Search Results (8,848)

Background: Despite its crucial role in overall health, oral health is frequently overlooked within healthcare systems, partly due to the misconception that oral diseases are neither life-threatening nor directly disabling. This perception has led to an underestimation of the psychological, social, and economic burden associated with oral diseases. Τhe present study aimed to assess oral health status and oral health-related quality of life among dental patients attending a public Special Care Center in Greece. Methods: A cross-sectional study was conducted among 400 dental patients aged 18 years and older who visited a public Special Care Center for a routine check-up or a dental problem between September and October 2024. Data was collected through personal interviews and clinical examinations after informed consent was obtained. Oral health-related quality of life was evaluated using the Oral Health Impact Profile-14 (OHIP-14) and the Oral Impacts on Daily Performance (OIDP) questionnaires. Categorical variables were presented as absolute and relative frequencies, while quantitative variables were summarized as mean, standard deviation, median, minimum, and maximum. Normality was assessed using the Kolmogorov–Smirnov test. Bivariate analyses and multivariate linear regression models were performed, with statistical significance set at p < 0.05. Statistical analyses were conducted using IBM SPSS 23.0. Results: The majority of participants were female (56.3%) with a mean age of 50.4 years (SD = 14.9). Overall oral health-related quality of life was moderate (OHIP-14: Mean = 21.0, SD = 14.8; OIDP: Mean = 14.0, SD = 12.8). Patients who attended the center due to a dental problem reported significantly poorer oral health outcomes than those attending routine check-ups (p < 0.001). Poorer self-rated oral health, having ≥12 missing teeth, prosthetic restoration, and foreign nationality were significantly associated with worse oral health-related quality of life. Conclusions: Dental patients attending the Special Care Center demonstrated moderate oral health status, which was associated with psychological distress, physical disability, and social limitations. These findings underline the need for targeted public oral health interventions, especially for vulnerable population groups. Full article

The International Roughness Index (IRI) is calculated from elevation profiles acquired by high-speed profilers or laser scanners, but these raw data often contain measurement noise and extraneous wavelength components that can degrade the accuracy of IRI calculations. Existing filtering methods expose a limitation in removing IRI-insensitive wavelength components. Thus, this paper proposes a Gaussian filtering algorithm based on the Nyquist sampling theorem to remove IRI-insensitive components of the longitudinal profile. The proposed approach first adaptively determines Gaussian template lengths according to sampling intervals, and then incorporates a boundary padding strategy to ensure processing stability. The proposed method enables precise wavelength selection within the IRI-sensitive band of 1.3–29.4 m while maintaining computational efficiency. The method was validated using the Paris–Lille dataset and the U.S. Long-Term Pavement Performance (LTPP) program dataset. The filtered profiles were evaluated by Power Spectral Density (PSD), and IRI values were calculated and compared with those obtained by conventional profile filtering methods. The results show that the proposed method is effective in removing the non-sensitive components of IRI and obtaining highly accurate IRI values. Compared with the standard IRI provided by the LTPP dataset, mean absolute error of the IRI values from the proposed method reaches 0.051 m/km, and mean relative error is less than 4%. These findings indicate that the proposed method improves the reliability of IRI calculation. Full article

The convergence of food insecurity, water scarcity, and environmental degradation has intensified the global search for sustainable agricultural models. Integrated Microalgal–Aquaponic Systems (IAMS) have emerged as a novel multi-trophic platform that unites aquaculture, hydroponics, and microalgal cultivation into a closed-loop framework for resource-efficient food production and water recovery. This critical review synthesizes empirical findings and engineering advancements published between 2008 and 2024, evaluating IAMS performance relative to traditional agriculture and recirculating aquaculture systems (RAS). Reported under controlled laboratory and pilot-scale conditions, IAMS have achieved nitrogen and phosphorus recovery efficiencies exceeding 95% while potentially reducing water consumption by up to 90% compared to conventional farming. The integration of microalgal photobioreactors enhances nutrient retention, may contribute to internal carbon capture, and enables the generation of diversified co-products, including biofertilizers and protein-rich aquafeeds. Nevertheless, significant barriers to commercial scalability persist, including the biological complexity of maintaining multi-trophic synchrony, high initial capital expenditure (CAPEX), and regulatory ambiguity regarding the safety of waste-derived algal biomass. Technical challenges such as photobioreactor upscaling, biofouling control, and energy optimization are critically discussed. Finally, the review evaluates the alignment of IAMS with UN Sustainable Development Goals 2, 6, and 13, and outlines future research priorities in techno-economic modeling, automation, and policy development to facilitate the transition of IAMS from pilot-scale innovations to viable industrial solutions. Full article

