Search Results (2,079)

Spray-induced gene silencing (SIGS) represents a transformative paradigm in sustainable pest management, utilizing the exogenous application of double-stranded RNA (dsRNA) to achieve sequence-specific silencing of essential genes in arthropod pests. Unlike transgenic approaches, sprayable RNA interference (RNAi) biopesticides offer superior versatility across crop systems, flexible application timing, and a more favorable regulatory and public acceptance profile. The 2023 U.S. EPA registration of Ledprona, the first sprayable dsRNA biopesticide targeting Leptinotarsa decemlineata, marks a significant milestone toward the commercialization of non-transformative RNAi technologies. Despite the milestone, large-scale field deployment faces critical bottlenecks, primarily environmental instability, enzymatic degradation by nucleases, and variable cellular uptake across pest taxa. This review critically analyzes the mechanistic basis of spray-applied RNAi and synthesizes the recent technological breakthroughs designed to overcome physiological and environmental barriers. We highlight advanced delivery strategies, including nuclease inhibitor co-application, liposome encapsulation, and nanomaterial-based formulations that enhance persistence on plant foliage and uptake efficiency. Furthermore, we discuss how innovations in microbial fermentation have drastically reduced synthesis costs, rendering industrial-scale production economically viable. Finally, we outline the roadmap for broad adoption, addressing essential factors such as biosafety assessment, environmental fate, resistance management protocols, and the path toward cost-effective manufacturing. Full article

Children with rare tumours and malformations may benefit from innovative imaging, including patient-specific 3D models that can enhance communication and surgical planning. The primary aim was to evaluate the impact of patient-specific 3D models on communication with families. The secondary aims were to assess their influence on medical management and to establish an efficient post-processing workflow. From 2021 to 2024, we prospectively included patients aged 3 months to 18 years with rare tumours or malformations. Families completed questionnaires before and after the presentation of a 3D model generated from MRI sequences, including peripheral nerve tractography. Treating physicians completed a separate questionnaire before surgical planning. Analyses were performed in R. Among 21 patients, diagnoses included 11 tumours, 8 malformations, 1 trauma, and 1 pancreatic pseudo-cyst. Likert scale responses showed improved family understanding after viewing the 3D model (mean score 3.94 to 4.67) and a high overall evaluation (mean 4.61). Physicians also rated the models positively. An efficient image post-processing workflow was defined. Although manual 3D reconstruction remains time-consuming, these preliminary results show that colourful, patient-specific 3D models substantially improve family communication and support clinical decision-making. They also highlight the need for supporting the development of MRI-based automated segmentation softwares using deep neural networks, which are clinically approved and usable in routine practice. Full article

Industrial gateways serve as critical data aggregation points within the Industrial Internet of Things (IIoT), enabling seamless data interoperability that empowers enterprises to extract value from equipment data more efficiently. However, their role exposes a fundamental trade-off between computational efficiency and prediction accuracy—a contradiction yet to be fully resolved by existing approaches. The rapid proliferation of IoT devices has led to a corresponding surge in network traffic, posing significant challenges for traffic forecasting methods, while deep learning models like Transformers and GNNs demonstrate high accuracy in traffic prediction, their substantial computational and memory demands hinder effective deployment on resource-constrained industrial gateways, while simple linear models offer relative simplicity, they struggle to effectively capture the complex characteristics of IIoT traffic—which often exhibits high nonlinearity, significant burstiness, and a wide distribution of time scales. The inherent time-varying nature of traffic data further complicates achieving high prediction accuracy. To address these interrelated challenges, we propose the lightweight and theoretically grounded DOA-MSDI-CrossLinear framework, redefining traffic forecasting as a hierarchical decomposition–interaction problem. Unlike existing approaches that simply combine components, we recognize that industrial traffic inherently exhibits scale-dependent temporal correlations requiring explicit decomposition prior to interaction modeling. The Multi-Scale Decomposable Mixing (MDM) module implements this concept through adaptive sequence decomposition, while the Dual Dependency Interaction (DDI) module simultaneously captures dependencies across time and channels. Ultimately, decomposed patterns are fed into an enhanced CrossLinear model to predict flow values for specific future time periods. The Dream Optimization Algorithm (DOA) provides bio-inspired hyperparameter tuning that balances exploration and exploitation—particularly suited for the non-convex optimization scenarios typical in industrial forecasting tasks. Extensive experiments on real industrial IoT datasets thoroughly validate the effectiveness of this approach. Full article

