Search Results (250)

As public demand for health and well-being continues to rise, rural landscapes are increasingly valued as settings for stress reduction and psycho-physiological restoration. Drawing on five “Beautiful Villages” in Jiangning District, Nanjing (China), this study categorizes rural landscapes into three types—farmland production landscapes, rural settlement landscapes, and rural mountain–water landscapes—based on the proportional dominance of key landscape elements. Audio-visual stimuli were developed from on-site photography and field recordings to construct controlled rural audio-visual environments. Using a combination of physiological indicators and self-reported psychological assessments, we systematically compare restorative responses across modalities (visual, auditory, and audio-visual) and across landscape types, and examine how specific landscape elements relate to restorative outcomes. Results show that (1) auditory stimuli generally produce stronger restorative responses than visual stimuli, and audio-visual interactions are evident; (2) restorative benefits vary significantly across the three rural landscape types; and (3) visually natural and structurally rich elements are associated with greater restoration, while auditory cues can direct visual attention and natural sounds are positively linked to restorative outcomes. These findings advance understanding of multi-sensory restorative processes in rural landscapes and provide evidence for sustainable rural landscape planning and design by supporting healthier, more restorative, and more human-centered rural environments. Full article

Speech sound disorder (SSD) is common in childhood and can persist, adversely affecting language, literacy, and social functioning. Yet consonant error patterns in school-age children, particularly in non-English-speaking populations, remain insufficiently characterized. Short-term memory (STM) supports phonological processing and speech learning, but its relationship with SSD severity in school-age children is not well established. This study profiles consonant errors and short-term memory in school-age Chinese children with SSD and examines short-term memory correlates and predictors of disorder severity to inform targeted interventions. A total of 142 Mandarin-speaking school-age children with SSD were recruited. For the short-term memory analyses, we randomly selected 70 children with SSD and recruited 70 typically developing controls. Speech was assessed using a word-level picture-naming task to derive consonant accuracy and characterize error types/patterns, and short-term memory was measured with the WISC-IV Digit Span (forward and backward). Substitutions predominated for most consonants, and individual phonemes often exhibited co-occurring error patterns. In addition, school-age children with SSD showed significantly poorer short-term memory than typically developing peers across multiple indices. Notably, backward digit span was positively associated with consonant accuracy and remained an independent predictor of consonant accuracy. These results advance our understanding of the mechanisms underlying SSD and provide an evidence-based rationale for future interventions that combine speech-focused therapy with cognitive training to enhance clinical outcomes. Full article

Infant cries serve as a primary indicator of distress and pain; however, distinguishing pain-related cries from those triggered by other needs remains a challenging task, even for trained professionals. Timely and accurate pain assessment is essential for appropriate medical intervention, particularly in preverbal infants who cannot express their needs verbally. Recently, Deep Learning (DL) models have demonstrated significant potential in addressing this challenge by enabling automated and efficient pain assessment through audio signal processing. In this survey, we review methods for pain assessment from infant cry sounds, covering deep learning architectures, modern Transformer-based models, and emerging Vision-Language Model (VLM) pipelines. The review includes approaches that integrate Mel-spectrogram representations of cry audio with multimodal model frameworks to improve robustness, interpretability, and cross-modal reasoning in pain detection. By summarizing recent advancements and identifying limitations and open challenges in current methodologies, this review aims to provide insights into future research directions that may enhance the robustness, generalizability, and clinical applicability of automated infant pain assessment tools. Full article

The evaluation of vibro-acoustic comfort in vehicle cabins is frequently limited by fragmented treatment of noise and vibration indicators and by the absence of structured, reproducible assessment frameworks. This study proposes an advanced, correlation-driven and process-oriented methodology for vibro-acoustic comfort evaluation, designed to support systematic analysis and decision-making across varying vehicle operating conditions. The proposed framework is formulated as a sequential process comprising experimental data acquisition, signal preprocessing, statistical correlation analysis, and decision-oriented interpretation. The framework was experimentally validated on five special-purpose armored platforms under both stationary and dynamic operating regimes, with repeated measurement trials to ensure robustness. Interior and exterior sound pressure levels, together with vibration-related parameters, are experimentally measured under stationary and dynamic operating regimes. Pearson correlation coefficients are employed to quantify interdependencies among vibro-acoustic variables and identify dominant contributors affecting comfort-related conditions. The results indicate statistically significant correlations between interior noise levels and selected vibration indicators, revealing distinct correlation patterns associated with different operating states. Based on these findings, correlation strength was classified as weak (|r| < 0.3), moderate (0.3 ≤ |r| < 0.6), and strong (|r| ≥ 0.6), enabling structured contributor ranking. The primary contribution of this work consists in elevating correlation analysis from a descriptive statistical technique to a formalized assessment process suitable for integration into predictive modeling and optimization workflows. The framework provides a transferable methodological structure, validated within the investigated vehicle category. Full article

