Search Results (19,821)

To cope with the increasing global demand of energy and significant energy wastage caused by the use of different home appliances, smart load monitoring is considered a promising solution to promote proper activation and scheduling of devices and reduce electricity bills. Instead of installing a sensing device on each electric appliance, non-intrusive load monitoring (NILM) enables the monitoring of each individual device using the total power reading of the home smart meter. However, for a high-accuracy load monitoring, efficient artificial intelligence (AI) and deep learning (DL) approaches are needed. To that end, this paper thoroughly reviews traditional AI and DL approaches, as well as emerging AI models proposed for NILM. Unlike existing surveys that are usually limited to a specific approach or a subset of approaches, this review paper presents a comprehensive survey of an ensemble of topics and models, including deep learning, generative AI (GAI), emerging attention-enhanced GAI, and hybrid AI approaches. Another distinctive feature of this work compared to existing surveys is that it also reviews actual cases of NILM system design and implementation, covering a wide range of technical enablers including hardware, software, and AI models. Furthermore, a range of new future research and challenges are discussed, such as the heterogeneity of energy sources, data uncertainty, privacy and safety, cost and complexity reduction, and the need for a standardized comparison. Full article

Staphylococcus aureus is the most common pathogen causing osteomyelitis, a hardly recoverable bone infection that generates significant burden to patients. Osteomyelitis mouse models have long and successfully served to provide phenomenological insights into both pathogenesis and host response. However, direct in situ monitoring of bone microbial pathogenesis and immune response at the cellular level is still conspicuously missing in the published literature. Here, we update a standard pyogenic osteomyelitis in Wistar rat model, in order to investigate bacterial localization and immune response in osteomyelitis of rat tibia upon adding in situ analyses by spectrally resolved Raman spectroscopy. Raman experiments were performed one and five weeks post infections upon increasing the initial dose of bacterial inoculation in rat tibia. Label-free in situ Raman spectroscopy clearly revealed the presence of Staphylococcus aureus through exploiting peculiar signals from characteristic carotenoid staphyloxanthin molecules. Data were collected as a function of both initial bacteria inoculation dose and location along the tibia. Such strong Raman signals, which relate to single and double bonds in the carbon chain backbone of carotenoids, served as efficient bacterial markers even at low levels of infection. We could also detect strong Raman signals from cytochrome c (and its oxidized form) from bone cells in response to infection and inflammatory paths. Although initial inoculation was restricted to a single location close to the medial condyle, bacteria spread along the entire bone down to the medial malleolus, independent of initial infection dose. Raman spectroscopic characterizations comprehensively and quantitatively revealed the metabolic state of bacteria through specific spectroscopic biomarkers linked to the length of staphyloxanthin carbon chain backbone. Moreover, the physiological response of eukaryotic cells could be quantified through monitoring the level of oxidation of mitochondrial cytochrome c, which featured the relative intensity of the 1644 cm⁻¹ signal peculiar to the oxidized molecules with respect to its pyrrole ring-breathing signal at 750 cm⁻¹, according to the previously published literature. In conclusion, we present here a novel Raman spectroscopic approach indexing bacterial concentration and immune response in bone tissue. This new approach enables locating and characterizing in situ bone infections, inflammatory host tissue reactions, and bacterial resistance/adaptation. Full article

Accurate and timely detection of postural control impairments in stroke patients is crucial for effective rehabilitation and fall prevention. Traditional clinical assessments often rely on qualitative observation and handcrafted features, which may fail to capture the nonlinear and uncertain nature of postural deficits. This study addresses these limitations by introducing a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) with Type-2 fuzzy logic activation to robustly classify sensory dysfunction under altered balance conditions. Using an EquiTest-derived dataset of 8316 labeled samples from 700 participants across six standardized sensory manipulation scenarios, the proposed method achieved 97% accuracy, 96% precision, 97% sensitivity, and 96% specificity, outperforming conventional CNN and other baseline classifiers. The approach demonstrated resilience to measurement noise down to 1 dB SNR, confirming its robustness in realistic clinical environments. These results suggest that the proposed system can serve as a practical, non-invasive tool for clinical diagnosis and personalized rehabilitation planning, supporting data-driven decision-making in stroke care. Full article

