Search Results (58)

This review systematically examines recent advances in self-powered wireless sensing technologies based on triboelectric nanogenerators (TENGs), focusing on innovative methods that leverage breakdown discharge effects to achieve high-precision and long-distance signal transmission. These methods offer novel technical pathways and theoretical frameworks for next-generation wireless sensing systems. To address the core limitations of conventional wireless sensors, such as a restricted transmission range, high power consumption, and suboptimal integration, this analysis elucidates the mechanism of the generation of high-frequency electromagnetic waves through localized electric field ionization induced by breakdown discharge. Key research directions are synthesized to enhance TENG-based sensing capabilities, including novel device architectures, the optimization of RLC circuit models, the integration of machine learning algorithms, and power management strategies. While current breakdown discharge sensors face challenges such as energy dissipation, multimodal coupling complexity, and signal interpretation barriers, future breakthroughs in material engineering and structural design are anticipated to drive advancements in efficiency, miniaturization, and intelligent functionality in this field. Full article

Interior permanent magnet synchronous motors (IPMSMs) are widely applied as drive motors in electric vehicles because they have the advantages of high power density, high efficiency, and excellent dynamic performance. This paper introduces a framework for multi-objective optimization, tailored for the demands of V-Shaped IPMSMs, which involves high-dimensional variables. The framework is divided into three parts. Firstly, a proportional parametric finite element analysis (FEA) model for V-Shaped IPMSMs was established to reduce the probability of size interference among motor design parameters. Secondly, a surrogate model was trained using the design of experiments (DOE) approach and was utilized to substitute the FEA model. The accuracy of the surrogate model was then verified. Thirdly, the surrogate model was used as a fitness function, and a non-dominated sorting genetic algorithm II (NSGA-II) was employed as the optimization method to acquire the optimal goals rapidly. Based on the optimal design parameters, a prototype of the electrical motor was fabricated. Finally, the effectiveness of optimization was proven by experimental testing. Full article

Timber–concrete composite (TCC) floors are gaining popularity in sustainable construction due to their enhanced stiffness and structural efficiency. However, excessive vibrations, particularly those induced by human activity, pose significant challenges to occupant comfort and structural integrity. This study investigates the application of Tuned Mass Dampers (TMDs) to mitigate vibrations in TCC floors, with a focus on enhancing damping performance through the incorporation of pre-strained Shape Memory Alloys (SMAs) (Kellogg’s Research Labs, New Boston, NH, USA). A novel pre-strained SMA–TMD system was designed and experimentally tested to evaluate its effectiveness in vibration control under various loading conditions. The results demonstrate that pre-straining significantly increases the damping ratio of the SMA–TMD, improving its vibration mitigation capability. Compared to non-pre-strained SMA–TMD, the pre-strained SMA–TMD system exhibited superior adaptability and robustness in reducing floor vibrations, achieving a peak acceleration reduction of up to 49.91%. These findings provide valuable knowledge into the development of advanced damping solutions for timber floors, contributing to the broader application of vibration control strategies in sustainable and high-performance building systems. Full article

The challenging problem of chlorine “poisoning” SnO₂ for poorly recoverable detection of dichloromethane has been solved in this work. The materials synthesized by Ni or/and Mo doping SnO₂ were spread onto the micro-hotplates (<1 mm³) to fabricate the MEMS sensors with a low power consumption (<45 mW). The sensor based on Mo·Ni co-doped SnO₂ is evidenced to have the best sensing performance of significant response and recoverability to dichloromethane between 0.07 and 100 ppm at the optimized temperature of 310 °C, in comparison with other sensors in this work and the literature. It can be attributed to a synergetic effect of Mo·Ni co-doping into SnO₂ as being supported by characterization of geometrical and electronic structures. The sensing mechanism of dichloromethane on the material is investigated. In situ infrared spectroscopy (IR) peaks identify that the corresponding adsorbed species are too strong to desorb, although it has demonstrated a good recoverability of the material. A probable reason is the formation rates of the strongly adsorbed species are much slower than those of the weakly adsorbed species, which are difficult to form significant IR peaks but easy to desorb, thus enabling the material to recover. Theoretical analysis suggests that the response process is kinetically determined by molecular transport onto the surface due to the free convection from the concentration gradient during the redox reaction, and the output steady voltage thermodynamically follows the equation only formally identical to the Langmuir–Freundlich equation for physisorption but is newly derived from statistical mechanics. Full article

