Background: Chronic kidney disease (CKD) affects over 35.5 million US adults. Serum α1-acid glycoprotein (α1-AGP), an acute-phase protein, exhibits anti-inflammatory properties in animal models, but its association with CKD in younger women remains underexplored. This study investigated the relationship between serum α1-AGP and CKD risk in US women aged 20–49 years. Methods: This nationally representative cross-sectional study used data on female adults in the US aged 20–49 years from the National Health and Nutrition Examination Survey 2015–2018 cycles. 2,137 individuals were included in the study after excluding individuals without serum α1-AGP, urine albumin, and creatinine data. Multivariate logistic regression models evaluated the association between serum α1-AGP and CKD. Moreover, we performed stratified and interaction analyses to see if the relationship was stable in different subgroups. Results: Among 2,137 participants (mean age 34.6 years, mean eGFR 111.7 mL/min/1.73 m²), CKD prevalence was 8.8% (n=188). Higher serum α1-AGP levels were associated with lower CKD risk in the fully adjusted model (OR 0.37, 95% CI 0.16–0.84, P = 0.017), with a dose-response trend across quartiles (P = 0.041). The association was stronger in women aged 40–49 years (OR 0.20, 95% CI 0.05–0.76) and Mexican Americans (OR 0.07, 95% CI 0.01–0.56), though interaction terms were not significant (P > 0.05). Conclusions: Higher serum α1-AGP levels are associated with lower CKD prevalence in young women, suggesting a protective role. Longitudinal studies are needed to confirm causality and explore α1-AGP as a biomarker for CKD risk stratification.

This article presents a mathematical model designed to simulate the impact of targeted interventions aimed at preventing HIV transmission among female sex workers (FSWs) and their clients, while also analyzing their effects on the health of the general population. The compartmental model distinguishes between high-risk populations (FSWs and their clients) and low-risk populations (sexually active men and women in the general population), and links prevention efforts in high-risk groups to the evolution of the epidemic in the low-risk population. The fundamental properties of the model, such as the positivity of solutions and the boundedness of the system, have been verified, and the basic reproduction number R₀ has been calculated. Finally, the stability of the model was studied using Varga’s theorem and the Lyapunov method. Simulation results show that targeted prevention among FSWs and their clients reduces HIV incidence in the general population. This framework provides a valuable tool for guiding policymakers in the design of effective strategies to combat the epidemic, especially relevant in the context of suspension of USAID funding.

A residual-set framework is introduced for analyzing additive prime conjectures, with particular emphasis on the Strong Goldbach Conjecture (SGC). For each even integer $E_n\ge 4$, the residual set $ℛ\left(E_n\right)=\{E_n-p\mid$ $p<E_n,p\in \mathbb{P}\}$ is defined, and the universal residual set ${ℛ}_{\mathbb{E}}={{{\displaystyle \cup }}^{\text{}}}_{E_n}ℛ\left(E_n\right)$ is constructed. It is shown that ${ℛ}_{\mathbb{E}}$ contains infinitely many primes. A nontrivial constructive lower bound is derived, establishing that the number of Goldbach partitions satisfies $G\left(E\right)\ge 2$ for all $E\ge 8$, and that the cumulative partition count satisfies ${\displaystyle \sum }_{E\le N}G\left(E\right)\gg \frac{N^2}{{\text{log}}^{4}N}$. An optimized deterministic algorithm is implemented to verify the SGC for even integers up to 16,000 digits. Each computed partition $E_n=p+q$ is validated using elliptic curve primality testing, and no exceptions are observed. Runtime variability observed in the empirical tests corresponds with known fluctuations in prime density and modular residue distribution. A recursive construction is formulated for generating Goldbach partitions, using residual descent and leveraging properties of the residual sets. The method extends naturally to Lemoine's Conjecture, asserting that every odd integer $n\ge 7$ can be expressed as $n=p+2q$, where $p,q\in \mathbb{P}$. A corresponding residual formulation is developed, and it is proven that at least two valid partitions exist for all $n\ge 9$. Comparative analysis with the Hardy-Littlewood and Chen estimates is provided to contextualize the cumulative growth rate. The residual-set methodology offers a deterministic, scalable, and structurally grounded approach to additive problems in prime number theory, supported by both theoretical results and large-scale computational evidence.

