Search Results (3,112)

The valorization of agri-food by-products represents a key strategy for improving sustainability and promoting circular economy principles in food systems. Pumpkin seed press cake is a protein-rich by-product with potential application in bakery products. The aim of this study was to evaluate the feasibility of using defatted pumpkin seed press cake flour (PPSF) as a major ingredient in cookie formulations and to optimize its incorporation in order to maximize nutritional quality and sensory acceptability. Chemical characterization showed that PPSF has a superior nutritional profile compared to wheat flour, containing 55.75% protein, 8.78% minerals, and 6.15% total dietary fiber, along with significantly higher levels of total phenolics, total carotenoids, and β-carotene (0.26 mg/100 g). Formulation optimization using response surface methodology (RSM) enabled a high inclusion level of 69.61% PPSF, with 41.32% sugar and a baking time of 9 min and 29 s. The developed predictive models for diameter, thickness, overall acceptability, and bending stiffness were highly significant (p < 0.05) with a non-significant lack of fit (p > 0.05), confirming their statistical reliability for exploring the design space. The optimized C-PPSF (defatted pumpkin seed press cake flour) cookies showed a significant nutritional improvement, with protein content increasing from 13.05% to 30.17% and antioxidant capacity (DPPH) rising from 2.90% to 7.10%. While the enriched cookies had a darker color (L* 51.98) and reduced snapping force (39.7 N) due to gluten dilution, they maintained stable geometric parameters and achieved higher sensory scores for aroma, taste, and overall acceptability compared to the control. The main finding of this study is that PPSF can replace a substantial proportion of wheat flour in cookies while maintaining consumer acceptability and significantly improving nutritional quality. The optimized formulation with approximately 70% PPSF shows that this by-product has the potential to serve as a major ingredient in bakery products rather than only as a nutritional supplement. These results confirm that PPSF is a powerful functional ingredient that supports zero-waste manufacturing and provides a foundation for its broader use in bakery formulations within circular economy approaches. Future research should focus on shelf-life stability, bioaccessibility of bioactive compounds, volatile aroma profiling (e.g., GC–MS analysis), and industrial-scale validation of PPSF-based formulations. Full article

ARGLU1 (Arginine and Glutamate Rich1) is a newly identified nuclear protein with suggested multifunctional roles that may be implicated in the pathogenesis and therapeutic resistance of glioma, the most common primary malignant brain tumor. The high heterogeneity and treatment resistance of gliomas pose central challenges in clinical management. ARGLU1 has been implicated in maintaining genomic stability and may contribute to tumor progression by regulating RNA splicing and DNA damage repair pathways. This review systematically summarizes the structural and functional features of ARGLU1 and discusses its potential molecular mechanisms in glioma. These include its influence on the spliceosome assembly, alternative splicing events, and key DNA repair pathways such as homologous recombination (HR) and Fanconi anemia (FA). Furthermore, it discusses the hypothesis that ARGLU1 may enhance DNA repair capacity and thereby influence glioma resistance to temozolomide (TMZ) and radiotherapy. Targeting ARGLU1 may offer a strategy to overcome this resistance. Finally, the review outlines current research limitations and future directions, aiming to provide a new theoretical foundation for the precision treatment of glioma. Full article

Natural microbial pigments offer important advantages and are widely studied for food applications. We investigated the biosynthetic pathways, characteristics, and bioactivities of the orange–red pigment produced by Bacillus velezensis YM–3, a strain isolated from the traditional Pixian Douban condiment. Whole-genome sequencing revealed complete pathways for melanin, phytoene, and heme biosynthesis. The purified extracellular pigment was characterized using ultraviolet–visible spectroscopy, Fourier-transform infrared spectroscopy, nuclear magnetic resonance spectroscopy, and ultra-performance liquid chromatography–high-resolution mass spectrometry; it was preliminarily characterized as melanin-like pigment. The pigment was highly soluble in alkaline solutions, moderately soluble in water, and insoluble in common organic solvents. It exhibited strong photostability and remained stable at low temperature, precipitated under acidic conditions, and showed high stability under alkaline environments. Furthermore, the pigment demonstrated in vitro free radical scavenging activity. Hence, this study provides a scientific foundation for exploring the potential utility of B. velezensis YM–3 and its pigment metabolites as functional agents. Full article

