Search Results (705)

Flat electronic bands, characterized by a nearly dispersionless energy spectrum, have emerged as fertile ground for exploring strong correlation effects, unconventional magnetism, and topological phases. This review paper provides an overview of the theoretical basis, material realization, and emergent phenomena associated with flat bands. We begin by discussing the geometric and topological origins of flat bands in lattice systems, emphasizing mechanisms such as destructive interference and compact localized states. We will also explain the relationship between quantum metrics and flat bands, which are recent theoretical findings. We then survey various classes of materials—ranging from engineered lattices and Moiré structures to transition metal compounds—where flat bands have been theoretically predicted or experimentally observed. The interplay between flat-band physics and strong correlations is explored through recent developments in ferromagnetism, superconductivity, and various Hall effects. Finally, we outline open questions and potential directions for future research, including the quest for ideal flat-band systems, the role of spin–orbit coupling, and the impact of disorder. This review aims to bridge fundamental concepts with cutting-edge advances, highlighting the rich physics and material prospects of flat bands. Full article

Lapis lazuli is a valued gemstone that displays a wide spectrum of blue hues, yet the quantitative link between its color and internal sulfur speciation remains unresolved. This study integrates colorimetry with electron probe microanalysis and UV-Vis, Raman, and X-ray photoelectron spectroscopy to establish this relationship and build a robust grading framework within the CIE 1976 L*a*b* color space. X-ray diffraction was employed to determine the mineral composition and confirm that the chromogenic elements originated from lazurite. K-means clustering with Fisher’s discriminant validation classifies samples into four grades: Fancy Blue, Fancy Intense Blue, Fancy Deep Blue, and Fancy Dark Blue. Multimodal analyses identify three sulfur species—[S₃]^·−, S²⁻, and ${\mathrm{SO}}_4^{2-}$—and show that higher sulfur content correlates with lower lightness, reduced chroma, and a violetish-blue shift. [S₃]^·− is confirmed as the dominant chromophore, producing the strong 600 nm absorption that defines the blue hue. A weak absorption band observed near 400 nm in some samples can be attributed to S²⁻ and ${\mathrm{SO}}_4^{2-}$ species, but no visually perceptible effect of this band on the overall color was detected. Full article

Background: Cognitive Reserve (CR) is a theoretical construct developed to explain individual differences in resilience to age-related cognitive decline. Empirical evidence supports its positive role across multiple cognitive domains. However, behavioral research has primarily focused on areas either vulnerable to aging, such as memory, or relatively preserved, such as language. In contrast, the relationship between CR and task-specific performance in domains like visuospatial processing—a domain critical for everyday functioning—remains underexplored. This study investigates whether CR, as measured by the Cognitive Reserve Index Questionnaire (CRIq), predicts performance in mental rotation tasks in healthy older adults. Methods: Participants (age 55–85) completed two tasks: (1) a hand laterality task, requiring judgments about whether a rotated hand image (palm or back view) was left or right; and (2) a letter-congruency task, in which participants determined whether simultaneously presented rotated letters were identical or mirror-reversed. Results: Generalized and linear mixed-effects models revealed a protective effect of cognitive reserve, with higher CRIq scores significantly predicting greater accuracy in both tasks. Efficiency benefits (i.e., shorter reaction times) were evident mainly in the easiest conditions, suggesting that CR supports processing resources more effectively under moderate rather than maximal task demands. This pattern indicates that cognitive reserve does not uniformly enhance performance but instead modulates the allocation of cognitive resources in a context-dependent manner. Conclusions: To our knowledge, this is the first study to demonstrate a modulatory role of CR on visuospatial abilities in healthy older adults. These findings open new avenues for investigating how CR may differentially affect performance across a broader spectrum of cognitive functions, including attention, executive control, and spatial processing. A better understanding of these mechanisms could inform targeted cognitive interventions to strengthen resilience and promote successful aging. Full article

Individuals with high autistic traits typically face a higher risk of depression, making it necessary to explore the relationship between autistic traits and depression in depth. Building on previous research, this study further investigates the roles of interpersonal competence, social avoidance and distress in the relationship between autistic traits and depression. A total of 674 college students were surveyed online using the Chinese version of the Autism-Spectrum Quotient (AQ) questionnaire, the Chinese version of the Interpersonal Competence Questionnaire (ICQ), the Chinese version of the Social Avoidance and Distress Scale (SAD), and the Chinese version of the 13-item Beck Depression Inventory (BDI). Correlation analysis results indicate that autistic traits exhibit a significant negative correlation with interpersonal skills, while showing a significant positive correlation with social distress, social avoidance, and depression levels. Conversely, interpersonal skills demonstrate a significant negative correlation with social avoidance, distress, and depression. Social avoidance and distress showed a significant positive correlation with depression. Chain mediation analysis revealed that interpersonal skills exerted a chain mediating effect between autistic traits and depression via social avoidance and distress. These findings provide insights for further exploration of the relationship and mechanisms underlying autistic traits and depression in individuals. Full article

