Search Results (242)

The Kuroshio Current’s flow velocity imposes exacting requirements on underwater vehicle propulsive systems. Ecological preservation necessitates low-noise propeller designs to mitigate operational disturbances. As technological evolution advances toward greater intelligence and system integration, intelligent unmanned systems are positioning themselves as a critical frontier in marine innovation. In recent years, the global research community has increased its efforts towards the development of high-maneuverability underwater vehicles. However, propeller design optimization ignores the key balance between acoustic performance and hydrodynamic efficiency, as well as the appropriate speed threshold for blade rotation. In order to solve this problem, the propeller design of the NACA 65A010 airfoil is optimized by using OpenProp v3.3.4 and XFlow 2022 software, aiming at innovating the propulsion system of shallow water agile submersibles. The study presents an integrated design framework combining lattice Boltzmann method (LBM) simulations synergized with fully Lagrangian-LES modeling, implementing rotational speed thresholds to detect cavitation inception, followed by advanced acoustic propagation analysis. Through rigorous comparative assessment of hydrodynamic metrics, we establish an optimization protocol for propeller selection tailored to littoral zone operational demands. Studies have shown that increasing the number of propeller blades can reduce the single-blade load and delay cavitation, but too many blades will aggravate the complexity of the flow field, resulting in reduced efficiency and noise rebound. It is concluded that the propeller with five blades, a diameter of 234 mm, and a speed of 500 RPM exhibits the best performance. Under these conditions, the water efficiency is 69.01%, and the noise is the lowest, which basically realizes the balance between hydrodynamic efficiency and acoustic performance. This paradigm-shifting research carries substantial implications for next-generation marine vehicles, particularly in optimizing operational stealth and energy efficiency through intelligent propulsion architecture. Full article

The human voice is an invaluable tool for communication, carrying information about a speaker’s emotional state and cognitive health. Recent research highlights the potential of acoustic biomarkers to detect early signs of mental health and neurodegenerative conditions. Despite their promise, vocal biomarkers remain underutilized in clinical settings, with limited standardized protocols for assessment. This Perspective article argues for the integration of acoustic biomarkers into digital health solutions to improve the detection and monitoring of cognitive impairment and emotional disturbances. Advances in speech analysis and machine learning have demonstrated the feasibility of using voice features such as pitch, jitter, shimmer, and speech rate to assess these conditions. Moreover, we propose that singing, particularly simple melodic structures, could be an effective and accessible means of gathering vocal biomarkers, offering additional insights into cognitive and emotional states. Given its potential to engage multiple neural networks, singing could function as an assessment tool and an intervention strategy for individuals with cognitive decline. We highlight the necessity of further research to establish robust, reproducible methodologies for analyzing vocal biomarkers and standardizing voice-based diagnostic approaches. By integrating vocal analysis into routine health assessments, clinicians and researchers could significantly advance early detection and personalized interventions for cognitive and emotional disorders. Full article

Background: Snoring is a common symptom within the spectrum of sleep-disordered breathing, often occurring independently or in association with obstructive sleep apnea syndrome (OSAS). Despite its prevalence, treatment strategies remain variable and lack standardization, particularly regarding surgical interventions. This review aims to evaluate and summarize the outcomes of soft palate and pharyngeal surgeries for adult snoring based on recent literature. Methods: A systematic review was conducted using the PubMed database, identifying studies published between 2014 and 2024 that involved adult patients undergoing upper airway surgery for snoring. Inclusion criteria required pre- and postoperative snoring assessment using the Visual Analog Scale (VAS). Studies were categorized by surgical technique (anterior vs. lateral/circumferential), anesthesia type, presence of tonsillectomy, BMI, OSAS severity (based on AHI), and use of Drug-Induced Sleep Endoscopy (DISE). Descriptive analysis was performed on the changes in VAS scores. Results: A total of 43 studies involving 2713 patients were included, with 18 eligible for quantitative analysis (716 patients). Across all patients, mean VAS scores improved from 7.29 to 3.50 (ΔVAS 3.79). Both anterior and lateral/circumferential techniques yielded significant symptom reduction (ΔVAS 4.12 and 3.68, respectively). General anesthesia showed slightly better outcomes than local anesthesia. Notably, tonsillectomy was associated with greater symptom improvement (ΔVAS 5.17 vs. 4.49). Patients with lower BMI and milder OSAS showed higher baseline VAS but similar improvements. Limited objective measures and heterogeneity in surgical protocols were key limitations. Conclusions: Surgical interventions for snoring provide subjective symptom relief regardless of surgical approach or OSAS severity. Tonsillectomy may enhance outcomes. Future efforts should prioritize standardized, objective outcome measures and personalized treatment planning, potentially incorporating DISE and wearable acoustic technologies. Full article

