Search Results (14,288)

Sweet basil is a medicinal herb valued for its culinary and therapeutic applications, primarily due to its secondary metabolite content. Therefore, optimizing its cultivation is essential for growers seeking to improve both the quality and nutritional value of the plants. Two cultivars of Ocimum basilicum L., ‘Lettuce Leaf’ (LL) and ‘Purple Opal’ (PO), were evaluated under various nutritional regimes (mineral, organic, and organo-mineral). The assessment included measurements of total protein, fat, and ash content, as well as total polyphenol levels, phenolic acid content, and antioxidant activity. HPLC analysis was performed to evaluate the composition of selected phenolic and chlorogenic acids, flavonoids, and catechins. Additionally, mineral content was analyzed using OES-ICP. Gene expression of key genes involved in the phenylpropanoid pathway (PAL, C4H, 4Cl, CAD, and CVOMT) and the transcription factor OscWRKY1 was analyzed through RT-qPCR. The key findings indicated that the nutritional variants significantly impacted both primary and secondary metabolism in the assessed plants. Additionally, there was a significant (p < 0.05) cultivar-specific response to the different nutritional variants. The results suggest that the optimal nutritional strategy may vary depending on the target metabolite. Variant 4 was associated with the most favorable overall response in basil, including increased protein levels, higher total polyphenol content, and a balanced mineral composition. However, variant 5 showed the highest antioxidant activity for both cultivars. Rutin and protocatechuic acid were detected only in PO, and cryptochlorogenic acid was detected only in LL. A marked varietal difference was observed in gallocatechin content, with the LL variety containing more than fourfold higher levels than the PP variety. The results of RT-qPCR were fluctuating and strongly dependent on the cultivar. Full article

In response to the challenges associated with suboptimal route efficiency, insufficient environmental adaptability as well as unsmooth paths in global path planning for mobile robots using the conventional A* algorithm, this paper introduces an adaptive A* algorithm. Initially, an adaptive estimation function is put forward by utilizing the positional relationship between the robot’s current and target position. Through tuning the coefficients with the heuristic function dynamically, path generation time is curtailed. Subsequently, the distance function model is optimized. The arithmetic mean of Euclidean distance and Manhattan distance is utilized to enhance the algorithm’s adaptability to diverse environmental maps. Ultimately, the redundant point deletion strategy is implemented to remove unnecessary nodes along the route, thereby enhancing path smoothness. Experimental results show that across three varying maps, the proposed algorithm, relative to the conventional A* algorithm, on average achieves a 69% reduction in path generation time, a decrease in path length of 2.66 m, and a decline in the quantity of mean steering angles exceeding or equaling 45 degrees of 38.1%. Moreover, when compared with several classic A* algorithm variants and recent improved algorithms, the proposed approach is capable of generating the shortest and most smooth path, confirming its superior planning performance while fulfilling both efficiency and smoothness demands. Full article

This work examines the machining responses of dry turning in ultrasonic-assisted stir-squeeze cast A356 hybrid nanocomposites reinforced with zirconia (ZrO₂) and graphene oxide (GO). Accordingly, flank wear (VBc) ranged from 0.061 to 0.238 mm, influenced by abrasion, adhesion, built-up edge (BUE) formation, and diffusion mechanisms. Cutting speed had the most significant effect on flank wear (65.65%), followed by depth of cut (18.2%) and feed rate (11.13%), supported by a well-fitted regression model (R² = 0.987; p < 0.05). Surface roughness (Ra) ranged from 1.733 to 7.012 μm, with cutting speed, feed rate, and depth of cut contributing 70.42%, 15.43%, and 9.56%, respectively. The cutting temperature was limited to 127 °C, primarily influenced by cutting speed (60.68%), whereas cutting power varied between 0.353 and 0.644 kW, mainly governed by cutting speed (68.71%) and depth of cut (25.92%). The chip morphology showed a segmented sawtooth pattern due to cyclic fracture initiation during material removal. Multi-criteria optimization using complex proportional assessment (COPRAS) identified v = 90 m/min, f = 0.06 mm/rev, and d = 0.1 mm as the optimal parameters, yielding a tool life of 22.6 min and a machining cost of INR 58.69 per item. This research is further focused on the implementation of different cooling lubrication techniques utilizing environmentally friendly cutting fluids, including Minimum-Quantity Lubrication and nano-MQL, among other types of environments. Full article