The tidal mudflat of the South Yellow Sea is characterized by complex sediment environments that preserve rich paleoenvironmental signals, making it an important area for understanding land–sea interactions and promoting sustainable coastal development. Thus, accurate identification of sediment sequences and layer thicknesses becomes crucial for interpreting sediment dynamics and paleoenvironmental reconstruction. While borehole data have elucidated local sediment facies, their spatially discontinuous nature hinders a holistic reconstruction of regional depositional history. To overcome this limitation, ground-penetrating radar (GPR) surveys were conducted across the tidal mudflat of the South Yellow Sea, enabling systematic correlation between radar reflection patterns and sediment architectures. Based on the relationship between the dielectric permittivity and wave velocity, short-time Fourier transform (STFT) was applied to derive the peak-weighted average frequency in the frequency domain for individual soil layers, revealing its dependence on dielectric properties. Sediment interfaces and layer thicknesses were determined using three methods: the radar image waveform method, the Hilbert spectrum instantaneous phase method, and the generalized S-transform time–frequency analysis method. The results indicate the following: (1) GPR enables high-fidelity imaging of subsurface stratigraphy, successfully resolving three distinct radar facies: F1: high-amplitude, horizontal, continuous reflections with parallel waveforms; F2: moderate-to-high-amplitude, sinuous continuous reflections with parallelism; and F3: medium-amplitude, discontinuous chaotic reflections. (2) All three methods effectively characterize subsurface soil stratification, but positioning accuracy decreases systematically with depth. Excluding anomalous errors at one site, the relative error for most layers within the 1 m depth is below 15%, and remains ≤25% at the 1–2 m depth. Beyond the 2 m depth, reliable stratification becomes unattainable due to severe signal attenuation. (3) Comparative analysis demonstrates that the Hilbert spectral instantaneous phase method significantly enhances GPR signals, achieving an optimal performance with positioning errors consistently below 5 cm for most soil layers. The application of this approach along the tidal mudflat of the South Yellow Sea significantly enhances the precision of sediment layer boundary identification. Our analysis systematically interpreted radar facies, demonstrating the effectiveness of the Hilbert spectrum instantaneous phase method in delineating soil stratification. These findings offer reliable technical support for interpreting GPR data in comparable sediment environments. Full article

The rapid and accurate identification of radionuclides and the quantitative analysis of their activities have long been key research areas in the field of nuclear spectrum data processing. Traditional nuclear spectrum analysis methods heavily rely on manual feature extraction, making them highly susceptible to interference from factors such as energy resolution, calibration drift, and spectral peak overlap when dealing with complex mixed-radionuclide spectra, ultimately leading to degraded identification performance and accuracy. Based on multi-nuclide energy spectral data acquired via Geant4 simulation, this study compares the performance of partial least squares regression (PLSR), random forest (RF), a convolutional neural network (CNN), and a hybrid CNN–Meta model for radionuclide identification and quantitative activity analysis under conditions of raw energy spectra, Z-score normalization, and min-max normalization. To maximize the potential of each model, principal component selection, Bayesian hyperparameter optimization, iteration tuning, and meta-learning optimization were employed. Model performance was comprehensively evaluated using the coefficient of determination (R²), root mean square error (RMSE), mean relative error (MRE), and computational time. The results demonstrate that deep learning models can effectively capture nonlinear relationships within complex energy spectra, enabling accurate radionuclide identification and activity quantification. Specifically, the CNN achieved a globally optimal test RMSE of 0.00566 and an R² of 0.999 with raw energy spectra. CNN–Meta exhibited superior adaptability and generalization under min-max normalization, reducing test error by 70.8% compared to RF, while requiring only 49% of the total computation time of the CNN model. RF was relatively insensitive to preprocessing but yielded higher absolute errors, whereas PLSR was limited by its linear nature and failed to capture the nonlinear characteristics of complex energy spectra. In conclusion, the CNN–Meta hybrid model demonstrates superior performance in both accuracy and efficiency, providing a reliable and effective approach for the rapid identification of radionuclides and quantitative analysis of activity in complex energy spectra. Full article

