Search Results (3,415)

This review provides a comprehensive analysis of advanced control strategies and operational optimization of energy systems, focusing on heat pumps, with an emphasis on their role in enhancing energy efficiency and operational flexibility. The study concentrates on methods supported by artificial intelligence algorithms, highlighting Model Predictive Control (MPC), Reinforcement Learning (RL), and hybrid approaches that combine the advantages of both. These methods aim to optimize both the operation of heat pumps and their interaction with thermal energy storage (TES) systems, renewable energy sources, and power grids, thereby enhancing the flexibility and adaptability of the systems under real operating conditions. Through a systematic analysis of the existing literature, 95 studies published after 2019 were examined to identify research trends, key challenges such as computational requirements and algorithm interpretability, and future opportunities. Furthermore, significant benefits of applying advanced control compared to conventional practices were highlighted, such as reduced operational costs and lower CO₂ emissions, emphasizing the importance of heat pumps in the energy transition. Thus, the analysis highlights the need for digital solutions, robust and adaptive control frameworks, and holistic techno-economic evaluation methods in order to fully exploit the potential of heat pumps and accelerate the transition to sustainable and flexible energy systems. Full article

This study aimed to elucidate the impact of Persian Gum (PG; Amygdalus scoparia Spach) on the heat-induced aggregation and interfacial behavior of whey protein isolate (WPI). To achieve this, pure WPI and mixed WPI-PG systems were subjected to thermal treatments between 25 and 85 °C, and their structural and functional changes were characterized using fluorescence spectroscopy, UV-vis absorption, turbidity and bulk viscosity measurements, interfacial shear and dilatational rheology, and foaming assessments. The presence of PG altered the aggregation pathway of WPI in a temperature-dependent manner, producing smaller, more soluble complexes with lower turbidity, particularly at higher temperatures. Both pure WPI and WPI-PG mixtures exhibited increased surface hydrophobicity upon heating; however, PG generally reduced the dilatational elastic modulus except at 85 °C, where the mixed system showed a higher modulus than WPI alone. In contrast, the interfacial shear modulus increased over time in all samples, with consistently higher values observed for WPI-PG mixtures at both 25 °C and 85 °C. Notably, three complementary methods were employed to evaluate foaming properties and interfacial behavior in this study, revealing that factors such as concentration, measurement time, and methodological approach strongly influence the observed responses, highlighting the complexity of interpreting protein-polysaccharide interactions. The ability of PG to modulate WPI unfolding and limit the formation of large aggregates during heating demonstrates a previously unreported mechanism by which PG tailors heat-induced protein network formation. These findings underscore the potential of Persian Gum as a functional polysaccharide for designing heat-treated food systems with controlled aggregation behavior and optimized interfacial performance. Full article

Introduction: Functionality, anthropometric measurements (BMI, arm circumference), and handgrip strength (HGS) are crucial for assessing the health of older adults, as HGS is a strong predictor of frailty and independence, correlating with muscle mass loss (sarcopenia) and the risk of falls. Background/Objectives: To analyze the relationship between functional capacity, anthropometric measurements, and handgrip strength in community-dwelling older adults. Methods: A descriptive, exploratory, cross-sectional study with a quantitative approach was conducted with 225 older adults monitored at two Family Health Units, using the Barthel Scale, Lawton and Brody Scale, anthropometric measurements (body mass index, waist, calf, and brachial circumferences), and dynamometry as instruments. Spearman’s test was used for correlations, with interpretation by shared variance and comparison of magnitudes by Steiger r-to-z method. A higher frequency of females (65.8%) was observed, in the age range between 60 and 68 years (51.1%), independent in Basic Activities of Daily Living (76.9%) and dependent in Instrumental Activities of Daily Living (99.1%). The analysis revealed that waist circumference showed a significant correlation with waist-to-hip ratio (ρ-value 0.604; p-value < 0.01) and body mass index (ρ-value = 0.696; p-value < 0.01). These associations showed shared variances of 36.5% (waist circumference and waist-to-hip ratio) and 48.4% (waist circumference and body mass index). Waist-to-hip ratio showed a significant positive correlation with waist-to-hip ratio (ρ-value = 0.256; p-value < 0.01) and body mass index (ρ-value = 0.198; p-value < 0.01). However, these relationships showed lower shared variances at 6.5% with waist-to-hip ratio and 3.9% with BMI. The Lawton scale showed a statistically significant negative correlation with hand grip strength (ρ-value = −0.176; p-value < 0.01). Conclusions: There is a significant relationship between functional capacity, anthropometric measurements, and hand grip strength in community-dwelling older adults, reflecting the interaction between physical performance, body composition, and autonomy. Full article