Mapping marine habitats is fundamental for biodiversity conservation and ecosystem-based management in oceanic regions under increasing anthropogenic and climatic pressures. In the context of global initiatives—such as marine protected area expansion and international agreements—habitat mapping has become mandatory for regional and global conservation policies. It provides spatial data to delineate essential habitats, support connectivity analyses, and assess pressures, enabling ecosystem-based marine spatial planning aligned with EU directives (2008/56/EC; 2014/89/EU). Beyond biodiversity, macrophytes, rhodolith beds, and coral reefs deliver key ecosystem services—carbon sequestration, coastal protection, nursery functions, and fisheries support—essential to local socioeconomies. This systematic review (PRISMA guidelines) examined 69 peer-reviewed studies across Central-Eastern Atlantic archipelagos (Macaronesia: the Azores, Madeira, the Canaries, and Cabo Verde) and the Mid-Atlantic Ridge. We identified knowledge gaps, methodological trends, and key challenges, emphasizing the integration of cartographic, ecological, and technological approaches. Although methodologies diversified over time, the lack of survey standardization, limited ground truthing, and heterogeneous datasets constrained the production of high-resolution bionomic maps. Regional disparities persist in technology access and habitat coverage. The Azores showed the highest species richness (393), dominated by acoustic mapping in corals. Madeira was most advanced in the remote mapping of rhodoliths; the Canaries focused on shallow macrophytes with direct mapping; and Cabo Verde remains underrepresented. Harmonized protocols and regional cooperation are needed to improve data interoperability and predictive modeling. Full article

Thermoacoustic devices, such as refrigerators and heat pumps, present unique measurement challenges due to the simultaneous presence of rapidly fluctuating acoustic parameters and more stable thermal variables. Accurate and informative measurements during operation are crucial for developing effective control algorithms and optimizing performance under specific conditions. However, issues like inappropriate sampling frequencies and excessive data storage can lead to unintended averaging, compromising measurement quality. This study introduces a comprehensive experimental procedure aimed at enhancing the reliability of measurements in thermoacoustic systems. The approach encompasses meticulous experimental design, identification of measurement uncertainties and influencing factors during standard operation, and a statistical uncertainty analysis. Experimental findings reveal a significant reduction in temperature measurement uncertainty with increased thermoacoustic channel length and highlight the substantial impact of device structural features on performance. These insights are instrumental for refining measurement protocols and advancing the development of efficient thermoacoustic technologies. Full article

The risk of developing symptomatic knee osteoarthritis (KOA) during a lifetime, i.e., pain, aching, or stiffness in a joint associated with radiographic KOA, was estimated in 2008 to be around 40% in men and 47% in women. The clinical and scientific communities lack an efficient diagnostic method to effectively monitor, evaluate, and predict the evolution of KOA before and during the therapeutic protocol. In this review, we summarize the main methods that are used or seem promising for the diagnosis of osteoarthritis, with a specific focus on non- or low-invasive methods. As standard diagnostic tools, arthroscopy, magnetic resonance imaging (MRI), and X-ray radiography provide spatial and direct visualization of the joint. However, discrepancies between findings and patient feelings often occur, indicating a lack of correlation between current imaging methods and clinical symptoms. Alternative strategies are in development, including the analysis of biochemical markers or acoustic emission recordings. These methods have undergone deep development and propose, with non- or minimally invasive procedures, to obtain data on tissue condition. However, they present some drawbacks, such as possible interference or the lack of direct visualization of the tissue. Other original methods show strong potential in the field of KOA monitoring, such as electrical bioimpedance or near-infrared spectrometry. These methods could permit us to obtain cheap, portable, and non-invasive data on joint tissue health, while they still need strong implementation to be validated. Also, the use of Artificial Intelligence (AI) in the diagnosis seems essential to effectively develop and validate predictive models for KOA evolution, provided that a large and robust database is available. This would offer a powerful tool for researchers and clinicians to improve therapeutic strategies while permitting an anticipated adaptation of the clinical protocols, moving toward reliable and personalized medicine. Full article