Melatonin, a potent endogenous antioxidant, holds promise for enhancing stress tolerance in woody plants, yet its molecular mechanism under saline–alkali stress remains poorly understood. This study systematically investigated the effects of exogenous melatonin on Ulmus pumila ‘Zhonghua Jinye’ by integrating physiological assays, transcriptomics, and metabolomics. Two-year-old cuttings were subjected to 150 mmol·L⁻¹ saline–alkali stress and treated with varying melatonin concentrations (0, 50, 100, 200, 400 μmol·L⁻¹; three replicates). Physiological evaluations identified 100 μmol·L⁻¹ melatonin (SMT100) as optimal, significantly enhancing antioxidant enzyme activities (SOD, CAT, APX, GR) by 28.7–41.5% and reducing reactive oxygen species (H₂O₂ by 31.5%; O₂⁻ by 38.2%) compared to untreated stressed controls. Integrated omics analysis (CK, S, SMT100 groups) revealed that saline–alkali stress suppressed the flavonoid biosynthesis pathway, down-regulating key genes such as UpANS1 (10.74-fold), UpANS2, UpHCT1, and UpDFR2, thereby reducing the accumulation of protective flavonoids like quercetin and kaempferol. Conversely, melatonin treatment reactivated this pathway, significantly up-regulating UpANS1 (17.36-fold induction), UpDFR2 (5.55-fold), UpCHS1, UpF3H6, and UpLAR2. This genetic reconfiguration promoted the synthesis of antioxidant flavonoids, enhancing the plant’s overall stress resilience, thus identifying UpANS1 as candidates associated with treatment response. The study provides a scientific basis for cultivating U. pumila ‘Zhonghua Jinye’ in saline–alkali soils and clarifies the molecular mechanism by which melatonin alleviates combined saline–alkali stress via flavonoid pathway regulation. Full article

With the rapid growth of data center (DC) energy consumption and the large-scale integration of renewable energy, DCs increasingly face time-varying power upper-bound constraints jointly shaped by grid power supply capability, renewable energy fluctuations, and demand response mechanisms. Meanwhile, DC power consumption exhibits a typical information-load-driven characteristic. The computing tasks hosted by virtual machines affect server-side IT power consumption through resource utilization states such as CPU, memory, disk I/O, and network I/O, and are further coupled with non-IT auxiliary power consumption from cooling, power distribution, and networking equipment. In such cyber–physical operation scenarios, physical-layer sensing data and hypervisor-level virtualization monitoring data jointly provide the state basis for power estimation, power warning, and migration decisions. To address the mismatch between dynamic power upper bounds and time-varying information loads, this paper investigates the information load scheduling problem under constrained power loads and proposes a two-stage virtual machine (VM) migration optimization framework. In the VM selection stage, a Multi-Factor Balanced (MFB) algorithm is designed. By introducing a warning-line trend model based on the arctangent function, MFB comprehensively considers resource utilization, power load variation trends, and service level agreement (SLA) violation levels to dynamically identify candidate VMs for migration. In the VM placement stage, a Multi-Factor Equilibrium Ant Colony Optimization (MFEACO) algorithm incorporating a Random Roulette Wheel (RRW) selection mechanism is proposed. By constructing normalized multi-dimensional equilibrium factors, MFEACO coordinates the trade-off among energy consumption, load balancing, and SLA violations. Simulation experiments are conducted on an improved CloudSim platform using real-world cluster trace data from Google and Alibaba. The results show that, while satisfying dynamic power constraints, the proposed MFB–MFEACO framework achieves a favorable comprehensive trade-off among energy consumption control, SLA violation suppression, and migration reduction. Compared with traditional heuristic methods and a power-constrained genetic algorithm baseline, the proposed framework demonstrates better dynamic adaptability and scheduling stability. Full article