The development of KRAS G12D inhibitors represents an effective therapeutic strategy for treating oncological pathologies. Existing quantitative structure-activity relationship (QSAR) models for KRAS G12D inhibitors have several limitations, primarily the lack of applicability domain determination and virtual screening implementation. In this study, we propose a set of regression QSAR models for KRAS G12D inhibitors by employing various molecular descriptors and machine learning methods. Our consensus model achieved a Q² test value of 0.70 on an external test set, covering 78% of the data within the applicability domain. We integrated this consensus model into our Python-based framework KRASAVA. The platform predicts inhibitory activity while considering the applicability domain, assesses compounds for compliance with Muegge’s bioavailability rules, and identifies PAINS, toxicophores, and Brenk filters. Furthermore, we structurally interpreted the QSAR models to propose several promising inhibitors and performed molecular docking on these candidates using GNINA. For the reference inhibitor MRTX1133, we reproduced the crystal structure pose with an RMSD of 0.76 Å (PDB ID: 7T47). The key interactions with amino acid residues Asp12, Asp69, His95, Arg68, and Gly60, identified for both MRTX1133 and our proposed compounds, demonstrate a strong consistency between the molecular docking and QSAR results. Full article

Endogenous selective attention, the cognitive process of selectively attending to non-literal, ambiguous, or multistable interpretations of sensory input, remains poorly understood at the network level. To address this gap, we applied Granger causality (GC) analysis to electroencephalographic (EEG) recordings to characterize effective connectivity during sustained attention to ambiguous visual stimuli. Participants viewed the Necker cube, whose left and right faces were modulated at 6.67 Hz and 8.57 Hz, respectively, enabling objective tracking of perceptual dominance via steady-state visually evoked potentials (SSVEPs). GC analysis revealed robust directed connectivity between frontal and occipito-parietal areas during sustained perception of a specific cube orientation. We found that the magnitude of the GC-derived F-statistics correlated positively with attention performance indices during the left-face orientation task and negatively during the right-face orientation task, indicating that interregional causal influence scales with cognitive engagement in ambiguous interpretation. These results establish GC as a sensitive and reliable approach for characterizing dynamic, directional neural interactions during perceptual ambiguity, and, most notably, reveal, for the first time, an occipito-frontal effective connectivity architecture specifically recruited in support of endogenous selective attention. The methodology and findings hold translational potential for applications in neuroadaptive interfaces, cognitive diagnostics, and the study of disorders involving impaired symbolic processing. Full article

A wide range of non-microbial contaminants—such as heavy metals, pesticide residues, antibiotics, as well emerging foodborne contaminants like micro- and nanoplastics and persistent organic pollutants—can enter the human body through daily diet and exert subtle yet chronic effects that are increasingly recognized to be gut microbiota-dependent. However, the relationships among multi-contaminant exposure profiles, dynamic microbial community structures, microbial metabolites, and diverse clinical or subclinical phenotypes are highly non-linear and multidimensional, posing major challenges to traditional analytical approaches. Artificial intelligence (AI) is emerging as a powerful tool to untangle the complex interactions between foodborne non-microbial contaminants, the gut microbiota, and host health. This review synthesizes current knowledge on how key classes of non-microbial food contaminants modulate gut microbial composition and function, and how these alterations, in turn, influence intestinal barrier integrity, immune homeostasis, metabolic regulation, and systemic disease risk. We then highlight recent advances in the application of AI techniques, including machine learning (ML), deep learning (DL), and network-based methods, to integrate multi-omics and exposure data, identify microbiota and metabolite signatures of specific contaminants, and infer potential causal pathways within “contaminant–microbiota–host” axes. Finally, we discuss current limitations, such as data heterogeneity, small-sample bias, and interpretability gaps, and propose future directions for building standardized datasets, explainable AI frameworks, and human-relevant experimental validation pipelines. Overall, AI-enabled analysis offers a promising avenue to refine food safety risk assessment, support precision nutrition strategies, and develop microbiota-targeted interventions against non-microbial food contaminants. Full article