Autism Spectrum Disorder presents one of the most complex challenges in contemporary neuroscience. This review adopts an unconventional narrative structure, drawing inspiration from song titles by The Beatles to explore the multifaceted biological, developmental, and social dimensions of autism. Spanning historical perspectives to embryonic origins and adult cognition, we examine critical topics including cortical folding, sensory processing, and the contributions of various brain regions such as the cerebellum and brainstem. The role of mirror neurons and other neural systems in shaping social behavior is discussed, alongside insights from animal models that have advanced our understanding of autism’s underlying mechanisms. Ultimately, this manuscript argues that autism is not merely a biomedical challenge, but a broader societal issue intersecting with education, human rights, and identity. Following the long and winding road of scientific discovery, we advocate for a more empathetic, interdisciplinary, and human-centered approach to autism research. Though the path ahead remains uncertain, every step informed by evidence and driven by collaboration brings us closer to deeper understanding, greater inclusion, and more effective support. Full article

Neuromuscular robotic prostheses have emerged as a critical convergence point between biomedical engineering, machine learning, and human–machine interfaces. This work provides a narrative state-of-the-art review regarding recent developments in robotic prosthetic technology, emphasizing sensor integration, actuator architectures, signal acquisition, and algorithmic strategies for intent decoding. Special focus is given to non-invasive biosignal modalities, particularly surface electromyography (sEMG), as well as invasive approaches involving direct neural interfacing. Recent developments in AI-driven signal processing, including deep learning and hybrid models for robust classification and regression of user intent, are also examined. Furthermore, the integration of real-time adaptive control systems with surgical techniques like Targeted Muscle Reinnervation (TMR) is evaluated for its role in enhancing proprioception and functional embodiment. Finally, this review highlights the growing importance of modular, open-source frameworks and additive manufacturing in accelerating prototyping and customization. Progress in this domain will depend on continued interdisciplinary research bridging artificial intelligence, neurophysiology, materials science, and real-time embedded systems to enable the next generation of intelligent prosthetic devices. Full article

Spherical shells exhibit superior strength-to-geometry efficiency, making them ideal for industrial applications such as fluid storage tanks, architectural domes, naval vehicles, nuclear containment systems, and aeronautical and aerospace components. Given their critical role, careful attention to the design parameters and engineering constraints is essential. The present paper investigates the buckling responses of bio-inspired helicoidal laminated composite spherical shells under normal and torsional loading, including the effects of a Winkler elastic medium. The pre-buckling equilibrium equations are derived using linear three-dimensional (3D) elasticity theory and the principle of virtual work, solved via the classical finite element method (FEM). The buckling load is computed using a nonlinear Green strain formulation and a generalized geometric stiffness approach. The shell material employed in this study is a T300/5208 graphite/epoxy carbon fiber-reinforced polymer (CFRP) composite. Multiple helicoidal stacking sequences—linear, Fibonacci, recursive, exponential, and semicircular—are analyzed and benchmarked against traditional unidirectional, cross-ply, and quasi-isotropic layups. Parametric studies assess the effects of the normal/torsional loads, lamination schemes, ply counts, polar angles, shell thickness, elastic support, and boundary constraints on the buckling performance. The results indicate that quasi-isotropic (QI) laminate configurations exhibit superior buckling resistance compared to all the other layup arrangements, whereas unidirectional (UD) and cross-ply (CP) laminates show the least structural efficiency under normal- and torsional-loading conditions, respectively. Furthermore, this study underscores the efficacy of bio-inspired helicoidal stacking sequences in improving the mechanical performance of thin-walled composite spherical shells, exhibiting significant advantages over conventional laminate configurations. These benefits make helicoidal architectures particularly well-suited for weight-critical, high-performance applications in aerospace, marine, and biomedical engineering, where structural efficiency, damage tolerance, and reliability are paramount. Full article

We developed a machine learning model for disease classification based on the International Classification of Diseases, 10th Revision (ICD-10) codes, analyzing male and female groups using seven features. The three most prevalent ICD-10 classes covered over 98% of the data. Features were selected using the least absolute shrinkage and selection operator, ridge, and elastic net, followed by the mean decrease in accuracy and impurity. A random forest classifier with five-fold cross-validation showed improved performance with more features. Using Shapley additive explanations, age, BMI, respiratory rate, and body temperature were identified as key predictors, with gender-specific variations. Integrating gender-specific insights into predictive modeling supports personalized medicine and enhances early diagnosis and healthcare resource allocation. Full article

