Search Results (52,211)

Modeling the dynamic response of saturated marine soils is crucial yet computationally challenging for traditional methods. Meanwhile, purely data-driven models suffer from sparse data and lack of physical interpretability. To overcome these limitations, this study proposes an intelligent engineering framework based on a frequency-domain physics-informed neural network (FD-PINN) for the forward simulation and inverse parameter identification of saturated seabed soils. Constrained directly by physical laws during the learning process, FD-PINN remains highly reliable even when training data is sparse. By formulating the governing equations in the frequency domain, it directly predicts complex-valued displacement and pore-pressure phasors. Multiscale Fourier feature mappings mitigate spectral bias and capture boundary layers and high-frequency effects. For inverse problems, a phase-sensitive lock-in extraction strategy transforms time-domain measurements into robust frequency-domain targets, enabling the accurate and noise-tolerant identification of poroelastic parameters with clear physical meaning (nondimensional storage parameter S and permeability parameter $\Gamma$). Numerical experiments show that FD-PINN substantially outperforms conventional time-domain PINN, achieving relative $L^2$ errors of ${10}^{-2}\sim {10}^{-3}$ for single- and multi-frequency excitations typical of wave-induced loadings. In particular, $\Gamma$ is consistently recovered with sub-percent relative error, while S can be reliably identified with multi-frequency data. The framework offers a data-efficient, noise-robust approach for high-fidelity modeling and robust parameter inversion, which is particularly valuable in offshore environments where high-quality data is scarce. Full article

Fungal extracts have garnered considerable attention in recent years due to their diverse pharmaceutical potential. The present study investigates the secondary metabolite profile and biological activities of Alternaria citri, a fungal strain associated with citrus fruits. Metabolites were extracted from A. citri grown in Potato Dextrose Broth (PDB) using ethyl acetate and subsequently evaluated for antimicrobial, antioxidant, and cytotoxic activities, alongside gas chromatography–mass spectrometry (GC–MS) profiling. GC–MS analysis identified 14 bioactive compounds in the fungal extract. The extract exhibited antimicrobial activity against Aspergillus flavus, Trichoderma hamatum, Staphylococcus aureus, and Escherichia coli. Moderate total phenolic and flavonoid contents were observed, which correlated with concentration-dependent antioxidant activity as determined by the DPPH assay. Cytotoxic evaluation using NIH/3T3 cells demonstrated potential anticancer activity, with an IC₅₀ value of 126.63 µg/mL. A. citri is an interesting source of bioactive metabolites with potential therapeutic applications. These findings further strengthen the evidence that Alternaria species can serve as promising sources of natural antioxidants and antimicrobials, thereby supporting their potential applications in pharmaceutical and biomedical formulations. This study expands current knowledge of fungal metabolite diversity and establishes A. citri as a potential source of novel therapeutic agents. Full article

This study investigates European citizens’ perspectives on farmers’ roles, highlighting gender, age, education, political orientation, community size, social class, and attitudes towards the EU. This study was developed using 21,002 interviews with European Citizens from all 27 EU countries. A quantitative data analysis methodology was used from the European Eurobarometer 97.1 survey. Seven models were formulated and tested. It is shown that men prioritise economic growth and food stability, while women emphasise environmental protection and animal welfare. Younger individuals focus on rural job creation, whereas older citizens value food security. Higher education levels correlate with environmental and animal welfare concerns. Right-leaning citizens favour economic development, whereas left-leaning individuals prioritise ecological issues. Larger communities emphasise economic growth, while smaller ones focus on environmental preservation. Social class influences priorities, with higher classes concerned about sustainability and lower classes about job creation. Pessimistic views about the EU correlate with food safety concerns, while optimistic views align with environmental and animal welfare priorities. These findings suggest that aligning agricultural and food policies with citizens’ diverse needs can foster a more sustainable and resilient European food system. Full article

