Search Results (324)

The Low-Strain Pile Integrity Test (LSPIT), standardized in ASTM D5882, is widely used as a rapid and economical non-destructive technique for assessing pile continuity in deep foundation systems. However, interpretation of LSPIT reflectograms remains strongly dependent on expert judgment and is influenced by soil–pile interaction effects such as damping and radiation losses, which can alter waveform morphology and confound automated defect screening. This study proposes a soil-aware deep learning framework that combines image-based reflectogram features with categorical geotechnical context describing the dominant soil regime at the measurement site. Reflectogram images are processed with a pretrained ConvNeXt-Large backbone, while soil information derived from Unified Soil Classification System (USCS) logs is represented as a categorical auxiliary input and mapped to a learnable embedding. The resulting multimodal design conditions waveform interpretation based on site context rather than relying on signal morphology alone. The framework is examined on an assembled benchmark of 510 expert-labeled reflectograms (404 intact and 106 defective), including a nine-site subset of 182 field records with explicit soil annotations. On the assembled benchmark, the model yields 99.41% accuracy and a weighted F1-score of 0.9941; on the nine-site subset, the observed accuracy is 99.45% with zero missed defective cases. Balanced accuracy, specificity, missed-detection rate, false-alarm rate, and confidence intervals are additionally reported to better align the evaluation with engineering screening practice. The study also states the current limits of the evidence base, including partial soil annotation, dominant-soil simplification, restricted soil coverage, and the absence of leave-site-out and interpretability-focused validation. Overall, the results support soil-aware multimodal learning as a promising proof-of-concept direction for more context-aware automated LSPIT interpretation, while also identifying the validation steps still required for broad field deployment. Full article

High-geothermal tunnels are subjected to complex thermo–hydro–mechanical (THM) coupling effects, where the interaction of temperature, seepage, and stress significantly influences the stability of surrounding rock. To address the limitations of conventional models assuming uniform initial temperature, a THM-coupled numerical model incorporating an in situ temperature gradient is established based on the Sangzhuling Tunnel. The concept of the thermal influence zone is quantitatively defined by an equivalent-radius method, and its spatiotemporal evolution is systematically investigated. In addition, the distinct roles of temperature and pore water pressure in controlling deformation and plastic-zone evolution are comparatively clarified. The results show that the thermal influence zone expands nonlinearly with increasing initial rock temperature and gradually stabilizes over time. Temperature and pore water pressure both promote the development of the plastic zone, which predominantly propagates along directions approximately 45° to the horizontal. Under the geological and boundary conditions considered in this study, temperature plays a dominant role by inducing thermal stress and degrading mechanical properties, leading to significant expansion of the plastic zone and increased vault deformation. In contrast, pore water pressure mainly reduces effective stress, thereby influencing deformation distribution, especially at the tunnel invert. Overall, THM coupling significantly amplifies surrounding rock failure compared with single-field conditions. The findings provide quantitative insights into the evolution of the thermal influence zone and its coupled control on deformation and plasticity, offering a theoretical basis for support design and stability control in high-geothermal tunnels. Full article

The installation of floating offshore wind turbines presents significant operational challenges due to coupled vessel platform dynamics and sensitivity to environmental conditions. This study proposes a response-based methodology for defining allowable operational limits and assessing operability for floating wind turbine generator (WTG) installation using the Nordic Wind concept. The approach integrates hydrodynamic modelling, time-domain simulations, and probabilistic weather-window analysis to evaluate installation feasibility under realistic offshore conditions. The methodology explicitly accounts for coupled vessel spar interactions, heading-dependent system response, and response-based failure criteria, including relative motion, gripper forces, and impact velocity. Allowable sea-state limits are derived for key installation phases and applied to multiple case studies representing different geographical locations and project scales. The results show that installation operability is governed primarily by system response rather than environmental parameters alone. Peak wave period and wave heading are identified as dominant factors, with longer wave periods leading to reduced operability due to response amplification. Across all case studies, the mating phase is found to be the most restrictive operation, controlling overall installation feasibility. Head sea conditions generally provide improved operability, while following seas lead to increased relative motion and reduced performance. The comparative analysis further demonstrates that environmental severity and project scale jointly influence installation duration. Milder environments result in higher operability, whereas harsher conditions, particularly those characterised by long-period swell, significantly reduce feasible weather windows. Larger installation campaigns increase cumulative exposure to weather downtime, even under favourable conditions. The proposed framework extends existing operability assessment methods by incorporating coupled multi-body dynamics and response-based criteria specific to floating wind installations. The results provide a quantitative basis for defining operational limits and support improved planning and decision making for offshore wind turbine installation. Full article

