Search Results (7,165)

The prediction of real estate values is vital for taxation, transactions, mortgages, and urban policy development. Values can be predicted more accurately by statistical or advanced methods together when the size of the data is huge. In metropolitan cities like İstanbul, where size of the real estate data is vast and complex, mass appraisal methods supported by Machine Learning offer a scalable and consistent alternative. This study employs six algorithms: Artificial Neural Network, Extreme Gradient Boosting, K-Nearest Neighbors, Support Vector Regression, Random Forest, and Semi-Log Regression, to estimate the values of real estate on both the Asian and European continent parts of İstanbul. In total, 168,099 residential properties were utilized along with 30 of their features from both sides of the Bosphorus. The results show that RF yielded the best performance in Beşiktaş, while XGBoost performed best in Üsküdar. ANN also produced competitive results, although slightly less accurate than those of XGBoost and RF. In contrast, traditional SVR and SLR models underperformed, especially in terms of R² and RMSE values. With its large-scale dataset, focusing on one of the greatest metropolitan areas, Istanbul, and the usage of multiple ML algorithms, this study stands as a comprehensive and practical contribution to the field of automated real estate valuation. Full article

The accurate and effective prediction of PM2.5 concentrations is crucial for mitigating air pollution, improving environmental quality, and safeguarding public health. To address the challenge of strong temporal correlations in PM2.5 concentration forecasting, this paper proposes a novel hybrid model that integrates the Particle Optimization Algorithm (POA) and Variational Mode Decomposition (VMD) with the Long Short-Term Memory (LSTM) network. First, POA is employed to optimize VMD by adaptively determining the optimal parameter combination [k, α], enabling the decomposition of the original PM2.5 time series into subcomponents while reducing data noise. Subsequently, an LSTM model is constructed to predict each subcomponent individually, and the predictions are aggregated to derive hourly PM2.5 concentration forecasts. Empirical analysis using datasets from Beijing, Tianjin, and Tangshan demonstrates the following key findings: (1) LSTM outperforms traditional machine learning models in time series forecasting. (2) The proposed model exhibits superior effectiveness and robustness, achieving optimal performance metrics (e.g., MAE: 0.7183, RMSE: 0.8807, MAPE: 4.01%, R²: 99.78%) in comparative experiments, as exemplified by the Beijing dataset. (3) The integration of POA with serial decomposition techniques effectively handles highly volatile and nonlinear data. This model provides a novel and reliable tool for PM2.5 concentration prediction, offering significant benefits for governmental decision-making and public awareness. Full article

Accurate and non-destructive monitoring of leaf chlorophyll content (LCC) is essential for assessing crop photosynthetic activity and nitrogen status in precision agriculture. This study introduces a phenology-aware machine learning framework that combines hyperspectral reflectance data with various regression models to estimate leaf chlorophyll content (LCC) in cotton at six key reproductive stages. Field experiments utilized synchronized spectral and SPAD measurements, incorporating spectral transformations—such as vegetation indices (VIs), first-order derivatives, and trilateration edge parameters (TEPs, a new set of geometric metrics for red-edge characterization)—for evaluation. Five regression approaches were evaluated, including univariate and multivariate linear models, along with three machine learning algorithms: Random Forest, K-Nearest Neighbor, and Support Vector Regression. Random Forest consistently outperformed the other models, achieving the highest R² (0.85) and the lowest RMSE (4.1) during the bud stage. Notably, the optimal prediction accuracy was achieved with fewer than five spectral features. The proposed framework demonstrates the potential for scalable, stage-specific monitoring of chlorophyll dynamics and offers valuable insights for large-scale crop management applications. Full article