Atomic Layer Deposition (ALD) is a key thin-film fabrication technique that enables the growth of ultra-thin, conformal, and compositionally controlled layers for applications in nanoelectronics, optoelectronics, and energy devices. However, the high cost and operational complexity of commercial ALD systems limit their accessibility in academic and emerging research environments. In this work, a low-cost, automated thermal ALD system is designed, assembled, and experimentally validated for the deposition of zinc oxide (ZnO) thin films. The developed system enables precise control of precursor dosing, purge sequences, and substrate temperature via an integrated LabVIEW–Arduino control architecture, allowing reproducible and stable thin-film growth. The design allows the use of various precursors through high-precision three-way diaphragm valves. In addition, the system allows continuous purge gas flow in the reaction chamber, which enhances the drag velocity of the precursor gas, reducing dosage requirement, accelerating chamber saturation time and lowering the total consumption of precursors per deposition cycle. ZnO thin films were successfully grown on silicon and glass substrates at 200 °C using diethylzinc (DEZ) as the metal precursor and hydrogen peroxide (H₂O₂) as the oxidant. The process exhibited self-limiting growth characteristics typical of ALD, yielding a growth per cycle of approximately 0.8 Å. The deposited ZnO films exhibited optical transparency of 70–80% in the visible region, a refractive index of approximately 1.9, and an optical bandgap close to 3.4 eV, which are consistent with values reported for high-quality ZnO films grown in commercial ALD systems. These results demonstrate that the proposed low-cost platform is capable of producing functional ZnO thin films with properties comparable to those obtained with conventional commercial reactors. Overall, this work presents an accessible and scalable thermal ALD system that significantly reduces equipment costs while maintaining reliable process control and film quality, offering a practical framework for expanding thin-film research capabilities across microelectronics, optoelectronics, and nanotechnology laboratories. Full article

Ultraviolet (UV) radiation is a significant environmental stressor that exerts profound impacts on insect physiology, behaviour and survival. Although some insects can use UV light for spatial orientation and navigation, it can induce DNA damage, oxidative stress, and impair critical biological functions, ultimately reducing ecological fitness. Sclerodermus alternatusi Yang (Hymenoptera: Bethylidae) is a dominant ectoparasitoid of the early instar larvae of Monochamus alternatus and plays a key role in the biological control of this pest in forestry systems; however, it faces intense UV exposure in the field environment. Despite its ecological importance, the molecular mechanisms underlying its responses to UV-induced stress remain poorly understood. In this study, newly emerged adult wasps (within 24 h post-eclosion) were exposed to UVA (365 nm) and UVC (253.7 nm) radiation for 9 h under controlled laboratory conditions. Total RNA was extracted from treated and control individuals for transcriptomic analysis using RNA-Seq. A total of 505 differentially expressed genes (DEGs) were identified; gene ontology enrichment analysis revealed that UVA exposure significantly upregulated genes involved in cellular respiration and oxidative phosphorylation, suggesting an enhanced metabolic response. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that UV stress modulates energy metabolism through the activation of oxidative phosphorylation and thermogenesis-related pathways, highlighting the reallocation of energy resources in response to UV-induced stress. To validate the RNA-Seq data, four representative DEGs were selected for quantitative real-time PCR (RT-qPCR) analysis. The qPCR results were consistent with the transcriptomic trends, confirming the reliability of the sequencing data. Collectively, this study provides a comprehensive overview of the molecular response mechanisms of S. alternatusi to UV stress, offering novel insights into its environmental adaptability and laying a theoretical foundation for its application in biological pest control under field conditions. Full article

In the domain of marine remote sensing, the real-time monitoring of ocean waves is a research hotspot, which employs acquired X-band radar images to retrieve wave information. To enhance the accuracy of the classical spectrum method using the extracted signal-to-noise ratio (SNR) from an image sequence, data from the preferred analysis area around the upwind is required. Additionally, the accuracy requires further improvement in cases of low wind speed and swell. For shore-based radar, access to the preferred analysis area cannot be guaranteed in practice, which limits the measurement accuracy of the spectrum method. In this paper, a method using extracted SNRs and an optimized genetic algorithm back-propagation (GABP) neural network model is proposed to enhance the inversion accuracy of significant wave height. The extracted SNRs from multiple selected analysis regions, included angles, and wind speed are employed to construct a feature vector as the input parameter of the GABP neural network. Considering the not-completely linear relationship of wave height to the SNR derived from radar images, the GABP network model is used to fit the relationship. Compared with the classical SNR-based method, the correlation coefficient using the GABP neural network is improved by 0.14, and the root mean square error is reduced by 0.20 m. Full article

