Search Results (5,414)

The fractional PDE-ODE coupled system with Neumann boundary condition is considered in this paper. We design a boundary state feedback controller using the Backstepping method to stabilize the considered system. According to operator semigroup theory, we obtain a unique solution of the investigated system. Based on this, by using the fractional Lyapunov scheme, we prove that the system is Mittag-Leffler stable. Full article

Natural enemies commonly probe larval bodies and frass with their antennae for prey hunting. However, the attractants to natural enemies emitted directly from hosts and host-associated tissues remained largely unknown. Here, we used two generalist noctuid species, Helicoverpa armigera (Hübner) and Spodoptera frugiperda (JE Smith), along with the larval endoparasitoid Microplitis mediator (Haliday) to address the question. Extracts of larval frass of both the noctuid species were strongly attractive to M. mediator females when hosts were fed either maize, cotton, soybean leaves, or an artificial diet without leaf tissues. By using a combination of electrophysiological measurements and behavioral tests, we found that the attractiveness of frass mainly relied on a volatile compound ethyl palmitate. The compound was likely to be a by-product of host digestion involving gut bacteria because an antibiotic supplement in diets reduced the production of the compound in frass and led to the decreased attractiveness of frass to the parasitoids. In contrast, extracts of the larval bodies of both the noctuid species appeared to be less attractive to the parasitoids than their respective fecal extracts, independently of types of food supplied to the larvae. Altogether, larval frass of the two noctuid species was likely to be more important than their bodies in attracting the endoparasitoid species, and the main attractant of frass was probably one of the common metabolites of digestion involving gut microbes, and its emission is likely to be independent of host plant species. Full article

Quantum state discrimination (QSD) is a fundamental task in quantum information processing, improving the computation efficiency and communication security. Non-Hermitian (NH) PT-symmetric systems were found to be able to discriminate two quantum states better than the Hermitian strategy. In this work, we propose a QSD approach based on P-pseudo-Hermitian systems with real spectra. We theoretically prove the feasibility of realizing QSD in the real-spectrum phase of a P-pseudo-Hermitian system, i.e., two arbitrary non-orthogonal quantum states can be discriminated by a suitable P-pseudo-Hermitian Hamiltonian. In detail, we decide the minimal angular separation between two non-orthogonal quantum states for a fixed P-pseudo-Hermitian Hamiltonian, and we find the orthogonal evolution time is able to approach zero under suitable conditions, while both the trace distance and the quantum relative entropy are employed to judge their orthogonality. We give a criterion to choose the parameters of a P-pseudo-Hermitian Hamiltonian that evolves the two initial orthogonal states faster than a fixed arbitrary PT-symmetric one with an identical energy difference. Our work expands the NH family for QSD, and can be used to explore real quantum systems in the future. Full article

Globally, technologically integrated housing solutions are increasingly relevant in addressing the challenges of aging populations and sustainable urban development. Drawing on the Technology Acceptance Model (TAM), this research investigates how perceptions of usefulness, ease of use, and attitudes influence relocation intention to smart retirement villages (SRVs), while also examining any significant differences between the socio-demographic variables and such intention. A total of 305 individuals aged 55 and above participated in an online survey, with data analyzed using IBM SPSS Statistics version 27 and AMOS-SEM version 25. The findings reveal that elderly individuals of Chinese ethnicity, those who are married, and those aged between 66 and 70 are more inclined to relocate to SRVs. Attitude and perceived usefulness significantly predict relocation intention, while perceived ease of use exerts an indirect effect through usefulness. These results highlight the importance of integrating user-centered technological design with socio-cultural and demographic considerations in the development of age-friendly built environments. The study offers insights for urban planners, policymakers, and developers seeking to create inclusive and sustainable smart housing solutions for aging populations. Full article