Pistachio nuts are among the 50 best foods with the highest antioxidant potential. They have a balanced content of mono- (~70%) and polyunsaturated (~20%) fatty acids, minerals, and bioactive compounds such as tocopherols, phytosterols, and phenolic compounds, which have shown rapid accessibility in the stomach. Pistachio consumption provides several health benefits, primarily due to its antioxidant properties and high content of essential nutrients. In this study, we analyzed the mineral composition, total phenolic content (TP), antioxidant activity (AA), and UHPLC/MS-MS polyphenolic profile of three Argentinian pistachio crops. Additionally, the physicochemical parameters and the elemental profiles of the growing soils were determined, as they influence mineral uptake and the synthesis of bioactive compounds in pistachio kernels. The TP was not significantly modified by the growing soils, with Crop3 presenting the highest TP content (276 ± 14 mg GA/100 g DW). Crop3 exhibited 18% higher TP content compared to Crop2. Similarly, FRAP values ranged from 28.0 to 36.5 mmol TE/100 g DW, with Crop1 showing a 30% increase compared to Crop2. DPPH values varied from 19.0 to 24.3 mmol TE/100 g DW, with Crop1 displaying 28% higher activity than Crop2. However, the polyphenolic profile was similar for all crops analyzed. Thirty compounds were identified; only Crop 1 contained the flavanone eriodyctiol and the isoflavone genistein, while the flavanone naringenin and the flavone luteolin were identified in Crop1 and Crop3. Regarding mineral content, the pistachio kernels mainly contained K, Ca, and Mg. Multivariate analyses revealed distinct elemental and antioxidant profiles among crops. LDA achieved classification accuracies of 77.7% for soils and 74.4% for kernels, with Pb, Zn, Cu, Rb, Sr, and Mn as key discriminants. CCA confirmed strong soil–kernel mineral correlations (r = 1), while GPA showed higher congruence between antioxidant traits and kernel composition than with soil geochemistry. These findings underscore the importance of soil composition in determining the nutritional quality of pistachio kernels, thereby supporting the beneficial health effects associated with pistachio consumption. Full article

Governments worldwide have set ambitious targets for decarbonising energy grids, driving the need for increased renewable energy generation and improved energy efficiency. One key strategy for achieving this involves enhanced energy management in buildings, often using machine learning-based forecasting methods. However, such methods typically rely on extensive historical data collected via costly sensor installations—resources that many buildings lack. This study introduces a novel forecasting approach that eliminates the need for large-scale historical datasets or expensive sensors. By integrating custom-built models with existing energy data, the method applies calculated weighting through a distance matrix and accuracy coefficients to generate reliable forecasts. It uses readily available building attributes—such as floor area and functional type to position a new building within the matrix of existing data. A Euclidian distance matrix, akin to a K-nearest neighbour algorithm, determines the appropriate neural network(s) to utilise. These findings are benchmarked against a consolidated, more sophisticated neural network and a long short-term memory neural network. The dataset has hourly granularity over a 24 h horizon. The model consists of five bespoke neural networks, demonstrating the superiority of other models with a 610 s training duration, uses 500 kB of storage, achieves an R² of 0.9, and attains an average forecasting accuracy of 85.12% in predicting the energy consumption of the five buildings studied. This approach not only contributes to the specific goal of a fully decarbonized energy grid by 2050 but also establishes a robust and efficient methodology for maintaining standards with existing benchmarks while providing more control over the method. Full article

Forest volume is an important information for assessing the economic value and carbon sequestration capacity of forest resources and serves as a key indicator for energy flow and biodiversity. Although remote sensing technology is applied to estimate volume, optical remote sensing data have limitations in capturing forest vertical height information and may suffer from reflectance saturation. While LiDAR data can provide more detailed vertical structural information, they come with high processing costs and limited observation range. Therefore, improving the accuracy of volume estimation through multi-source data fusion has become a crucial challenge and research focus in the field of forest remote sensing. In this study, we integrated Sentinel-2 multispectral data, Resource-3 stereoscopic imagery, UAV-based LiDAR data, and field survey data to quantitatively estimate the forest volume in Saihanwula Nature Reserve, located in Inner Mongolia, China, on the southern part of Daxing’anling Mountains. The study evaluated the performance of multi-source remote sensing features by using recursive feature elimination (RFE) to select the most relevant factors and applied four machine learning models—multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF), and gradient boosting regression tree (GBRT)—to develop volume estimation models. The evaluation metrics include the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). The results show that (1) forest Canopy Height Model (CHM) data were strongly correlated with forest volume, helping to alleviate the reflectance saturation issues inherent in spectral texture data. The fusion of CHM and spectral data resulted in an improved volume estimation model with R² = 0.75 and RMSE = 8.16 m³/hm², highlighting the importance of integrating multi-source canopy height information for more accurate volume estimation. (2) Volume estimation accuracy varied across different tree species. For Betula platyphylla, we obtained R² = 0.71 and RMSE = 6.96 m³/hm²; for Quercus mongolica, R² = 0.74 and RMSE = 6.90 m³/hm²; and for Populus davidiana, R² = 0.51 and RMSE = 9.29 m³/hm². The total forest volume in the Saihanwula Reserve ranges from 50 to 110 m³/hm². (3) Among the four machine learning models, GBRT consistently outperformed others in all evaluation metrics, achieving the highest R² of 0.86, lowest RMSE of 9.69 m³/hm², and lowest rRMSE of 24.57%, suggesting its potential for forest biomass estimation. In conclusion, accurate estimation of forest volume is critical for evaluating forest management practices and timber resources. While this integrated approach shows promise, its operational application requires further external validation and uncertainty analysis to support policy-relevant decisions. The integration of multi-source remote sensing data provides valuable support for forest resource accounting, economic value assessment, and monitoring dynamic changes in forest ecosystems. Full article

