control parameter to efficiently explore the multi-dimensional design space and effectively balance exploration with exploitation. The framework successfully identified the optimal configuration for maximizing voltage output, predicting a theoretical maximum of approximately 70.67 V. This optimal setup notably involved a high applied load of 100 N, the 6CA multi-pointed tapper configuration, and the three-support boundary condition, which is consistent with the experimentally validated results. The computational findings demonstrated excellent agreement with empirical results while providing significantly higher resolution for design insights. This validated, predictive tool offers a substantial advancement for the future scaling and design optimization of piezoelectric energy harvesters, minimizing the need for extensive physical prototyping and ensuring efficient stress transfer without mechanical failure. Full article

Street seating serves as a critical medium for stimulating spatial vitality and holds substantial design value in the refined planning of commercial upgrading and quality enhancement in aging districts. As urban regeneration and the optimization of existing built environments have become dominant paradigms in global urban development, the improvement of street quality—given its role as the primary setting for everyday public life—has increasingly depended on the fine-grained design and precise regulation of micro-scale environmental elements. This study takes Pingdeng Street in Zhengzhou, China, and its 33 seating installations as an empirical case. A multi-level analytical framework—comprising the seating ontology level, the seating space level, and the street environment level—was developed to quantitatively examine the relationships between multi-level spatial elements and street vitality intensity. Through correlation and regression analyses, the study systematically investigated the mechanisms by which seating-related elements at different levels influence street vitality. The results indicate that the Green View Index (GVI) is the core driver of street vitality, with the most significant enhancement observed when GVI ranges between 28% and 35%. The synergistic coupling of multi-level seating elements is essential for maximizing street vitality, while optimization pathways vary across different functional seating types. In design practice, high-comfort seating with backrests is recommended, with seating continuity controlled within 0.63–0.90. Seating spaces should adopt moderately enclosed spatial forms, such as eave-covered areas, and be supplemented with adequate lighting facilities. At the street environment level, a GVI of 28–35% and spatial openness of 9–18% should be maintained. The multi-level analytical framework and quantified indicator thresholds established in this study offer a new perspective on the mechanisms linking seating and street vitality. The findings provide a scientific theoretical basis and offer context-sensitive design guidance for the refined renewal of aging urban districts under comparable conditions. Full article

Reconfigurable intelligent surfaces (RISs) hold promising technical prospects for 6G wireless communications to enhance system capacity, coverage and sum-rate. Unlike existing studies deploying only passive or active RISs, this paper adopts a novel hybrid RIS architecture that optimally allocates the number of active and passive elements. Under fixed quantities of both RIS element types in the fixed hybrid RIS, it simultaneously increases the number of base station antennas and served users, focusing on solving rate optimization for hybrid RIS-assisted MISO systems deployed in various scenarios. This paper establishes a fundamental model for hybrid RIS reflection signals. To better characterize the performance of the proposed hybrid RIS architecture, an optimization problem is formulated to maximize the sum-rate of the hybrid RIS-assisted multi-user, multiple-input, single-output (MU-MISO) system. An efficient algorithm is proposed combining fractional programming (FP), alternating optimization, and Lagrange duality transformation. Simulation results demonstrate that with hybrid RIS assistance, the system’s sum-rate gain increases by 49.1% and 40%, respectively, compared to systems with only active RIS deployment. This achieves higher sum-rate gains at lower power consumption. Full article

Polycrystalline diamond compact (PDC) bits often experience localized heating during impact rock breaking in complex formations, resulting in reduced service life and lower drilling efficiency. An optimized structural design of PDC cutters can significantly enhance bit performance, mitigate thermal concentration, and extend operational longevity. Inspired by previous work on PDC cutter surface topography, five saw-type tooth-shaped cutter designs—featuring one to five saw-type teeth were developed. To evaluate their rock-breaking effectiveness and identify the optimum design, the impact-induced rock fragmentation processes of these cutters were compared using the finite element method. Key indicators, including cutting force, mechanical specific energy (MSE), and cutter surface temperature, were analyzed to determine the superior tooth configuration. Among the five designs, the four-saw-tooth cutter induced the most pronounced stress concentration in the rock. Its optimized number of saw-type teeth ensured full participation of all teeth in rock cutting, enabling efficient rock removal and maximizing breakage performance. Compared with other designs, this cutter exhibited the smallest fluctuations and mean cutting force. The specific mechanical energy decreased initially and then increased with the number of saw-type teeth, reaching a minimum for the four saw-type tooth design. Moreover, it showed the lowest surface temperature and the mildest temperature variation, which helps alleviate localized heating and improve wear resistance. The cutting performance of the four saw-type tooth was further influenced by cutting depth and back rake angle, with optimal values identified as 1.5 mm and 20°, respectively. Compared with a conventional cutter, the four saw-type tooth design reduced the overall surface temperature by approximately 10.69%, with temperature rise confined mainly to the grooves between adjacent saw-type teeth and no widespread thermal concentration observed, confirming its design superiority. Full-scale rock-breaking simulations demonstrated that the bit equipped with four saw-type tooth achieved greater penetration depth and required lower torque than the conventional design, indicating enhanced rock-breaking ability and higher drilling efficiency. In conclusion, the four saw-type tooth PDC cutter design offers a promising approach for developing high-performance drill bits and reducing drilling costs. Full article

