Search Results (766)

In the severe environment of Hunshandake Sandy Land, the uncommon and indigenous Chinese tree species Picea mongolica is an important biological component. Conventional seed propagation in P. mongolica is constrained by low germination rates, prolonged breeding cycles, and hybrid offspring genetic instability, limiting efficient varietal improvement. In contrast, somatic embryogenesis (SE) offers superior propagation efficiency, exceptional germination synchrony, and strict genetic fidelity, enabling rapid mass production of elite regenerants. However, SE in P. mongolica is hampered by severe genotype dependence, poor mature embryo induction rates, and loss of embryogenic potential during long-term cultures, restricting the production of high-quality seedlings. In this study, we aimed to analyze the transcriptome and metabolome of three crucial phases of SE: mature somatic embryos (MSEs), globular somatic embryos (GSEs), and embryogenic calli (EC). Numerous differentially expressed genes (DEGs) were found, especially in pathways linked to ribosomal functions, flavonoid biosynthesis, and the metabolism of starch and sucrose. Additionally, 141 differentially accumulated metabolites (DAMs) belonging to flavonoids, organic acids, carbohydrates, lipids, amino acids, and other metabolites were identified. An integrated study of metabolomic and transcriptome data indicated considerable enrichment of DEGs and DAMs in starch and sucrose metabolism, as well as phenylpropanoid biosynthesis pathways, all of which are required for somatic embryo start and development. This study revealed a number of metabolites and genes linked with SE, offering important insights into the molecular mechanisms driving SE in P. mongolica and laying the groundwork for the development of an efficient SE system. Full article

This study investigates the deformation and fracture mechanisms of a Ti₆₁Al₁₆Cr₁₀Nb₈V₅ multi-principal element alloy (Ti61V5 alloy) under quasi-static and dynamic compression. The alloy comprises an equiaxed BCC matrix (~35 μm) with uniformly dispersed nano-sized B2 precipitates and a ~3.5% HCP phase along grain boundaries, exhibiting a density of 4.82 g/cm³, an ultimate tensile strength of 1260 MPa, 12.8% elongation, and a specific strength of 262 MPa·cm³/g. The Ti61V5 alloy exhibits a pronounced strain-rate-strengthening effect, with a strain rate sensitivity coefficient (m) of ~0.0088 at 0.001–10/s. Deformation activates abundant {011} and {112} slip bands in the BCC matrix, whose interactions generate jogs, dislocation dipoles, and loops, evolving into high-density forest dislocations and promoting screw-dominated mixed dislocations. The B2 phase strengthens the alloy via dislocation shearing, forming dislocation arrays, while the HCP phase enhances strength through a dislocation bypass mechanism. At higher strain rates (960–5020/s), m increases to ~0.0985. Besides {011} and {112}, the BCC matrix activates high-index slip planes {123}. Intensified slip band interactions generate dense jogs and forest dislocations, while planar dislocations combined with edge dislocation climb enable obstacle bypassing, increasing the fraction of edge-dominated mixed dislocations. The Ti61V5 alloy shows low sensitivity to adiabatic shear localization. Under forced shear, plastic-flow shear bands form first, followed by recrystallized shear bands formed through a rotational dynamic recrystallization mechanism. Microcracks initiate throughout the shear bands; during inward propagation, they may terminate upon encountering matrix microvoids or deflect and continue when linking with internal microcracks. Full article

Intelligent reflecting surface (IRS) has emerged as a promising solution to establish propagation paths in non-line-of-sight (NLoS) scenarios, effectively mitigating blockage challenges in direct vehicle-to-infrastructure (V2I) links. This study investigates a time-varying multi-IRS-assisted multiple-input multiple-output (MIMO) communication system, aiming to maximize the system sum rate through the joint optimization of base station (BS) precoding and IRS phase configurations. The formulated problem exhibits inherent non-convexity and time-varying characteristics, posing significant optimization challenges. To address these, we propose a low-complexity dimension-wise sine maximization (DSM) algorithm, grounded in the sum path gain maximization (SPGM) criterion, to efficiently optimize the IRS phase shift matrix. Concurrently, the water-filling (WF) algorithm is employed for BS precoding design. Simulation results demonstrate that compared with traditional methods, the proposed DSM algorithm achieves a 14.9% increase in sum rate, while exhibiting lower complexity and faster convergence. Furthermore, the proposed multi-IRS design yields an 8.7% performance gain over the single-IRS design. Full article