Constructed-response items offer rich evidence of writing proficiency, but the linguistic signals they contain vary with grade level. This study presents a cross-sectional analysis of 5638 English Language Arts essays from Grades 6–12 to identify which linguistic features predict proficiency and to characterize how their importance shifts across grade levels. We extracted a suite of lexical, syntactic, and semantic-cohesion features, and evaluated their predictive power using an interpretive dual-model framework combining LASSO and XGBoost algorithms. Feature importance was assessed through LASSO coefficients, XGBoost Gain scores, and SHAP values, and interpreted by isolating both consensus and divergences of the three metrics. Results show moderate, generalizable predictive signals in Grades 6–8, but no generalizable predictive power was found in the Grades 9–12 cohort. Across the middle grades, three findings achieved strong consensus. Essay length, syntactic density, and global semantic organization served as strong predictors of writing proficiency. Lexical diversity emerged as a key divergent feature, it was a top predictor for XGBoost but ignored by LASSO, suggesting its contribution depends on interactions with other features. These findings inform actionable, grade-sensitive feedback, highlighting stable, diagnostic targets for middle school while cautioning that discourse-level features are necessary to model high-school writing. Full article

The vibrational properties of the chiral sulfoxide methyl-p-tolyl-sulfoxide (Metoso) were investigated by infrared and Raman spectroscopy in the solid, liquid and aqueous solution phases, for both the enantiopure compounds and their racemic mixture. Experimental data were complemented by DFT calculations on the isolated enantiomer and on the two RR and RS dimeric conformers to support spectral interpretation and mode assignment. The IR and Raman spectra of the crystalline enantiomer and racemic mixture are similar, indicating comparable molecular organization and intermolecular interactions in the solid state. Upon melting, band broadening and frequency shifts are observed, consistent with molecular disorder and the breaking of weak intramolecular interactions, accompanied by changes in the S-O, S-CH₃ and C-H stretching frequencies. In aqueous solution, further broadening and opposite shifts in these bands reflect the formation of Metoso-H₂O complexes through hydrogen bonds. Theoretical spectra reproduce the observed trends and confirm that either solvent or phase transitions control the balance between intra- and intermolecular interactions thus influencing the vibrational degrees of freedom of the model chiral sulfoxide. Full article

Guided by principles of symmetry to achieve a proper balance among model consistency, accuracy, and complexity, this paper proposes a new approach for identifying the unknown parameters of nonlinear one-degree-of-freedom mechanical systems using nonlinear regression methods. To this end, the steps followed in this study can be summarized as follows. Firstly, given a proper set of input time histories and a virtual model with all parameters known, the dynamic response of the mechanical system of interest, used as output data, is evaluated using a numerical integration scheme, such as the classical explicit fixed-step fourth-order Runge–Kutta method. Secondly, the numerical values of the unknown parameters are estimated using the Levenberg–Marquardt nonlinear regression algorithm based on these inputs and outputs. To demonstrate the effectiveness of the proposed approach through numerical experiments, two benchmark problems are considered, namely a mass-spring-damper system and a simple pendulum-damper system. In both mechanical systems, viscous damping is included at the kinematic joints, whereas dry friction between the bodies and the ground is accounted for and modeled using the Coulomb friction force model. While the source of nonlinearity is the frictional interaction alone in the first benchmark problem, the finite rotation of the pendulum introduces geometric nonlinearity, in addition to the frictional interaction, in the second benchmark problem. To ensure symmetry in explaining model behavior and the interpretability of numerical results, the analysis presented in this paper utilizes five different input functions to validate the proposed method, representing the initial phase of ongoing research aimed at applying this identification procedure to more complex mechanical systems, such as multibody and robotic systems. The numerical results from this research demonstrate that the proposed approach effectively identifies the unknown parameters in both benchmark problems, even in the presence of nonlinear, time-varying external input actions. Full article