Kiln-fired clay bricks are energy-intensive and carbon-heavy. This study develops and validates kiln-free, pressure-free, and ambient-cured geopolymer (GPM) bricks made from uncalcined clay and Class F fly ash. A two-stage experimental program screened 33 mixes (12–16 M NaOH and 396 cubes tested at 14–90 days) and then scaled six optimized mixes to 90 full-size bricks for mechanical, durability, and microstructural evaluation. Bricks with an optimal mix of 20–30% clay and 70–80% fly ash achieved a compressive strength of up to 32.5 MPa, satisfying ASTM C62 (for severe weathering) requirements. Relative to fired clay units, GPM bricks delivered +61% average compressive strength (up to +91%), +56.5% average modulus of rupture (up to +103%), 6–29% lower water absorption, and 42–84% higher UPV while their strength losses after 28-day immersion in 5% H₂SO₄ or 3.5% NaCl were only ~3–5%. SEM confirmed a dense N-A-S-H gel matrix with reduced porosity. Eco-efficiency analysis showed ~95% lower embodied CO₂ (0.26–0.31 vs. 5.5 kg eCO₂ per brick) and ~35% lower cost per MPa of strength than fired clay bricks. The findings demonstrate a practical, low-carbon brick manufactured without mechanical pressing or heat curing, delivering verified performance and durability under ambient conditions. Full article

Timely and accurate crop yield estimation is vital for food security and management decision-making. Integrating remote sensing with machine learning provides an effective solution. In this study, based on canopy hyperspectral data collected by an ASD FieldSpec 3 handheld spectrometer during the critical growth stages of winter wheat, 18 vegetation indices (VIs) were systematically calculated, and their correlation with yield was analyzed. At the same time, a continuous projection algorithm, Successive Projections Algorithm (SPA), was used to screen the characteristic bands. Recursive Feature Elimination (RFE) was employed to select optimal features from VIs and characteristic spectral bands, facilitating the construction of a multi-temporal fusion feature set. To identify the superior yield estimation approach, a comparative analysis was conducted among four machine learning models: Deep Forest (DF), Support Vector Regression (SVR), Random Forest (RF), and Gaussian Process Regression (GPR). Performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). Results indicate that the highest correlations between VIs and grain yield were observed during the flowering and grain-filling stages. Independent analysis showed that VIs reached absolute correlations of 0.713 and 0.730 with winter wheat yield during the flowering and grain-filling stages, respectively, while the SPA further identified key bands primarily in the near-infrared and short-wave infrared regions. On this basis, integrating multi-temporal features through RFE significantly improved the accuracy of yield estimation. Among them, the DF model with the fusion of flowering and filling stage features performed best (R² = 0.786, RMSE = 641.470 kg·hm⁻², rRMSE = 15.67%). This study demonstrates that combining hyperspectral data and VIs from different growth stages provides complementary information. These findings provide an effective method for crop yield estimation in precision agriculture. Full article

Overfitting remains a critical challenge in data-driven financial modelling, where machine learning (ML) systems learn spurious patterns in historical prices and fail out of sample and in deployment. This paper introduces the GT-Score, a composite objective function that integrates performance, statistical significance, consistency, and downside risk to guide optimization toward more robust trading strategies. This approach directly addresses critical pitfalls in quantitative strategy development, specifically data snooping during optimization and the unreliability of statistical inference under non-normal return distributions. Using historical stock data for 50 S&P 500 companies spanning 2010–2024, we conduct an empirical evaluation that includes walk-forward validation with nine sequential time splits and a Monte Carlo study with 15 random seeds across three trading strategies. In walk-forward validation, GT-Score improves the generalization ratio (validation return divided by training return) by 98% relative to baseline objective functions. Paired statistical tests on Monte Carlo out-of-sample returns indicate statistically detectable differences between objective functions (p < 0.01 for comparisons with Sortino and Simple), with small effect sizes. These results suggest that embedding an anti-overfitting structure into the objective can improve the reliability of backtests in quantitative research. Full article