The construction industry is undergoing a significant transformation due to the increasing influence of digital technology, sustainability requirements, and diverse stakeholder expectations, which highlights the need to update the existing service quality models accordingly. However, the traditional service quality models often fail to address these evolving demands comprehensively. This study systematically reviews 44 peer-reviewed articles to identify the key service quality dimensions and offer clear guidance for future research that can address the complexities of modern construction. The findings reveal that reliability, tangibles, and communication remain the most emphasized dimensions across the reviewed literature, whereas critical areas, such as digital integration, sustainability indicators, and service recovery, are significantly underexplored. This contrast explicitly links the limitations of the classic frameworks to these emerging demands, highlighting their difficulty in accommodating the industry’s growing reliance on real-time data, an environmentally friendly performance, and multi-stakeholder collaboration. Because the construction industry typically contributes 6–10 per cent of the national GDP and underpins wider economic development, inadequate service quality models can propagate cost overruns, productivity losses, and reputational damage across the economy; conversely, improved models enhance project efficiency, and thus support sustained economic growth. This review is limited by its reliance on the Scopus and Web of Science databases, which may exclude relevant regional or non-English studies. Furthermore, many reviewed articles are context-specific, potentially reducing the generalizability of the findings. Despite these limitations, this review offers an evidence-based framework that integrates advanced digital tools, sustainability measures, and diverse stakeholder perspectives. Future studies should demonstrate this framework’s efficacy and applicability in different circumstances. Full article

Large-fruited cranberry (Vaccinium macrocarpon Aiton) is a species known for its highly valued fruit and is typically propagated vegetatively through the rooting of stem cuttings. Studies on the rooting of stem cuttings of large-fruited cranberry have shown that the morphological traits of the root system are a key indicator of the effectiveness of this process. To support rooting, gel coatings based on polysaccharides and containing auxins, especially the indole-3-butyric acid (IBA) W4 variant, were developed and applied. These significantly influenced root length (increase of 44.6% compared to control W0), surface area (increase of 32.4% compared to W0), volume (increase of 26.7% compared to W0), and average thickness, which translated into better nutrient uptake and a higher degree of plant nourishment. The W4 coating, combining mineral components, polysaccharides, and IBA, reduced transpiration and maintained moisture, promoting effective rooting. The associated metabolic changes were confirmed by analyses of oxidative stress markers and chlorophyll fluorescence. The study demonstrated that enhanced root system development was closely linked with the increased accumulation of macro- and micronutrients in the aerial parts of the plants, directly contributing to improved growth and potential yield. These findings highlight that effective rooting—achieved through the targeted metabolic stabilisation of the rooting environment—is essential for the successful vegetative propagation of large-fruited cranberry. Full article

Our study lies at the intersection of three fields: finite-time thermodynamics, relativity theory, and the theory of hyperbolic conservation laws. Each of these fields has its own requirements and richness, and in order to link them together as effectively as possible, we have simplified each one, reducing it to its fundamental principles. The example chosen concerns the propagation of chemical changes in a very large reactor, as found in geology. We ask ourselves two sets of questions: (1) How do the finiteness of propagation speeds modeled by hyperbolic problems (diffusion is neglected) and the finiteness of the time allocated to transformations interact? (2) How do the finiteness of time and that of resources interact? The similarity in the behavior of the pairs of variables (x, t and resources, resource flows) in Lorentz relativistic transformations allows us to put them on the same level and propose complementary-type relationships between the two classes of finiteness. If times are finite, so are resources, which can be neither zero nor infinite. In hyperbolic problems, a condition is necessary to select solutions with a physical sense among the multiplicity of weak solutions: this is given by the entropy production, which is Lorentz invariant (and not entropy alone). Full article