To address the critical safety challenges of flooding induced by ship–ice collisions in Arctic shipping routes, this study proposes an Observe–Orient–Predict–Decide–Act (OODA-P)-enhanced closed-loop intelligent damage control decision-support framework integrated with predictive simulation. To address the limitations of existing systems—namely, weak polar adaptability and the absence of a decision feedback loop—this research presents three core findings: (1) A fast time-domain floating condition model was developed by coupling topside icing with progressive flooding. Numerical simulations indicate that neglecting ice accretion leads to an underestimation of the long-term heel angle and transverse stability by 4.4% and 4.5%, respectively, validating the necessity of incorporating coupled ice loads. (2) A serial dual-channel prediction and evaluation mechanism, integrating “situation evolution prediction” and “decision efficacy evaluation,” was designed. This mechanism can proactively forecast long-term deterioration trends in the floating condition within 0.3147 s of acquiring damage information, capable of identifying and flagging potentially high-risk emergency plans before their execution, thus preventing adverse outcomes. (3) The proposed framework was validated through typical polar scenarios and 111 damage control training sessions across three batches, with the full-loop logic flow completing in under 3 s. Compared with the traditional OODA loop, the average emergency response time was reduced from 26.9 to 22.7 min (a 15.5% reduction), while the initial response success rate improved from 74.7% to 97.3% in a simulated training environment. By enabling “virtual trial-and-error” prior to execution, this framework demonstrates the potential to augment traditional experience-based damage control with proactive, simulation-driven decision support, marking a step towards more intelligent interventions. Through the explicit coupling of topside icing and progressive flooding into real-time predictions, this work provides a foundation for further development of polar-adaptable intelligent damage control systems. Full article

In order to improve the speed regulation accuracy, dynamic response and operation robustness of an automatic guided vehicle (AGV) in a complex road disturbance environment, this paper studies an adaptive AGV speed regulation system based on EKF state estimation on the basis of AGV dynamics modeling and adaptive control. Firstly, through the electrical-mechanical coupling modeling of the AGV drive system, state space construction and external unknown disturbance equivalent design, a unified electromechanical coupling simulation and physical verification environment is built, which lays a model foundation for the research of the speed control algorithm. Secondly, based on the optimal control model of PID and LQR with first-order lead compensation, an EKF adaptive speed regulation model is constructed by combining the extended Kalman filter and adaptive control to realize the online estimation and dynamic compensation of unknown disturbances. Finally, based on MATLAB/Simulink simulation platform and the STM32 embedded experimental platform, the rationality and robustness of the proposed speed control strategy are verified by speed-mutation conditions, load-disturbance condition and a physical verification experiment. The results show that the overshoot of the EKF adaptive control strategy is only 1.8%, which is 84.1% lower than that of PID control and 61.7% lower than that of LQR control. The rise time is 42% shorter than PID and 23% shorter than LQR. The recovery time under load disturbance is 58% shorter than that of PID and 31% shorter than that of LQR. EKF adaptive control is significantly better than PID and LQR in overshoot, rise time and control stability. The disturbance rejection ability and dynamic recovery speed are greatly improved, which can ensure the high robustness and smooth operation of the AGV speed control system under complex working conditions, effectively enhance the response and compensation ability of the system to sudden disturbances, and better meet the actual needs of AGV speed control in complex engineering scenarios. Full article