Individual tree species classification is essential for detailed forest inventories, ecosystem monitoring, and biodiversity assessment. While UAV-acquired RGB and multispectral (MS) imagery have advanced tree species mapping, most studies focus on a single sensor type. In practice, UAV platforms carry diverse sensors with varying spatial resolutions, spectral bands, radiometric responses, and noise characteristics, introducing domain shifts that limit model generalization across datasets. To overcome these challenges, we propose a supervised cross-sensor transfer learning approach, leveraging a DenseNet-121 model pretrained on high-resolution UAV RGB imagery to improve classification on lower-resolution multispectral imagery with limited labelled data. The adapted model achieved 75% overall accuracy and a macro-F1 score of 0.706, significantly improving over models trained from scratch. Its performance was further evaluated on downsampled UAV MS imagery simulating conventional airborne multispectral photographs, demonstrating robustness and practical applicability for regional-scale forest inventories. This study highlights cross-domain transfer learning as a pathway toward sensor-independent, efficient, and operationally scalable tree species classification. Full article

Dielectric permittivity describes how a material becomes polarized in response to a time-varying electric field and provides a powerful framework for probing the physical organization of biological systems. This review aims to clarify the origin of dielectric permittivity in plants, offering a conceptually grounded interpretation while keeping mathematical formalism to the level necessary for biological interpretation. We first outline the fundamental mechanisms of polarization, their characteristic time scales, and the frequency-dependent nature of the dielectric response, including the concept of complex permittivity, together with commonly used measurement approaches in biological materials. Particular attention is given to water, whose dielectric properties play a dominant role in plant tissues. We then examine how permittivity varies across different plant organs, including leaves, fruits, and roots, highlighting the relationship between dielectric response and structural and compositional features. Modeling strategies linking microscopic organization to macroscopic dielectric behavior are also discussed. Because dielectric permittivity is intrinsically connected to plant structure and composition, non-invasive measurements offer significant potential for assessing plant physiological status, including the detection of changes induced by abiotic and biotic stresses. By bridging engineering approaches with plant physiology, this review provides a unified framework to interpret dielectric measurements in plants and supports their application in plant science and phenotyping. Full article

A total of 3930 blackfin flounder (Glyptocephalus stelleri) individuals were collected continuously on a monthly basis from the East Sea of Korea in 2024 (n = 1800) and 2025 (n = 2130). The total length ranged from 10.6 to 44.0 cm in 2024 and from 11.9 to 49.7 cm in 2025. The major prey items differed between the years. In 2024, polychaetes (75.3%) and amphipods (12.2%) were the dominant prey items, whereas in 2025, euphausiids (33.1%), polychaetes (33.7%), and fish (17.5%) were the most important prey groups, indicating a clear interannual variation in diet composition. PERMANOVA revealed that diet composition varied significantly with year, season, and size class (p < 0.05), with a significant interaction between the year and season. These patterns were consistently supported by the CAP ordination, which showed a clear separation of samples along the seasonal gradient on the CAP1 axis, with additional variations associated with the year and size class observed within the respective seasonal groupings. Ultimately, these results suggest that G. stelleri functions as an opportunistic feeder that is capable of shifting its diet in response to environmental fluctuations. This study aims to provide scientific data for efficient fishery resource management and ecosystem-based assessments in response to future climate change. Full article