Polyphenols have attracted considerable scientific interest over recent years due to their broad spectrum of biological activities, including antioxidant, cardioprotective, anti-inflammatory, antidiabetic, and anticancer properties. However, their practical application is often limited by unfavorable physicochemical characteristics, particularly low aqueous solubility. Consequently, amorphous solid dispersions (ASDs) have been extensively investigated as a formulation strategy to overcome these limitations. This article represents the first part of a two-part review and presents the current state of the art in amorphous solid dispersions of polyphenols. The available literature is systematically summarized with respect to the investigated polyphenolic compounds, the employed carriers (with particular emphasis on polymeric systems), the preparation methods, and the solid-state characterization techniques used to confirm amorphization. Both single-component systems and binary combinations of polyphenols reported in the literature are considered. The collected data are presented in tabular form and complemented by a heat map illustrating the frequency of reported polyphenol–carrier combinations. The aim of this review is to organize the available knowledge, identify the most extensively studied systems, and highlight research areas that remain underexplored. A detailed discussion of the pharmaceutical benefits and mechanistic aspects of polyphenols in ASD systems will be provided in Part II. Full article

High-quality training data are essential for the performance and generalization of artificial intelligence systems, particularly in dynamic environments such as adaptive stream processing for disaster response. However, constructing large and representative datasets remains costly and time-consuming, especially in domains where real data are scarce or difficult to obtain. Large Language Models (LLMs) provide powerful capabilities for synthetic text generation, yet the quality of generated data strongly depends on the design of input prompts. Prompt engineering is therefore critical, but it remains largely manual and difficult to scale, particularly in black-box settings where model internals are inaccessible. This work introduces EVOLMD-MO, a multi-objective evolutionary framework for automated prompt learning aimed at generating high-quality synthetic text datasets using black-box LLMs. The proposed approach formulates prompt optimization as a multi-objective search problem in which candidate prompts evolve through genetic operators guided by two complementary objectives: semantic fidelity to reference data and generative diversity of the produced samples. To support scalable optimization, the framework integrates a modular multi-agent architecture that decouples prompt evolution, LLM interaction, and evaluation mechanisms. The evolutionary process is implemented using the NSGA-II algorithm, enabling the discovery of diverse Pareto-optimal prompts that balance semantic preservation and diversity. Experimental evaluation using large-scale disaster-related social media data demonstrates that the proposed approach consistently improves prompt quality across generations while maintaining a stable trade-off between fidelity and diversity. Compared with a single-objective baseline, EVOLMD-MO explores a significantly broader semantic search space and produces more diverse yet semantically coherent synthetic datasets. These results indicate that multi-objective evolutionary prompt learning constitutes a promising strategy for black-box LLM-driven data generation, with potential applicability to adaptive data analytics and real-time decision-support systems in highly dynamic environments, pending broader validation across domains and models. Full article

The theory of physical degrees of freedom (DoF) developed by Franceschetti–Migliore–Minero (FMM) establishes a fundamental phase transition in the singular-value spectrum of electromagnetic radiation operators under maximal rotational symmetry. In this work, we revisit this result from a symmetry-explicit operator-theoretic perspective and extend it to scenarios with reduced and controllable symmetries, with particular emphasis on reconfigurable intelligent surfaces (RISs). We model the radiation process as a compact operator acting between admissible source and observation spaces and characterize its symmetry through group equivariance. This formulation enables a systematic decomposition of the operator into irreducible representation sectors associated with the effective symmetry group, defined as the intersection of symmetries supported jointly by the source architecture, RIS geometry and programmability, receiver configuration, and propagation environment. We show that the FMM phase transition persists within each symmetry sector and that the total DoF budget is redistributed across sectors according to symmetry constraints. A key outcome of this analysis is the distinction between physical and effective degrees of freedom. While breaking the maximal $\mathrm{SO}\left(2\right)$ symmetry does not increase the total number of electromagnetic DoF dictated by physics, symmetry reduction modifies their allocation across sectors, potentially lifting degeneracies and increasing the number of degrees of freedom that can be effectively addressed by a given excitation, RIS control, and measurement architecture, even when the total number of physical DoF remains fixed by fundamental limits. This clarifies the role of controlled symmetry breaking as a design mechanism rather than a means to surpass fundamental limits. The proposed framework bridges electromagnetic operator theory, representation theory, and RIS-enabled system design, providing both rigorous symmetry-resolved DoF accounting and actionable insights for excitation, surface programmability, and measurement strategies under practical architectural constraints. Full article