Glucocorticoid-induced hypertension affects over 30% of treated patients, yet its underlying mechanisms remain unclear, particularly how glucocorticoids regulate renin within the renin-angiotensin-aldosterone system (RAAS). Modeling these dynamics is difficult because only four dose-response measurements are available at a single 24-h timepoint (36 observations total), while the system depends on roughly eleven biochemical parameters spanning minutes-long receptor interactions to days-long protein secretion. Classical parameter estimation becomes unreliable in this extremely underdetermined setting, and purely data-driven methods offer limited biological interpretability. In this paper, we introduce a physics-informed neural network (PINN) framework that integrates ELISA measurements from As4.1 juxtaglomerular cells, ordinary differential equations describing glucocorticoid receptor signaling and renin transcription, and automatic differentiation to enforce mechanistic constraints. By systematically tuning synthetic-data weights (SW in {0.2, 0.3, 0.5}), we identify an intermediate value of SW = 0.3 that provides the best overall balance between predictive accuracy, accepted ensemble size, and biologically plausible parameter estimates among the tested configurations. The framework uses adaptive constraint scheduling with a plateau ramp to reduce premature convergence and introduces calibrated plausibility thresholds reflecting experimental noise. The accepted PINN ensemble (n = 5, 50% success rate) achieved R² = 0.803, compared with 0.759 for the SW = 0.5 baseline and −0.220 for the ODE-only baseline, with RMSE = 0.024. Key learned parameters (IC₅₀ = 2.925 ± 0.012 mg/dL, Hill = 1.950 ± 0.009) are biologically plausible within the model assumptions, and the best single accepted model attained R² = 0.891. Information criteria favored the PINN over the ODE model, with improvements of approximately 77× (AIC) and 5.9× (BIC). Despite training on a single timepoint, the PINN also infers full 48-h trajectories and reproduces non-monotonic dose-response behavior. This work presents, to our knowledge, the first PINN framework for glucocorticoid-mediated renin regulation and should be interpreted as a proof-of-concept approach for integrating sparse biomedical data with mechanistic constraints. The inferred parameters and temporal dynamics are best viewed as model-dependent, hypothesis-generating estimates rather than validated biological quantities. Full article

Chronic obstructive and inflammatory lung diseases share overlapping clinical manifestations and spirometric features, complicating differential diagnosis and monitoring. In this study, we performed an integrative real-time proton-transfer-reaction time-of-flight mass spectrometry (PTR-TOF-MS) breathomics analysis to assess whether exhaled volatile organic compound (VOC) profiles enable multiclass discrimination among bronchial asthma (BA), chronic obstructive pulmonary disease (COPD), cystic fibrosis (CF), and lymphangioleiomyomatosis (LAM), with healthy individuals as controls. Breath VOC data from 843 subjects were analyzed using a stratified 70/30 train/test split. An ensemble feature selection strategy based on gradient boosting (XGBoost with SMOTE within cross-validation) identified stable VOC panels (top 25% selection probability), yielding 29 VOCs and 31 features including clinical covariates. On the independent test set, the VOC-only model achieved a macro-averaged one-vs-one (OvO) AUC of 0.866 (95% CI 0.829–0.903), while the combined model improved to 0.888 (95% CI 0.853–0.919), indicating modest value of clinical variables. Pairwise analysis demonstrated highest discrimination for CF (AUC up to 0.988), whereas BA and LAM showed lower sensitivity (<0.60), likely reflecting heterogeneity and limited sample size. Given differences in age, sex, BMI, and smoking status across cohorts, confounding effects were assessed, confirming that VOC signatures retain independent diagnostic information. Disease-specific VOC interaction networks revealed distinct remodeling patterns, with central metabolites not captured by univariate analysis. Overall, PTR-TOF-MS breathomics demonstrates proof-of-concept multiclass discrimination across chronic lung diseases. Full article

