Search Results (46,716)

With the continuous maturation of prefabricated buildings, the errors and efficiency issues in the design of prefabricated buildings have gradually drawn the attention of architectural designers. The characteristics of standardized design for prefabricated buildings also provide a foundation for the application of computer-learning methods in the field of architectural design, thereby improving design quality and efficiency. This study combined BIM technology to construct the information data on prefabricated buildings, applied the transfer-learning method to build the training model, and utilized the traditional architectural design collision concept to construct a prediction model applicable to the collision detection of prefabricated building design. The training set and test set were constructed in a 9:1 ratio, and the loss function and accuracy function were calculated. The error rate of the model was verified to be within 10% through trial calculations based on engineering cases. The results show that, in the selected engineering cases, the collision detection accuracy of the model reached 90.3%, with an average absolute error (MAE) of 0.199 and a root mean square error (RMSE) of 0.245. The prediction error rate was controlled within 10%, representing an approximately 65% improvement in efficiency compared to traditional manual inspections. This method significantly improves the efficiency and accuracy of collision detection, providing reliable technical support for the optimization of prefabricated building design. Full article

Between 2014 and 2016, traps baited with synthetic generic lures were used to collect data on insect diversity, with a focus on Lepidoptera pest assemblages in the surroundings of Velyka Dobron’ in Transcarpathia, West Ukraine. Traps captured a large number of insects from various taxa, including Hymenoptera, providing valuable data on both the poorly known regional fauna and the attractiveness of the lures to different Hymenoptera groups. In total, 1214 individuals were recorded, representing 11 families and 39 species, of which 17 species are reported from Transcarpathia for the first time. The majority of the sample (97%) belonged to the families Apidae and Vespidae. The Apidae and Halictidae species were predominantly attracted to phenylacetaldehyde-based lures, while Vespidae species responded more to semisynthetic lures containing isoamyl alcohol. The parallel use of the tested lures with different selectivity appears to be an effective, user-friendly, standardized method for studying Hymenoptera assemblages. Furthermore, knowledge of lure selectivity allows for the target collection of species belonging to different ecotypes. Full article

Fog computing is increasingly preferred over cloud computing for processing tasks from Internet of Things (IoT) devices with limited resources. However, placing tasks and allocating resources in distributed and dynamic fog environments remains a major challenge, especially when trying to meet strict Quality of Service (QoS) requirements. Deep reinforcement learning (DRL) has emerged as a promising solution to these challenges, offering adaptive, data-driven decision-making in real-time and uncertain conditions. While several surveys have explored DRL in fog computing, most focus on traditional centralized offloading approaches or emphasize reinforcement learning (RL) with limited integration of deep learning. To address this gap, this paper presents a comprehensive and focused survey on the full-scale application of DRL to the task offloading problem in fog computing environments involving multiple user devices and multiple fog nodes. We systematically analyze and classify the literature based on architecture, resource allocation methods, QoS objectives, offloading topology and control, optimization strategies, DRL techniques used, and application scenarios. We also introduce a taxonomy of DRL-based task offloading models and highlight key challenges, open issues, and future research directions. This survey serves as a valuable resource for researchers by identifying unexplored areas and suggesting new directions for advancing DRL-based solutions in fog computing. For practitioners, it provides insights into selecting suitable DRL techniques and system designs to implement scalable, efficient, and QoS-aware fog computing applications in real-world environments. Full article