Background/Objectives: In this work, a series of six new indazole-benzimidazole hybrids (M1–M6) were designed, synthesized, and fully characterized. The design of these compounds was based on the combination of two pharmacophoric units, indazole and benzimidazole, both known for their broad spectrum of biological activities. Methods: The molecular hybridization strategy was planned to combine these scaffolds through an effective synthetic pathway, using 6-nitroindazole, two 2-mercaptobenzimidazoles, and 1,3- or 1,5-dihaloalkanes as key precursors, affording the desired hybrids in good yields and with enhanced biological activity. Quantum chemical calculations were performed to investigate the structural, electronic, and electrostatic properties of M1–M6 molecules using Density Functional Theory (DFT) at the B3LYP/6-311++G(d,p) level. The antimicrobial activity efficacy of these compounds was assessed in vitro against four Gram-positive bacteria (Staphylococcus aureus, Enterococcus faecalis, Bacillus cereus, and Lactobacillus plantarum), four Gram-negative bacteria (Salmonella enteritidis, Escherichia coli, Campylobacter coli, Campylobacter jejuni), and four fungal strains (Saccharomyces cerevisiae, Candida albicans, Candida tropicalis, and Candida glabrata) using ampicillin and tetracycline as reference standard drugs. Results: Among the series, compound M6 exhibited remarkable antimicrobial activity, with minimum inhibitory concentrations (MIC) of 1.95 µg/mL against S. cerevisiae and C. tropicalis, and 3.90 µg/mL against S. aureus, B. cereus, and S. enteritidis, while the standards Ampicillin (AmB) (MIC ≥ 15.62 µg/mL) and Tetracycline (TET) (MIC ≥ 7.81 µg/mL) exhibited higher MIC values. To gain molecular insights into the compounds, an in silico docking study was performed to determine the interactions of M1–M6 ligands against the antimicrobial target beta-ketoacyl-acyl carrier protein (ACP) synthase III complexed with malonyl-COA (PDB ID: 1HNJ). Molecular modeling data provided valuable information on the structure-activity relationship (SAR) and the binding modes influencing the candidate ligand-protein recognition. Amino acid residues, such as Arg249, located in the solvent-exposed region, were essential for hydrogen bonding with the nitro group of the 6-nitroindazole moiety. Furthermore, polar side chains such as Asn274, Asn247, and His244 participated in interactions mediated by hydrogen bonding with the 5-nitrobenzimidazole moiety of these compound series. Conclusions: The hybridization of indazole and benzimidazole scaffolds produced compounds with promising antimicrobial activity, particularly M6, which demonstrated superior potency compared to standard antibiotics. Computational and docking analyses provided insights into the structure–activity relationships, highlighting these hybrids as potential candidates for antimicrobial drug development. Full article

Arboviruses such as dengue (DENV), Zika (ZIKV), and chikungunya (CHIKV) remain major global health threats, especially in tropical regions, with no effective antiviral treatments available. Recent research highlights progress in identifying antiviral compounds from natural sources against arboviruses belonging to the flavivirus genus, such as DENV and ZIKV. These compounds, derived from plants, marine organisms, and microorganisms, fall into several key chemical classes: quinones, flavonoids, phenolics, terpenoids, and alkaloids. Quinones inhibit viral entry and replication by targeting envelope proteins and proteases. Flavonoids disrupt RNA synthesis and show virucidal activity. Phenolic compounds reduce expression of non-structural proteins and inhibit enzyme function. Terpenoids demonstrate broad-spectrum activity against multiple arboviruses, while alkaloids interfere with early infection stages or viral enzymes. To support the reviewed literature, we performed molecular docking analyses of selected natural compounds and some arboviral proteins included as illustrative examples. These analyses support the structure–activity relationships reported for some natural compounds and highlight their potential interactions with essential viral targets such as the NS2B-NS3 protease and NS5 polymerase. Together, these literature and computational insights highlight the potential of natural products as scaffolds for antiviral drug development. Full article

The Faster-than-Nyquist (FTN) technology is widely used in optical wireless communication (OWC) systems to improve data rates and spectrum efficiency. However, it introduces inter-symbol interference (ISI), which can affect communication reliability. To address this issue, we propose a pre-equalization algorithm based on a deep neural network (DNN). The performance analysis primarily focuses on the bit-error-rate (BER) under a Gamma-Gamma atmospheric turbulence channel with varying acceleration factors. Simulation results show that our scheme effectively reduces the degradation in BER caused by ISI. Additionally, we observe an inverse relationship between the BER performance and the atmospheric refractive index constants as well as transmission distance, while a direct proportionality exists with respect to the filter roll-off factor and laser wavelength. Furthermore, comparing with conventional minimum mean square error (MMSE) and zero-forcing (ZF) algorithms highlights the superior performance of our proposal. Full article