The need to investigate ecological and sustainable approaches to weed management, as well as to reduce the negative environmental impact of chemical herbicides, is becoming increasingly important in modern agriculture and land management. Among alternative strategies, allelopathy is a natural mechanism by which particular plant species release bioactive compounds that can influence the germination, growth, and development of neighboring plants. Harnessing allelopathic interactions offers an opportunity to develop environmentally friendly alternatives to synthetic herbicides and helps preserve ecological balance within agroecosystems. This review examines the potential of allelopathic plant-derived substances for weed control in agricultural systems, with particular emphasis on managing weed populations in vegetable crops and gardens in urban and peri-urban areas. This study introduces the concept of allelopathy with definitions and general information. Subsequently, the paper analyzes the phenomenon’s presence at the plant level, its interactions, and the extracts obtained from allelopathic plants. The paper focuses on essential oils and fatty acid-derived compounds, such as pelargonic acid, which have demonstrated significant inhibitory effects on weed germination and biomass accumulation. Overall, the presented results establish a scientific basis for developing bioherbicides and support implementing sustainable, environmentally responsible horticultural practices. Full article

This exploratory study analyzes the relationship between cooperative oral reading and intercultural competence within the field of teacher education (i.e., training of pre-service FL teachers in primary education) at the University of Cádiz (Spain), aiming to move beyond traditional, Eurocentric conceptions of interculturality, by aligning the framework with the United Nations Sustainable Development Goals (SDGs), particularly SDGs 4, 5, 10, and 16. A mixed-methods design is adopted, combining quantitative and qualitative approaches through cooperative oral reading activities based on selected literary texts in English, French, and German addressing diversity, identity, inclusion, among others. Data are collected via recording forms administered to language assistants and two focus groups involving students and language assistants. The quantitative indicators of the study suggest that cooperative oral reading may contribute to foreign language learning, strengthen engagement between students and assistants, promote collaborative dialogue, and provide opportunities to challenge stereotypes, while interaction with native speakers (i.e., assistants) deepens understandings of cultural diversity and identity. Overall, the research proposes that cooperative oral reading is an illustrative pedagogical strategy for fostering inclusive intercultural competence and that linking classroom practices to the SDGs can contribute not only to language development but also to broader goals of equity, inclusion, and social justice. Full article

Dietary antioxidants are widely valued for their potential health benefits, but incorporating them into functional foods is not straightforward. Polyphenols are among the most abundant and important antioxidants in foods, and this review focuses on them because the same structural features linked to their health-promoting effects can also cause pronounced bitterness and astringency, ultimately limiting consumer acceptance. This review examines how these challenges are interconnected across three levels: food matrix interactions, bioavailability, and consumer psychobiology. We describe how non-covalent interactions between polyphenols, proteins, and polysaccharides can have both positive and negative effects. While these interactions may alter oral lubrication and flavour release, they also protect highly reactive bioactive compounds from gastric degradation. Furthermore, we broaden the concept of bioavailability by exploring the microbiota-mediated “colonic rescue” of polyphenols that are not released during earlier digestion. We also highlight the role of extraoral bitter taste receptors (TAS2Rs) along the gastrointestinal (GI) tract. Activation of these receptors during digestion can trigger relevant metabolic and endocrine responses, indicating that systemic absorption is not the only pathway to bioactivity. Finally, we connect these mechanisms to individual differences in food acceptance, showing that genetic factors (e.g., TAS2R38 and the salivary proteome) and psychological traits (such as neophobia and reward sensitivity) can shape rejection or flavour-nutrient learning. Overall, the successful development of functional foods will require a “sensory-by-design” approach. This strategy utilises matrix interactions strategically to improve both consumer acceptance and physiological efficacy. Full article