Nickel-based metal–organic frameworks (Ni-MOFs) have received enormous amounts of attention from the scientific community due to their excellent porosity, larger specific surface area, tunable structure, and intrinsic redox properties. In previous years, Ni-MOFs and their hybrid composite materials have been extensively explored for electrochemical sensing applications. As per the reported literature, Ni-MOF-based hybrid materials have been used in the fabrication of electrochemical sensors for the monitoring of ascorbic acid, glucose, L-tryptophan, bisphenol A, carbendazim, catechol, hydroquinone, 4-chlorophenol, uric acid, kaempferol, adenine, _L-cysteine, etc. The presence of synergistic effects in Ni-MOF-based hybrid materials plays a crucial role in the development of highly selective electrochemical sensors. Thus, Ni-MOF-based materials exhibited enhanced sensitivity and selectivity with reasonable real sample recovery, which suggested their potential for practical applications. In addition, Ni-MOF-based hybrid composites were also adopted as electrode modifiers for the development of supercapacitors. The Ni-MOF-based materials demonstrated excellent specific capacitance at low current densities with reasonable cyclic stability. This review article provides an overview of recent advancements in the utilization of Ni-MOF-based electrode modifiers with metal oxides, carbon-based materials, MXenes, polymers, and LDH, etc., for the electrochemical detection of environmental pollutants and biomolecules and for supercapacitor applications. In addition, Ni-based bimetallic and trimetallic catalysts and their composites have been reviewed for electrochemical sensing and supercapacitor applications. The key challenges, limitations, and future perspectives of Ni-MOF-based materials are discussed. We believe that the present review article may be beneficial for the scientific community working on the development of Ni-MOF-based materials for electrochemical sensing and supercapacitor applications. Full article

Phenolic compounds contribute significantly to the nutritional and functional properties of wheat, particularly due to their antioxidant activity. In this study, a genome-wide association study was conducted to elucidate the genetic basis of total phenolic content (TPC) and antioxidant activity (AA) in a panel of 144 tetraploid wheat accessions representing diverse subspecies. The panel was evaluated under two different environments, located in Chile and Italy, to assess the influence of genotype, environment, and their interaction. Significant variability was observed for both TPC and AA, with TPC ranging from 0.26 to 0.82 mg gallic acid equivalent (GAE)/g and AA from 0.04 to 0.99 µmol Trolox equivalent (TE)/g. Substantial phenotypic variation and high broad-sense heritability were observed for both traits, underscoring the predominant genetic control. The genome-wide association study, using a mixed linear model (MLM), and the Bayesian information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) approaches identified 17 significant marker–trait associations, including quantitative trait loci on chromosomes 2B, 3A, 4B, 5A, 5B, and 6B. Notably, QTLs on chromosome 5A were co-localized for both TPC and AA, suggesting potential pleiotropic loci. Candidate genes linked to these loci included flavonol 3-sulfotransferase and peptidylprolyl isomerase, which are involved in phenylpropanoid metabolism and oxidative stress response, respectively. These findings offer valuable insights into the genetic basis of wheat phenolic traits and provide molecular targets for the development of biofortified cultivars through marker-assisted selection. Full article

The refined aerodynamic design optimization of multistage compressors is a typical high-dimensional and expensive optimization problem. This study proposes an integrated surrogate model-assisted evolutionary algorithm combined with a Directly Manipulated Free-Form Deformation (DFFD)-based parametric dimensionality reduction method, establishing a high-precision and efficient global parallel aerodynamic optimization platform for multistage axial compressors. The DFFD method achieves a balance between flexibility and low-dimensional characteristics by directly controlling the surface points of blades, which demonstrates a particular suitability for the aerodynamic design optimization of multistage axial compressors. The integrated surrogate model enhances prediction accuracy by simultaneously identifying optimal solutions and the most uncertain solutions, effectively addressing highly nonlinear design space challenges. A three-stage axial compressor in a heavy-duty gas turbine is selected as the optimization object. The results demonstrate that the optimization task takes less than 48 h and achieves an improvement of 0.6% and 4% in the adiabatic efficiency and surge margin, respectively, while maintaining a nearly unchanged flow rate and pressure ratio at the design point. The proposed approach provides an efficient and reliable solution for complex aerodynamic optimization problems. Full article

This study presents the development of a noise-monitoring system for the storage ring at the Siam Photon Source, designed to detect and classify noise patterns in real time using beam position monitor (BPM) data. Noise patterns were categorized into four classes: broad peak, multipeak, normal peak, and no beam. Two BPMs located at the multipole wiggler section, BPM-MPW1 and BPM-MPW2, were selected for detailed monitoring based on consistent noise trends observed across the ring. The dataset was organized in two complementary formats: two-dimensional (2D) images used for training and validating the models and one-dimensional (1D) CSV files containing the corresponding raw numerical signal data. Pre-trained deep learning and 1D convolutional neural network (CNN) models were employed to classify these patterns, achieving an overall classification accuracy of up to 99.83%. The system integrates with the EPICS control framework and archiver log data, enabling continuous data acquisition and long-term analyses. Visualization and monitoring features were developed using CS-Studio/Phoebus, providing both operators and beamline scientists with intuitive tools to track beam quality and investigate noise-related anomalies. This approach highlights the potential of combining beam diagnostics with machine learning to enhance operational stability and optimize the synchrotron radiation performance for user experiments. Full article