The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is crucial for enhancing urban development, environmental monitoring, and advancing smart city governance. LiDAR, known for its high-resolution 3D data capture capabilities, paired with AI, particularly deep learning algorithms, facilitates advanced analysis and interpretation of urban areas. This combination supports precise mapping, real-time monitoring, and predictive modeling of urban growth and infrastructure. For instance, AI can process LiDAR data to identify patterns and anomalies, aiding in traffic management, environmental oversight, and infrastructure maintenance. These advancements not only improve urban living conditions but also contribute to sustainable development by optimizing resource use and reducing environmental impacts. Furthermore, AI-enhanced LiDAR is pivotal in advancing autonomous navigation and sophisticated spatial analysis, marking a significant step forward in urban management and evaluation. The reviewed paper highlights the geometric properties of LiDAR data, derived from spatial point positioning, and underscores the effectiveness of machine learning algorithms in object extraction from point clouds. The study also covers concepts related to LiDAR imaging, feature selection methods, and the identification of outliers in LiDAR point clouds. Findings demonstrate that AI algorithms, especially deep learning models, excel in analyzing high-resolution 3D LiDAR data for accurate urban feature identification and classification. These models leverage extensive datasets to detect patterns and anomalies, improving the detection of buildings, roads, vegetation, and other elements. Automating feature extraction with AI minimizes the need for manual analysis, thereby enhancing urban planning and management efficiency. Additionally, AI methods continually improve with more data, leading to increasingly precise feature detection. The results indicate that the pulse emitted by continuous wave LiDAR sensors changes when encountering obstacles, causing discrepancies in measured physical parameters.

Background: The study of phenolic compounds and their potential to contribute to health is a major interest in research. This work was to determine phenolic compound contents as well as antioxidant properties of roasted maize-peanut snack product with and without spices. Methods: HPLC was used to determine the phenolic composition of the maize flours, peanut flour and their composite snacks with and without spices. Total phenolic content (TPC), total flavonoid content (TFC), tannin content (TC) and radical scavenging activity (measured by 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2-azino-bis (3- ethylbenzothiazoline-6-sulphonicacid) (ABTS) and hydrogen peroxide radical scavenging assays was also used. Results: TPC of the extract of roasted maize flour, roasted peanut flour and composite roasted maize-peanut flour ranged from 48.93 to 178.31 mg GAE/100 g, while the TFC was 3.18–25.94 mg CE/100 g and TC (0.22 – 0.73 mg CE/g). The dominant phenolic acid was protocatechuic acid ranged from 13.73 to 1643.54 µg/g. Among the flavonoids, quercetin and catechin were dominant. The extracts of the free soluble fraction exhibited 23.88 – 81.52 %, 49.59 – 85.17 % and 0.58 -5.13 µmol AAE/g of DPPH, hydrogen peroxide and ABTS radical scavenging abilities respectively. Conclusion: Maize–peanut product showed potential ability in contributing to alleviating radical induced oxidative stress.

This paper investigates the constructability of angles multiples of 3 within the framework of Euclidean geometry. It makes a significant contribution by presenting the first geometric construction for all such angles, offering a rigorous solution to a longstanding geometric problem. The paper reaffirms the efficacy of Euclidean geometry in providing precise constructions and robust proofs for these angles, demonstrating the enduring strength of Euclidean principles from classical to modern times. The presented workflow goes beyond Euclidean geometry to examine non-Euclidean methods, particularly analytical approaches, revealing misconceptions that compromise the genetic and geometric rigor of Euclidean principles. The paper exposes incongruities when algebraic proofs related to angle constructability are applied to the Euclidean system, emphasizing the misalignment of fundamental geometric concepts. A notable result in the paper is the construction of a angle, introducing the “ angle chord” as a novel geometric property. This property challenges assumptions made by non-Euclidean methods and highlights the nuanced geometric properties crucial for rigorous constructions. The paper refutes the fallacy of relying solely on algebra for solutions to angles multiples of , emphasizing the necessity of embracing Euclidean geometry for geometric discoveries. The paper underscores the merits and resilience of Euclidean geometry, showcasing its independence and depth across historical and modern perspectives. The newly presented geometric construction not only resolves a longstanding question but also emphasizes the intrinsic strength and uniqueness of Euclidean principles in contrast to alternative methodologies.