This paper presents a novel framework addressing the fundamental challenge of concurrent real-time audio acquisition and motor control in resource-constrained mobile robotics. The ESP32-based system integrates a digital MEMS microphone with rover mobility through a unified Bluetooth protocol. Key innovations include (1) a dual-thread architecture enabling non-blocking concurrent operation, (2) an adaptive eight-bit compression algorithm optimizing bandwidth while preserving audio quality, and (3) a mathematical model for real-time resource allocation. A comprehensive empirical evaluation demonstrates consistent control latency below 150 ms with 90–95% audio packet delivery rates across varied environments. The framework enables mobile acoustic sensing applications while maintaining responsive motor control, validated through comprehensive testing in 40–85 dB acoustic environments at distances up to 10 m. A performance analysis demonstrates the feasibility of high-fidelity mobile acoustic sensing on embedded platforms, opening new possibilities for environmental monitoring, surveillance, and autonomous acoustic exploration systems. Full article

Artificial intelligence and bioacoustics represent a paradigm shift in non-invasive poultry welfare monitoring through advanced vocalization analysis. This comprehensive systematic review critically examines the transformative evolution from traditional acoustic feature extraction—including Mel-Frequency Cepstral Coefficients (MFCCs), spectral entropy, and spectrograms—to cutting-edge deep learning architectures encompassing Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, attention mechanisms, and groundbreaking self-supervised models such as wav2vec2 and Whisper. The investigation reveals compelling evidence for edge computing deployment via TinyML frameworks, addressing critical scalability challenges in commercial poultry environments characterized by acoustic complexity and computational constraints. Advanced applications spanning emotion recognition, disease detection, and behavioral phenotyping demonstrate unprecedented potential for real-time welfare assessment. Through rigorous bibliometric co-occurrence mapping and thematic clustering analysis, this review exposes persistent methodological bottlenecks: dataset standardization deficits, evaluation protocol inconsistencies, and algorithmic interpretability limitations. Critical knowledge gaps emerge in cross-species domain generalization and contextual acoustic adaptation, demanding urgent research prioritization. The findings underscore explainable AI integration as essential for establishing stakeholder trust and regulatory compliance in automated welfare monitoring systems. This synthesis positions acoustic AI as a cornerstone technology enabling ethical, transparent, and scientifically robust precision livestock farming, bridging computational innovation with biological relevance for sustainable poultry production systems. Future research directions emphasize multi-modal sensor integration, standardized evaluation frameworks, and domain-adaptive models capable of generalizing across diverse poultry breeds, housing conditions, and environmental contexts while maintaining interpretability for practical farm deployment. Full article

Background: Urethral stricture disease involves fibrotic scarring that narrows the urethral lumen and can occur at any site. Sonourethrography (SUG) is increasingly used because it depicts both luminal anatomy and periurethral fibrosis, yet little is known about patient or lesion features that influence its diagnostic performance. Methods: We conducted a prospective single-center study of 170 men who underwent SUG before anterior urethroplasty between May 2016 and May 2021. Anthropometric data, comorbidities, and detailed ultrasonographic measurements were recorded and compared with intra-operative findings, which served as the reference standard. Accuracy was analyzed with Wald chi-square testing and Spearman correlation. Results: SUG length estimates matched intra-operative measurements in 139/170 strictures (81.8%). Length accuracy was higher in patients ≥ 60 years (89.2% vs. 77.0%, p = 0.03) and in those with type 2 diabetes (92.3% vs. 80.9%, p = 0.02) in conditions associated with pronounced spongiofibrosis that enhances echo contrast. Among stricture-specific factors, proximal location (63.6% vs. 84.5%, p = 0.01) and complete luminal occlusion (68.8% vs. 84.8%, p = 0.02) reduced precision, largely because deeper anatomy and absent saline flow hinder acoustic delineation. The Chiou ultrasonographic grade was the strongest determinant of performance; higher grades yielded clearer margins and better length estimation (p < 0.001). Conclusions: SUG is a reliable bedside technique for assessing anterior urethral strictures, but its accuracy varies with age, diabetes status, stricture site, degree of occlusion, and fibrosis grade. Recognizing these determinants allows clinicians to judge when SUG alone is sufficient and when complementary imaging or heightened caution is warranted. The findings support tailored imaging protocols and underscore the need for multi-center studies that include operators with diverse experience to confirm generalisability. Full article