Background/Objectives: In the treatment of autism spectrum disorders, families express the need for dedicated clinical spaces to manage emotional overload and to develop effective relational skills. Parent training addresses this need by supporting the parent–child relationship and fostering the child’s development. This study proposes a clinical protocol designed for psychotherapists and behavior analysts, based on the Autism Open Clinical Model (A.-O.C.M.), which integrates the rigor of Applied Behavior Analysis (ABA) with a phenomenological and embodied perspective. The model acknowledges technology—particularly artificial intelligence—as an opportunity to structure adaptive and personalized intervention tools. Methods: A multi-level prompt design system was developed, grounded in the principles of the A.-O.C.M. and integrated with generative AI. The tool employs clinical questions, semantic constraints, and levels of analysis to support the clinician’s reasoning and phenomenologically informed observation of behavior. Results: Recurrent relational patterns emerged in therapist–caregiver dynamics, allowing the identification of structural elements of the intersubjective field that are useful for personalizing interventions. In particular, prompt analysis highlighted how the quality of bodily and emotional attunement influences readiness for change, suggesting that intervention effectiveness increases when the clinician can adapt their style according to emerging phenomenological resonances. Conclusions: The design of clinical prompts rooted in embodied cognition and supported by AI represents a new frontier for psychotherapy that is more attuned to subjectivity. The A.-O.C.M. stands as a theoretical–clinical framework that integrates phenomenology and intelligent systems. Full article

Background: Previous studies have indicated that the gut microbiome and plasma metabolites play key roles in autism spectrum disorder (ASD), but their causal relationships remain unclear. Linkage disequilibrium score regression (LDSC) and Mendelian randomization (MR) are powerful tools for assessing genetic causality. This study uses LDSC and MR to investigate the genetic links between the gut microbiome and ASD and explore the mediating role of plasma metabolites. Methods: To explore the genetic relationships between the gut microbiome, plasma metabolites, and ASD, we obtained summary statistics from large-scale genome-wide association studies (GWAS). Gut microbiome data came from a MiBioGen consortium meta-analysis (N = 18,340), ASD data from the Danish Psychiatric Central Research Register (DPCRR) (N = 18,382), and plasma metabolite data from the Canadian Longitudinal Study of Aging (CLSA) (N = 8299). We applied LDSC and bidirectional MR to analyze the genetic associations between the gut microbiome and ASD and plasma metabolites and ASD. Mediation MR was used to assess the mediating role of plasma metabolites in the gut microbiome-ASD relationship. Results: LDSC analysis revealed significant genetic correlations between the gut microbiota Lachnospiraceae NK4A136 group and Sellimonas with ASD. Moreover, bidirectional MR demonstrated causal effects of five gut microbial genera on ASD risk, as indicated by inverse variance weighted (IVW) methods. Similarly, we identified 49 plasma metabolites that exhibited genetic correlations with ASD, and 58 metabolites had causal effects on ASD in MR analysis. Mediation analysis revealed that specific bacteria, Ruminiclostridium5, reduce the occurrence of ASD through metabolites Delta-CEHC and Docosadioate (C22-DC). Furthermore, Ruminococcaceae UCG005 and Sutterella modulate ASD by inhibiting Serotonin and N-acetyl-L-glutamine, respectively. Conclusions: This study provides evidence of a causal relationship between the gut microbiome and ASD, with plasma metabolites acting as a potential mediator. Our findings offer new insights into the causal mechanisms linking the gut microbiome and ASD and provide a theoretical foundation for microbiome-based therapeutic strategies. Full article

As the Internet of Things (IoT) expands and spectrum resources become increasingly scarce, Automatic Modulation Classification (AMC) has become critical for enabling dynamic spectrum access, interference mitigation, and spectrum monitoring without coordination or prior signaling. Most deep learning-based AMC methods (e.g., CNNs, LSTMs, Transformers) operate in Euclidean spaces and therefore overlook the non-Euclidean relationships inherent in modulated signals. We propose KGNN, a graph-based AMC architecture that couples a KNN-driven graph representation with GraphSAGE convolutions for neighborhood aggregation. In the KNN stage, each feature vector is connected to its nearest neighbors, transforming temporal signals into structured graphs, while GraphSAGE extracts relational information across nodes and edges for classification. On the RML2016.10b dataset, KGNN attains an overall accuracy of 64.72%, outperforming strong baselines (including MCLDNN) while using only one-eighth the number of parameters used by MCLDNN and preserving fast inference. These results highlight the effectiveness of graph convolutional modeling for AMC under practical resource constraints and motivate further exploration of graph-centric designs for robust wireless intelligence. Full article