Industrial plants now stream thousands of temperature, pressure, flow rate, and composition measurements at minute-level intervals. These multi-sensor records often contain variable transport or residence time delays that hinder accurate disturbance analysis. This study applies lag-specific transfer entropy (LSTE) to historical sensor logs to identify the instrument that first deviates from normal operation and the time required for that deviation to appear at downstream points. A self-prediction optimization step removes each sensor’s own information storage, after which LSTE is computed at candidate lags and tested against time-shifted surrogates for statistical significance. The method is benchmarked on a nonlinear simulation, the Tennessee Eastman plant, a three-phase separator test rig, and a full-scale blast furnace line. Across all cases, LSTE locates the disturbance origin and reports propagation times that match known process physics, while significantly reducing false links compared to classical transfer entropy. Full article

Remotely sensed cropland abandonment monitoring is crucial for providing spatially explicit references for maintaining sustainable agricultural practices and ensuring food security. However, abandoned cropland is commonly detected based on multi-date classification or the dynamics of a single vegetation index, with the interactions between vegetation and soil time series often being neglected, leading to a failure to understand its full-life-cycle succession processes. To fill this gap, we propose a new full-life-cycle modeling framework based on the interactive trajectories of vegetation–soil-related endmembers to identify abandoned and reclaimed cropland in Jinan from 2000 to 2022. In this framework, highly accurate annual fractional vegetation- and soil-related endmember time series are generated for Jinan City for the 2000–2022 period using spectral mixture models. These are then used to integrally reconstruct temporal trajectories for complex scenarios (e.g., abandonment, weed invasion, reclamation, and fallow) using logistic and double-logistic models. The parameters of the optimization model (fitting type, change magnitude, start timing, and change duration) are subsequently integrated to develop a rule-based hierarchical identification scheme for cropland abandonment based on these complex scenarios. After applying this scheme, we observed a significant decline in green vegetation (a slope of −0.40% per year) and an increase in the soil fraction (a rate of 0.53% per year). These pathways are mostly linked to a duration between 8 and 15 years, with the beginning of the change trend around 2010. Finally, the results show that our framework can effectively separate abandoned cropland from reclamation dynamics and other classes with satisfactory precision, as indicated by an overall accuracy of 86.02%. Compared to the traditional yearly land cover-based approach (with an overall accuracy of 77.39%), this algorithm can overcome the propagation of classification errors (with product accuracy from 74.47% to 85.11%), especially in terms of improving the ability to capture changes at finer spatial scales. Furthermore, it also provides a better understanding of the whole abandonment process under the influence of multi-factor interactions in the context of specific climatic backgrounds and human disturbances, thus helping to inform adaptive abandonment management and sustainable agricultural policies. Full article

Network-based GWAS (NetWAS) has advanced brain imaging research by identifying genetic modules associated with brain alterations. However, how imaging risk genes exert functions in brain diseases, particularly their mediation through imaging quantitative traits (iQTs), remains underexplored. We propose a module-level polygenic risk score (MPRS)-based NetWAS framework to uncover genetic modules associated with Alzheimer’s disease (AD) through the mediation of an iQT, using amygdala density as a case study. Our framework integrates genotype data, brain imaging phenotypes, clinical diagnosis of AD, and protein–protein interaction (PPI) networks to identify AD-relevant modules (ADMs) influenced by iQT-associated genetic variants. Specifically, we conducted a genome-wide association study (GWAS) of amygdala density ($N=1515$) to identify variants associated with iQT. These variants were mapped onto a PPI network and network propagation was performed to prompt amygdala modules. The meta-GWAS of AD ($N_1=63,926$; $N_2=455,267$) was used to calculate MPRS to further identify AD-relevant modules (ADMs). Four modules that showed significant differences in MPRS between AD and controls were identified as ADM. Post-hoc analyses revealed that these ADMs demonstrated strong modularity, showed increased sensitivity to early stages of AD, and significantly mediated the link between ADMs and AD progression through the amygdala. Furthermore, these modules exhibited high tissue specificity within the amygdala and were enriched in AD-related biological pathways. Our MPRS-based framework bridges genetics, intermediate traits, and clinical outcomes and can be adapted for broader biomedical applications. Full article