Background/Objectives: Semaglutide and tirzepatide demonstrate substantial efficacy in obesity treatment, yet individual responses vary markedly. The incretin system—comprising glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP)—is frequently dysregulated in obesity, but whether fasting incretin levels predict differential pharmacological responses remains unexplored. We investigated whether combinatorial fasting GLP-1/GIP tertile profiles predict the initial weight-loss response to semaglutide versus tirzepatide in patients with severe obesity. Methods: This prospective, parallel-group, open-label pilot study enrolled 90 treatment-naïve patients with BMI > 40 kg/m² (mean 42.5 ± 3.5 kg/m²) at the University of Catania, Italy. Fasting serum GLP-1 (0.8–50 pg/mL) and GIP (1–16 ng/mL) were measured by chemiluminescence immunoassay and distributed into tertiles, generating nine combinatorial profiles (P1–P9; n = 10 per profile). Within each profile, five patients were randomly assigned to semaglutide (escalated to 2.4 mg/week) or tirzepatide (escalated to 15 mg/week). Primary outcome was pharmacological response category at six months: low (<5% body weight reduction), intermediate (5–15%), or optimal (>15%). Results: Baseline characteristics were balanced across profiles (age 48 ± 8 years, BMI 42.5 ± 3.5 kg/m², waist circumference 134 ± 12 cm, HOMA-IR 8.5 ± 3.0; all p > 0.05). Tirzepatide achieved optimal response in profiles with low GIP tertile regardless of GLP-1 level (P1, P6, P8), while semaglutide achieved optimal response when GLP-1 was low and GIP was intermediate-to-high (P4, P5). Both drugs showed low response in the high GLP-1/high GIP profile (P3). Mean weight loss in optimal-response groups was 18.2 ± 2.1% for tirzepatide and 16.8 ± 1.9% for semaglutide. Waist circumference reductions paralleled weight loss patterns. HOMA-IR decreased significantly in all optimal-response groups (mean reduction 4.2 ± 1.1 units). Conclusions: In this hypothesis-generating pilot study, fasting GLP-1/GIP combinatorial profiling, obtained from a single fasting blood sample, was associated with differential pharmacological responses to semaglutide and tirzepatide in severe obesity. Low GIP levels were tentatively associated with optimal tirzepatide response; low GLP-1 with intermediate-to-high GIP was tentatively associated with optimal semaglutide response. These preliminary findings provide proof-of-concept for incretin-guided personalised obesity pharmacotherapy but require confirmation in larger, adequately powered randomised trials before any clinical recommendations can be made. The inability to discriminate incretin secretory deficiency from receptor resistance using fasting measurements alone, the absence of a placebo control, and the six-month follow-up (shorter than the 12–18 months at which maximal efficacy is typically observed) remain critical limitations. Full article

This study investigates the influence of normal and cryogenic grinding on the antioxidant properties and bioactive compound profiles of fennel seed (Foeniculum vulgare) powder at varying particle sizes. Methanolic and ethanolic extracts were evaluated for DPPH radical scavenging activity (AAO), total phenolic content (TPC), and total flavonoid content (TFC) using conditions optimised through Response Surface Methodology (RSM) employing an I-optimal quartic design. Statistical analysis confirmed that grinding type and solvent type were the dominant factors influencing all three responses, with their interaction significantly governing flavonoid content recovery. The optimal extraction conditions were identified as cryogenic grinding, methanol as solvent, and a particle size of 200 µm, with an overall desirability of 0.887. Validated experimental values under these conditions were AAO = 87.11%, TPC = 11.31 mg GAE/g, and TFC = 14.18 mg QE/g, with prediction errors within ±5% of model-predicted values confirming the robustness of the developed models. HRLC-MS analysis confirmed that cryogenic grinding preserves a broader and more concentrated phytochemical profile compared to normal grinding. These findings demonstrate that cryogenic grinding combined with methanol extraction at fine particle sizes significantly enhances the yield of antioxidant-rich phytochemicals from fennel seeds, supporting their potential application in functional food and nutraceutical development. Full article