Molecular research for genetic variants underlying body weight (BW) provides crucial information for this important selected trait when developing productive poultry breeds, lines and crosses. We searched for molecular markers—single nucleotide polymorphisms (SNPs)—and candidate genes associated with this trait in 240 F₂ resource population Japanese quails (Coturnix japonica). This population was produced by crossing two breeds with contrasting growth phenotypes, i.e., Japanese (with lower growth) and Texas White (with higher growth). The birds were genotyped using the genotyping-by-sequencing method followed by a genome-wide association study (GWAS). Using 74,387 SNPs, GWAS resulted in 142 significant SNPs and 42 candidate genes associated with BW at the age of 1, 14, 28, 35, 42, 49 and 56 days. Hereby, 25 SNPs simultaneously associated with BW at more than one age were established that colocalized with nine prioritized candidate genes (PCGs), including ITM2B, SLC35F3, ADAM33, UNC79, LEPR, RPP14, MVK, ASTN2, and ZBTB16. Twelve PCGs were identified in the regions of two or more significant SNPs, including MARCHF6, EGFR, ADGRL3, ADAM33, NPC2, LTBP2, ZC2HC1C, SATB2, ASTN2, ZBTB16, ADAR, and LGR6. These SNPs and PCGs can serve as molecular genetic markers for the genomic selection of quails with desirable BW phenotypes to enhance growth rates and meat productivity. Full article

Nanovaccines have emerged as a transformative platform in immunotherapy, distinguished by their capabilities in targeted antigen delivery, enhanced immunogenicity, and multifunctional integration. By leveraging nanocarriers, these vaccines achieve precise antigen transport, improve immune activation efficiency, and enable synergistic functions such as antigen protection and adjuvant co-delivery. This review comprehensively explores the foundational design principles of nanovaccines, delves into the diversity of nanovaccine design strategies—including the selection of primary carrier materials, functionalization modification, synergistic delivery of immune adjuvants, and self-assembled nano-delivery systems—and highlights their applications in cancer immunotherapy, infectious disease and autoimmune diseases. Furthermore, it critically examines existing technical challenges and translational barriers, providing an integrative reference to guide future research and development in this dynamic field. Full article

Against the backdrop of the growing popularity of blockchain technology, this study investigates blockchain adoption strategies for the fresh food supply chain (FFSC) under a carbon cap-and-trade (CAT) policy. Taking a two-echelon supply chain consisting of a supplier and a retailer as an example, we designed four blockchain adoption modes based on the supplier’s strategy (adopt or not) and the retailer’s strategy (adopt or not). Combining influencing factors such as consumers’ low-carbon preference, consumers’ freshness preference, and carbon trading price (CTP), we established four game-theoretic models. Using backward induction, we derived the equilibrium strategies for the supplier and retailer under different modes and analyzed the impact of key factors on these equilibrium strategies. The analysis yielded four key findings: (1) BB mode (both adopt blockchain) is the optimal adoption strategy for both FFSC parties when carbon prices are high, and consumers exhibit strong dual preferences. It most effectively mitigates the negative price impact of rising carbon prices by synergistically enhancing emission reduction efforts and freshness preservation efforts, thereby increasing overall profits and achieving a Pareto improvement in the benefits for both parties. (2) Consumers’ low-carbon preference and freshness preference exhibit an interaction effect. These two preferences mutually reinforce each other’s incentive effect on FFSC efforts (emission reduction/freshness preservation). Blockchain’s information transparency makes these efforts more perceptible to consumers, forming a synergistic “emission reduction-freshness preservation” cycle that further drives sales and profit growth. (3) The adoption of blockchain by either the supplier or the retailer significantly lowers the cost threshold for the other party to adopt blockchain, thereby increasing their willingness to adopt. (4) CAT and consumer preferences jointly influence the adoption strategies of suppliers and retailers. Additionally, the adoption strategies of FFSC participants are also affected by the other party’s blockchain adoption status. Drawing on the above conclusions, this study provides actionable guidance for suppliers and retailers in selecting optimal blockchain adoption strategies. Full article