The depletion of fossil fuel resources and the growing need for sustainable energy solutions have increased interest in vertical axis wind turbines (VAWTs), which offer advantages in urban and variable-wind environments but often exhibit limited performance at low tip speed ratios (TSRs). This study optimizes VAWT aerodynamic behavior across a wide TSR range by varying three geometric parameters: maximum thickness position ($a/b$), relative thickness (m), and pitch angle ($\beta$). A two-dimensional computational fluid dynamics (CFD) framework, combined with the Metamodel of Optimal Prognosis (MOP), was used to build surrogate models, perform sensitivity analyses, and identify optimal profiles through gradient-based optimization of the integrated $C_p$–$\lambda$ curve. The Joukowsky transformation was employed for efficient geometric parameterization while maintaining aerodynamic adaptability. The optimized airfoils consistently outperformed the baseline NACA 0021, yielding up to a 14.4% improvement at $\lambda =2.64$ and an average increase of 10.7% across all evaluated TSRs. Flow-field analysis confirmed reduced separation, smoother pressure gradients, and enhanced torque generation. Overall, the proposed methodology provides a robust and computationally efficient framework for multi-TSR optimization, integrating Joukowsky-based parameterization with surrogate modeling to improve VAWT performance under diverse operating conditions. Full article

Refractory geopolymers derived from aluminosilicate sources and alkaline activation are a promising alternative to traditional fired bricks, particularly when low-cost, waste-derived raw materials are used. This study improves the workability of a refractory brick formulated with clays (Kaolin and Tepozan–Bauwer), seashell waste, sodium silicate, potassium hydroxide, and water by incorporating sodium lignosulfonate (LS) and polycarboxylate (PC) plasticizers. Clays from Comonfort, Guanajuato, Mexico, and seashells were ground and sieved to pass a 100 Tyler mesh. A base mixture was prepared and evaluated using the Mini Slump Test, varying plasticizer content from 0 to 2% relative to the solid fraction. Based on workability, 0.5% LS and 1% PC (by solids) increased the slump, and a blended plasticizer formulation (1.5% by solids, 80%PC+20%LS) produced the highest workability. These additives act through different mechanisms, with LS primarily promoting electrostatic repulsion and PC steric repulsion. Bricks with and without plasticizers exhibited thermal resistance up to 1200 °C. After four calcination cycles, compressive strength values were 354.74 kg_f/cm² for the brick without plasticizer, 597.25 kg_f/cm² for 1% PC, 433.63 kg_f/cm² for 0.5% LS, and 519.05 kg_f/cm² for 1.5% of the 80%PC+20%LS blend. Strength was consistent with changes in porosity and apparent density, and 1% PC provided a favorable combination of high workability and high compressive strength after cycling. Because the cost of clays and seashells is negligible, formulation selection was based on plasticizer cost per brick. Although 1% PC and the 1.5% of 80%PC+20%LS blend showed statistically comparable strength after cycling, 1% PC was selected as the preferred option due to its lower additive cost ($0.0449 per brick) compared with the blend ($0.0633 per brick). Stereoscopic microscopy indicated pore closure after calcination with no visible cracking, and SEM–EDS identified O, Si, and Al as the significant elements, with traces of S and K. Overall, the study provides an integrated assessment of workability, multi-cycle calcination, microstructure, and performance for refractory bricks produced from readily available clays and seashell waste. Full article

The economic dispatch (ED) problem is a major challenge in power system optimization. In this article, an Enhanced Social Group Optimization (ESGO) algorithm is presented for solving the economic dispatch problem with or without wind units, considering various characteristics related to valve-point effects, ramp-rate constraints, prohibited operating zones, and transmission power losses. The Social Group Optimization (SGO) algorithm models the social dynamics of individuals within a group—through mechanisms of collective learning, behavioral adaptation, and information exchange—and leverages these interactions to guide the population efficiently towards optimal solutions. ESGO extends SGO along three complementary directions: redefining the update relations of the original SGO, introducing stochastic operators into the heuristic mechanisms, and dynamically updating the generated solutions. These modifications aim to achieve a more robust balance between exploration and exploitation, enable flexible adaptation of search steps, and rapidly integrate improved-fitness solutions into the evolutionary process. ESGO is evaluated in six distinct cases, covering systems with 6, 40, 110, and 220 units, to demonstrate its ability to produce competitive solutions as well as its performance in terms of stability, convergence, and computational efficiency. The numerical results show that, in the vast majority of the analyzed cases, ESGO outperforms SGO and other known or improved metaheuristic algorithms in terms of cost and stability. It incorporates wind generation results at an operating cost reduction of approximately 10% compared to the thermal-only system, under the adopted linear wind power model. Moreover, relative to the size of the analyzed systems, ESGO exhibits a reduced average execution time and requires a small number of function evaluations to obtain competitive solutions. Full article