Transcranial pulse stimulation (TPS) is a non-invasive neuromodulation method that uses, high-intensity acoustic shockwaves to deliver focused mechanical stimulation to neural tissue with minimal thermal effects. The mechanism of action includes but is not limited to promotion of blood flow and angiogenesis through mechanotransduction. Clinical data to date are limited and preliminary. In Alzheimer’s disease (AD), TPS has demonstrated cognitive and mood improvements in pilot studies and secondary endpoint analysis in first randomized trials. The enhancement of gamma-band oscillations and network connectivity has been reported. Clinical observations in Parkinson’s disease (PD) suggest TPS as a hypothesis-generating approach to address non-motor symptoms—such as depression, cognitive decline, and the freezing of gait—through theoretical modulation of basal ganglia–cortical circuits. TPS is CE-marked in Europe for AD and shows a favorable safety profile; however, ethical considerations arise from the limited evidence base, potential impairment of patient autonomy and judgment in dementia, and the risk of withholding established treatments. TPS should only be offered under structured scientific protocols or within patient registries to ensure rigorous oversight. Ensuring that consent processes account for cognitive capacity, and that TPS is applied as adjunct rather than replacement therapy, is paramount. Future research must include large-scale randomized controlled trials (RCTs), standardize stimulation protocols, deepen mechanistic insight, and embed robust ethical frameworks. Full article

Unmanned Underwater Vehicles (UUVs) play an irreplaceable role in marine exploration, environmental monitoring, and national defense. The UUV depends on underwater acoustic communication (UAC) technology to enable reliable data transmission and support efficient collaboration. As the complexity of UUV missions has increased, secure UAC has become a critical element in ensuring successful mission execution. However, underwater channels are inherently characterized by high error rates, limited bandwidth, and signal interference. These problems severely limit the efficacy of traditional security methods and expose UUVs to the risk of data theft and signaling attacks. Cryptography-based security methods are important means to protect data, effectively balancing security requirements and resource constraints. They provide technical support for UUVs to build secure communication. This paper systematically reviews key advances in cryptography-based secure UAC technologies, focusing on three main areas: (1) efficient authentication protocols, (2) lightweight cryptographic algorithms, and (3) fast cryptographic synchronization algorithms. By comparing the performance boundaries and application scenarios of various technologies, we discuss the current challenges and critical issues in underwater secure communication. Finally, we explore future research directions, aiming to provide theoretical references and technical insights for the further development of secure UAC technologies for UUVs. Full article

Ultrasound technology has emerged as a transformative tool in modern food science, offering non-destructive, real-time assessment and enhancement of food quality attributes. This review systematically explores the fundamental mechanisms by which ultrasound interacts with food matrices, including mechanical effects such as acoustic cavitation, localized shear forces, and microstreaming, as well as thermal and acoustic attenuation phenomena. Applications of ultrasound in food texture evaluation are discussed across multiple sectors, with particular emphasis on its role in assessing moisture distribution, fat content, structural integrity, and microstructural alterations in meat, dairy, fruits, and vegetables. The versatility of ultrasound—spanning low-intensity quality assessments to high-intensity processing interventions—makes it an invaluable technology for both quality control and product innovation. Moreover, emerging innovations such as ultrasound-assisted extraction, non-thermal pasteurization, and real-time quality monitoring are highlighted, demonstrating the synergy between ultrasound and advanced technologies like AI-driven data interpretation and portable, handheld sensing devices. Despite these advances, challenges related to technical limitations in heterogeneous food systems, high initial investment costs, scalability, and the absence of standardized protocols remain critical barriers to widespread adoption. The future directions emphasize the integration of ultrasound with multi-modal approaches, the development of miniaturized and cost-effective equipment, and the establishment of global regulatory standards to facilitate its broader application. Overall, ultrasound is positioned as a key enabler for sustainable, efficient, and non-invasive quality assurance across the global food industry. Full article