Metaverses have been hailed as the next arena for a wide spectrum of technovation and business opportunities. This research (∑ N = 714) focuses on the three underexplored areas of virtual commerce in AI-enabled metaverses: blockchain-powered cryptocurrencies, non-fungible tokens (NFTs), and AI-powered virtual influencers. Study 1 reports the mediating effects of (dis)trust in AI-enabled blockchain technologies and the moderating effects of consumers’ technopian perspectives in explaining the relationship between blockchain transparency perception and intention to use cryptocurrencies in AI-powered metaverses. Study 1 also reports the mediating effects of Neo-Luddism perspectives regarding metaverses and the moderating effects of consumers’ social phobia in explaining the relationship between AI-algorithm awareness and behavioral intention to engage with AI-powered virtual influencers in metaverses. Study 2 reports the serial mediating effects of general perception of NFT ownership and psychological ownership of NFTs as well as the moderating effects of the investment value of NFTs in explaining the relationship between acknowledgment of the nature of NFTs and intention to use NFTs in AI-enabled metaverses. Theoretical contributions to the literature on digital materiality and psychological ownership of blockchain/cryptocurrency-powered NFTs as emerging forms of digital consumption objects are discussed. Practical implications for NFT-based branding/entrepreneurship and creative industries in blockchain-enabled metaverses are provided. Full article

Load spectrum enhancement is a pivotal accelerated fatigue testing methodology employed to substantially reduce test duration and associated costs. This technique operates by strategically elevating load amplitudes while ensuring the preservation of the original failure mechanism. In this study, a novel fatigue life prediction model for variable amplitude loading is developed by integrating the theories of Equivalent Initial Flaw Size (EIFS) and the Equivalent Plastic Zone (EPZ). This integrated approach explicitly accounts for both the small crack effect and load interaction effects, which are critical yet often oversimplified aspects of fatigue damage accumulation. The model is subsequently applied to quantitatively establish the relationship between the Load Enhancement Factor (LEF) and the test time or compression ratio. Finally, fatigue tests on typical 2A14 aluminum alloy structures under variable amplitude loading are conducted to validate the proposed model. The results demonstrate a significant life reduction with increasing LEF, achieving a remarkable test time reduction of over 50% at an LEF of 1.2. All experimental data fall within a scatter band of three, relative to the model prediction. Additionally, the predicted mean compression ratio exhibits approximate agreement with the experimental data, with errors within an acceptable range. This work provides a physically grounded and practically validated framework for implementing efficient and reliable load spectrum enhancement. Full article

Background/Objectives: Nitrous oxide (N₂O), commonly known as laughing gas, is increasingly being used recreationally. While neurological risks are recognized, psychiatric effects remain underexplored. This study investigates neuropsychiatric presentations among patients referred to the Emergency Mental Health (EMH) team at Sunshine Hospital, Melbourne, Australia, associated with recreational N₂O use. Methods: We conducted a retrospective observational review of EMH referrals between August 2020 and July 2024. Inclusion criteria were patients with documented recreational N₂O use within the preceding 12 months. Cases were operationally defined as presenting with either predominantly psychiatric features (psychosis or suicidal ideation/self-harm documented by clinician) or predominantly neurological features (ataxia, paresthesia, pyramidal signs, or other focal deficits). Primary outcomes included type and severity of neuropsychiatric presentation, concurrent substance use, and disposition from the Emergency Department. Results: Of 25 identified patients, 23 met inclusion criteria (12 males, 11 females; mean age 29.3 ± 8.3 years). Psychotic symptoms were reported in 11/23 (47.8%, 95% CI 27.3–69.0) and suicidal ideation or self-harm in 8/23 (34.8%, 95% CI 17.2–55.7). Neurological symptoms, including paraesthesia and ataxia, occurred in 5/23 (21.7%, 95% CI 7.5–43.7). Concurrent substance use was documented in 19/23 (82.6%, 95% CI 61.2–95.0), most frequently cannabis, alcohol, and tobacco. Over half of patients (12/23; 52.2%, 95% CI 30.6–73.2) identified as culturally and linguistically diverse (CALD). Conclusions: Among EMH-referred ED patients, recreational N₂O use is associated with a spectrum of neuropsychiatric presentations, including psychosis, suicidality, and neurological symptoms. These findings reflect clinical associations rather than causal relationships and highlight the need for early recognition, targeted assessment, and appropriate follow-up in high-risk patients. Full article