Earthquakes are the result of the dynamic processes occurring beneath the Earth’s crust; specifically, the movement and interaction of tectonic/lithospheric plates. When one plate shifts relative to another, stress accumulates and is eventually released as seismic energy. This process is continuous and unstoppable. This phenomenon is well recognized in the Mediterranean region, where significant seismic activity arises from the northward convergence (4–10 mm per year) of the African plate relative to the Eurasian plate along a complex plate boundary. Consequently, our research will focus on the Mediterranean region, specifically examining seismic activity from 1990 to 2015 within the latitude range of 33–44° and longitude range of 17–44°. These geographical coordinates encompass 28 seismic zones, with the most active areas being Turkey and Greece. In this paper, we applied Grammatical Evolution for artificial feature construction in earthquake prediction, evaluated against machine learning approaches including MLP(GEN), MLP(PSO), SVM, and NNC. Experiments showed that feature construction (FC) achieved the best performance, with a mean error of 9.05% and overall accuracy of 91%, outperforming SVM. Further analysis revealed that a single constructed feature $\left(N_f=1\right)$ yielded the lowest average error (8.21%), while varying the number of generations indicated that $N_g=200$ provided an effective balance between computational cost and predictive accuracy. These findings confirm the efficiency of FC in enhancing earthquake prediction models through artificial feature construction. Our results, as will be discussed in greater detail within the research, yield an average error of approximately 9%, corresponding to an overall accuracy of 91%. Full article

About 16% of typhoons making landfall in China strike Hainan Island, where near-surface extreme winds in dense urban areas exhibit a strong spatiotemporal heterogeneity that is difficult to capture with current observations and mesoscale models. Focusing on Haikou during Super Typhoon Yagi (2024)—the strongest autumn typhoon to hit China since 1949—we developed a multiscale ERA5–WRF–PALM framework to conduct 30 m resolution large-eddy simulations. PALM results are in reasonable agreement with most of the five automatic weather stations, while performance is weaker at the most sheltered park site. Mean near-surface wind speeds increased by 20–50% relative to normal conditions, showing a coastal–urban gradient: maps of weighted cumulative exposure to strong winds (≥Beaufort force 8) show much longer and more intense events along open coasts than within built-up urban cores. Urban morphology exerted nonlinear effects: wind speeds followed a U-shaped relation with both the open-space ratio and mean building height, with suppression zones at ~0.5–0.7 openness and ~20–40 m height, while clusters of super-tall buildings induced Venturi-like acceleration of 2–3 m s⁻¹. Spatiotemporal analysis revealed banded swaths of high winds, with open areas and islands sustaining longer, broader extremes, and dense street grids experiencing shorter, localized events. Methodologically, this study provides a rare, systematically evaluated application of a multiscale ERA5–WRF–PALM framework to a real typhoon case at 30 m resolution in a tropical coastal city. These findings clarify typhoon–city interactions, quantify morphological regulation of extreme winds, and support risk assessment, urban planning, and wind-resilient design in coastal megacities. Full article

Hydrogen is widely regarded as a promising clean energy carrier, and blending hydrogen into existing natural gas pipelines is considered a cost-effective and practical pathway for large-scale deployment. Supplying hydrogen-enriched natural gas to buildings requires careful consideration of the safe operation of pipelines and appliances without introducing new risks. In this study, on-site demonstrations and experimental tests were conducted in two high-rise buildings in Weifang to evaluate the impact of hydrogen addition on high-rise building natural gas distribution systems. The results indicate that hydrogen blending up to 20% by volume does not cause stratification in building risers and leads only to a relatively minor increase in additional pressure, approximately 0.56 Pa/m for every 10% increase in hydrogen addition. While hydrogen addition may increase leakage primarily in aging indoor gas systems, gas meter leakage rates under a 10% hydrogen blend remain below 3 mL/h, satisfying safety requirements. In addition, in-service domestic gas alarms remain effective under hydrogen ratios of 0–20%, with average response times of approximately 19–20 s. These findings help clarify the safety performance of hydrogen-blended natural gas in high-rise building distribution systems and provide practical adjustment measures to support future hydrogen injection projects. Full article