This paper addresses the optimization challenges in urban logistics with the aim of enhancing the sustainability of last-mile delivery. By focusing on the collaborative delivery between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), we propose a novel approach to reducing energy consumption and operational inefficiencies. A bilevel mixed-integer linear programming (Bilevel-MILP) model is developed, integrating road network topology with dynamic energy constraints. Departing from traditional single-delivery modes, the paper establishes a multi-task continuous delivery framework. By incorporating a dynamic charging point selection strategy and path–energy coupling constraints, the model effectively mitigates energy limitations and the issue of repeated returns for UAV charging in complex urban road networks, thereby promoting more efficient resource utilization. At the algorithmic level, a Collaborative Delivery Path Optimization (CDPO) framework is proposed, which embeds an Improved Sparrow Search Algorithm (ISSA) with directional initialization and a Hybrid Genetic Algorithm (HGA) with specialized crossover strategies. This enables the synergistic optimization of UAV delivery sequences and UGV charging decisions. The simulation results demonstrate that, in scenarios with a task density of 20 per 100 km², the proposed CDPO algorithm reduces the total delivery time by 33.9% and shortens the UAV flight distance by 24.3%, compared to conventional fixed charging strategies (FCSs). These improvements directly contribute to lowering energy consumption and potential emissions. The road network discretization approach and dynamic candidate charging point generation confirm the method’s adaptability in high-density urban environments, offering a spatiotemporal collaborative optimization paradigm that supports the development of sustainable and intelligent urban logistics systems. The obtained results provide practical insights for the design and deployment of efficient UAV–UGV collaborative logistics systems in urban environments, particularly under high-task-density and energy-constrained conditions. Full article

With the growing global popularity of Android apps, ensuring their quality and reliability has become increasingly important, as low-quality apps can lead to poor user experiences and potential business losses. A common approach to testing Android apps involves automatically generating event sequences that interact with the app’s graphical user interface (GUI) to detect crashes. To support this, we developed ACE (Android Crawler), a tool that systematically generates events to test Android apps by automatically exploring their GUIs. However, ACE’s original heuristic-driven exploration can be inefficient in complex application states. To address this, we extend ACE with a deep reinforcement learning-based crawling strategy, called Reinforcement Learning Strategy (RLS), which tightly integrates with ACE’s GUI exploration process by learning to intelligently select GUI components and interaction actions. RLS leverages the Proximal Policy Optimization (PPO) algorithm for stable and efficient learning and incorporates an action mask to filter invalid actions, thereby reducing training time. We evaluate RLS on 15 real-world Android apps and compare its performance against the original ACE and three state-of-the-art Android testing tools. Results show that RLS improves code coverage by an average of 2.1% over ACE’s Nearest unvisited event First Search (NFS) strategy and outperforms all three baseline tools in terms of code coverage. Paired t-test analyses further confirm that these improvements are statistically significant, demonstrating its effectiveness in enhancing automated Android GUI testing. Full article

Non-alcoholic fatty liver disease (NAFLD) is closely linked to metabolic syndrome and circadian rhythm disruption, yet the mechanisms by which lifestyle interventions restore circadian organization remain incompletely understood. In this study, we employed a stringent 3 h time-restricted feeding (TRF) regimen in a mouse model of high-fat diet (HFD)-induced metabolic dysfunction. TRF markedly improved metabolic outcomes, including lipid accumulation, glucose tolerance, and behavioral and physiological rhythms. Importantly, through transcriptomic profiling using RNA sequencing, we found that TRF induced circadian rhythmicity in previously arrhythmic hepatic genes. This approach revealed that TRF promotes transcriptional synchronization within key metabolic pathways. Genes involved in autophagy, fatty acid metabolism, and protein catabolism exhibited coherent peak expression at defined time windows, suggesting that TRF temporally restructures gene networks to enhance metabolic efficiency. This intra-pathway synchronization likely minimizes energy waste and enables cells to execute specialized functions in a temporally optimized manner. Together, our findings identify temporal reorganization of metabolic pathways as a mechanistic basis for the benefits of TRF, providing new insight into circadian-based strategies for managing metabolic disease. Full article