Chemical warfare agents (CWAs), including hydrogen cyanide (AC), 2-[fluoro(methyl)phosphoryl]oxypropane (GB), 3-[fluoro(methyl)phosphoryl]oxy-2,2-dimethylbutane (GD), ethyl S-(2-diisopropylaminoethyl) methylphosphonothioate (VX), and di-2-chloroethyl sulfide (HD), pose a great threat to public safety; therefore, it is important to develop sensing technology for CWAs. Herein, a sensor array consisting of 24 metal oxide semiconductor (MOS)-based MEMS sensors with good gas sensing performance, a simple device structure (0.9 mm × 0.9 mm), and low power consumption (<10 mW on average) was developed. The experimental results show that there are always several sensors among the 24 sensors that show good sensing performance in relation to each CWA, such as a relatively significant response, a broad detection range (AC: 5.8–89 ppm; GB: 0.04–0.47 ppm; GD: 0.06–4.7 ppm; VX: 9.978 × 10⁻⁴–1.101 × 10⁻³; HD: 0.61–4.9 ppm), and a low detection limit that is lower than the immediately dangerous for life and health (IDLH) level of the five CWAs. This indicates that these sensors can meet the needs for qualitative detection and can provide an early warning regarding low concentrations of CWAs. In addition, features were extracted from the initial kinetic characteristics and dynamic change characteristics of the sensing response. Finally, principal component analysis (PCA) and machine learning algorithms were applied for CWA classification. The obtained PCA plots showed significant differences between groups, and the narrow neural network among the machine learning algorithms achieves a prediction accuracy of nearly 100.0%. In summary, the proposed MOS-based MEMS sensor array driven by pattern recognition algorithms can be integrated into portable devices, showing great potential and practical applications in the rapid, in situ, and on-site detection and identification of CWAs. Full article

In the domain of swine production, body weight (BW) and average daily gain (ADG) are recognized as the primary performance indicators. Nevertheless, the genetic architecture of ADG and BW in Dongliao black (DLB) pigs remains to be fully elucidated. In this study, we performed a genome-wide association analysis of BW, ADG, and body mass index (BMI) in 358 DLB pigs of different days of age. The genome-wide association study (GWAS) showed the following: (1) The most significant single nucleotide polymorphism (SNP) detected for BW was on Sus scrofa chromosome (SSC) 11:100,808 (p-value = 1.16 × 10⁻⁶) that was also the most significant SNP for ADG. (2) The most significant SNP associated with BMI was SSC17:51,463,521 (p-value = 5.16 × 10⁻⁸). (3) SNPs SSC10:6,523,844 and SSC17:23,852,682 were identified in both BW and ADG. A meta-analysis was conducted on BW at different days and demonstrated SSC5:39,028,335 (p-value = 8.37 × 10⁻⁶) which was not identified in the results of each single trait. The regions of two SNPs (SSC11:100,808, SSC4:10,703,277) exhibited considerable influence on both BW and ADG and the related regions were selected for linkage disequilibrium (LD) analyses that exhibited a notable linkage. In addition, several genes were identified that are associated with obesity and play roles in lipid metabolism, including MACROD2, PHLPP2, CYP2E1, and STT3B. Full article

This study examined the genetic and evolutionary features of influenza A/H3N2 viruses in Hangzhou (2010–2022) by analyzing 28,651 influenza-like illness samples from two sentinel hospitals. Influenza A/H3N2 coexisted with other subtypes, dominating seasonal peaks (notably summer). Whole-genome sequencing of 367 strains was performed on GridION platforms. Phylogenetic analysis showed they fell into 16 genetic groups, with multiple clades circulating simultaneously. Shannon entropy indicated HA, NA, and NS gene segments exhibited significantly higher variability than other genomic segments, with HA glycoprotein mutations concentrated in antigenic epitopes A–E. Antiviral resistance showed no inhibitor resistance mutations in PA, PB1, or PB2, but NA mutations were detected in some strains, and most strains harbored M2 mutations. A Bayesian molecular clock showed the HA segment exhibited the highest nucleotide substitution rate (3.96 × 10⁻³ substitutions/site/year), followed by NA (3.77 × 10⁻³) and NS (3.65 × 10⁻³). Selective pressure showed A/H3N2 strains were predominantly under purifying selection, with only sporadic positive selection at specific sites. The Pepitope model demonstrated that antigenic epitope mismatches between circulating H3N2 variants and vaccine strains led to a significant decline in influenza vaccine effectiveness (VE), particularly in 2022. Overall, the study underscores the complex circulation patterns of influenza in Hangzhou and the global importance of timely vaccine strain updates. Full article