Ultrasonic testing is a prevalent method for non-destructive evaluation of railway rails; however, conventional Time-of-Flight (ToF) approaches applied in practical dry-coupled inspections often rely on simplified assumptions regarding wave propagation velocity and neglect complex waveform characteristics. This paper presents a robust depth estimation framework for surface-breaking cracks that enhances sizing accuracy through effective velocity calibration and Hilbert envelope extraction. Unlike standard methods that assume the free-space speed of sound in air (343 m/s) for wave propagation within the air-filled gap of a surface-breaking crack, we propose an effective velocity model derived from in situ calibration to account for the boundary layer viscosity and thermal conduction effects within narrow crack geometries. The signal processing chain incorporates spectral analysis, band-pass filtering, and Hilbert Transform-based envelope detection to mitigate noise and resolve phase ambiguities. Experimental validation on steel specimens with controlled defects (0.2–10.0 mm) demonstrates that the proposed method achieves an exceptional linear correlation (R² ≈ 0.9976). The calibrated effective velocity was determined to be 289.3 m/s, approximately 15.6% lower than the speed of sound in air, confirming the significant influence of confinement effects. Furthermore, excitation parameters were optimized, identifying that high-voltage excitation (≥110 V) and a tuned pulse width (≈150 ns) are critical for maximizing the signal-to-noise ratio. The results confirm that combining physical model calibration with advanced signal analysis significantly reduces systematic errors, paving the way for portable, high-precision rail inspection systems. Full article

Subsurface exploration of Mars is essential for understanding its geological evolution and potential water ice distribution. Subsurface radar sounding is an effective technique for detecting layered structure and physical parameters beneath the Martian surface. However, existing methods often neglect the influence of loss tangent and rely on data-driven approaches without physical constraints, limiting their accuracy in high-lossy environments and reducing their physical interpretability. To overcome these limitations, this paper proposes a dual-branch physics-informed network (DBPINet) for the joint inversion of layer thickness, permittivity, and loss tangent of Martian layered media. This method introduces a dual signal loss tangent branch (DSLT-Branch) to extract frequency-dependent attenuation features from dual-frequency radar signals and incorporates a physics-informed loss function based on the electromagnetic transmission-line model to embed physical laws into the learning process. Multiple numerical and measured experiments demonstrate the effectiveness of DBPINet. Compared with the MLP-based baseline and the more advanced LMPINet, DBPINet achieves significant improvements in different layered subsurface models. Specifically, on the three-layer models, the mean absolute percentage error (MAPE) for layer thickness, permittivity, and loss tangent is reduced by 4.793%, 3.600% and 4.559%, respectively. Meanwhile, DBPINet exhibits enhanced robustness under noisy conditions. When applied to real Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) data acquired over the Medusae Fossae Formation (MFF) region, the inversion results reveal a three-layer subsurface structure (a volcanic ash surface layer, an ice-mixed basaltic middle layer, and a basaltic basement) that is consistent with existing geological interpretations. Full article