Accomplishing China’s national targets of carbon peaking and carbon neutrality necessitates proactive solutions, hinging critically on fundamentally transforming rural construction models. Current construction practices in rural areas are characterized by inefficiency, high resource consumption, and reliance on imported materials. These shortcomings not only jeopardize the attainment of climate objectives, but also hinder equitable development between urban and rural regions. Using the Digital Industrial Park in Yongtai County, Fuzhou City, as a case study, this study focuses on prefabricated public buildings in regions with extreme hot–humid climate, and innovatively integrates BIM (Building Information Modeling)-driven carbon modeling with the Gaussian Two-Step Floating Catchment Area (G2SFCA) method for spatial accessibility assessment to investigate the carbon emissions and economic benefits of prefabricated buildings during the embodied stage, and analyzes the spatial accessibility of prefabricated building material suppliers in Fuzhou City and identifies associated bottlenecks, seeking pathways to promote sustainable rural revitalization. Compared with traditional cast-in-situ buildings, embodied carbon emissions of prefabricated during their materialization phase significantly reduced. This dual-perspective approach ensures that the proposed solutions possess both technical rigor and logistical feasibility. Promoting this model across rural areas sharing similar climatic conditions would advance the construction industry’s progress towards the dual carbon goals. Full article

Visible-Infrared Person Re-Identification (VI-ReID) is of crucial importance in applications such as monitoring and security. However, challenges faced from intra-class variations and cross-modal differences are often exacerbated by inaccurate infrared analysis and insufficient structural modeling. To address these issues, we propose Parsing-guided Differential Enhancement Graph Learning (PDEGL), a novel framework that learns discriminative representations through a dual-branch architecture synergizing global feature refinement with part-based structural graph analysis. In particular, we introduce a Differential Infrared Part Enhancement (DIPE) module to correct infrared parsing errors and a Parsing Structural Graph (PSG) module to model high-order topological relationships between body parts for structural consistency matching. Furthermore, we design a Position-sensitive Spatial-Channel Attention (PSCA) module to enhance global feature discriminability. Extensive evaluations on the SYSU-MM01, RegDB, and LLCM datasets demonstrate that our PDEGL method achieves competitive performance. Full article

Detecting power transformer winding degradations at an early stage is very important for the safe operation of nuclear power plants. Most transformer failures are caused by insulation breakdown; the winding turn-to-turn short circuit fault is frequently encountered. Experience has shown that routine testing techniques, e.g., winding resistance, leakage inductance, and sweep frequency response analysis (SFRA), are not sensitive enough to identify minor turn-to-turn short defects. The SFRA technique is effective only if the fault is in such a condition that the flux distribution in the core is prominently distorted. This paper proposes the frequency domain reflectometry (FDR) technique for detecting and locating transformer winding defects. FDR measures the wave impedance and its change along the measured windings. The wire over a plane model is selected as the transmission line model for the transformer winding. The effectiveness is verified through lab experiments on a twist pair cable simulating the transformer winding and field testing on a real transformer. The FDR technique successfully identified and located the turn-to-turn short fault that was not detected by other testing techniques. Using FDR as a complementary tool for winding condition assessment will be beneficial. Full article

Nuclear receptors are involved in multiple biological processes, among which RORγ can regulate the expression of inflammation-related genes and is thus frequently used as a therapeutic target for cancer. Canine mammary cancer is one of the most common tumor diseases in dogs, with a relative incidence rate of 46.71% for CMT in China over the past five years, severely threatening the life and health of dogs. Therefore, the search for novel drugs targeting canine mammary cancer is of great significance. This study aims to investigate how the RORγ inhibitors W6134 and XY018 affect the expression of inflammatory genes through histone modifications in CMT-N7 cells. These results show that W6134 and XY018 can upregulate signaling pathways related to inflammation and apoptosis and influence the expression of associated genes. The close link between RORγ and inflammation-related genes further confirms that RORγ may serve as a therapeutic target for canine cancer. Additionally, ChIP-qPCR was used to detect the enrichment of histone markers such as P300, H3K27ac, H3K4me1, H3K9la, and H3K9bhb at the target loci of CXCL10 and MECOM genes. Collectively, our findings provide molecular evidence for the protective role of RORγ in canine mammary cancer, potentially by regulating inflammatory pathways via histone modifications, offering new insights for improving the cure rate and survival of affected dogs. Full article