Disease resistance is one of the most important target traits for sugarcane genetic improvement. Sugarcane brown stripe (SBS) caused by Helminthosporium stenospilum is one of the most destructive foliar diseases, which not only reduces harvest cane yield but also sugar content. This study aimed to identify quantitative trait loci (QTL) and candidate genes associated with SBS resistance. Here, the phenotypic investigation in six field habitats showed a continuous normal distribution, revealing that the SBS resistance trait is a quantitative trait. Two high-density linkage maps based on the single-dose markers calling from the Axiom Sugarcane100K SNP chip were constructed for the dominant sugarcane cultivars YT93-159 (SBS-resistant) and ROC22 (SBS-susceptible) with a density of 2.53 cM and 2.54 cM per SNP marker, and mapped on 87 linkage groups (LGs) and 80 LGs covering 3069.45 cM and 1490.34 cM of genetic distance, respectively. A total of 32 QTL associated with SBS resistance were detected by QTL mapping, which explained 3.73–11.64% of the phenotypic variation, and the total phenotypic variance explained (PVE) in YT93-159 and ROC22 was 107.44% and 79.09%, respectively. Among these QTL, four repeatedly detected QTL (qSBS-Y38-1, qSBS-Y38-2, qSBS-R8, and qSBS-R46) were considered stable QTL. Meanwhile, two major QTL, qSBS-Y38 and qSBS-R46, could account for 11.47% and 11.64% of the PVE, respectively. Twenty-five disease resistance candidate genes were screened by searching these four stable QTL regions in their corresponding intervals, of which Soffic.01G0010840-3C (PR3) and Soffic.09G0017520-1P (DND2) were significantly up-regulated in YT93-159 by qRT-PCR, while Soffic.01G0040620-1P (EDR2) was significantly up-regulated in ROC22. These results will provide valuable insights for future studies on sugarcane breeding in combating this disease. Full article

Mixing enthalpy (ΔH_mix), mixing entropy (ΔS_mix), atomic-size difference (δ), and valence electron concentration (VEC) are the indicators determining the phase structures of multi-principal element alloys. Exploring the relationships between the structures and properties of multi-principal element films is a fundamental study. TiZrHf films with a ΔH_mix of 0.00 kJ/mol, ΔS_mix of 9.11 J/mol·K (1.10R), δ of 3.79%, and VEC of 4.00 formed a hexagonal close-packed (HCP) solid solution. Exploring the characterization of TiZrHf films after solving Ta, Y, and Cr atoms with distinct atomic radii is crucial for realizing multi-principal element alloys. This study fabricated TiZrHf, TiZrHfTa, TiZrHfY, and TiZrHfCr films through co-sputtering. The results indicated that TiZrHfTa films formed a single body-centered cubic (BCC) solid solution. In contrast, TiZrHfY films formed a single HCP solid solution, and TiZrHfCr films formed a nanocrystalline BCC solid solution. The crystallization of TiZrHf(Ta, Y, Cr) films and the four indicators mentioned above for multi-principal element alloy structures were correlated. The mechanical properties and thermal stability of the TiZrHf(Ta, Y, Cr) films were investigated. Full article