Permanent magnet synchronous motor (PMSM)-based servo actuators are fundamental to high-performance electromechanical systems. However, in energy-sensitive aerospace applications, the impact of tracking error on system-level efficiency remains insufficiently quantified. This paper establishes an energy-oriented analytical framework linking PMSM tracking accuracy to vehicle-level energy consumption and flight range. By employing a specific mechanical energy formulation, we demonstrate that tracking deviations modify aerodynamic drag and introduce additional dissipative work. Specifically, the accumulated dissipation is shown to admit a lower bound proportional to the integral of the squared tracking error, from which a range degradation bound is derived. These results reveal that “tracking-error energy” imposes a fundamental limit on achievable flight distance. A Lyapunov-based analysis further proves that minimizing this error energy reduces total aerodynamic dissipation without requiring modifications to propulsion scheduling or guidance laws. Numerical simulations comparing a conventional sliding mode controller with an advanced fuzzy-adaptive nonsingular terminal sliding mode controller confirm that enhanced servo precision directly improves velocity retention and range performance. This framework offers practical insights for designing energy-aware PMSM control strategies in energy-constrained aerospace platforms. Full article

Transmission towers, as critical infrastructure in power systems, are frequently threatened by multiple hazards such as strong winds and flood scour. Traditional structural health monitoring methods face limitations in data feedback timeliness and mechanical interpretation, making real-time condition awareness and early warning under disaster scenarios challenging. To address these issues, this paper proposes a digital twin framework for transmission tower structures, integrating Building Information Modeling (BIM), Internet of Things (IoT) technology, and the Finite Element Method (FEM) for structural health monitoring and visual warning under wind loads and flood scour effects. The framework achieves cross-platform collaboration through the FEM Open Application Programming Interface (OAPI) and Python scripts. In the physical domain, fluctuating wind loads are simulated based on the Davenport spectrum, flood scour depth is modeled using the HEC-18 formulation, and foundation constraint degradation is represented through nonlinear spring stiffness reduction. In the FEM domain, dynamic time-history analyses are conducted to obtain structural responses. In the BIM domain, a three-level warning mechanism based on stress change rate (ΔR) is established to achieve intuitive rendering and dynamic feedback of structural damage. A 44.4 m high latticed angle steel tower is employed as the case study for validation. Results demonstrate that the simulated wind spectrum closely matches the theoretical target spectrum, confirming the validity of the load input. A critical scour evolution threshold of 40% is identified, beyond which the first two natural frequencies exhibit nonlinear decay with a maximum reduction of 80.9%. Non-uniform scour induces significant load transfer, with axial forces at leeside nodes increasing from 27 kN to 54 kN. During the 0–60 s wind loading process, BIM visualization accurately captures the full stress evolution from the tower base to the upper structure, showing excellent agreement with FEM results. The proposed framework establishes a closed-loop interaction mechanism of “physical sensing–digital simulation–visual warning”, effectively enhancing the timeliness and interpretability of structural health monitoring for transmission towers under multiple hazards, providing an innovative approach for intelligent disaster prevention in power infrastructure. Full article

Local antibiotic delivery has gained a central role as an adjunct to radical debridement in chronic osteomyelitis, allowing high antimicrobial concentrations at the infection site while reducing systemic toxicity. This narrative review summarizes the current clinical evidence on commercially available antibiotic-loadable bone substitutes, with particular focus on calcium sulfate (CaSO₄)-based systems and biphasic calcium sulfate/hydroxyapatite (CaS/HA) composites. Nineteen studies were included. Differences in formulation, resorption kinetics, antibiotic elution profile and osteoconductive behavior are discussed, alongside clinical outcomes including recurrence of infection, reoperation rates and complication patterns. Finally, based on the currently available evidence and expert recommendations, practical guidance is proposed to support carrier selection in different clinical scenarios (cavitary vs. corticomedullary defects; high-risk soft tissue; polymicrobial or resistant infections). Across published series, although heterogeneous, infection eradication rates are generally high when local carriers are integrated into structured surgical protocols. Calcium sulfate carriers provide rapid resorption and robust early antibiotic release but are associated with higher rates of sterile wound drainage. In contrast, CaS/HA biocomposites demonstrate more gradual remodeling and radiographic integration, potentially improving defect consolidation and reducing wound-related morbidity, although leakage and cost considerations remain relevant. Full article