Elodea nuttallii is a significant submerged macrophyte utilized in shrimp and crab aquaculture, yet it exhibits low thermotolerance. This study investigated the physiological responses and transcriptomic characteristics of E. nuttallii under high-temperature stress (HTS). The results indicated that HTS significantly reduced the absolute growth rate (AGR) and photosynthetic efficiency of E. nuttallii while concurrently elevating antioxidant enzyme activities, malondialdehyde (MDA) content, and concentrations of osmotic adjustment compounds. Furthermore, the apical segments of E. nuttallii demonstrated greater sensitivity to HTS compared to the middle segments. Under exposure to 35 °C and 40 °C, antioxidant enzyme activities, MDA content, and osmotic adjustment compound levels were significantly higher in the apical segments than in the middle segments. Transcriptomic analysis revealed 7526 differentially expressed genes (DEGs) in the apical segments at 35 °C, a number substantially exceeding that observed in the middle segments. Enrichment analysis of DEGs revealed significant upregulation of key metabolic regulators under HTS, including carbohydrate metabolism genes (HXK, FRK) and phenylpropanoid biosynthesis enzymes (4CL, COMT). This transcriptional reprogramming demonstrates E. nuttallii’s adaptive strategy of modulating carbon allocation and phenolic compound synthesis to mitigate thermal damage. Our findings not only elucidate novel thermotolerance mechanisms in aquatic plants but also provide candidate genetic targets (HXK, 4CL) for molecular breeding of heat-resilient cultivars through transcriptomic screening. Full article

Depression is a common mental disorder with high economic burden, characterized by high disability and mortality rates. The etiology of depression remains unclear to date, and there are various hypotheses regarding the pathogenesis of depression in clinical practice, including the monoamine neurotransmitter hypothesis, the hypothalamic–pituitary–adrenal (HPA) axis dysregulation hypothesis, the inflammatory cytokine hypothesis, and the neurotrophic factor hypothesis. These theories offer specific directional aid in the clinical management of individuals suffering from depression. Medicinal intervention stands as a critical approach within the spectrum of depression treatments, and this article reviews the specific mechanisms of different hypotheses on the pathogenesis of depression in recent years, as well as the research progress on related therapeutic drugs. Full article

Browning significantly impacts the commercial value of luffa (luffa cylindrica) and is primarily driven by the metabolic processes of phenolic acids. Investigating changes in phenolic acids during browning aids in understanding the physiological mechanisms underlying this process and provides a basis for improving storage, processing, variety breeding, and utilization of germplasm resources. This study compared browning-resistant (‘30’) and browning-sensitive (‘256’) luffa varieties using high-throughput sequencing and metabolomics techniques. The results revealed 55 genes involved in the phenylpropanoid biosynthesis pathway, including 8 phenylalanine ammonia-lyase (PAL) genes, 20 peroxidase (POD) genes, 2 polyphenol oxidase (PPO) genes associated with tyrosine metabolism, and 37 peroxisome-related genes. Real-time quantitative (qPCR) was employed to validate 15 browning-related genes, revealing that the expression levels of LcPOD21 and LcPOD6 were 12.5-fold and 25-fold higher in ‘30’ compared to ‘256’, while LcPAL5 and LcPAL4 were upregulated in ‘30’. Enzyme analysis showed that catalase (CAT) and phenylalanine ammonia-lyase (PAL) activities were higher in ‘30’ than in ‘256’. Conversely, superoxide dismutase (SOD) and polyphenol oxidase (PPO) activities were reduced in ‘30’, whereas CAT activity was upregulated. The concentrations of cinnamic acid, p-coumaric acid, trans-5-O-(4-coumaroyl)mangiferic acid, and caffealdehyde were lower in browning-resistant luffa ‘30’ than in browning-sensitive luffa ‘256’, suggesting that their levels influence browning in luffa. These findings elucidate the mechanisms underlying browning and inform strategies for the storage, processing, and genetic improvement of luffa. Full article