In modern buildings, the air-conditioned indoor environment is vital for occupant productivity and well-being, yet fan noise and airflow turbulence can significantly compromise these benefits. Human–environmental interactions are complex processes that traditional energy-based acoustic metrics are often insufficient to model. Therefore, this study aims to advance the multidimensional sound quality assessment framework for building acoustics. Three methods, the conventional regression approach (CRA), general prediction model (GPM), and psychoacoustic machine learning (PML) assessment methods, were evaluated for predicting three perceptual dimensions (Evaluation, Potency, Activity; EPA) and negative noise impacts on occupant well-being (O1: Discomfortable, O2: Annoying, O3: Stressful, and O4: Unacceptable). Based on 432 multidimensional sound quality assessments across four general types of air-conditioned built environments, the PML achieved the best goodness-of-fit for the EPA-score perdition (adjusted R² = 0.61) compared to CRA (0.32) and GPM (0.15) and effectively predicted all negative noise impacts (adjusted R² = 0.53–0.61). The PML assessment method offers a smart and reliable solution for sound quality and well-being prediction through psychoacoustic heatmaps encoding time-varying psychoacoustic features in 227 × 227 pixels from 30 s soundtracks of the built environment for sustainable building design. Full article

As a non-invasive, quantitative, and objective evaluation method, the analysis of bowel sounds has shown significant potential in clinical practice. In recent years, with the continuous advancement of signal processing techniques and analysis methods, research on bowel sounds has made significant progress. In this review, we discuss the main clinical applications of bowel sounds and summarize the commonly used analysis methods for bowel sounds at present. It is necessary to explore more comprehensive and effective signal processing technologies and methods in the future, and also establish a well-organized bowel sound database and scientific classification standards. This will promote better application of bowel sounds in clinical practice. Full article

Real-time lithology identification while drilling is widely applied in oil and gas exploration, development drilling, geo-steering, unconventional resource extraction, well logging, and environmental monitoring, enhancing efficiency and accuracy in subsurface operations. This study investigates the frequency characteristics of rock-drilling sounds generated during drilling operations and explores their potential for real-time lithology identification. Experiments were conducted using 8 mm and 14 mm drill bits at both high and low rotational speeds on four types of rock samples: sandstone, limestone, granite, and shaly sandstone. Sound signals were recorded both within the rock and in air using high-fidelity sensors. The results reveal distinct frequency patterns for each rock type, with sandstone exhibiting dominant low-frequency energy, limestone and granite showing broader frequency bands with strong high-frequency components, and shaly sandstone displaying a mix of low- and high-frequency energy. Quadratic polynomial regression models between the Vp or Vs and the peak frequencies of the four distinct rock samples are built, and the corresponding coefficients of determination are 0.9878 and 0.9799. The study also demonstrates that drilling parameters, such as drill bit diameter and revolutions per minute (RPM), significantly influence the frequency distribution of rock-drilling sounds, with larger drill bits and higher RPMs producing broader frequency bands and stronger high-frequency energy. Comparisons between in-rock and in-air recordings show that the latter captures richer high-frequency information, though the overall trends remain consistent. These findings provide an experimental foundation for using rock-breaking sounds as a potential tool for lithology identification during drilling operations. The study highlights the importance of considering rock heterogeneity and drilling conditions when interpreting acoustic data and suggests future work to validate the method in field conditions and integrate advanced data processing techniques. Full article

Reliable tool-wear monitoring is essential for maintaining machining quality and preventing unscheduled downtime in manufacturing. This investigation presents a sound-based classification framework for identifying wear states in the turning of AISI 316L stainless steel using advanced gradient-boosting models. Acoustic signals were recorded under constant cutting parameters to eliminate process-induced variability, and each recording was divided into standardized 2 s segments. A total of 540 multidomain features—including RMS, ZCR, spectral descriptors, Mel-spectrogram statistics, MFCCs and their derivatives, and discrete wavelet energies—were extracted to capture both stationary and transient characteristics of tool–workpiece interactions. Feature selection was performed using a three-stage pipeline comprising Boruta, LASSO, and SHAP analysis, resulting in a compact subset of highly informative descriptors. LightGBM, XGBoost, and CatBoost classifiers were trained using stratified 10-fold cross-validation across three wear states: Unworn, Slight wear, and Severe wear. LightGBM and XGBoost achieved the best performance, with mean accuracies above 0.96 and strong PRC–AUC and ROC–AUC values (0.98–1.00). Although Slight wear remained the most difficult class due to its transitional acoustic characteristics, all models showed clear separability for Unworn and Severe wear conditions. The results confirm that boosted decision-tree methods combined with SHAP-enhanced feature selection provide an effective, low-cost, and non-contact solution for tool-wear classification in 316L turning. Full article

