Taro (Colocasia esculenta), one of the neglected and underutilized crop species (NUCS), is regarded as a climate-smart crop due to its ability to tolerate harsh environmental conditions associated with climate change. The crop plays a critical role in enhancing food and nutritional security while promoting resilience through crop diversification, particularly in smallholder farming systems in developing countries. Despite its recognized potential, a substantial knowledge gap exists regarding the identification of optimal areas for maximizing yield in local contexts. Hence, this study employed the Maximum Entropy (MaxEnt) machine learning algorithm to conduct a Land Suitability Analysis (LSA) for taro within rural landscapes dominated by smallholder farmers. To quantify the area suitable for cultivation, we evaluated topographic and edaphic influences at 0-20 and 20-50 cm using five models: (1) topographic- variables, (2) edaphic variables at 0-20 cm, (3) a combined model using topographic + edaphic variables at 0-20 cm, (4) edaphic variables at 20-50 cm, (5) a combined model using topographic + edaphic variables at 20-50 cm. Model 5 achieved the highest mean AUC (0.821 ± 0.083 SD) based on 10 replicated validations, although paired t-tests revealed that this improvement was statistically significant only when compared to the lowest-performing Model 1 (p = 0.005), while comparisons with Models 2, 3, and 4 were not significant (all p > 0.05). The results from Model 5 indicated that 7.92%, 11.04%, 16.80%, and 26.64% of the study area were classified as highly suitable, suitable, moderately suitable, and marginally suitable for taro cultivation, respectively. Based on the MaxEnt Jackknife test, slope, Topographic Wetness Index (TWI), and the soil’s calcium, iron, and phosphorus were the most important variables for taro growth. These findings provide practical information to support improved land-use planning, promote the integration of NUCS into local farming systems, and contribute to food and nutritional security, particularly within smallholder farming communities in rural areas.
Tailoring surfactant topology offers a powerful route to govern interfacial phenomena and fluid displacement in harsh subsurface environments. Herein, we systematically examine the influence of headgroup multiplication across three homologous tetradecyl quaternary ammonium architectures: single-chain MC14, dimeric GC14, and trimeric TC14. Spectroscopic characterization (1H/13C-NMR, FTIR) and thermogravimetric analysis (TGA) validated the target chemical frameworks and their thermal robustness, while XRD confirmed the predominantly calcitic, highly crystalline matrix of the evaluated carbonate substrate. Increasing the oligomeric degree substantially boosted surface activity and self-assembly propensity at 25 °C, lowering the critical micelle concentration (CMC) by two orders of magnitude from 3.50 mM (MC14) down to 0.114 mM (GC14) and 0.035 mM (TC14), alongside superior counterion association and surface packing efficiency. Dynamic light scattering (DLS) confirmed architecture-controlled supramolecular aggregation, tracking hydrodynamic sizes from 4.15 nm to 8.12 nm. Thermodynamic evaluations indicated spontaneous, increasingly exergonic micellization and elevated halotolerance against NaCl from monomeric to trimeric configurations. Under harsh thermal conditions (80 °C), TC14 outperformed lower-order homologues by reducing oil-water interfacial tension to a nadir of 0.21 mN/m and effectively reversing oil-wet carbonate wettability (contact angle shifted from 153° to 56° at 2 ×CMC). Synergistic tuning with sulfate ions further decoupled conditions required for minimal interfacial tension from those favoring maximal hydrophilicity. Crucially, capillary-dominated spontaneous imbibition experiments revealed that wettability-tailored TC14 solutions yielded a peak recovery of 22.1%, outperforming baseline seawater (12.7%). These insights elucidate the mechanistic advantages of multivalent trimeric surfactants as ultra-low-dosage, salt-tolerant agents for interfacial engineering and enhanced hydrocarbon recovery in carbonate formations.