This study investigates the strengthening mechanisms of char in silicon dioxide thermal reduction through systematic high-temperature experiments using three char types (YQ1, CW1, HY1) characterized by X-ray diffraction, Raman spectroscopy, transmission electron microscopy, and scanning electron microscopy. HY1 char demonstrated superior reactivity due to its highly ordered microcrystalline structure, characterized by the largest aromatic cluster size (L_a) and lowest defect ratio (I_D/I_G = 0.37), which directly correlated with enhanced reaction completeness. The carbon–silicon reaction reactivity increased progressively with temperature, achieving optimal performance at 1550 °C. Addition of Fe and Fe₂O₃ significantly accelerated the reduction process, with Fe₂O₃ exhibiting superior catalytic performance by reducing activation energy and optimizing reaction kinetics. The ferrosilicon formation mechanism proceeds through a two-stage pathway: initial char-SiO₂ reaction producing SiC and CO, followed by SiC–iron interaction generating FeSi, which catalytically promotes further reduction. These findings establish critical structure–performance relationships for char selection in industrial silicon production, where microcrystalline ordering emerges as the primary performance determinant. The identification of optimal temperature and additive conditions provides practical pathways to enhance energy efficiency and product quality in silicon metallurgy, enabling informed raw material selection and process optimization to reduce energy consumption and improve operational stability. Full article

In this work, we demonstrate high-efficiency 385 nm AlGaN-based near-ultraviolet micro light emitting diode (NUV-Micro LED) arrays. The epi structure is prepared using a novel AlN-inserted superlattice electrical blocking layer which enhances hole spreading in the p-type region significantly. The NUV-Micro LED arrays in this work comprise 228 chips in parallel with wavelengths at 385 nm, and each single chip size is 15 × 30 μm². Compared with conventional bulk AlGaN-based EBL structures, the NUV-Micro LED arrays that implemented the new hole spreading enhanced superlattice electrical blocking layer (HSESL-EBL) structure proposed in this work had a remarkable increase in light output power (LOP) at current density, increasing the range down from 0.02 A/cm² to as high as 97 A/cm². The array’s light output power is increased up to 1540% at the lowest current density 0.02 A/cm², and up to 58% at the highest current density 97 A/cm², measured under room temperature (RT); consequently, the WPE is increased from 13.4% to a maximum of 30.18%. This AlN-inserted HESEL-EBL design significantly enhances both the lateral expansion efficiency and the hole injection efficiency into the multi quantum well (MQW) in the arrays, improving the concentration distribution of the holes in MQW while maintaining good suppression of electron leakage. The array’s efficiency droop has also been greatly reduced. Full article

This study adopts a dynamic Expectancy–Disconfirmation framework to investigate the evolving nature of user satisfaction across three product categories: durable goods, fast-moving consumer goods (FMCG), and digital products. A 25-day longitudinal experiment involving 128 participants was conducted, during which users engaged with their most recently purchased products and provided repeated subjective evaluations over time. The findings reveal dynamic changes in the influence of expectations and perceived performance on satisfaction throughout the product usage cycle. For durable goods and FMCG, both expectations and perceived performance gradually declined, accompanied by a weakening effect of expectations on satisfaction. In contrast, digital products exhibited greater volatility, lacking a stable experiential baseline and resulting in greater fluctuations in satisfaction trajectories. Moreover, external contextual and emotional factors were found to play a more significant role in shaping satisfaction with physical products, beyond the scope of the traditional expectancy–performance model. These insights offer theoretical and managerial implications for sustainable product and experience design. In particular, they highlight the importance of implementing experience-stabilizing strategies in digital consumption contexts to support user well-being and enhance continuous product utilization, thereby maximizing product potential and reducing waste. Full article

Knowledge-graph-based recommendation aims to provide personalized recommendation services to users based on their historical interaction information, which is of great significance for shopping transaction rates and other aspects. With the rapid growth of online shopping, the knowledge graph constructed from users’ historical interaction data now incorporates multiattribute information, including timestamps, images, and textual content. The information of multiple modalities is difficult to effectively utilize due to their different representation structures and spaces. The existing methods attempt to utilize the above information through simple embedding representation and aggregation, but ignore targeted representation learning for information with different attributes and learning effective weights for aggregation. In addition, existing methods are not sufficient for effectively modeling temporal information. In this article, we propose MTR, a knowledge graph recommendation framework based on mixture of experts network. To achieve this goal, we use a mixture-of-experts network to learn targeted representations and weights of different product attributes for effective modeling and utilization. In addition, we effectively model the temporal information during the user shopping process. A thorough experimental study on popular benchmarks validates that MTR can achieve competitive results. Full article