Blockchain is widely promoted as a tool for enhancing transparency, trust, and sustainability in business, yet little is known about how creative micro, small, and medium enterprises (MSMEs) in emerging economies can meaningfully adopt it for finance and accounting purposes in times of global uncertainty. This study explores how blockchain can be harnessed for transparent and sustainable accounting in Indonesian creative MSMEs amid rapid digital disruption. Using an exploratory qualitative design, we conducted semi-structured, in-depth interviews with 18 owners and key decision-makers across diverse creative subsectors and analysed the data thematically through an integrated Technology Acceptance Model (TAM) and Diffusion of Innovation (DOI) lens. The findings show that participants recognise blockchain’s potential benefits for transaction transparency, verifiable records, intellectual property protection, and secure payments, but adoption is constrained by technical complexity, financial constraints, limited digital and accounting capabilities, and perceived regulatory and reputational risks. Government initiatives are seen as important for legitimacy yet insufficient without concrete guidance, capacity-building, and financial support. The study extends TAM–DOI applications to blockchain-enabled accounting in creative MSMEs and highlights the need for sequenced, ecosystem-based interventions to translate blockchain’s technical promise into accessible, ESG- and SDG-oriented accounting solutions in the creative economy. Full article

Cholesterol plays a fundamental role in the human body—it stabilizes cell membranes, modulates gene expression, and is a precursor to steroid hormones, vitamin D, and bile salts. Its correct level is crucial for homeostasis, while both excess and deficiency are associated with serious metabolic and health consequences. Excessive accumulation of cholesterol leads to the development of atherosclerosis, while its deficiency disrupts the transport of fat-soluble vitamins. Magnetic resonance spectroscopy (MRS) enables the detection of cholesterol esters and the differentiation between their liquid and crystalline phases, but the technical limitations of clinical MRI systems require the use of dedicated coils and sequence modifications. This study demonstrates the feasibility of using MRS to identify cholesterol-specific spectral signatures in atherosclerotic plaque through ex vivo analysis. Using a custom-designed experimental coil adapted for small-volume samples, we successfully detected characteristic cholesterol peaks from plaque material dissolved in chloroform, with spectral signatures corresponding to established NMR databases. To further enhance spectral quality, a deep-learning denoising framework based on a 1D U-Net architecture was implemented, enabling the recovery of low-intensity cholesterol peaks that would otherwise be obscured by noise. The trained U-Net was applied to experimental MRS data from atherosclerotic plaques, where it significantly outperformed traditional denoising methods (Gaussian, Savitzky–Golay, wavelet, median) across six quantitative metrics (SNR, PSNR, SSIM, RMSE, MAE, correlation), enhancing low-amplitude cholesteryl ester detection. This approach substantially improved signal clarity and the interpretability of cholesterol-related resonances, supporting more accurate downstream spectral assessment. The integration of MRS with NMR-based lipidomic analysis, which allows the identification of lipid signatures associated with plaque progression and destabilization, is becoming increasingly important. At the same time, the development of high-resolution techniques such as μOCT provides evidence for the presence of cholesterol crystals and their potential involvement in the destabilization of atherosclerotic lesions. In summary, nanotechnology-assisted MRI has the potential to become an advanced tool in the proof-of-concept of atherosclerosis, enabling not only the identification of cholesterol and its derivatives, but also the monitoring of treatment efficacy. However, further clinical studies are necessary to confirm the practical usefulness of these solutions and their prognostic value in assessing cardiovascular risk. Full article

Fluorine-containing polyarylates (F-PARs) were synthesized via interfacial polycondensation of hexafluorobisphenol A (BPAF), bisphenol A (BPA), and two acyl chloride monomers under four feeding strategies. Sequential feeding affords the highest M_w (2.02 × 10⁵ g/mol) and high alternating sequence content; the one-pot method gives intermediate M_w and a random sequence; and segmented and parallel methods yield lower-M_w polymers and pseudo-block sequences. Time-resolved GPC results reveal that the concentration of -CF₃-activated acyl chloride termini during chain propagation controls the subsequent chain propagation and, thus, the final M_w. Consequently, sequential feeding delivers the highest T_g (215 °C) and stiffness (2.51 GPa) for thermal–mechanical loads; the one-pot protocol maximizes optical clarity (T₄₅₀ = 85%) for transparent films. Systematic variation in the BPAF/BPA ratio via sequential feeding further reveals that higher BPAF content increases M_w, enhances thermal stability, and blue-shifts UV absorption, whereas BPA-rich compositions improve the tensile strength and modulus. These findings provide a quantitative roadmap for the rational design of F-PAR chain architectures, enabling on-demand tuning of thermal, mechanical, and optical properties without additional synthetic complexity. Full article