Topological data analysis (TDA) has evolved into a flexible and robust paradigm for obtaining qualitative, geometry-inspired insights from high-dimensional, noisy, and complex data. Grounded in algebraic topology, geometry, statistics, and machine learning (ML), TDA provides multiscale descriptions through persistent homology, Mapper (a graph-based method that summarizes the shape of high-dimensional data), and related topological signatures that are often inaccessible to standard linear and metric methods. In recent years, and especially during 2024–2025, TDA has expanded rapidly across science, engineering, biomedical research, and socio-economic studies, while also being integrated with modern learning paradigms such as deep learning (DL) and graph learning. This survey summarizes recent developments in TDA using a carefully selected set of articles, with emphasis on 2024–2025. We first present the mathematical and computational foundations of TDA, covering simplicial complexes, filtrations, persistent homology, the Mapper algorithm, and computational advances such as data simplification, stability, and efficiency. We then review applications in time series and dynamical systems, biomedical imaging and precision medicine, engineering and physical sciences, finance and risk analysis, DL and interpretability, and security and critical infrastructure systems. Throughout, we highlight how TDA can extract informative features, function as a model component, and provide a conceptual lens for studying complex systems. However, the survey also emphasizes recurrent failure patterns: TDA performance is highly sensitive to filtration, embedding, and vectorization choices; aggressive simplification can dilute or remove informative topological signals; and integration into standard ML workflows still lacks uniform validation and reporting protocols. We conclude by outlining key challenges—including scalability, statistical foundations, interpretability, and compatibility with rapidly evolving artificial intelligence (AI) paradigms—and by identifying directions for future research. The survey also provides a unifying design perspective for TDA systems, highlighting methodological trade-offs and emerging research directions for integrating topology with modern ML. Full article

This review examines the emerging role of machine learning (ML) in pharmaceutical chemistry, with emphasis on molecular design, synthetic feasibility, and structure–property–performance (SPP) relationships. By enabling pre-synthesis prediction of physicochemical properties, reaction pathways, and pharmaceutical performance, ML can reduce empirical trial-and-error experimentation and support more efficient exploration of chemical space. A structured narrative review design with PRISMA-aligned systematic search elements was used to evaluate 101 studies, enabling transparent literature identification, eligibility screening, and thematic synthesis across heterogeneous ML applications in pharmaceutical chemistry. This review examines structure–property relationships (SPRs) and property–performance relationships (PPRs), with emphasis on key pharmaceutical endpoints such as solubility, permeability, stability, dissolution, and bioavailability. An integrated SPP framework is proposed to connect molecular structure, intermediate properties, and final performance outcomes while incorporating retrosynthetic analysis and experimental feedback and closed-loop optimization. Recent frontier developments are also discussed, including molecular foundation models, multimodal language–graph models, diffusion-based molecular generation, E(3)-equivariant models, and MolMIM-like latent-space optimization. This review also covers co-folding and joint ligand–protein modeling, Boltz-2-like affinity prediction, AlphaFold 3-related biomolecular interaction modeling, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction. Key limitations include dataset leakage, benchmark inconsistency, assay variability, conformational and protonation-state effects, reproducibility challenges, regulatory constraints, and the gap between computational prediction and prospective experimental validation. Future progress is expected to depend on hybrid physics–ML models, uncertainty-aware prospective validation, autonomous experimentation, explainable artificial intelligence, and sustainability-aware molecular design. Overall, ML is evolving from a predictive tool into a chemically informed decision-support framework for rational, synthesis-aware, and experimentally validated pharmaceutical development. Full article