As the electrification of the automotive industry accelerates, the importance of small-scale motors used in applications such as HVAC systems and water pumps is growing. To design small motors that exhibit high efficiency and high output within limited spaces, applying axial flux motors (AFMs) instead of conventional radial flux motors (RFMs) can maximize the power density within the same volume, offering advantages in both weight reduction and miniaturization. This study proposes an optimized end-turn layout design to mitigate back-EMF imbalance in AFMs utilizing PCB stators. Optimization results demonstrated that the structure employing a non-adjacent end-turn layout with equalized average end-turn heights (BCAACB type) exhibited the best performance in terms of average resistance and phase resistance variance, effectively mitigating back-EMF imbalance. The validity of the optimized end-turn structure was verified through finite element analysis (FEA). The analysis confirmed that the motor’s back-EMF balance was improved, and the magnitude of phase resistance was reduced. This reduction led to lower copper loss, thereby increasing overall efficiency. Furthermore, the variance in resistance for each phase was minimized, resulting in enhanced electrical balance. The results of this study are expected to contribute to enhancing the applicability of PCB stators in small motor design. Full article

Dimensioning the energy storage systems for a heavy-duty fuel cell hybrid electric vehicle is not straightforward. This study proposes a methodology to address this challenge, aiming to maximize efficiency while mitigating the aging effects on the energy storage systems. Various configurations of storage system ratios have been analyzed using the concept of hybridization percentage, which represents the ratio between the supercapacitor weight and the total weight of the energy storage elements. Simulations were conducted using models developed in AVL Cruise MTM. A case study is included to test the methodology, incorporating commercial components, a standard driving cycle, and a rule-based energy management strategy. The conclusions of this application example illustrate the types of results that can be obtained by using this hybrid energy storage system sizing methodology. Findings for this case study suggest that for cycles lacking extreme power peaks, non-hybridized configurations can be the optimal solution, as the battery size reduction outweighs the benefits of hybridization in terms of efficiency, achieving 76.08% without supercapacitors compared to 65.7% with a high hybridization grade of 32.4%, and overall cost. However, sensitivity analysis reveals that if the optimization weights are adjusted to prioritize aging over efficiency, the optimal configuration shifts to a 6.48% hybridization grade at a 0.3C threshold. Full article

Modern agriculture requires optimizing available resources to maximize production while minimizing environmental impact without increasing economic costs. Hydroponic agriculture replaces soil with inert media that provide physical support for plants but do not supply nutrients. In this type of agricultural production, fertilization with nutrient solutions is essential, as they supply the 15 elements necessary for proper plant development. These solutions consist of mixtures of different amounts of fertilizers dissolved in water. In this context, a method based on a simulated annealing algorithm is proposed, a metaheuristic that optimizes fertilizer quantities in grams to achieve target concentrations in parts per million for six macronutrients and nine micronutrients. The algorithm addresses a multi-objective optimization problem, balancing two competing goals: first, maximizing the accuracy of the fertilizer balance to achieve the required nutritional levels, and second, minimizing the total cost of the fertilizer mixture. The algorithm’s fitness function weights the total cost of the fertilizers used and the total relative error between the concentrations obtained and those desired, allowing the relative importance of cost and accuracy in the nutrient solution to be adjusted. The results of three experiments with varying nutrient levels are presented for a 1000-L water tank. The first experiment consisted of three macronutrients and two micronutrients. The second configuration added three macronutrients and two micronutrients, for a total of ten nutrients. Finally, five micronutrients were added to complete the 15 essential nutrients for plants. It is important to note that there are several methods for calculating micronutrients that contribute to precision agriculture, increasing the complexity of finding a solution that meets established nutritional requirements. The nutrient concentrations in parts per million required for tomato cultivation during the vegetative development stage. To balance nutrient accuracy and solution cost, we applied weighting factors of $0.65,0.75,0.85,0.90,0.95,$ and $1.0$ for accuracy. The corresponding weights for cost were calculated as the complement of these values (totaling 1). By favoring nutrient accuracy with a weighting of 1, accuracies of 0.00500, 0.02618, and 0.03077 parts per million were achieved in each experiment, respectively. Meanwhile, the lowest cost is $2.06$, $2.72$, and $2.70$ USD for the aforementioned experiments. Full article