The current global population aging trend has intensified, especially in rural areas. As vital spatial carriers supporting multiple activities of older adults, rural human settlements have become key settings for addressing the challenges of aging. However, current efforts to improve rural human settlements primarily focus on enhancing the overall appearance of villages. This approach fails to adequately address the specific needs of older adults. Chongqing is a typical mountainous city, facing deep aging and significant regional disparities. It is also confronted with realities such as spatial fragmentation, scattered facilities, and low service accessibility. So Chongqing urgently requires systematic assessment and targeted interventions. To transcend the traditional one-size-fits-all governance in rural human settlements, the concept of “rural human settlements for aging” is introduced in this article, to establish an age-sensitive governance logic. Based on 2023 cross-sectional data, this article evaluates the level of the rural human settlements in Chongqing by establishing an index system, and employs global spatial correlation and local spatial correlation to analyze the spatial correlation patterns. The geographic detector model and the obstacle degree model are used to delve into the key obstacle factors influencing and hindering rural human settlements. The results indicate that despite exhibiting a pronounced spatial clustering pattern, spatial disparities remain quite evident. The spatial differentiation presents a pattern of “high in the west and low in the east, led by a single core area.” Elderly service facilities constitute the main external obstacle. The relationship between social security and family support within welfare systems represents the primary internal obstacle. Transportation conditions serve as the key interactive obstacle. Based on an analysis of the primary obstacles in each region, the promotion strategy is categorized into three types: facility enhancement type, characteristic amplification type and comprehensive upgrading type. This article aims to advance the transformation of rural human settlements from “universal design” to “age-friendly design.” It provides a reference framework for rural human settlements development in the context of an aging population. Full article

Although the transition from the Waterfall model to Agile practices has accelerated software delivery, it has often weakened documentation practices, contributing to persistent information asymmetry between Product Managers and Developers. This study introduces an LLM-based reverse engineering framework designed to assist product management workflows by analyzing source code and generating enriched development tickets. The proposed Interactive Product Management Assistant leverages the long-context capabilities of Gemini 1.5 Pro together with a context-caching mechanism to analyze large codebases, identify ambiguities in product requests, highlight potential edge cases, detect possible cascading dependencies (“domino effects”), and generate code pointers that guide developers to relevant implementation areas. The framework was evaluated through case studies on several open-source projects, including WordPress, ERPNext, Ghost, and Odoo. The results suggest that the system can support requirement clarification, improve visibility of potential implementation impacts, and reduce exploratory effort during code analysis. In addition, the implemented preprocessing and caching mechanisms reduce analysis costs and improve operational efficiency during iterative interactions. Rather than providing a large-scale quantitative before-and-after comparison, this paper presents a qualitative case study and a proof-of-concept implementation to demonstrate the feasibility of the proposed approach. Overall, the findings demonstrate the feasibility of using LLM-assisted reverse engineering to support requirements analysis and product–developer collaboration, highlighting the potential of AI-based tools to complement traditional requirements engineering practices in complex software projects. Full article