Background/Objectives: Sexual dysfunction is a prevalent and multifactorial condition affecting a large proportion of the global population, with limited therapeutic options beyond pharmacological approaches primarily targeting erectile dysfunction. This has increased interest in botanical supplements for sexual health, although mechanistic evidence and clear links between phytochemical composition and biological activity remain scarce. The present study provides an integrative evaluation of a commercial Turnera diffusa extract (Liboost^®) formulated to support sexual health by combining detailed phytochemical characterization with targeted in vitro mechanistic assays. Methods: The extract was characterized by HPLC-DAD-HRMS, enabling the identification and semi-quantification of its major constituents. A total of 49 compounds were detected, predominantly flavonoids, including luteolin- and apigenin-derived glycosides, flavonols, methoxyflavones, flavanones, and coumaroyl derivatives, with a total quantified flavonoid content of 15.9 mg·g⁻¹. Biological activity was evaluated in human cell models without cytotoxic effects at the tested concentrations. Results: Liboost^® significantly reduced PDE5 expression, inhibited aromatase activity, and moderately increased nitric oxide production. These complementary effects suggest a multi-target modulation of pathways involved in sexual function, integrating vascular, endocrine, and nitrergic mechanisms. Conclusions: Although limited to in vitro models, the findings provide mechanistic support for the biological activity of T. diffusa extracts and highlight the importance of linking phytochemical composition with functional evidence when evaluating botanical supplements. Full article

Deep learning has accelerated progress in plant disease recognition, providing strong technical support for early diagnosis and precision management. However, models often lack robustness and generalization when confronted with novel crops absent from the training set, leading to a marked performance drop in cross-unseen-crop scenarios. Cross-crop generalization for plant disease recognition requires models to identify known disease categories in crop domains never observed during training. A central challenge is that disease symptoms are strongly coupled with crop-specific appearance cues, which severely degrades generalization. Here, TDC (Text-guided feature Disentanglement Contrast) is introduced as a feature-disentanglement framework for cross-crop plant disease recognition. The proposed method employs a dual-branch visual encoder to separately capture disease semantic representations and crop-domain representations, and it leverages a frozen CLIP text encoder to use disease and crop prompts for text-guided semantic anchoring. A semantic-anchor-only contrastive disentanglement strategy is further formulated under a hybrid label space, where crop-branch features are incorporated as stop-gradient hard negatives to suppress semantic–domain information leakage and strengthen the intra-class aggregation of the same disease across crops. Residual domain-discriminative cues are mitigated via domain-adversarial learning. During inference, only the disease branch is retained for classification, improving generalization while reducing deployment overhead. Experiments demonstrate that under the PlantVillage cross-crop setting, the method achieves 98.04% and 74.29% Top-1 accuracy on seen and unseen crop domains, respectively. Moreover, it attains 81.99% on a real-world field dataset of strawberry powdery mildew and 76.31% on a low-illumination degradation set, validating robustness under realistic imaging distribution shifts. Full article

Polyvinyl chloride (PVC) is widely used in engineering applications; however, its inherent thermal instability associated with dehydrochlorination limits its processing window and long-term performance. While blending with thermoplastic polyurethane (TPU) and plasticization are common strategies to improve flexibility, their combined influence on the thermal behavior and stability of PVC, particularly when bio-based plasticizers are employed, has not been thoroughly investigated. In this study, the thermal behavior and stability of PVC/TPU blends plasticized with glycerol diacetate monolaurate, a bio-based plasticizer derived from waste cooking oil, were investigated. Dynamic mechanical analysis (DMA) and Fourier transform infrared spectroscopy (FTIR) were used to examine segmental mobility and intermolecular interactions, while scanning electron microscopy (SEM) provided insight into microstructural organization. Thermal stability was evaluated through conductivity-based dehydrochlorination measurements, complemented by thermogravimetric and derivative thermogravimetric analyses (TGA/DTG) to assess degradation behavior. The results showed that neither TPU nor the bio-plasticizer alone improved the resistance of PVC to dehydrochlorination. In contrast, ternary PVC/TPU/bio-plasticizer blends exhibited a pronounced delay in HCl evolution, accompanied by a more homogeneous phase distribution and interaction-driven modification of the molecular environment. TGA/DTG analysis indicated that this stabilization arises from altered degradation kinetics rather than a simple shift in degradation onset. Overall, the findings clarify the thermal behavior of PVC-based blends and demonstrate a sustainable formulation approach for achieving flexible and thermally balanced PVC materials while reducing reliance on potentially toxic phthalate plasticizers. Full article