Avian coccidiosis, caused by protozoan parasites of the genus Eimeria, poses a major threat to the poultry industry worldwide, leading to severe economic losses through reduced growth rates, poor feed efficiency, and increased mortality. Although the conventional management of this disease has relied on anticoccidial drugs, the overwhelming use of these agents has led to the rapid emergence and spread of drug-resistant Eimeria isolates, highlighting the urgent need for novel therapeutic approaches. This study employed computational approaches to identify novel inhibitors targeting Eimeria tenella prolyl-tRNA synthetase (EtPRS). Based on the virtual screening of a library of 3045 natural compounds, 42 high-confidence inhibitors were identified. Three compounds, including Chelidonine, Bicuculline, and Guggulsterone, demonstrated strong and selective binding to EtPRS through stable interactions within the active site. ADMET predictions revealed favorable safety profiles, while molecular dynamic simulations confirmed binding stability. Overall, this research established a solid framework for the development of effective anticoccidial agents targeting PRS, contributing to the advancement of therapeutic strategies for combating parasitic infections in the poultry industry. Full article

Lotus leaves combine both edible and medicinal properties and are rich in nutrients and bioactive compounds. In this study, the lotus leaf tea was prepared using a black tea fermentation process, and the functional components and microbial changes during fermentation were investigated. The results indicated that the activity of polyphenol oxidase showed an initial rise followed by a decline as fermentation progressed, peaked at 3 h with 1.07 enzyme activity units during fermentation. The lotus leaf fermented tea has high levels of soluble sugars (20.92 ± 0.53 mg/g), total flavonoids (1.59 ± 0.05 mg GAE/g), and total polyphenols (41.34 ± 0.87 mg RE/g). Its antioxidant activity was evaluated using ABTS, DPPH, and hydroxyl radical scavenging assays, with results of 18.90 ± 1.02 mg Vc/g, 47.62 ± 0.51 mg Vc/g, and 17.58 ± 1.06 mg Vc/g, respectively. The microbial community also shifted during fermentation. Fusarium played a significant role during the fermentation process. This study demonstrated that the black tea fermentation process improved the functional components and biological activity of lotus leaf tea by optimizing the synergistic effect of enzymatic oxidation and microbial fermentation. The findings not only realized the comprehensive utilization of lotus leaf resources but also provided a foundation for developing innovative functional beverages with enhanced bioactive properties. Full article

Accurate crop yield prediction is crucial for formulating agricultural policies, guiding agricultural management, and optimizing resource allocation. This study proposes a method for predicting yields in China’s major winter wheat-producing regions using MOD13A1 data and a deep learning model which incorporates an Improved Gray Wolf Optimization (IGWO) algorithm. By adjusting the key parameters of the Convolutional Neural Network (CNN) with IGWO, the prediction accuracy is significantly enhanced. Additionally, the study explores the potential of the Green Normalized Difference Vegetation Index (GNDVI) in yield prediction. The research utilizes data collected from March to May between 2001 and 2010, encompassing vegetation indices, environmental variables, and yield statistics. The results indicate that the IGWO-CNN model outperforms traditional machine learning approaches and standalone CNN models in terms of prediction accuracy, achieving the highest performance with an R² of 0.7587, an RMSE of 593.6 kg/ha, an MAE of 486.5577 kg/ha, and an MAPE of 11.39%. The study finds that April is the optimal period for early yield prediction of winter wheat. This research validates the effectiveness of combining deep learning with remote sensing data in crop yield prediction, providing technical support for precision agriculture and contributing to global food security and sustainable agricultural development. Full article

The advancement of drone technology has underscored the critical need for adaptability and enhanced functionality in unmanned aerial vehicles (UAVs). Morphing quadrotors, capable of dynamically altering their structure during flight, offer a promising solution to extend and optimize the operational capabilities of conventional drones. This paper presents a comprehensive review of current advancements in morphing quadrotor research, focusing on morphing concept, actuation mechanisms and flight control strategies. We examine various active morphing approaches, including the integration of smart materials and advanced actuators that facilitate real-time structural adjustments to meet diverse mission requirements. Key design considerations—such as structural integrity, weight distribution, and control algorithms—are meticulously analyzed to assess their impact on the performance and reliability of morphing quadrotors. Despite their significant potential, morphing quadrotors face challenges related to increased design complexity, higher energy consumption, and the integration of sophisticated control systems. The discussion on challenges and opportunities highlights the necessity for ongoing advancements in morphing quadrotor technologies, particularly in addressing adaptive control problems associated with highly nonlinear and dynamic morphing aircraft systems, and in the potential integration with smart materials. By synthesizing the latest research and outlining prospective directions, this paper aims to serve as a valuable reference for researchers and practitioners dedicated to advancing the field of morphing quadrotor technologies. Full article