Surface water-groundwater interaction zones are critical ecohydrological interfaces in arid regions, yet quantitative spatiotemporal patterns and soil-vegetation responses under coupled water-salt-heat gradients remain poorly documented. Based on a one-year monitoring period (August 2024–August 2025) at four sites along a river-to-desert transect (LW3: 25 m, LW2: 200 m, LW1: 300 m, LW4: 400 m from the Niya River) in the hyper-arid Tarim Basin, this study reveals the following quantitative patterns. Groundwater depth increased with distance from the river and followed an annual decrease-increase trend, with an anomalous shallow peak in March 2025 (−20 cm) linked to precipitation recharge. Soil temperature stability increased with depth: the 20 cm layer recorded the widest annual fluctuation (e.g., −1.5 °C to 24 °C at LW1), whereas the 80 cm layer varied only between approximately −0.2 °C and 28 °C. Proximity to the river dampened thermal extremes. Shallow soil moisture was highly dynamic (with a coefficient of variation [CV] reaching 40–50% at LW1 and LW4), while deeper layers remained stable; LW3 near the river stayed saturated year-round (CV = 0). Soil electrical conductivity (EC) decreased with distance from the river: LW3 exhibited the highest surface values (5000–16,000 μS cm⁻¹), whereas LW1 recorded the lowest (1000–2700 μS cm⁻¹). Vegetation performance was governed by coupled water-salt conditions rather than moisture alone: P. australis at LW1 achieved the tallest growth (>200 cm) and highest photosynthetic rates (20.25–37.38 μmol m⁻² s⁻¹), outperforming LW3 (104 cm, winter photosynthesis dropping to 2.01) and LW4 (~100 cm). Correlation analysis further showed strong vertical temperature coupling (r > 0.96 across all depths) and depth-stratified water-salt relationships (e.g., EC-volumetric water content r = 0.95 at 20 cm in LW4), reflecting spatial differentiation driven by freeze-thaw cycles, evaporative enrichment, and homogeneous silt-textured soils (54–96% fine fraction). These quantitative findings provide a detailed observational baseline for riparian ecohydrology in hyper-arid inland rivers and underscore that sustainable vegetation management requires balancing water availability against salinity stress. Full article

Coccocypselum lanceolatum is a tropical, perennial, creeping, herbaceous C3 plant species that is found in deeply shaded humid forests. This species has potential for medicinal and culinary uses. Knowledge about this species and other herbaceous Rubiaceae is confined to phytocoenological and morpho-anatomical studies. Here, it was hypothesized that (1) leaf gas exchange dynamics over a one-year period in C. lanceolatum are related to light conditions, phenology and environmental seasonal changes; (2) photosynthetic performance is focused on enhanced carbon gains through a high leaf net assimilation rate (A_net) relative to light availability, a low dark respiration rate (R_d) and a light compensation point (LCP); and (3) these parameters will vary over leaf age. The photosynthetic photon flux density (PPFD), characterizing the growth and development of C. lanceolatum, was reduced to 4–11% of incoming light in the open area, while the red-to-far-red light ratio (R:FR) was reduced from 1.15 to mean diurnal values of 0.45–0.81, depending on forest canopy dynamics. Leaf gas exchange parameters [A_net, stomatal conductance (g_s), leaf transpiration (E), and intrinsic water use efficiency (iWUE)] were observed over a one-year period. A_net, g_s, and E were correlated with energy factors (PPFD and air temperature) during vegetative growth, while only iWUE showed a correlation with leaf gas exchange parameters during blooming and fruiting, indicating that seasonality and phenology were additional drivers of leaf gas exchange. As a deep-shade forest species, C. lanceolatum displayed low iWUE (3–21 μmol m⁻² s⁻¹) and was adapted to maximize carbon gain and prioritize high g_s rather than water economy. The extremely low LCP (4.2 μmol m⁻² s⁻¹), low R_d (0.2 to 0.43 μmol m⁻² s⁻¹), maximum net photosynthesis (A_max, 5 μmol m⁻² s⁻¹), and apparent quantum efficiency of CO₂ assimilation (Φ of 0.04 µmol µmol⁻¹) were adaptational traits of this species for low light. Finally, the A_net, g_s, E, iWUE, gross photosynthesis under light saturation, R_d, LCP, and light saturation point values were different when comparing young and adult leaves. The ecophysiological responses over a one-year period shown here could assist in the success of C. lanceolatum as a sustainable soil-cover plant in shaded areas. Full article