Millimeter wave (mmWave) technology, with its ultra-high bandwidth and low latency, holds significant promise for vehicle-to-everything (V2X) communications. However, it faces challenges such as high propagation losses and limited coverage in dense urban vehicular environments. Intelligent Reflecting Surfaces (IRSs) help address these issues by enhancing mmWave signal paths around obstacles, thereby maintaining reliable communication. This paper introduces a novel Contextual Multi-Armed Bandit (C-MAB) algorithm designed to dynamically adapt beam and IRS selections based on real-time environmental context. Simulation results demonstrate that the proposed C-MAB approach significantly improves link stability, doubling average beam sojourn times compared to traditional SNR-based strategies and standard MAB methods, and achieving gains of up to four times the performance in scenarios with IRS assistance. This approach enables optimized resource allocation and significantly improves coverage, data rate, and resource utilization compared to conventional methods. Full article

High-resolution seismic reflection profiles from the offshore segment of the Littoral Fault Zone (LFZ) near Nan’ao Island were analyzed to investigate fault activity and its potential link to the 1918 M7.3 earthquake. The data reveal a ~19 km-wide graben bounded by seaward- and landward-dipping normal faults, with fault-propagation folds and growth faults reaching the seafloor. Forward modeling of the fault-propagation fold indicates three discrete episodes of normal dip-slip displacement (~20 m per phase), separated by prolonged quiescent periods, suggesting episodic fault activity and seismic-scale strain accumulation. Despite the regional NW–SE compressional stress regime, active normal faulting is observed, implying vertical stress as the dominant driving force. A gravitational seismic model driven by upper crustal loading is proposed to explain both the fault motion and the down-draw tsunami observed during the 1918 event. These findings offer new insights into intraplate seismogenic mechanisms and associated hazards along the South China coast. Full article

With the growth of Internet of Things (IoT) business demands, NB-IoT integrating low earth orbit (LEO) satellite communication systems is considered a crucial component for achieving global coverage of IoT networks in the future. However, the long propagation delay and significant Doppler frequency shift of the satellite-to-ground link pose substantial challenges to the uplink and downlink synchronization in LEO satellite-based NB-IoT networks. To address this challenge, we first propose a Multiple Segment Auto-correlation (MSA) algorithm to detect the downlink Narrow-band Primary Synchronization Signal (NPSS), specifically tailored for the large Doppler frequency shift of LEO satellites. After detection, downlink synchronization can be realized by determining the arrival time and frequency of the NPSS. Then, to complete the uplink synchronization, we propose a position-based scheme to obtain the Timing Advance (TA) values and pre-compensated Doppler shift value. In this scheme, we formulate a time difference of arrival (TDOA) equation using the arrival times of NPSSs from different satellites or at different times as observations. After solving the TDOA equation using the Chan method, the uplink synchronization is completed by obtaining the TA values and pre-compensated Doppler shift value from the terminal position combined with satellite ephemeris. Finally, the feasibility of the proposed scheme is verified in an Iridium satellite constellation. Compared to conventional GNSS-assisted methods, the approach proposed in this paper reduces terminal power consumption by 15–40%. Moreover, it achieves an uplink synchronization success rate of over 98% under negative SNR conditions. Full article

This study develops a novel framework for supply chain financial resilience (SCFR) by integrating complex adaptive systems theory with supply chain finance and resilience concepts. To explore how disruption risks propagate through the supply chain, we propose an SEIJR epidemic model that categorizes node enterprises into five distinct states: susceptible (S), exposed (E), infected (I), quarantined (J), and recovered (R). Transitions between these states are captured using differential equations. Through numerical simulations linking this epidemiological approach to financial resilience metrics, we demonstrate several key findings: first, disruption risks temporarily reduce resilience; second, properly managed risk propagation through timely isolation and effective mitigation can transform disruptions into opportunities for systemic improvement; third, isolation measures need to work alongside recovery mechanisms to significantly improve the overall resilience of supply chain finance. Our results show that optimal isolation strategies enable the system to reach a risk-free equilibrium while simultaneously elevating the supply chain’s long-term financial resilience above initial levels. These findings offer theoretical and practical guidance for dynamic, adaptive risk management strategies in supply chain finance. Empirical validation and other research topics will be explored in subsequent studies. Full article