Time series and spatio-temporal forecasting are fundamental tasks for complex system modeling and intelligent decision-making, with broad applications in transportation, meteorology, finance, healthcare, and public safety. Compared with simple univariate time series, real-world spatio-temporal data exhibit rich temporal dynamics and intricate spatial interactions, leading to heterogeneity, non-stationarity, and evolving topologies. Addressing these challenges requires modeling frameworks that can simultaneously capture temporal evolution, spatial correlations, and cross-domain regularities. This survey provides a comprehensive synthesis of forecasting methods, spanning statistical algorithms, traditional machine learning approaches, neural architectures, and recent generative and causal paradigms. We review the methodological evolution from classical linear models to deep learning–based temporal modules and emphasize the role of attention-based Transformers as general-purpose sequence architectures. In parallel, we distinguish these architectural advances from pre-trained foundation models for time series and spatio-temporal data (e.g., large models trained across diverse domains), which leverage self-supervised objectives and exhibit strong zero-/few-shot transfer capabilities. We organize the review along both data-type and architectural dimensions—single long-term time series, Euclidean-structured spatio-temporal data, and graph-structured spatio-temporal data—while also examining advanced paradigms such as diffusion models, causal modeling, multimodal-driven frameworks, and pre-trained foundation models. Through this taxonomy, we highlight common strengths and limitations across approaches, including issues of scalability, robustness, real-time efficiency, and interpretability. Finally, we summarize open challenges and future directions, with a particular focus on the joint evolution of graph-based, causal, diffusion, and foundation-model paradigms for next-generation spatio-temporal forecasting. Full article

Efficient interaction between PHP (Hypertext Preprocessor) applications and MySQL databases is essential for the performance of modern web systems. This study systematically compares the two most widely used PHP APIs for working with MySQL databases—MySQLi (MySQL Improved extension) and PDO (PHP Data Objects)—under identical experimental conditions. The analysis covers execution time, memory consumption, and the stability and variability of results across different types of SQL (Structured Query Language) queries (simple queries, complex JOIN, GROUP BY/HAVING). A specialized benchmarking tool was developed to collect detailed metrics over several hundred repetitions and to enable graphical and statistical evaluation. Across the full benchmark suite, MySQLi exhibits the lowest mean wall-clock execution time on average (≈15% overall). However, under higher query complexity and in certain connection-handling regimes, PDO prepared statement modes provide competitive latency with improved predictability. These results should be interpreted as context-aware rankings for the tested single-host environment and workload design, and as a reusable benchmarking framework intended for replication under alternative deployment models. Statistical analysis (Kruskal–Wallis and Mann–Whitney tests) confirms significant differences between the methods, while Box-plots and histograms visualize deviations and the presence of outliers. Unlike earlier studies, this work provides a controlled and replicable benchmarking environment that tests both MySQLi and PDO across multiple API modes and isolates the impact of native versus emulated prepared statements. It also evaluates performance under complex-query workloads that reflect typical reporting and analytics patterns on the ClassicModels schema. To our knowledge, no previous study has analyzed these factors jointly or provided a reusable tool enabling transparent comparison across PHP–MySQL access layers. The findings provide empirical evidence and practical guidelines for choosing the optimal API depending on the application scenario, as well as a tool that can be applied for further testing in various web environments. Full article

Organic anion-transporting polypeptides (OATPs) are key transporters of hepatic uptake for endogenous compounds and xenobiotics. Human OATP1B1 and OATP1B3 are well-studied due to their role in drug–drug interactions. In contrast, data on murine OATP1B2, the rodent orthologue of these transporters, are limited, despite its importance in early drug development. Here, we systematically compared the transport characteristics of mouse and rat OATP1B2 under identical experimental conditions. The K_m values for estrone-3-sulfate (E₁S) and taurocholate (TCA) were 242 and 73 µM for mOATP1B2 and 90 and 16 µM for rOATP1B2. Nine clinically relevant drugs were evaluated for inhibitory effects, showing strong correlation between species. Cyclosporine A, ritonavir, odevixibat, rosuvastatin, and rifampicin markedly inhibited uptake. Rifampicin demonstrated species-specific differences, with higher IC₅₀ values for mOATP1B2 (E₁S: 9.6 µM; TCA: 7.7 µM) than rOATP1B2 (E₁S: 1.1 µM; TCA: 2.4 µM). A comparison of the rodent data with the human orthologues revealed similar inhibition patterns but distinct substrate selectivity: hOATP1B1 showed high affinity for E₁S but negligible TCA uptake, while hOATP1B3 transported TCA weakly but not E₁S. This study provides insights into species-specific differences in OATP-mediated hepatic uptake and is therefore valuable for the interpretation of preclinical studies and their transfer to human pharmacology. Full article