As technologies become increasingly diverse and complex, Intelligent Massage Systems (IMS) are evolving from traditional mechanically executed modes toward personalized and predictive health interventions. However, the field still lacks a unified grading standard for intelligence, making it difficult to quantitatively assess a system’s overall intelligence level. To address this gap, this paper proposes a task-driven six-level (L0–L5) classification framework and constructs a Massage-Driven Task (MDT) model that decomposes the massage process into six subtasks (S1–S6). Building on this, we design a three-dimensional evaluation scheme comprising a Functional Delegation Structure (FDS), an Anomaly Perception Mechanism (APM), and a Human–Machine Interaction Boundary (HMIB), and we select eight key performance indicators to quantify IMS intelligence across the perception–decision–actuation–feedback closed loop. We then determine indicator weights via the Delphi method and the Analytic Hierarchy Process (AHP), and obtain dimension-level scores and a composite intelligence score S0 using normalization and weighted aggregation. Threshold intervals for L0–L5 are defined through equal-interval partitioning combined with expert calibration, and sensitivity is verified on representative samples using ±10% data perturbations. Results show that, within typical error ranges, the proposed grading framework yields stable classification decisions and exhibits strong robustness. The framework not only provides the first reusable quantitative basis for grading IMS intelligence but also supports product design optimization, regulatory certification, and user selection. Full article

Mountain glaciers are the most direct and sensitive indicators of climate change. In the context of global warming, monitoring changes in glacier elevation has become a crucial issue in modern cryosphere research. The Global Ecosystem Dynamics Investigation (GEDI) is a full-waveform laser altimeter with a multi-beam that provides unprecedented measurements of the Earth’s surface. Many studies have investigated its applications in assessing the vertical structure of various forests. However, few studies have assessed GEDI’s performance in detecting variations in glacier elevation in land ice in high-mountain Asia. To address this limitation, we selected the Southeastern Tibetan Plateau (SETP), one of the most sensitive areas to climate change, as a test area to assess the feasibility of using GEDI to monitor glacier elevation changes by comparing it with ICESat-2 ATL06 and the reference TanDEM-X DEM products. Moreover, this study further analyzes the influence of environmental factors (e.g., terrain slope and aspect, and altitude distribution) and glacier attributes (e.g., glacier area and debris cover) on changes in glacier elevation. The results show the following: (1) Compared to ICESat-2, in most cases, GEDI overestimated glacier thinning (i.e., elevation reduction) to some extent from 2019 to 2021, with an average overestimation value of about −0.29 m, while the annual average rate of elevation change was relatively close, at −0.70 ± 0.12 m/yr versus −0.62 ± 0.08 m/yr, respectively. (2) In terms of time, GEDI reflected glacier elevation changes at interannual and seasonal scales, and the trend of change was consistent with that found with ICESat-2. The results indicate that glacier accumulation mainly occurred in spring and winter, while the melting rate accelerated in summer and autumn. (3) GEDI effectively monitored and revealed the characteristics and patterns of glacier elevation changes with different terrain features, glacier area grades, etc.; however, as the slope increased, the accuracy of the reported changes in glacier elevation gradually decreased. Nonetheless, GEDI still provided reasonable estimates for changes in mountain glacier elevation. (4) The spatial distribution of GEDI footprints was uneven, directly affecting the accuracy of the monitoring results. Thus, to improve analyses of changes in glacier elevation, terrain factors should be comprehensively considered in further research. Overall, these promising results have the potential to be used as a basic dataset for further investigations of glacier mass and global climate change research. Full article