An improved Mass–Spring Model (iMSM) is developed by adding central springs to the conventional Mass–Spring Models (MSMs) of tubular structures. This improvement is necessary to model fibers that have enough stiffness so that they do not collapse under transverse loading. Such is the case with many pulp fibers used in papermaking. Four different types of pulp fibers (Aspen CTMP, Aspen BCTMP, Birch BCTMP, and Spruce BKP) were simulated in the study. A geometric model and iMSM of a single fiber were developed, in which the topological structure of iMSM is explained in detail. The mass of mass points and the elastic coefficient of different springs in iMSM were calculated using axial tensile and torsional responses. A dynamic simulation of transverse bending of the fiber over a rigid cylinder and subjected to a transverse pressure was used to determine the effective elastic modulus for four different single fibers and compared to experimental values with an average relative error of 8.49%. The dynamic simulations were completed in 1.04–2.64 min for the four different paper fibers representing sufficient speeds to meet the needs of most real application scenarios. The acceptable accuracy and the fast simulation speed with the developed iMSM fiber model demonstrate the feasibility of the methodology in analyzing paper structures as well as similar fiber-based materials. Full article

Printed flexible materials have garnered considerable attention as next-generation materials for bioelectronic applications, particularly hydrogels and elastomers, owing to their intrinsic softness, tissue-like mechanical compliance, and electrical conductivity. In contrast to conventional fabrication approaches, printing technologies enable precise spatial control, design versatility, and seamless integration with complex biological interfaces. This review provides a comprehensive overview of the progress in printable soft conductive materials, with a particular emphasis on the composition, processing, and functional roles of conductive hydrogels and elastomers. This review first introduces traditional fabrication methods for conductive materials and explains the motivation for using printing techniques. We then introduce two major classes of soft conductive materials, hydrogels and elastomers, and describe their applications in both in vitro systems, such as biosensors and soft stimulators, and in vivo settings, including neural interfaces and implantable devices. Finally, we discuss current challenges and propose future directions for advancing printed soft bioelectronics toward clinical translation. Full article

Introduction: Sperm morphology is a fundamental parameter in the evaluation of male infertility, offering critical insights into reproductive health. However, traditional manual assessments under microscopy are limited by operator dependency and subjective interpretation caused by biological variation. To overcome these limitations, there is a need for accurate and fully automated classification systems. Objectives: This study aims to develop a two-stage, fully automated sperm morphology classification framework that can accurately identify a wide spectrum of abnormalities. The framework is designed to reduce subjectivity, minimize misclassification between visually similar categories, and provide more reliable diagnostic support in reproductive healthcare. Methods: A novel two-stage deep learning-based framework is proposed utilizing images from three staining-specific versions of a comprehensive 18-class dataset. In the first stage, sperm images are categorized into two principal groups: (1) head and neck region abnormalities, and (2) normal morphology together with tail-related abnormalities. In the second stage, a customized ensemble model—integrating four distinct deep learning architectures, including DeepMind’s NFNet-F4 and vision transformer (ViT) variants—is employed for detailed abnormality classification. Unlike conventional majority voting, a structured multi-stage voting strategy is introduced to enhance decision reliability. Results: The proposed framework consistently outperforms single-model baselines, achieving accuracies of 69.43%, 71.34%, and 68.41% across the three staining protocols. These results correspond to a statistically significant 4.38% improvement over prior approaches in the literature. Moreover, the two-stage system substantially reduces misclassification among visually similar categories, demonstrating enhanced ability to detect subtle morphological variations. Conclusions: The proposed two-stage, ensemble-based framework provides a robust and accurate solution for automated sperm morphology classification. By combining hierarchical classification with structured decision fusion, the method advances beyond traditional and single-model approaches, offering a reliable and scalable tool for clinical decision-making in male fertility assessment. Full article

Dry Eye Disease (DED) is a complex, multifaceted ocular disease characterized by tear film instability and inflammation. It can sometimes be elusive to identify the type of DED in patients, given the overlapping symptoms with other conditions like allergies and the multitude of stimuli that might trigger DED onset. There is also difficulty due to limitations on the diagnostic testing available to clinicians, as poor reliability and a lack of standardization plague accurate diagnoses. Identified biomarkers can help identify DED pathophysiology and category, and these include molecular biomarkers like matrix metalloproteinase-9 (MMP-9), cytokines, lactotransferrin, and lacritin, as well as functional biomarkers such as tear osmolarity. Diagnostic tools, such as the InflammaDry and I-Pen Tear Osmolarity System, also now allow for point-of-care measurement of select biomarkers, including MMP-9 and osmolarity. Nonetheless, there remains a critical need for additional, reliable, and accurate diagnostic devices to better aid in the diagnosis and management of DED. This review uniquely combines a review on the current understanding of various biomarkers with an overview of the emerging technologies available to healthcare providers, aiding in better-informed diagnosis and treatment of DED. Full article