Jamu is a traditional Indonesian herbal beverage widely consumed for its perceived health benefits; however, its broader acceptance is often constrained by intense sensory characteristics. This study investigated the sensory characteristics and consumer acceptance of five commonly consumed jamu beverages (wedang jahe, beras kencur, kunyit asam, temulawak, and pahitan) using an integrated sensory and consumer research approach. Commercial powdered jamu products were evaluated by 120 consumers familiar with jamu. Significant differences in consumer acceptance were observed among formulations (p < 0.05), with beras kencur and wedang jahe showing the highest liking, kunyit asam moderate acceptance, and temulawak and pahitan the lowest. Sensory characterization revealed a clear perceptual continuum across jamu beverages, ranging from sweet, refreshing, and spicy profiles to strongly bitter, herbal, and medicinal characteristics. Analysis of sensory intensity perception indicated that excessive bitterness and herbal intensity, as well as insufficient sweetness, were the primary contributors to reduced consumer liking. Furthermore, consumer responses to jamu were heterogeneous, with distinct acceptance patterns observed across different consumer groups. By linking sensory perception with consumer preference, this study provides scientifically grounded insights to support the development, reformulation, and targeted positioning of jamu beverages as modern functional drinks while preserving their traditional identity. Full article

The aim of this study was to design and validate a questionnaire to assess students’ perceptions of the use of Artificial Intelligence (AI) tools in academic tasks (PEHIA-TA). To determine the psychometric properties of the PEHIA-TA, a descriptive, exploratory and confirmatory factor analysis was carried out. The sample used in this study consisted of 546 students. The results confirmed that it is a valid and reliable scale with a five-factor structure: “Uses of Artificial Intelligence (AI)” (student opinion, knowledge and experience in relation to AI); “Perceptions of skills needed to use AI” (type of skills they consider necessary to work with this type of tool); “Plagiarism and lack of academic integrity” (issues related to what the student considers plagiarism and lack of academic integrity in order to identify possible risks or associated moral dilemmas); “Perception of the benefits of AI” (assessment of the beneficial aspects of the use of AI in the academic context by students); and “Perception of the problems of AI” (analyses how students assess the problems associated with the use of AI tools in the development of their tasks). The instrument allows for the traceability of training needs in digital literacy, as well as the formulation of institutional policies on the use of AI that contribute to the prevention of behaviours associated with academic dishonesty and ensure critical reflection by students on the risks and opportunities of AI in their educational process. Full article

This paper is a contribution to Einstein’s general relativity theory and is mostly a review of known work. It concentrates attention on four fourth-order tensors which arise on the space-time manifold describing this theory and which are very useful. These are the (Riemann) curvature tensor, the Weyl conformal tensor, the “E” tensor and the Weyl projective tensor. The first of these, the curvature tensor, plays an important role in the formulation and interpretation of Einstein’s theory. Next, the Weyl conformal tensor is introduced and its conformal properties described and with it, the Petrov classification of gravitational fields which arises from this tensor. This, in turn, gives rise to the Bel criteria for distinguishing Petrov types at a point by an alignment of certain null directions at that point. The third of these tensors, the “E” tensor, is an important tensor in calculations due to its close connection to the Ricci tensor. The fourth tensor, the Weyl projective tensor, is then described together with its properties relating to the geodesic structure of space-time. As examples of the combined usefulness of these tensors, pp-waves and generalised pp-waves are discussed and related, and a review of the geodesic structure of vacuum metrics is given. Full article