Proceroplatus (Keroplatidae: Keroplatinae) is a distinct keroplatid group comprising 39 described species. These species are found worldwide, but none had previously been reported in China prior to this study. In this paper, Proceroplatus is recorded for the first time from China, along with two new species: P. dapanshanussp. n. and P. biemarginatussp. n., which were collected from the southern region. Here, a worldwide distribution map of this genus is presented by species, including the new ones. Images and detailed morphological descriptions are provided for each new species, accompanied by molecular identification based on the standard mitochondrial cytochrome oxidase subunit I (COI) gene. To clarify the mitogenomic characteristics of Proceroplatus, the well-assembled and annotated mitogenome of P. dapanshanus was obtained and described in detail. The comparative analyses and phylogenetic tree indicate that the mitogenomic evolution of keroplatids is relatively conserved and influenced not only by mutation pressure but also by natural selection and other factors. Full article

To ensure the safe and reliable operation of hydrogen-blended natural gas (HBNG) pipelines in urban utility tunnels, this study conducted a comprehensive CFD simulation of the leakage and diffusion characteristics of HBNG in confined underground environments. Utilizing ANSYS CFD software (2024R1), a three-dimensional physical model of a utility tunnel was developed to investigate the influence of key parameters, such as leak sizes (4 mm, 6 mm, and 8 mm)—selected based on common small-orifice defects in utility tunnel pipelines (e.g., corrosion-induced pinholes and minor mechanical damage) and hydrogen blending ratios (HBR) ranging from 0% to 20%—a range aligned with current global HBNG demonstration projects (e.g., China’s “Medium-Term and Long-Term Plan for Hydrogen Energy Industry Development”) and ISO standards prioritizing 20% as a technically feasible upper limit for existing infrastructure, on HBNG diffusion behavior. The study also evaluated the adequacy of current accident ventilation standards. The findings show that as leak orifice size increases, the diffusion range of HBNG expands significantly, with a 31.5% increase in diffusion distance and an 18.5% reduction in alarm time as the orifice diameter grows from 4 mm to 8 mm. Furthermore, hydrogen blending accelerates gas diffusion, with each 5% increase in HBR shortening the alarm time by approximately 1.6 s and increasing equilibrium concentrations by 0.4% vol. The current ventilation standard (12 h⁻¹) was found to be insufficient to suppress concentrations below the 1% safety threshold when the HBR exceeds 5% or the orifice diameter exceeds 4 mm—thresholds derived from simulations showing that, under 12 h⁻¹ ventilation, equilibrium concentrations exceed the 1% safety threshold under these conditions. To address these gaps, this study proposes an adaptive ventilation strategy that uses variable-frequency drives to adjust ventilation rates in real time based on sensor feedback of gas concentrations, ensuring alignment with leakage conditions, thereby ensuring enhanced safety. These results provide crucial theoretical insights for the safe design of HBNG pipelines and ventilation optimization in utility tunnels. Full article

A novel series of multifunctional tacrine–quinoline hybrids were designed, synthesized, and evaluated as potential anti-Alzheimer’s agents. These compounds incorporate tacrine for cholinesterase’s inhibition and 8-hydroxyquinoline for metal chelation. Piperazine was selected as a linker to provide conformational flexibility and to create favorable cation–π interactions with residues in the mid-gorge region of AChE, enhancing dual-site binding with AChE to inhibit Aβ aggregation. In vitro studies demonstrated submicromolar inhibitory activity toward both AChE and BuChE, particularly for compounds 16e (IC₅₀ = 0.10 μM for AChE, 0.043 μM for BuChE) and 16h (IC₅₀ = 0.21 μM for AChE, 0.10 μM for BuChE). These compounds also exhibited potent inhibition of self-induced Aβ_1–42 aggregation (16e: 80.5% ± 4.4%, 16h: 93.2% ± 3.9% at 20 μM). Kinetic analyses revealed mixed-type inhibition, suggesting dual binding to both CAS and PAS of AChE. UV–vis spectrometry confirmed the chelation of Cu²⁺ and Zn²⁺ ions by the 8-hydroxyquinoline moiety. These findings highlight the tacrine–quinoline scaffold as a promising platform for the discovery of a multitarget-directed anti-AD drug. Full article