To address the challenges of low land use efficiency, soil degradation, and high management costs in Ilex asprella cultivation, this study established an I. asprella–Grona styracifolia intercropping system and systematically evaluated its effects on soil nutrient cycling, microbial communities, and crop growth. Field experiments were conducted in Yunfu City, Guangdong Province, with monoculture (LCK for I. asprella, DCK for G. styracifolia) and three intercropping densities (HDT, LDT, MDT). Combining 16S rRNA sequencing and metagenomics, we analyzed the functional profile of the rhizosphere microbiome. The results showed that intercropping significantly increased the biomass of G. styracifolia, with the medium-density (MDT) treatment increasing plant length and fresh weight by 41.2% and 2.4 times, respectively, compared to monoculture. However, high-density intercropping suppressed the accumulation of medicinal compounds. In terms of soil properties, intercropping significantly enhanced soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), and available nitrogen (AN) in the rhizosphere of both plants. Specifically, AN in the I. asprella rhizosphere increased by 18.9%. Soil urease and acid phosphatase activities were also elevated, while pH decreased. Microbial analysis revealed that intercropping reshaped the rhizosphere microbial community structure, significantly increased the Shannon diversity index of bacteria in the G. styracifolia rhizosphere, and enhanced the complexity of the microbial co-occurrence network. Metagenomic analysis further confirmed that intercropping enriched functional genes related to carbon fixation, nitrogen cycling (nitrogen fixation, assimilatory nitrate reduction), and organic phosphorus mineralization (the phoD gene), thereby driving the transformation and availability of soil nutrients. These findings demonstrate that the I. asprella–G. styracifolia intercropping system, particularly at medium density, effectively improves soil fertility and land use efficiency by regulating rhizosphere microbial functions, providing a theoretical basis for the sustainable ecological cultivation of I. asprella. Full article

This study evaluated the effect of clockwise reciprocation motion used in the original Optimum Torque Reverse kinematics, compared with clockwise continuous rotation, on the cyclic fatigue strength of nickel–titanium rotary instruments (NiTi) with different metallurgical characteristics. A total of 120 instruments, ProFile and EndoSequence in sizes 25/.04, 30/.04, and 35/.04, were tested under continuous rotation or reciprocation motions (n = 10 per subgroup). Instruments were examined by optical and scanning electron microscopy to exclude manufacturing defects. Phase transformation temperatures were determined by differential scanning calorimetry, and cyclic fatigue testing was conducted using a custom device simulating a curved canal with a 6 mm radius and an 86° curvature. The time to fracture was recorded, and the number of cycles to fracture was calculated. Statistical comparisons were performed using the Mann–Whitney U test with a significance level of p < 0.05. Differential scanning calorimetry showed that ProFile instruments were fully austenitic at the test temperature, while EndoSequence instruments exhibited a mixed R-phase and austenitic structure. Clockwise reciprocation motion significantly increased cyclic fatigue resistance in all groups compared with clockwise continuous rotation. Time to fracture increased by 241.3% to 337.5%, and EndoSequence instruments consistently demonstrated higher fatigue resistance. The greatest relative improvement was observed in ProFile 35/.04, with a 422.4% increase in the number of cycles to fracture. Overall, the reciprocation motion markedly enhanced cyclic fatigue strength irrespective of metallurgical phase composition, indicating a practical mechanical benefit that may reduce the risk of instrument separation during endodontic procedures. Full article

The two-parameter Birnbaum–Saunders (B-S) distribution is widely applied across various fields due to its favorable statistical properties. This study aims to enhance the efficiency of modified moment estimators for the B-S distribution by systematically incorporating auxiliary non-sample information. To this end, we developed and analyzed a suite of estimation strategies, including restricted estimators, preliminary test estimators, and Stein-type shrinkage estimators. A pretest procedure was formulated to guide the decision on whether to integrate the non-sample information. The relative performance of these estimators was rigorously evaluated through an asymptotic distributional analysis, comparing their asymptotic distributional bias and risk under a sequence of local alternatives. The finite-sample properties were assessed via Monte Carlo simulation studies. The practical utility of the proposed methods is demonstrated through applications to two real-world datasets: failure times for mechanical valves and bone mineral density measurements. Both numerical results and theoretical analysis confirm that the proposed shrinkage-based techniques deliver substantial efficiency gains over conventional estimators. Full article