Electric vehicles (EVs) rely on a battery pack as their primary energy source, making it a critical component for their operation. To guarantee safe and correct functioning, a Battery Management System (BMS) is employed, which uses variables such as State of Charge (SOC) to set charge/discharge limits and to monitor pack health. In this article, we propose a Multilayer Perceptron (MLP) network to estimate the SOC of a 14.8 V battery pack installed in a robotic vacuum cleaner. Both offline and online (real-time) tests were conducted under continuous load and with rest intervals. The MLP’s output is compared against two commonly used approaches: NARX (Nonlinear Autoregressive Exogenous) and CNN (Convolutional Neural Network). Performance is evaluated via statistical metrics, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), and we also assess computational cost using Operational Intensity. Finally, we map these results onto a Roofline Model to predict how the MLP would perform on an automotive-grade microcontroller unit (MCU). A generalization analysis is performed using Transfer Learning and optimization using MLP–Kalman. The best performers are the MLP–Kalman network, which achieved an RMSE of approximately 13% relative to the true SOC, and NARX, which achieved approximately 12%. The computational cost of both is very close, making it particularly suitable for use in BMS. Full article

In citrus cultivation practice, regular monitoring of leaf leaf relative water content (RWC) can effectively guide water management, thereby improving fruit quality and yield. When applying hyperspectral technology to citrus leaf moisture monitoring, the precise quantification of RWC still needs to address issues such as data noise and algorithm adaptability. The noise interference and spectral aliasing in RWC sensitive bands lead to a decrease in the accuracy of moisture inversion in hyperspectral data, and the combined sensitive bands of chlorophyll (LCC) in citrus leaves can affect its estimation accuracy. In order to explore the optimal prediction model for RWC of citrus leaves and accurately control irrigation to improve citrus quality and yield, this study is based on 401–2400 nm spectral data and extracts noise robust features through continuous wavelet transform (CWT) multi-scale decomposition. A high-precision estimation model for citrus leaf RWC is established, and the potential of CWT in RWC quantitative inversion is systematically evaluated. This study is based on the multi-scale analysis characteristics of CWT to probe the time–frequency characteristic patterns associated with RWC and LCC in citrus leaf spectra. Pearson correlation analysis is used to evaluate the effectiveness of features at different decomposition scales, and the successive projections algorithm (SPA) is further used to eliminate band collinearity and extract the optimal sensitive band combination. Finally, based on the selected RWC and LCC-sensitive bands, a high-precision predictive model for citrus leaf RWC was established using partial least squares regression (PLSR). The results revealed that (1) CWT preprocessing markedly boosts the estimation accuracy of RWC and LCC relative to the original spectrum (max improvements: 6% and 3%), proving it enhances spectral sensitivity to these two indices in citrus leaves. (2) Combining CWT and SPA, the resulting predictive model showed higher inversion accuracy than the original spectra. (3) Integrating RWC Scale7 and LCC Scale5-2224/2308 features, the CWT-SPA fusion model showed optimal predictive performance (R² = 0.756, RMSE = 0.0214), confirming the value of multi-scale feature joint modeling. Overall, CWT-SPA coupled with LCC spectral traits can boost the spectral response signal of citrus leaf RWC, enhancing its prediction capability and stability. Full article

Sequential decision-making is critical for applications ranging from personalized recommendations to resource allocation. This study evaluates three decision policies—Greedy, Thompson Sampling (via Monte Carlo Dropout), and a zero-shot Large Language Model (LLM)-guided policy (Gemini-1.5-Pro)—within a contextual bandit framework. To address covariate shift and assess subpopulation performance, we utilize a Collective Conditional Diffusion Network (CCDN) where covariates are partitioned into $B=10$ homogeneous blocks. Evaluating these policies across a high-dimensional treatment space ($K=5$, resulting in $2^5=32$ actions), we tested performance in a simulated environment and three benchmark datasets: Boston Housing, Wine Quality, and Adult Income. Our results demonstrate that the Greedy strategy achieves the highest Model-Relative Optimal (MRO) coverage, reaching 1.00 in the Wine Quality and Adult Income datasets, though performance drops significantly to 0.05 in the Boston Housing environment. Thompson Sampling maintains competitive regret and, in the Boston Housing dataset, marginally outperforms Greedy in action selection precision. Conversely, the zero-shot LLM-guided policy consistently underperforms in numerical tabular settings, exhibiting the highest median regret and near-zero MRO coverage across most tasks. Furthermore, Wilcoxon tests reveal that differences in empirical outcomes between policies are often not statistically significant (ns), suggesting an optimization ceiling in zero-shot tabular settings. These findings indicate that while traditional model-driven policies are robust, LLM-guided approaches currently lack the numerical precision required for high-dimensional sequential decision-making without further calibration or hybrid integration. Full article