This study presents a comparative analysis of cylindrical lithium-ion cell architectures, tracing the evolution from the conventional tabbed design (18650/21700) to the large-format 4680 cell with its tabless current collectors. This architectural shift is driven by the imperative to minimise internal ohmic resistance and enhance thermal management in high-power automotive battery applications. Forensic investigation reveals that the 4680 design replaces localised, high-resistance tab connections with a distributed, low-impedance interface, necessitating the adoption of advanced manufacturing techniques, including long ultrasonic torsional welding and highly controlled high-power density laser welding. Crucially, the welding of external aluminium busbars to the cell relies on sophisticated microstructural engineering, particularly for the challenging dissimilar Aluminium-Steel (Al-Steel) anode weld. This weld format employs a spiral laser path to limit the formation of brittle aluminium-iron (Al-Fe) intermetallic compounds (IMCs), leveraging the steel cell casing’s nickel plating to promote a more ductile Al-Fe-Ni phase for improved joint reliability. Furthermore, the 4680 cell incorporates a significantly thicker casing (≈0.54 to 0.7 mm) for enhanced mechanical strength. In conclusion, the 4680 cell achieves superior performance through robust mechanical design and advanced welding processes that prioritise microstructurally sound, low-resistance interfaces. Full article

Plant-parasitic nematodes (PPNs) cause big crop losses globally. Safe/reliable methods for their durable management strategies can harness various beneficial relationships among the plant immune system and related microbiomes. Molecular mechanisms basic to these relations reveal wide arrays of significant roles for plant-healthy growth. This review focuses on such relations of microbiomes to prime and immunize plants against PPNs. It also highlights molecular issues facing PPN-resistant varieties with possible solutions such as genetic breeding/engineering, grafting, PPN-antagonistic root exudates, and novel resistant cultivars. These issues call for optimal uses of various widespread groups of microbiomes. Related plant signaling hormones and transcription factors that regulate gene expression and modulate nematode-responsive genes to ease positive/negative adaptation are presented. Exploring PPN-resistance genes, their activation mechanisms, and signaling networks offers a holistic grasp of plant defense related to biotic/abiotic factors. Such factors relevant to systemic acquired resistance (SAR) via plant–microbe interactions to manage PPNs are stressed. The microbiomes can be added as inoculants and/or steering the indigenous rhizosphere ones. Consequently, SAR is mediated by the accumulation of salicylic acid and the subsequent expression of pathogenesis-related genes. To activate SAR, adequate priming and induction of plant defense against PPNs would rely on closely linked factors. They mainly include the engaged microbiome species/strains, plant genotypes, existing fauna/flora, compatibility with other involved biologicals, and methods/rates of the inoculants. To operationalize improved plant resistance and the microbiome’s usage, novel actionable insights for research and field applications are necessary. Synthesis of adequate screening techniques in plant breeding would better use multiple parameters (molecular and classical ones)-based ratings for PPN-host suitability designation. Sound statistical analyses and interpretation approaches can better identify genotypes with high-level, stable resistance to PPNs than the commonly used ones. Linking molecular mechanisms to consistent field relevance can be progressed via dissemination of many advanced techniques. The CRISPR/Cas9 system has been effective in knocking out both the OsHPP04 gene in rice to confer resistance against Meloidogyne graminicola and the GhiMLO3 gene in cotton to minimize the Rotylenchulus reniformis reproduction. Its genetic modifications in crops synthesized “transgene-free” PPN-resistant plants without decreased growth/yield. Characterizing microbiome species/strains needed to prime and immunize plants requires better molecular tools for fine-scale taxonomic resolution than the common ones used. The former can distinguish closely related ones that exhibit divergent phenotypes for key attributes like stability and production of enzymes and secondary metabolites. As PPN-control strategies via tritrophic interactions are more sensitive to the relevant settings than chemical nematicides, it is suggested herein to test these settings on a case-by-case basis to avoid erratic/contradictory results. Moreover, expanding the use of automated systems to expedite detection/count processes of PPN and related microbes with objectivity/accuracy is discussed. When PPNs and their related microbial distribution patterns were modeled, more aspects of their field distributions were discovered in order to optimize their integrated management. Hence, the feasibility of site-specific microbiome application in PPN–hotspot infections can be evaluated. The main technical challenges and controversies in the field are also addressed herein. Their conceptual revision based on harnessing novel techniques/tools is direly needed for future clear trends. This review also engages raising growers’ awareness to leverage such strategies for enhancing plant resistance and advancing the microbiome role. Microbiomes enjoy wide spectrum efficacy, low fitness cost, and inheritance to next generations in durable agriculture. Full article