In the context of rapid urbanization, the connection between humans and nature has progressively diminished. As an essential approach to fostering public ecological awareness and well-being, nature education requires greater integration into urban green space planning and management. This study examines 14 country parks, urban parks, and forest parks in Beijing, conducting questionnaire surveys in six representative parks and collecting 820 valid responses. Combining image semantic segmentation techniques, the research employs the PSPNet model trained on the ADE20K dataset to automatically extract landscape features of nature education spaces. These features are then integrated with visitor perception evaluations through univariate linear regression models to analyze the impact of spatial variables on visitor perceptions. Results indicate that building coverage, plant species density, interpretation sign density, number of artificial interpretations, and number of nature education activities offerings show significant positive correlations (p < 0.05) with visitor perceptions. In contrast, excessive artificial structures exert a negative influence. The R² values of each model ranged from 0.12 to 0.34, indicating that natural education space features possess explanatory power for visitor perceptions but remain influenced by multiple interacting factors. This study establishes a quantitative evaluation framework linking natural education space landscape features to visitor perceptions, providing a scientific basis for natural education planning and spatial optimization in parks within megacity contexts. Full article

Sustainable improvement of crop performance requires integrative approaches that link genomic variation to phenotypic expression through intermediate molecular pathways. Here, we present Reciprocal Best Linear Unbiased Prediction (Reciprocal BLUP), a predictability-guided multi-omics framework that quantifies the cross-layer relationships among the genome, metabolome, and microbiome to enhance phenotype prediction. Using a panel of 198 soybean accessions grown under well-watered and drought conditions, we first evaluated four direction-specific prediction models (genome → microbiome, genome → metabolome, metabolome → microbiome, and microbiome → metabolome) to estimate the predictability of individual omics features. We evaluated whether subsets of features with high cross-omics predictability improved phenotype prediction. These cross-layer models identify features that play physiologically meaningful roles within multi-omics systems, enabling the prioritization of variables that capture coherent biological signals enriched with phenotype-relevant information. Consequently, metabolome features were highly predictable from microbiome data, whereas microbiome predictability from metabolomic data was weaker and more environmentally dependent, revealing an asymmetric relationship between these layers. In the subsequent phenotype prediction analysis, the model incorporating predictability-based feature selection substantially outperformed models using randomly selected features and achieved prediction accuracies comparable to those of the full-feature model. Under drought conditions, the phenotype prediction models based on metabolomic or microbiomic kernels (MetBLUP or MicroBLUP) outperformed the genomic baseline (GBLUP) for several biomass-related traits, indicating that the environment-responsive omics layers captured phenotypic variations that were not explained by additive genetic effects. Our results highlight the hierarchical interactions among genomic, metabolic, and microbial systems, with the metabolome functioning as an integrative mediator linking the genotype, environment, and microbiome composition. The Reciprocal BLUP framework provides a biologically interpretable and practical approach for integrating multi-omics data, improving phenotype prediction, and guiding omics-based feature selection in plant breeding. Full article

This study investigates how virtual communication differs from in-person interaction across physiological and behavioral domains, with the goal of informing future interface design. Using a naturalistic setup, we recorded multimodal biosignals, including eye tracking, head and hand movement, heart rate, respiratory rate, and EEG during both in-person and video-based dialogues. Our results show that virtual communication significantly reduces movement and gaze dynamics, particularly in horizontal eye movements and lateral head motion, reflecting both sender- and receiver-side constraints. These physical limitations likely stem from the need to remain within the camera frame and the restricted access to nonverbal cues. Pupil dilation was significantly greater during in-person conversations, consistent with increased arousal during natural communication. Heart rate and EEG trends similarly suggested heightened engagement in face-to-face settings, though interpretation of EEG was limited by movement artifacts. Together, the findings highlight how virtual platforms alter embodied interaction, underscoring the need to address both mobility and visual access in future communication technologies to better support co-presence. Full article