Oxidative stress (OS) is a key factor in the generation of various pathophysiological conditions. Nuclear factor erythroid 2 (NF-E2)-related factor 2 (Nrf2) is a major transcriptional regulator of antioxidant reactions. Heme oxygenase-1 (HO-1), a gene regulated by Nrf2, is one of the most critical cytoprotective molecules. In recent years, Nrf2/HO-1 has received widespread attention as a major regulatory pathway for intracellular defense against oxidative stress. It is considered as a potential target for the treatment of inflammatory bowel disease (IBD). This review highlights the mechanism of action and therapeutic significance of Nrf2/HO-1 in IBD and IBD complications (intestinal fibrosis and colorectal cancer (CRC)), as well as the potential of phytochemicals targeting Nrf2/HO-1 in the treatment of IBD. The results suggest that the therapeutic effects of Nrf2/HO-1 on IBD mainly involve the following aspects: (1) Controlling of oxidative stress to reduce intestinal inflammation and injury; (2) Regulation of intestinal flora to repair the intestinal mucosal barrier; and (3) Prevention of ferroptosis in intestinal epithelial cells. However, due to the complex role of Nrf2/HO-1, a more nuanced understanding of the exact mechanisms involved in Nrf2/HO-1 is the way forward for the treatment of IBD in the future. Full article

Welded joints in naval ship hull structures are weak areas under explosive load, but there are relatively few studies investigating the failure characteristics of welded joints through dynamic fracture and explosion tests. In order to explore and predict the failure characteristics of welded joints under explosive load, instrumented Charpy impact tests, explosion tests, and numerical simulations were carried out. The dynamic fracture toughness of ultra-high strength ship hull structural steel welded joints was obtained, and the dynamic stress intensity factors, together with the correlation between stress wave and crack propagation at different positions, were acquired. The results showed that the stress state at the crack tip of a Charpy impact specimen was consistent with that of a welded joint under explosive loads, and the crack initiated when the dynamic stress intensity factor exceeded the dynamic fracture toughness. The results indicated that the dynamic fracture toughness obtained by instrumented Charpy impact tests could be used to predict the crack initiation characteristics of welded structures under explosive load, and the stress wave at the crack tip was basically perpendicular to the crack propagation surface and promoted the rapid propagation of cracks. Full article

Salient object detection (SOD) aims to accurately identify significant geographical objects in remote sensing images (RSI), providing reliable support and guidance for extensive geographical information analyses and decisions. However, SOD in RSI faces numerous challenges, including shadow interference, inter-class feature confusion, as well as unclear target edge contours. Therefore, we designed an effective Global Semantic-aware Aggregation Network (GSANet) to aggregate salient information in RSI. GSANet computes the information entropy of different regions, prioritizing areas with high information entropy as potential target regions, thereby achieving precise localization and semantic understanding of salient objects in remote sensing imagery. Specifically, we proposed a Semantic Detail Embedding Module (SDEM), which explores the potential connections among multi-level features, adaptively fusing shallow texture details with deep semantic features, efficiently aggregating the information entropy of salient regions, enhancing information content of salient targets. Additionally, we proposed a Semantic Perception Fusion Module (SPFM) to analyze map relationships between contextual information and local details, enhancing the perceptual capability for salient objects while suppressing irrelevant information entropy, thereby addressing the semantic dilution issue of salient objects during the up-sampling process. The experimental results on two publicly available datasets, ORSSD and EORSSD, demonstrated the outstanding performance of our method. The method achieved 93.91% S_α, 98.36% E_ξ, and 89.37% F_β on the EORSSD dataset. Full article

The accurate prediction of crop yields is crucial for enhancing agricultural efficiency and ensuring food security. This study assesses the performance of the CNN-LSTM-Attention model in predicting the yields of maize, rice, and soybeans in Northeast China and compares its effectiveness with traditional models such as RF, XGBoost, and CNN. Utilizing multi-source data from 2014 to 2020, which include vegetation indices, environmental variables, and photosynthetically active parameters, our research examines the model’s capacity to capture essential spatial and temporal variations. The CNN-LSTM-Attention model integrates Convolutional Neural Networks, Long Short-Term Memory, and an attention mechanism to effectively process complex datasets and manage non-linear relationships within agricultural data. Notably, the study explores the potential of using kNDVI for predicting yields of multiple crops, highlighting its effectiveness. Our findings demonstrate that advanced deep-learning models significantly enhance yield prediction accuracy over traditional methods. We advocate for the incorporation of sophisticated deep-learning technologies in agricultural practices, which can substantially improve yield prediction accuracy and food production strategies. Full article