Reproductive performance is a key determinant of overall livestock productivity. In both beef and dairy systems, reproductive failure represents a leading cause of cow culling. Reproductive traits are complex in nature and present a low heritability in general. Additionally, the collection of such phenotypes usually relies on indirect measures of fertility, such as conception success. Therefore, further investigation into genetic and non-genetic factors of reproductive traits in cattle is necessary. The hosts’ microbiome plays a crucial role in vertebrate biology, including reproduction. We, therefore, hypothesize that microbiome indicators may serve as a biomarker of fertility. This study explored the relationship between vaginal microbiome profiles and pregnancy among three beef cattle genetic groups using field data. Vaginal swabs were collected from 74 cows at Fort Keogh, MT, including 23 Angus, 23 Hereford Line 1, and 28 crossbreds, and DNA was extracted and analyzed via 16S rRNA gene amplification. Significant differences in alpha diversity (p < 0.05) were found among Line 1 cows compared to Angus and crossbreds in many indicators of alpha diversity. Pregnancy status did not influence alpha diversity of samples significantly, but trends toward significance were observed. PERMANOVA analysis indicated that genetic groups and pregnancy status affected microbial composition (p < 0.05), but their interaction was not significant. Each genetic group showed unique compositions of operational taxonomic units (OTUs), with higher proportions of Ureaplasma and Mycoplasma families in Line 1. Additionally, variations in microbial communities were observed between pregnant and non-pregnant cows, with certain uncultured bacteria more prevalent in non-pregnant cows. While field data are useful for such studies and represent a real production system, better-designed experiments are necessary to validate findings and test hypotheses. These results suggest variation in vaginal microbiomes across breeds and pregnancy status, emphasizing the need for further research to identify factors affecting these changes. Full article

Detonation-based combustion has re-emerged as a promising pathway for enhancing the efficiency and compactness of future aerospace propulsion systems, motivated by the intrinsic pressure-gain characteristics of detonative heat release. This review provides a comprehensive synthesis of the physical foundations, technological progress, and practical limitations associated with pulse detonation engines, rotating detonation engines, and standing or oblique detonation wave concepts. By tracing the evolution from early theoretical models and laboratory-scale demonstrations to engine-relevant configurations, this article highlights how detonation physics, ignition mechanisms, wave stability, and flow–structure interactions collectively govern propulsion performance. Particular attention is paid to recent experimental and numerical studies, with the review focusing on their technological impact and on the feasibility of integrating detonation-based propulsion concepts into practical aerospace systems. The analysis evaluates these approaches’ potential to enhance system-level performance compared to conventional propulsion technologies, while highlighting key challenges associated with scalability, operability, and compatibility with existing aerospace architectures. The review further identifies emerging design strategies, including geometry tailoring, adaptive flow control, and hybrid architectures, as key enablers for extending operability and system integration. Overall, the findings indicate that future progress in detonation-based propulsion will depend less on demonstrating detonation itself and more on achieving robust, controllable, and scalable implementations suitable for realistic aerospace applications. Full article

Diet is a major, modifiable determinant of cardiometabolic, cancer, and inflammatory disease risk, yet individuals frequently exhibit substantial heterogeneity in metabolic and clinical responses to similar dietary exposures. Genetic susceptibility and its interplay with diet plausibly contribute to this variability, motivating gene–diet (G×D) interaction research and the broader ambition of precision nutrition. Translation has lagged, however, because interaction effects are typically modest, context-dependent, and difficult to reproduce, particularly in the presence of pervasive dietary measurement error, heterogeneous exposure definitions, and stringent multiplicity correction. A methodologically oriented synthesis is presented across eight domains of contemporary G×D epidemiology: classical regression interaction models; efficient study designs; dietary assessment and measurement error; dietary patterns, mixtures, and non-linear modeling; genome-wide and polygenic approaches; causal inference frameworks; multi-omics integration; and machine learning. Central concepts include the recognition that “interaction” is a scale-dependent estimand and that transparent reporting of coding choices and effect-modification metrics—including additive interaction when relevant for public health interpretation—is essential. Credible inference further depends on high-quality, harmonized dietary phenotyping with explicit energy adjustment and, where feasible, biomarker calibration, alongside robust control of population structure and gene–diet correlation using ancestry adjustment, mixed models, and family-based designs. Genome-wide and polygenic risk-based approaches expand discovery potential but require disciplined multiplicity strategies, discovery-replication workflows, and explicit evaluation of portability and equity across ancestries. Causal inference methods can strengthen etiologic interpretation when assumptions are defensible and sensitivity analyses are routinely implemented. Multi-omics and machine learning may enhance mechanistic and predictive insight, but only under rigorous quality control, validation, and reproducible pipelines. Overall, harmonized measurement, clear estimands, multi-ancestry replication, and integrated evidence pipelines are pivotal for producing robust and actionable G×D evidence. Full article