Against the coordinated advancement of building a maritime power, high-quality development of marine tourism and ecological civilization construction, realizing positive interaction between marine resource conservation and tourism industrial development has emerged as a pivotal issue for high-quality growth in coastal regions. Taking 11 coastal provincial-level administrative regions in China spanning 2008 to 2024 as the research sample, this paper first establishes an evaluation indicator system covering marine resources and the tourism industry. It further adopts an integrated empirical framework encompassing the coupling coordination degree model, spatial Markov chain model, obstacle degree model, fixed-effect model and geographically and temporally weighted regression (GTWR) model to systematically unpack the spatiotemporal differentiation characteristics, internal restrictive obstacle factors and external driving determinants of the two-system coupling coordination. On this basis, a marine resource compensation mechanism for tourist destinations is formulated. Empirical results demonstrate four core findings: (1) In terms of temporal evolution, the overall coupling coordination level keeps rising and goes through three phases: initial development, rapid improvement and post-shock recovery. After a short-term decline triggered by the pandemic, the index rebounds markedly after 2023, showing that the two systems can recover and stabilize. (2) In terms of spatial layout, a persistent stratified spatial pattern featuring “higher coordination in southern coast versus lower coordination in northern coast with three-tier hierarchical differentiation” is identified; high-level neighboring regions exert prominent positive spatial spillover effects, whereas low-level adjacent areas are prone to fall into development lock-in traps. (3) For internal constraint obstacles, the marine resource subsystem is persistently restricted by resource exploitation limits and coastal spatial scarcity, while the dominant bottleneck of the tourism industrial subsystem shifts from insufficient market scale to inadequate human capital supply. (4) Regarding external driving forces, the proportion of tertiary industry and the digital infrastructure constitute core driving contributors, whereas marketization progress and opening-up degree act as primary restrictive factors, with pronounced spatial heterogeneity existing across all driving indicators. Finally, in line with the quasi-public-good attribute and ecological externality of marine resources, this study constructs a differentiated and synergistic marine resource compensation mechanism from three dimensions: stakeholder identification, compensation implementation pathways and institutional guarantee systems. The proposed framework provides theoretical references and practical policy options to facilitate high-level coupling and coordinated development between marine resource preservation and the coastal tourism industry. The marginal contribution of this research lies in integrating coupling coordination measurement, obstacle factor diagnosis, driving mechanism identification and compensation mechanism design into an integrated analytical framework, which delivers theoretical foundations and operable policy solutions for coastal marine resource protection, tourism industrial upgrading and differentiated compensation system construction. Full article

Per- and polyfluoroalkyl substances (PFASs) are persistent emerging contaminants characterized by high environmental stability and biotoxicity. Ubiquitous detection of these contaminants across aquatic environments poses severe threats to ecosystem stability and human health, while constructed wetlands (CWs) serve as a sustainable low-carbon alternative for the remediation of PFAS-laden wastewater. However, traditional mechanisms such as matrix adsorption, phytoaccumulation, and microbial transformation often suffer from low efficiency, rapid saturation, and incomplete degradation. To overcome the above drawbacks, the arbuscular mycorrhizal fungi (AMF)–plant–microbe synergistic consortium has become a promising remediation candidate, which facilitates PFAS immobilization and biodegradation via symbiotic crosstalk among three components. This paper reviews recent advancements in PFAS remediation within AMF-facilitated systems, examining fundamental synergistic mechanisms, treatment efficiencies, and key influencing factors. We propose several optimization strategies, including substrate modification, operational parameter refinement, and the integration of advanced technologies. Furthermore, we emphasize the necessity of elucidating the molecular pathways governing long-chain PFAS degradation and addressing current bottlenecks in engineering applications. Future research should prioritize molecular interaction level interaction mechanisms, the development of anti-interference systems, and field-scale validation. This review provides a theoretical foundation and technical framework for leveraging AMF–plant–microbe synergism to enhance PFAS removal in CWs. Full article

Background/Objectives: Otomycosis is a recurrent fungal infection of the external auditory canal. This disease is difficult to manage with current antifungal agents due to irritation, ototoxicity risk, and emerging resistance. Natural essential oils have been proposed as alternatives, yet their clinical application in otic formulations remains limited due to their poor solubility and stability. In this study, we report the first ear-drop formulation combining microemulsified Ocimum gratissimum essential oil and acetic acid for otomycosis treatment. Methods: The essential oil was quality-validated with eugenol content superior to 60%. A systematic formulation study was performed, and the Tween 20/isopropanol (4:1, w/w) mixture was selected as the optimal surfactant system, yielding a stable microemulsion with high encapsulation efficiency (~98%) and relevant physicochemical stability (up to 28 days). The final formulation containing 1% acetic acid and 0.3% micro-emulsified essential oil met pharmacopeial requirements in terms of appearance, pH, viscosity, and microbial limits. Results: Importantly, this micro-emulsified eardrop demonstrated significantly greater in vitro antifungal activity than 3% boric acid and 2% acetic acid eardrops in twelve clinical fungal isolates from Vietnamese swimmers, especially on Curvularia, Cunninghamella, Aspergillus terreus, and Bipolaris. Although less pronounced than 1% clotrimazole, the finalized formulation demonstrates better antifungal kinetics and a broader activity spectrum. Conclusions: This work provides relevant experimental evidence on the use of Ocimum gratissimum essential oil in a microemulsion delivery system and demonstrates its efficacy against clinically relevant otomycosis pathogens. The results establish a foundation for future in vivo and clinical studies. Full article