The growing need for ecologically sound and ethical protein sources has contributed to the development of meat analogs (MAs) and restructured meat products (RMPs). Next generation MA and RMP production requires sustainable structuring techniques to imitate the physical, chemical, and sensory characteristics of conventional meat. Innovative gelling techniques are essential for attaining optimal texture, chewiness, and structural firmness in MAs and RMPs. Food gels can modulate water and fat retention, as well as the physical and mechanical characteristics of MA and RMP. Different gelling systems such as hydrogels, emulsion gels, oleogels, and hybrid gels contribute to texture formation, water and fat retention, juiciness, and structural integrity, which are essential for mimicking conventional meat. The role of gels as key structuring elements is integrated with advanced processing technologies such as high-moisture extrusion and 3D printing. This review discusses how protein, polysaccharide, lipid, and hybrid gelling techniques facilitate the development of MAs and RMPs with enhanced texture, sensory quality, nutritional value, and sustainability. Advanced structuring techniques, such as high-moisture extrusion, shear cell processing, and 3D printing, are explained regarding their integration of tailored gels (hydrogels, emulsion gels, oleogels, and hybrid gels) to fabricate imitated meat structures. Moreover, this article investigates the sensory and nutritional ramifications of various gelling techniques, spanning their role in juiciness and flavor composition. This review emphasizes significant research deficiencies and suggests more extensive future studies to facilitate the further development of economically viable and sustainable MAs and RMPs. Full article

Microwave-assisted heating (MWH) has established itself as a transformative and energy-efficient paradigm for advanced materials processing. This review provides a comprehensive overview of the advances achieved at the CNR-SCITEC laboratories in Genoa. In this context, a customized microwave platform has been strategically employed for the synthesis, sintering, foaming, and melting of diverse inorganic, organic, and hybrid systems. The spectrum of materials investigated includes superconducting magnesium diboride (MgB₂), hydroxyapatite-based scaffolds, polyethylene components obtained via microwave-assisted rotational molding, cork-based sound-adsorbing composites, recycled expanded polystyrene (rEPS) panels, and polyvinylidene fluoride (PVDF) piezoelectric films. Across the case studies, MWH demonstrated a superior capacity for reducing energy consumption and processing times while maintaining—or even enhancing—the target functional properties. Furthermore, this work evaluates the technological maturity and emerging market opportunities of microwave-based processing, positioning it as a key and sustainable platform for next-generation materials development. Full article

Despite the increasing emphasis on residents’ prosperity aspirations in rural development initiatives, the lack of a psychometrically sound measure limits comparability and rigor, as existing studies primarily focus on structural and policy factors influencing community prosperity, with insufficient attention to residents’ psychological processes and subjective experiences. Drawing on community psychology, this study develops and validates a measure of rural residents’ aspirations for common prosperity, integrating personal fulfillment with collective advancement across material and spiritual domains. Employing a three-phase mixed-methods design, Study 1 used in-depth interviews and grounded theory procedures (N = 28) to develop a theoretical model comprising four dimensions: material–individual, material–collective, spiritual–individual, and spiritual–collective. Study 2 generated a 19-item, four-factor scale via exploratory factor analysis and exploratory graph analysis (N = 581). Study 3 confirmed the scale’s second-order factor structure and psychometric properties with confirmatory factor analysis (N = 659). The Common Prosperity Aspiration Scale (CPAS) demonstrated strong reliability and validity across its four dimensions and the overarching second-order factor. This pioneering study elucidates the psychological structure of common prosperity aspirations and provides a psychometrically reliable measure for rural contexts. It serves as a valuable tool to explore their influence on behaviors and promote sustainable community development. Full article