Modern hands-free and wearable communication devices employ multiple microphones and loudspeakers, leading to the joint presence of acoustic echo, background noise, and desired speech signals. While acoustic echo cancellation (AEC) and beamforming are commonly combined to address this challenge, existing architectures face a trade-off between computational complexity, stability, and adaptability. In particular, adaptive beamforming approaches require repeated estimation and inversion of covariance matrices, incurring high computational cost and introducing potential sensitivity to time-varying conditions. Conversely, fixed beamformers reduce online complexity and improve stability, but their performance can degrade when the acoustic scene differs from the calibration condition. In this work, we investigate low-complexity AEC–beamforming architectures that combine fixed minimum-variance distortionless response (MVDR) beamforming with adaptive AEC. Since the ordering of these stages yields two inequivalent architectures, we evaluate two configurations: AEC followed by beamforming (AEC-BF) and beamforming followed by AEC (BF-AEC). To reduce dependence on a single head pose in wearable devices, we use an offline orientation-averaged calibration strategy in which the undesired-signal covariance matrix and, when required, the relative echo transfer functions (RETFs) are estimated from calibration measurements averaged across multiple head orientations. The proposed methods are evaluated using real-device recordings from a six-microphone wearable device. The results show a clear architectural asymmetry: the fixed BF-AEC configuration achieves the highest average echo return loss enhancement (ERLE) and perceptual evaluation of speech quality (PESQ), with substantially lower online complexity than the fully adaptive baseline, whereas the fixed AEC-BF configuration provides a higher signal-to-distortion ratio (SDR) in the evaluated experiment. Additional calibration experiments show that orientation-averaged RETF calibration provides partial generalization across the measured head orientations, but also that the RETFs are not fully orientation-invariant. Overall, the results indicate that fixed BF-AEC provides a favorable trade-off between echo suppression, stability, and online complexity under the evaluated real-recording conditions. Full article

Sugarcane harvester performance varies substantially with field geometry, crop, and operator factors, yet separating these sources from telematics data while preserving engineering interpretability remains a methodological gap. This study models field efficiency (Eff) and harvesting capacity (C_a) separately from JDLink telematics, aligning model structure with each target’s response behavior. Operational data covered 105 plots across four seasons (2019/20–2022/23) from three John Deere CH570 chopper harvesters in eastern Thailand. Six engineering-relevant predictors were retained after multicollinearity screening, and linear (MLR), additive nonlinear (GAM), and tree-based models were compared under 5-fold grouped cross-validation by BaseField (87 groups). Eff was assigned to GAM (R²_CV = 0.621 ± 0.114) on the basis of its threshold-like response to turning frequency; C_a was retained for MLR (R²_CV = 0.681 ± 0.121), with GAM essentially tied. Train–validation gaps were substantially smaller for additive models (0.096–0.118) than for tuned tree-based candidates (GBR 0.210–0.302, RF 0.322–0.358). Turning frequency (TF) and perimeter-to-area ratio (PAR) were the strongest predictors, and a constant-turn-time partial-out test indicated that TF’s univariate effect on Eff is largely mediated by the time-budget identity. Tactical interventions (path planning, operator training, machine–field allocation) are immediately feasible, although strategic field-layout change remains constrained by smallholder land tenure. Full article

An automated CFD-based workflow for the design, evaluation, and comparative optimization of 2D tidal-stream turbine blade sections is presented for early-stage design exploration. The workflow is intended to efficiently derive an improved section using a consistent and higher fidelity evaluation approach, which is particularly relevant for floating tidal concepts where the effective angle of attack can vary. HEEDS is used to manage a SHERPA optimization loop, while candidate geometries are regenerated in Rhino Grasshopper through a control point parameterization with thickness bounds and smooth interpolation. STAR-CCM+ simulations are executed in an automated manner and the resulting lift and drag responses are returned to HEEDS to evaluate performance over four representative angles of attack, 0, 3, 6, and 9 deg. A total of 1000 design evaluations are conducted for a baseline NACA 63–815 section at Reynolds number 1 × 10⁷, using a two metric formulation that targets high mean lift to drag ratio while limiting the maximum drag coefficient within the same angle set. The optimization history shows rapid early improvement followed by a plateau and identifies a final best design at Design 746. Compared with the original section, the optimized section increases lift and improves the lift-to-drag ratio across the operating range, while keeping the peak drag constrained. Cavitation inception characteristics also improve, with the optimized section delaying inception at the same lift criterion and sustaining a cavitation free state at higher lift for the same cavitation number. Pressure coefficient distributions indicate that these changes are primarily associated with altered suction side loading in the front to mid chord region and modified pressure recovery behavior. A preliminary full 3D RANS CFD rotor comparison under a prescribed rotor geometry further shows that the optimized section can improve rotor power performance in the main operating TSR range, although the benefit becomes limited at high TSR. Full article