The European Common Agricultural Policy (CAP) commits, in its Treaty foundation, to a fair standard of living for the agricultural community and, in its post-2014 architecture, to enhanced territorial cohesion. Yet repeated reform cycles have left the regional concentration of payments in many Member States visibly untouched. This paper asks why. We document the persistence of the territorial concentration of CAP transfers across the 13 Greek NUTS-2 regions over the 2010–2025 period (€47.65 bn cumulative), identify the CAP design mechanisms that mechanically reproduce it, and quantify how much of the observed aggregate stationarity is the artefact of compositional shifts versus genuinely offsetting forces. Using the universe of payment disbursements aggregated to 13 NUTS-2 regions and 51 NUTS-3 prefectures, we (i) test for σ- and β-convergence and Lorenz dominance, (ii) decompose Theil-T between and within regions and across Pillar I/Pillar II, and (iii) run four counterfactual simulations: Pillar II share held at its 2010 level, Article: 17-style capping at a 12–15% NUTS-2 ceiling, an Article: 29-style lower-tail floor, and a concentration-elasticity perturbation of the top region. The territorial distribution of support proves strikingly stable: standard inequality measures stay within a narrow band for sixteen consecutive years, and the ranking of regions barely changes, so formal convergence tests detect no narrowing over time. Three messages follow. First, this persistence is not accidental but built into the architecture of the CAP—through historical-reference entitlement values, the per-hectare logic of the Basic Payment Scheme, the geographic concentration of coupled support in cotton and livestock, and the cadastral fragmentation of the island prefectures. Second, the apparent stability conceals two large and opposing forces: the post-2014 expansion of Pillar II has reduced regional disparities, while a widening of the Pillar I distribution has increased them by almost the same amount, so aggregate stationarity reflects policy effort cancelling out, not the absence of it. Third, the instruments already in the CAP toolbox have real redistributive power: capping the largest region’s envelope and redistributing the surplus to lagging regions, or introducing a lower-tail floor, would roughly halve measured inequality. Therefore, the spatial concentration of CAP transfers in Greece is a designed equilibrium rather than an unsolved residual, and reducing it requires instruments that act asymmetrically on the top of the distribution. Full article

Ground improvement for foundations supported on soft soils is traditionally problematic because of low bearing capacity and a large magnitude of settlement. One sustainable method for mitigating these problems is the use of stone columns (SCs), particularly geosynthetic-encased stone columns (GESCs), to improve load transfer, confinement, and consolidation. This review critically synthesizes recent advances in the analysis and design of SC systems using experimental investigations, numerical simulations, and machine learning (ML)-based methodologies. The article indicates that GESCs, when integrated with modern data-driven techniques, especially hybrid metaheuristic ML models, represent a reliable and sustainable solution for soft soil stabilization. Traditional analytical and empirical methods remain useful; however, they are often inadequate for very soft soils (Undrained shear strength (c_u) < 15 kPa), where excessive bulging and large deformations dominate system behavior. Consequently, intelligent hybrid modeling approaches are emerging as the next generation of optimized, data-driven design tools in geotechnical engineering. Different failure mechanisms of SCs, including bulging, punching shear, and general shear failure, are critically discussed along with the governing design parameters. Previous studies consistently indicate that spacing ratios within the range of s/D = 2–3 can improve the bearing capacity ratio (BCR) by approximately 50–100%. Numerical and experimental studies further demonstrate that SC systems can transfer nearly 60–80% of the applied load through stress concentration and soil arching mechanisms. Furthermore, the application of geosynthetic encasement enhances the performance of SCs in very soft soils by increasing confinement, reducing lateral deformation, and enhancing bearing capacity by nearly 3–6 times compared with ordinary SCs. The review also evaluates the growing role of artificial intelligence techniques in forecasting settlement and bearing capacity behavior. ML techniques such as artificial neural networks (ANN), support vector regression (SVR), random forest (RF), XGBoost, and hybrid metaheuristic–ML models have shown high predictive capability, often achieving prediction errors below 5%. Despite these advancements, many existing ML studies still suffer from limited datasets, a lack of generalization, and insufficient incorporation of physical mechanisms. Full article