The banana (Musa spp.) is a tropical fruit with excellent sensory characteristics in terms of aroma, flavor and texture, consumed worldwide and exploited in most tropical countries. Green banana flour is rich in flavonoids, which protect the gastric mucosa, has a high content of resistant starch, which acts in the body as a dietary fiber and thus has health benefits, and is an alternative option for bakery products, reducing waste of both the peel and the pulp. The aim of this study was to develop bread formulations with partial substitution of wheat flour with green banana flour (FBV), thus increasing the nutritional, technological and sensory value. 4 formulations, (A), standard sample; (B), bread with 10% FBV; (C), bread with 15% FBV and (D), bread with 20% FBV. Physico-chemical quality was assessed in terms of moisture content by drying at 105ºC, ash by incineration, fat by the Soxhlet method, protein by the biuret method, carbohydrates by difference calculation and calorific value by sum calculation and sensory analysis by affective methods. The data was evaluated using the RStudio 4.2.1 DCC statistical package. There were no significant differences in moisture content, lipids and calorific value. Differences were evident in the ash and protein content. Sensory acceptance of the standard formulation was 82.22%. The results obtained show that green banana flour can be used as a partial substitute for wheat flour to produce breads with functional properties.

Recently, we have studied drug-receptor interaction of the peptidic HIV-1 protease inhibitors based on polar and hydrophobic interactions. We have also studied pharmacokinetics of these inhibitors based on Lipinski’s rule of five and its extended form. After that there was a need to study intermolecular interactions. From literatures, drug-receptor interaction involves hydrogen bonds between acceptor and donor sites of drug and its receptor. These donor acceptor sites must be more than four to be dominant. As single intermolecular H-bond is relatively weak and unlikely to support this type of interaction. It is also clear from literature that this interaction contribute to the alignment of reacting species in proper three-dimensional space in such a position that strong and effective polar or hydrophobic or both interaction occurs to form drug-receptor adduct or enzyme inhibitor complex as appropriate. The strength of H-bonds formed between drug and receptor was judged by bond lengths, bond angles and bond orders. As well as, its nature (strong, moderate or weak) and its number, too. Along with H-bonding, we have also studied Van der Walls i.e. non-bonding type interaction. These non-bonding interactions were studied using charge transfer from donor to acceptor and this results transfer of electron flux from donor molecule (drug/receptor) towards acceptor (receptor/ drug). Thus, lowering of energy of the system under investigation will occur. For this resulted interaction energy was also studied that very clearly explain feasibility of interactions. As we know that all above phenomena are molecular properties and do not cover involvement of orbitals. To cover this we have also studied drug-receptor interaction involving molecular orbital. It was HOMO of one reacting molecule (B) that donates electron pair, electron cloud or electron density to LUMO of another reacting molecule (A) that accepts or accommodates this electron pair, electron cloud or electron density. The quantity of the electron flux from HOMO to LUMO was judged by the value of ∆E_LH. A lower value of this will support strong and effective drug-receptor interaction. Results of orbital based study have also been found to supports the results as abstracted from interaction energy.

Spline smoothing is a technique used to filter out noise in time series observations when predicting nonparametric regression models. Its performance depends on the choice of the smoothing parameter. Most of the existing smoothing methods applied to time series data tend to over fit in the presence of autocorrelated errors. This study aims to determine the optimum performance value, goodness of fit and model overfitting properties of the proposed Smoothing Method (PSM), Generalized Maximum Likelihood (GML), Generalized Cross-Validation (GCV), and Unbiased Risk (UBR) smoothing parameter selection methods. A Monte Carlo experiment of 1,000 trials was carried out at three different sample sizes (20, 60, and 100) and three levels of autocorrelation (0.2, 05, and 0.8). The four smoothing methods' performances were estimated and compared using the Predictive Mean Squared Error (PMSE) criterion. The findings of the study revealed that: for a time series observation with autocorrelated errors, provides the best-fit smoothing method for the model, the PSM does not over-fit data at all the autocorrelation levels considered ( the optimum value of the PSM was at the weighted value of 0.04 when there is autocorrelation in the error term, PSM performed better than the GCV, GML, and UBR smoothing methods were considered at all-time series sizes (T = 20, 60 and 100). For the real-life data employed in the study, PSM proved to be the most efficient among the GCV, GML, PSM, and UBR smoothing methods compared. The study concluded that the PSM method provides the best fit as a smoothing method, works well at autocorrelation levels (ρ=0.2, 0.5, and 0.8), and does not over fit time-series observations. The study recommended that the proposed smoothing is appropriate for time series observations with autocorrelation in the error term and econometrics real-life data. This study can be applied to; non – parametric regression, non – parametric forecasting, spatial, survival, and econometrics observations.