A single intratympanic application of the small-molecule drug AC102 was previously shown to promote significant recovery of hearing thresholds in a noise-induced hearing loss model in guinea pigs. Here, we report the effects of AC102 to revert synaptopathy of inner hair cells (IHCs) and behavioral signs of tinnitus in Mongolian gerbils following mild noise trauma. This experimental protocol led to minor hearing threshold shifts with no loss of auditory hair cells (HCs) but induced synaptopathy and a sustained and significant tinnitus percept. Treatment by intratympanic application of AC102 was evaluated in two protocols: 1. three weekly injections or 2. a single application. We evaluated hearing threshold changes using the auditory brainstem response (ABR) and the development of a tinnitus percept using the gap prepulse inhibition of acoustic startle (GPIAS) behavioral response. The number of IHC ribbon synapses along the cochlear frequency map were counted by immunostaining for the synaptic ribbon protein carboxy-terminal binding protein 2 (CTBP2). AC102 strongly and significantly reduced behavioral signs of tinnitus, as reflected by altered GPIAS. Noise-induced loss of IHC ribbon synapses was significantly reduced by AC102 compared to vehicle-treated ears. These results demonstrate that a single application of AC102 restores ribbon synapses following mild noise trauma thereby promoting recovery from tinnitus-related behavioral responses in vivo. Full article

Modeling complex, non-stationary dynamics remains challenging for deterministic neural networks. We present the Chaos-Integrated Synaptic-Memory Network (CISMN), which embeds controlled chaos across four modules—Chaotic Memory Cells, Chaotic Plasticity Layers, Chaotic Synapse Layers, and a Chaotic Attention Mechanism—supplemented by a logistic-map learning-rate schedule. Rigorous stability analyses (Lyapunov exponents, boundedness proofs) and gradient-preservation guarantees underpin our design. In experiments, CISMN-1 on a synthetic acoustical regression dataset (541 samples, 22 features) achieved R² = 0.791 and RMSE = 0.059, outpacing physics-informed and attention-augmented baselines. CISMN-4 on the PMLB sonar benchmark (208 samples, 60 bands) attained R² = 0.424 and RMSE = 0.380, surpassing LSTM, memristive, and reservoir models. Across seven standard regression tasks with 5-fold cross-validation, CISMN led on diabetes (R² = 0.483 ± 0.073) and excelled in high-dimensional, low-sample regimes. Ablations reveal a scalability–efficiency trade-off: lightweight variants train in <10 s with >95% peak accuracy, while deeper configurations yield marginal gains. CISMN sustains gradient norms (~2300) versus LSTM collapse (<3), and fixed-seed protocols ensure <1.2% MAE variation. Interpretability remains challenging (feature-attribution entropy ≈ 2.58 bits), motivating future hybrid explanation methods. CISMN recasts chaos as a computational asset for robust, generalizable modeling across scientific, financial, and engineering domains. Full article

This work presents an innovative bioimpedance spectroscopy device, developed as a support tool for decision-making during the evaluation of kidney viability for renal transplantation. Given the increasing demand for organs and the need to optimize donation criteria, the precise and objective assessment of renal graft functionality has become crucial. The device, based on a modular design and adapted to the surgical environment, uses a novel Cole model with a frequency-dependent membrane capacitance, which improves measurement accuracy and repeatability compared to conventional models. Adapting the device for operating room usege involved overcoming significant challenges, such as the need for sterilization and a visual, tactile and acoustic user interface that facilitates device usability. Optimizing the sensing stage has minimized the influence of measurement artifacts, which is crucial for obtaining accurate and representative measurements of renal tissue bioelectrical properties. In addition, a rigorous electrode sterilization protocol was designed, ensuring asepsis during the procedure. The results of tests on porcine renal models demonstrated the device’s ability to monitor pathophysiological changes associated with renal ischemia, with a notable improvement against measurement repeatability. Full article