This paper investigates the fundamental mechanisms of technological change in complex systems by analyzing how the evolution of embedded subsystems dictates the trajectory and sets the tempo of a host technology. Building on the theoretical framework of technological parasitism, the study conceptualizes host systems having a modular architecture—such as smartphones—as evolving through dynamic, coevolutionary interactions with their constituent subsystems. These relations gradually shift from parasitic reliance to mutualistic and ultimately symbiotic interactions. Central to this research is the concept of subsystems as pacemakers. Methodologically, this research employs a longitudinal, mixed-methods approach, combining an 18-year case study of the iPhone (2007–2025) with time-series regression and log–log hedonic pricing models. Key findings are: (a) Temporal precedence: Advances in subsystems (e.g., Bluetooth protocols) consistently precede host releases. The integration lag has contracted from three years to one, signaling an acceleration in symbiotic coupling and highlighting Bluetooth as a systemic pacemaker whose evolutionary tempo anticipates shifts in the wider smartphone architecture. (b) Differential evolutionary pressure in technological host systems: While camera resolution exhibited the highest exponential growth (+16.73%), it remained a secondary driver of systemic evolution. (c) Economic pacemakers: Hedonic analysis identifies battery life as the dominant evolutionary predictor (standardized beta = 0.77). With an elasticity of approximately 0.30, a 1% gain in battery performance correlates to a 0.3% increase in nominal price, whereas display and camera resolution exert significantly less influence on the system’s valuation and trajectory. These findings reveal that subsystems evolve—and exert influence—at different speeds and with different degrees of systemic leverage. Overall, the proposed theory shows that subsystem evolution functions as a leading indicator of forthcoming host–system transitions. By identifying which subsystems act as temporal pacemakers, this research contributes new design rules for forecasting technological generations and optimizing R&D strategies in complex, multi-component innovations. Hence, the study demonstrates that mastering complex innovation requires a granular understanding of the asynchronous rhythms between a host technology and its constitutive parts. Full article

Background: Machine learning (ML) has been widely adopted in decision-making, making fairness a central ethical and scientific priority. We developed the Themis chatbot, a Large Language Model (LLM) system designed to explain concepts of ML fairness in an accessible, conversational format. Methods: The development followed four stages: (1) curating a document corpus of 286 peer-reviewed publications on ML fairness; (2) development of Themis by combining a modern LLM (OpenAI’s GPT-4o) with Retrieval Augmented Generation (RAG); (3) creation of a 340-item benchmark dataset, the FairnessQA; and (4) evaluating performance against state-of-the-art non-augmented LLMs (DeepSeek R1, GPT-4o, GPT-5, and Grok 3). Results: For the multiple-choice questions, Themis achieved an accuracy of 96.7%, outperforming DeepSeek R1 (90.0%), GPT-4o (89.3%), GPT-5 (92.0%), and Grok 3 (86.7%), and the overall difference was statistically significant (χ²(4) = 10.1, p = 0.038). In the closed-ended questions, Themis achieved the highest accuracy (96.7%), while competing models ranged from 78.0% to 84.0%, and the overall difference was significant (χ²(4) = 23.9, p < 0.001). In the open-ended questions, Themis achieved the highest mean scores for correctness (M = 4.62), completeness (M = 4.59), and usefulness (M = 4.56), and differences were statistically significant (correctness: F(4, 195) = 20.91, p < 0.001; completeness: F(4, 195) = 7.76, p < 0.001; usefulness: F(4, 195) = 2.90, p < 0.001). By consolidating scattered research into an interactive assistant, Themis makes fairness concepts more accessible to educators, researchers, and policymakers. This work demonstrates that retrieval-augmented systems can enhance the public understanding of machine learning fairness at scale. Full article