The relationship between body mass index (BMI) and leukemia is still unclear, especially across various leukemia subtypes and mortality. We conducted an updated systematic review and dose-response meta-analysis of prospective cohort studies to quantify categorical, linear, and nonlinear associations between BMI and leukemia incidence and mortality. PubMed, Scopus, and Web of Science were searched for eligible prospective cohort studies. Twenty-one prospective cohort studies were included, of which 18 contributed leukemia incidence outcomes and three contributed leukemia mortality outcomes. To prevent duplicate counting, only one independent cohort-level risk estimate was included for each outcome, and pooled relative risks (RRs) were estimated using random-effects models fitted by restricted maximum likelihood (REML). Linear dose-response associations were standardized to a 5-kg/m² increment in BMI, and nonlinear associations were modeled using restricted cubic splines. Risk of bias was assessed using ROBINS-I, and the certainty of evidence was evaluated using GRADE. Higher BMI was associated with higher overall leukemia incidence in both categorical analyses comparing the highest available estimable BMI category with the study-specific reference category (9 studies; RR = 1.235, 95% CI 1.084–1.408; I²=49.8%) and linear dose-response analyses (10 studies; RR per 5 kg/m²=1.098, 95% CI 1.053–1.144; I²=82.4%). In categorical analyses, higher BMI was associated with higher incidence of ALL, AML, and CML, whereas the association with CLL was not statistically significant. In linear analyses, higher BMI was associated with higher incidence of AML and ALL; however, the ALL estimate was based on only two independent studies and should be considered exploratory. The associations for CML and CLL were not statistically significant. Higher BMI was also associated with leukemia-related mortality in both categorical (3 studies; RR = 1.870, 95% CI 1.372–2.550) and linear analyses (3 studies; RR per 5 kg/m²=1.329, 95% CI 1.121–1.575), although these estimates were derived from general-population cohorts with mortality linkage and should not be interpreted as evidence of higher post-diagnosis mortality among patients with leukemia. Restricted cubic spline analyses provided no evidence of nonlinear associations within the supported BMI ranges. The certainty of evidence was rated as low for overall leukemia incidence and very low for leukemia mortality. Higher BMI was associated with a modestly higher overall leukemia incidence and with leukemia-related mortality in prospective cohort studies. However, subtype-specific and mortality estimates were limited by the small number of independent studies, residual confounding, and between-study heterogeneity. No evidence of nonlinear associations was observed within the BMI ranges supported by the available data. These findings should be interpreted as epidemiological associations rather than evidence of causality.
Hypochlorous acid (HClO) is a vital constituent of the reactive oxygen species (ROS) family, predominantly generated during the myeloperoxidase-catalyzed reaction in macrophages and neutrophils. Clinically, HClO is considered a potential biomarker, with abnormal levels closely associated with the onset and progression of numerous diseases, such as atherosclerosis and rheumatoid arthritis. Therefore, a highly sensitive and specific HClO detection method is desired for its quantitative monitoring. Herein, a fluorescence detection method (ATP-controlled DNA nanodevice) is proposed for HClO detection. In this method, HClO can significantly enhance the system’s fluorescence signal by separating the FAM fluorophore from the Dabcyl quencher. The constructed device demonstrated a detection range of 0.01-1 µM with a limit of detection (LOD) of 8.774 nM under optimal conditions. Moreover, the proposed method successfully quantified HClO in practical samples, such as tap water. The presented experimental data confirm the promising efficacy of this method in relevant fields.
The rapid development of wireless sensor network (WSN)-enabled Internet of Things (IoT) systems increases the necessity of designing SRAM cells with very low power dissipation, excellent stability, and reliable low-voltage operation. Nevertheless, traditional SRAM cells have problems of insufficient read/write stability, half select disturb, and higher susceptibility to process variations in nanometer technology nodes. In this paper, a CNTFET-enabled 10T SRAM cell with independent read/write paths, Schmitt trigger storage inverter, virtual ground write assistance, and half select disturb free operation is proposed to enhance the stability and energy efficiency. The new design is analysed in HSPICE by using the Stanford 32 nm CNTFET model and compared with the state-of-the-art CNTFET SRAM cells. Simulation results show that the proposed cell has 81% increase in read static noise margin (RSNM), 94% increase in write static noise margin (WSNM), 80% reduction in write power, 76% reduction in read power, 74% reduction in leakage power, 29% reduction in minimum operational voltage, and 109% increase in critical charge over the traditional 6T SRAM cell. Also, the RSNM and WSNM yields of the 1000 samples in Monte Carlo analysis are the largest, indicating immunity to process variations.