The deterioration of uterine calcium transport capacity induced by aging is a common problem for late-laying period hens, causing decline in eggshell quality. This study aimed to investigate the effects and possible regulatory mechanisms of dietary puerarin (PU) on calcium transport and eggshell quality in aged hens. Two hundred eighty-eight Hubbard Efficiency Plus broiler breeder hens (50-week-old) were randomly allocated to three dietary treatments containing 0, 40, or 200 mg/kg puerarin (PU), with 8 replicates of 12 birds each, for an 8-week trial. The results demonstrated that dietary PU ameliorated the eggshell thickness and strength, which in turn reduced the broken egg rate (p < 0.05). Histological analysis showed that PU improved uterus morphology and increased epithelium height in the uterus (p < 0.05). Antioxidative capacity was significantly improved via upregulation of Nrf2, HO-1, and GPX1 mRNA expression in the uterus (p < 0.05), along with enhanced total antioxidant capacity (T-AOC) and glutathione peroxidase (GSH-PX) activity, and decreased levels of the oxidative stress marker malondialdehyde (MDA) (p < 0.05). Meanwhile, PU treatment reduced the apoptotic index of the uterus, followed by a significant decrease in expression of pro-apoptotic genes Caspase3 and BAX and the rate of BAX/BCL-2. Additionally, calcium content in serum and uterus, as well as the activity of Ca²⁺-ATPase in the duodenum and uterus, were increased by dietary PU (p < 0.05). The genes involved in calcium transport including ERα, KCNA1, CABP-28K, and OPN in the uterus were upregulated by PU supplementation (p < 0.05). The 16S rRNA gene sequencing revealed that dietary PU supplementation could reverse the age-related decline in the relative abundance of Bacteroidota within the uterus (p < 0.05). Overall, dietary PU can improve eggshell quality and calcium transport through enhanced antioxidative defenses and mitigation of age-related uterine degeneration. Full article

Maternal undernutrition (MUN) causes severe metabolic disruption in the offspring of mammals. Here we determined the role of histone modification in hepatic gene expression in late-gestation fetuses of nutritionally restricted cows, an established model using low-nutrition (LN) and high-nutrition (HN) conditions. The chromatin immunoprecipitation sequencing results show that genes with an altered trimethylation of histone 3 lysine 4 (H3K4me3) are associated with cortisol synthesis and secretion, the PPAR signaling pathway, and aldosterone synthesis and secretion. Genes with the H3K27me3 alteration were associated with glutamatergic synapse and gastric acid secretion. Compared to HN fetuses, promoter H3K4me3 levels in LN fetuses were higher in GDF15, IRF2BP2, PPP1R3B, and QRFPR but lower in ANGPTL4 and APOA5. Intriguingly, genes with the greatest expression changes (>1.5-fold) exhibited the anticipated up-/downregulation from elevated or reduced H3K4me3 levels; however, a significant relationship was not observed between promoter CpG methylation or H3K27me3 and the gene set with the greatest expression changes. Furthermore, the stress response genes EIF2A, ATF4, DDIT3, and TRIB3 were upregulated in the MUN fetal liver, suggesting activation by upregulated GDF15. Thus, H3K4me3 likely plays a crucial role in MUN-induced physiological adaptation, altering the hepatic gene expression responsible for the integrated stress response and systemic energy metabolism, especially circulating lipoprotein lipase regulation. Full article

Soil salinization severely restricts crop growth and presents a major challenge to global agriculture. In this study, a plant-growth-promoting rhizobacterium (PGPR) was isolated and identified as Proteus sp. through 16S rDNA analysis and was subsequently named Proteus sp. JHY1. Under salt stress, exogenous dopamine (DA) significantly enhanced the production of indole-3-acetic acid and ammonia by strain JHY1. Pot experiments revealed that both DA and JHY1 treatments effectively alleviated the adverse effects of 225 mM NaCl on rice, promoting biomass, plant height, and root length. More importantly, the combined application of DA-JHY1 showed a significant synergistic effect in mitigating salt stress. The treatment increased the chlorophyll content, net photosynthetic rate, osmotic regulators (proline, soluble sugars, and protein), and reduced lipid peroxidation. The treatment also increased soil nutrients (ammoniacal nitrogen and available phosphorus), enhanced soil enzyme activities (sucrase and alkaline phosphatase), stabilized the ion balance (K⁺/Na⁺), and modulated the soil rhizosphere microbial community by increasing beneficial bacteria, such as Actinobacteria and Firmicutes. This study provides the first evidence that the synergistic effect of DA and PGPR contributes to enhanced salt tolerance in rice, offering a novel strategy for alleviating the adverse effects of salt stress on plant growth. Full article