Potatoes, as the world’s fourth-largest staple crop, are vital for global food security. Efficient methods for assessing yield and quality are essential for policy-making and optimizing production. Traditional yield assessment techniques remain destructive, labor-intensive, and unsuitable for large-scale monitoring. While remote sensing has offered a promising alternative, current approaches largely depend on empirical correlations rather than physiological mechanisms. This limitation arises because potato tubers grow underground, rendering their traits invisible to aboveground sensors. This study investigated the mechanisms underlying hyperspectral remote sensing for assessing belowground yield traits in potatoes. Field experiments with four cultivars and five nitrogen treatments were conducted to collect foliar biochemistries (chlorophyll, nitrogen, and water and dry matter content), yield traits (tuber yield, fresh/dry weight, starch, protein, and water content), and leaf spectra. Two approaches were developed for predicting belowground yield traits: (1) a direct method linking leaf spectra to yield via statistical models and (2) an indirect method using structural equation modeling (SEM) to link foliar biochemistry to yield. The SEM analysis revealed that foliar nitrogen exhibited negative effects on tuber fresh weight (path coefficient b = −0.57), yield (−0.37), and starch content (−0.30). Similarly, leaf water content negatively influenced tuber water content (0.52), protein (−0.27), and dry weight (−0.42). Conversely, chlorophyll content showed positive associations with both tuber protein (0.59) and dry weight (0.56). Direct models (PLSR, SVR, and RFR) achieved higher accuracy for yield (R² = 0.58–0.84) than indirect approaches (R² = 0.16–0.45), though the latter provided physiological insights. The reduced accuracy in indirect methods primarily stemmed from error propagation within the SEM framework. Future research should scale these leaf-level mechanisms to canopy observations and integrate crop growth models to improve robustness across environments. This work advances precision agriculture by clarifying spectral–yield linkages in potato systems, offering a framework for hyperspectral-based yield prediction. Full article

This study investigates the electromagnetic performance of two carbon fiber monopole antennas integrated into a UAV copter frame, with emphasis on design adaptation, impedance matching, and propagation behavior. A comprehensive experimental campaign was conducted to characterize key parameters such as center frequency, bandwidth, gain, VSWR, and S₁₁. Both antennas exhibited dual-band resonance at approximately 381 MHz and 1.19 GHz, each achieving a 500 MHz bandwidth where VSWR ≤ 2. The modified antenna achieved a minimum reflection coefficient of –14.6 dB and a VSWR of 1.95 at 381.45 MHz, closely aligning with theoretical predictions. Gain deviations between measured (0.15–0.19 dBi) and calculated (0.19 dBi) values remained within 0.04 dB, while received power fluctuations did not exceed 1.3 dB under standard test conditions despite the composite material’s finite conductivity. Free-space link-budget tests at 0.5 m and 2 m of separation revealed received-power deviations of 0.9 dB and 1.3 dB, respectively, corroborating the Friis model. Radiation pattern measurements in both azimuth and elevation planes confirmed good directional behavior, with minor side lobe variations, where Antenna A displayed variations between 270° and 330° in azimuth, while Antenna B remained more uniform. A 90° polarization mismatch led to a 15 dBm signal drop, and environmental obstructions caused losses of 9.4 dB, 12.6 dB, and 18.3 dB, respectively, demonstrating the system’s sensitivity to alignment and surroundings. Additionally, signal strength changes observed in a Two-Ray propagation setup validated the importance of ground reflection effects. Small-scale fading analysis at 5 m LOS indicated a Rician-distributed envelope with mean attenuation of 53.96 dB, σ_dB = 5.57 dB, and a two-sigma interval spanning 42.82 dB to 65.11 dB; the fitted K-factor confirmed the dominance of the LOS component. The findings confirm that carbon fiber UAV frames can serve as effective directional antenna supports, providing proper alignment and tuning. These results support the future integration of lightweight, structure-embedded antennas in UAV systems, with potential benefits in communication efficiency, stealth, and design simplification. Full article