Background/Objectives: Oral delivery of biologics offers advantages for patient access and adherence compared to injection, but suffers from low bioavailability due to mucosal barriers and drug degradation in the gastrointestinal tract. We previously developed an oral self-pressurized aerosol (OSPRAE) capsule that uses effervescent excipients to generate CO₂ gas, building internal pressure to eject powdered drug at high velocity across intestinal mucosa. Methods: Here, we developed two key design improvements: (i) an enteric covering to protect the capsule delivery orifice in gastric fluids and (ii) reduced humidity content of capsules to extend shelf-life. Results: Enteric-covered capsules prevented drug release in simulated gastric fluid and then enabled rapid release upon transfer to simulated intestinal fluid. Burst pressure for enteric-covered capsules was ~3–4 times higher than non-covered capsules. After storage for up to three days, the capsules’ effervescent excipients pre-reacted, making them unable to achieve high pressure during subsequent use. To address this limitation, we prepared capsules under reduced humidity conditions, which inhibited pre-reaction of effervescent excipients during storage, and a polyurethane coating to improve water uptake into the capsule to drive the effervescence reaction in intestinal fluid. Conclusions: These design improvements enable improved functionality of OSPRAE capsules for continued translation in pre-clinical and future clinical development. Full article

Graphene quantum dots (GQDs) have emerged as promising nanomaterials due to their unique optical properties, high biocompatibility, and tunable surface functionalities. In this work, GQDs were synthesized via a one-pot hydrothermal method and further functionalized using polyethylene glycol (PEG) of various molecular weights and sodium hydroxide to tailor their photoluminescence (PL) behavior and enhance their applicability in thin-film illumination and biological staining. PEG-modified GQDs exhibited a pronounced red-shift and intensified fluorescence response due to aggregation-induced emission, with GQD-PEG (molecular weight: 300,000) achieving up to eight-fold enhancement in PL intensity compared to pristine GQDs. The influence of solvent environments on PL behavior was studied, revealing solvent-dependent shifts and emission intensities. Transmission electron microscopy confirmed the formation of core–shell GQD clusters, while Raman spectroscopy suggested improved structural ordering upon modification. The prepared GQD thin films demonstrated robust fluorescence stability under prolonged water immersion, indicating strong adhesion to glass substrates. Furthermore, the modified GQDs effectively labeled E. coli, Gram-positive, and Gram-negative bacteria, with GQD-PEG and GQD-NaOH displaying red and green emissions, respectively, at optimal concentrations. This study highlights the potential of surface-functionalized GQDs as versatile materials for optoelectronic devices and fluorescence-based bioimaging. Full article

Sodium alginate (SA) has the advantages of good biocompatibility, water absorption, oxygen permeability, non-toxicity, and film-forming properties. SA is compounded with other materials to formulate a spinning solution. Subsequently, electrospinning is employed to fabricate nanofiber membranes. These membranes undergo cross-linking modification or hydrogel composite functionalization, yielding nanofiber composites exhibiting essential properties, including biodegradability, biocompatibility, low immunogenicity, and antimicrobial activity. Consequently, these functionalized composites are widely utilized in tissue engineering, regenerative engineering, biological scaffolds, and drug delivery systems, among other biomedical applications. This work reviews the sources, characteristics, and electrospinning preparation methods of SA, with a focus on the application and research status of SA composite nanofibers in tissue engineering scaffolds, wound dressings, drug delivery, and other fields. It can be concluded that SA electrospun nanofibers have great development potential and application prospects in biomedicine, which could better meet the increasingly complex and diverse needs of tissue or wound healing. At the same time, the future development trend of SA composite nanofibers was prospected in order to provide some theoretical reference for the development of biomedical textiles and to promote its development in the direction of being green, safe, and efficient. Full article

Photoinitiators (PIs) are pivotal in enabling energy-efficient, spatiotemporally controlled photopolymerization for coatings. To address application-specific demands of coatings, diverse systems of Norrish-Type I (e.g., oxime esters, acylphosphine oxides) and Type II (e.g., onium salts, ketones) PIs have been engineered through systematic molecular design strategies. A comprehensive review necessitates highlighting recent achievements in designing PIs by various molecular engineering approaches. The π-conjugation extension, push–pull structures, and auxochrome incorporation boost strong and long-wavelength absorption; unimolecular PI systems with hydrogen-donor modifications improve reactivity and reduce oxygen inhibition; photobleaching via cleavable bonds and blocking conjugation enables colorless coating and deep-penetration curing; polymerizable macromolecular designs enhance migration resistance; organosilicon-functionalized structures optimize monomer compatibility. These strategies bridge molecular innovations with advanced applications in biomedical and deep-cured coatings. Full article