Alopecia, a prevalent dermatological disorder affecting over half of the global population, is strongly associated with psychological distress. Extracts from Periplaneta americana (L. PA), a medicinal insect resource, exhibit pharmacological activities (e.g., antioxidant, anti-inflammatory, microcirculation improvement) that align with core therapeutic targets for alopecia. This study aimed to systematically investigate the efficacy and mechanisms of PA extracts in promoting hair regeneration. A strategy combining network pharmacology prediction and in vivo experiments was adopted. The efficacy of a Periplaneta americana extract was validated by evaluating hair regrowth status and skin pathological staining in C57BL/6J mice. Transcriptomics, metabolomics, RT-qPCR, and 16s rRNA techniques were integrated to dissect the underlying mechanisms of its hair-growth-promoting effects. PA-011 significantly promoted hair regeneration in depilated mice via multiple mechanisms: enhanced skin superoxide dismutase activity and upregulated vascular endothelial growth factor expression; modulated FOXO/PI3K/AKT signaling pathway and restored skin microbiota homeostasis; and accelerated transition of hair follicles from the telogen to anagen phase. PA-011 exerts hair-promoting effects through synergistic modulation of FOXO/PI3K/AKT signaling and the skin microbiome. As a novel therapeutic candidate, it warrants further systematic investigation for clinical translation. Full article

In this study, particles of ground grape seeds were utilized to adsorb phosphate ions from a prepared solution, aiming to reduce phosphate concentration. Through a series of adsorption experiments, the effects of the adsorbent concentration, initial phosphate ion concentration, temperature, and pH on the phosphate ion uptake were studied. The removal efficiency of the phosphate ion decreased from 77 to 61% as a 25 to 45 °C increment in temperature was observed, which indicated the exothermicity in the adsorption process. The phosphate ion movement onto the adsorbent surface that exhibited the highest uptake value favored a neutral reaction environment with a pH value of seven. The experimental results, when compared using different adsorption isotherms, showed that the best fit was exhibited by the Jovanovic isotherm, which was further confirmed owing to its high 0.974 R² value. Intraparticle diffusion and pseudo second order models describe the kinetics of phosphate adsorption onto grape seeds, with reaction constants of 8.8 × 10⁻³ (mg/g min) and 0.412 (mg/g·min^0.5), respectively. The adsorption was physiosorptive, spontaneous, exothermic, and favorable. Furthermore, the negative entropy with a value of −0.0887 kJ/mol·K revealed reduced randomness in the adsorption process system. Full article

We consider the problem of privately updating a message out of K messages from N replicated and non-colluding databases where a user has an outdated version of the message ${\hat{W}}_{\theta }$ of length L bits that differ from the current version $W_{\theta }$ in at most f bits. The user also has a cache containing coded combinations of the K messages (with a pre-specified structure), which are unknown to the N databases (unknown prefetching). The cache Z contains ℓ linear combinations from all K messages in the databases with $r=\frac{\mathcal{l}}{L}$ being the caching ratio. The user needs to retrieve $W_{\theta }$ correctly using a private information retrieval (PIR) scheme without leaking information about the message index $\theta$ to any individual database. Our objective is to jointly design the prefetching (i.e., the structure of said linear combinations) and the PIR strategies to achieve the least download cost. We propose a novel achievable scheme based on syndrome decoding where the cached linear combinations in Z are designed to be bits pertaining to the syndrome of $W_{\theta }$ according to a specific linear block code. We derive a general lower bound on the optimal download cost for $0\le r\le 1$, in addition to achievable upper bounds. The upper and lower bounds match for the cases when r is exceptionally low or high, or when $K=3$ messages for arbitrary r. Such bounds are derived by developing novel cache-aided arbitrary message length PIR schemes. Our results show a significant reduction in the download cost if $f<{\displaystyle \frac{L}{2}}$ when compared with downloading $W_{\theta }$ directly using typical cached-aided PIR approaches. Full article

Hyers and Ulam considered the problem of whether there is a true isometry that approximates the $\epsilon$-isometry defined on a Hilbert space with a stability constant $10\epsilon$. Subsequently, Fickett considered the same question on a bounded subset of the n-dimensional Euclidean space ${\mathbb{R}}^n$ with a stability constant of $27{\epsilon }^{1/2^n}$. And Vestfrid gave a stability constant of $27n\epsilon$ as the answer for bounded subsets. In this paper, by applying singular value decomposition, we improve the previous stability constants by $C\sqrt{n}\epsilon$ for bounded subsets, where the constant C depends on the approximate linearity parameter K, which is defined later. Full article