Precision agriculture increasingly relies on high-resolution, long-term remote sensing to delineate sub-field management zones. However, traditional spatial zonation assumes temporal stationarity, utilizing seasonal aggregates that obscure transient, intra-annual stress signals. This study develops a data-driven framework to characterize both persistent and non-stationary crop water use dynamics by integrating monthly, 30-m evapotranspiration (ET) data from OpenET (2000–2025) with zero-shot temporal anomaly detection. A pre-trained time-series foundation model (Chronos-T5-Small) generated counterfactual expectations for sub-field ET, quantifying deviations using a mean absolute error-based anomaly score. Unsupervised clustering of these anomaly scores with longitudinal ET metrics partitioned the landscape into dynamic biophysical regimes. Cross-registered against legacy persistence mapping based on seasonal totals, the foundation model showed strong directional agreement (86.1%, Cohen’s Kappa = 0.716) in identifying chronically constrained zones across 869 shared active pixels. Crucially, the framework identified 966 historically persistent pixels undergoing stability decay, of which 95.3% were statistically verified via paired t-tests to have collapsed into the field’s baseline variance pool. Furthermore, counterfactual anomaly detection isolated zones of recent acute divergence, differentiating enduring edaphic constraints from sudden system disruptions. This approach demonstrates how foundation models can transition from purely predictive engines to diagnostic instruments, advancing operational precision agriculture. Full article

Housner’s classical rocking model assumes a rigid base, which often leads to inaccurate seismic assessments under real–world soil conditions. This study quantitatively establishes the applicability limits of the rigid–base assumption and defines a reference range for its validity. To address these limitations, a novel soil–structure interaction (SSI) rocking model was developed using Lagrange’s formulation, incorporating an event–driven spring–dashpot mechanism to characterize contact forces. Validation against LS–DYNA simulations and existing compliant base models confirms high predictive accuracy across diverse geometries and ground motions. Crucially, an empirical formulation for the interface stiffness of rocking structures was derived to ensure the alignment of the proposed analytical model with numerical observations, thereby enhancing its practical utility in industrial design. Our findings reveal that rocking behavior depends not only on soil stiffness but also on the inherent stiffness of the structure. Specifically, soft soils significantly alter rocking initiation thresholds and amplify peak angles. The proposed SSI–rocking model provides a computationally efficient and FE–compatible tool for optimizing the seismic stability of unanchored structures on flexible foundations. Full article

The launch of sixth-generation (6G) mobile networks is expected to introduce significant variability in Quality of Service (QoS), driven by environmental conditions, traffic heterogeneity, device diversity, and network slicing policies. Existing clustering-based QoS analysis methods rely primarily on using only KPI variables, such as latency, throughput, jitter and packet loss datasets, and classical geometric validity metrics, providing limited insight into the stability, predictive capability, and operational relevance of discovered clusters. To address these limitations, this study proposes a context-aware QoS modelling framework and a unified network-centric cluster evaluation protocol. A dataset comprising 2345 observations is constructed by integrating QoS indicators with contextual and operational variables, including weather conditions, time of day, geographic region, traffic type, device class, and slice identity. Four clustering paradigms, k-means, DBSCAN, spectral clustering, and Deep Embedded Clustering (DEC), are evaluated using both classical metrics and three proposed evaluation measures: Contextual Cluster Stability (CCS), QoS-Regime Predictive Consistency (QPC), and Slice-Level Reliability Separation (SLRS). The results demonstrate that classical clustering metrics alone are insufficient for assessing QoS regime quality. While DEC achieves strong structural performance in latent space, all methods exhibit near-zero predictive consistency and weak reliability separation. These findings reveal a consistent divergence between structural clustering quality and operational usefulness, indicating that unsupervised clustering alone is insufficient for QoS prediction and reliability-aware decision-making. The proposed framework provides a foundation for evaluating clustering methods in context-sensitive network environments and highlights the need for integrating temporal modelling and reliability-aware learning in future 6G network optimisation systems. Full article