Search Results (4,467)

To address limitations such as cleaning difficulties or secondary contamination/damage of cultural relics caused by the uncontrollable diffusion of water/cleaning agent/dirty liquids during the cleaning process in traditional cleaning methods, this study, using cotton textiles as an example, systematically investigated the cleaning efficacy of four in situ methods (blank gel, cleaning gel, ultrasonic emulsification, and gel + ultrasonic emulsification synergistic cleaning) on eight types of stains, including sand, clay, rust, blood, ink, oil, and mixed solid/liquid stains. Building upon this, this study proposed an efficient, targeted, in situ, and controllable cleaning strategy tailored for fragile, stained textile relics. Results demonstrated that, regardless of the stain type, the synergistic cleaning method of G+U (gel poultice + ultrasonic emulsification) consistently outperformed the cleaning methods of blank gel poultice, cleaning gel poultice, and ultrasonic emulsification. Furthermore, the gel loaded with cleaning agents was always more effective than the blank gel (unloaded cleaning agents). The poultice methods of blank gel and cleaning gel were better suited for solid stains, while the ultrasonic emulsification cleaning method was more effective for liquid stains. Meanwhile, it was also found that the optimal cleaning method proposed in this study (the G+U synergistic cleaning method) was a cleaning method that restricted the cleaning agent within the gel network/emulsion system, and utilized the porous network physical structure of gel, the chemical action of emulsion’s wetting/dissolving dirt, and the cavitation synergistic effect of ultrasound to achieve the targeted removal of contaminants from relics’ surfaces. Crucially, the cleaning process of G+U also had the characteristics of controlling the cleaning area at the designated position and effectively regulating the diffusion rate of the cleaning solution within the treatment zone, as well as the reaction intensity. Therefore, the proposed optimal (the synergistic cleaning method of G+U) cleaning method conforms to the significant implementation of the “minimal intervention and maximal preservation” principle in modern cultural heritage conservation. Consequently, the synergistic cleaning method of G+U holds promise for practical application in artifact cleaning work. Full article

The adoption of artificial intelligence (AI) in healthcare requires not only accurate predictions but also a clear understanding of its reliability. In safety-critical domains such as medical imaging and diagnosis, clinicians must assess the confidence in model outputs to ensure safe decision making. Uncertainty quantification (UQ) addresses this need by providing confidence estimates and identifying situations in which models may fail. Such uncertainty estimates enable risk-aware deployment, improve model robustness, and ultimately strengthen clinical trust. Although prior studies have surveyed UQ in centralized learning, a systematic review in the federated learning (FL) context is still lacking. As a privacy-preserving collaborative paradigm, FL enables institutions to jointly train models without sharing raw patient data. However, compared with centralized learning, FL introduces more complex sources of uncertainty. In addition to data uncertainty caused by noisy inputs and model uncertainty from distributed optimization, there also exists distributional uncertainty arising from client heterogeneity and personalized uncertainty associated with site-specific biases. These intertwined uncertainties complicate model reliability and highlight the urgent need for UQ strategies tailored to federated settings. This survey reviews UQ in medical FL. We categorize uncertainties unique to FL and compare them with those in centralized learning. We examine the sources of uncertainty, existing FL architectures, UQ methods, and their integration with privacy-preserving techniques, and we analyze their advantages, limitations, and trade-offs. Finally, we highlight key challenges—scalable UQ under non-IID conditions, federated OOD detection, and clinical validation—and outline future opportunities such as hybrid UQ strategies and personalization. By combining methodological advances in UQ with application perspectives, this survey provides a structured overview to inform the development of more reliable and privacy-preserving FL systems in healthcare. Full article

Tracheal intubation is a frequent and high-risk procedure in the intensive care unit (ICU). Unlike elective intubation in the operating room, ICU intubation is often performed under emergent conditions in physiologically unstable patients, leading to increased technical difficulty and higher complication rates. Among these, hypoxemia is particularly frequent and represents a major determinant of morbidity and mortality. Optimizing preoxygenation is therefore a cornerstone of safe airway management in critically ill patients. The aim of this review is to explore the advantages and limitations of each preoxygenation strategy and to provide clinicians with clear, practical guidance to optimize airway management in the ICU. Preoxygenation aims to increase oxygen reserves in order to prolong the duration of safe apnea. Conventional methods include high-flow oxygen delivery through a tightly fitted face mask, though efficacy depends on minimizing leaks. More advanced strategies include non-invasive ventilation (NIV), which improves both alveolar oxygen fraction and lung volume, and high-flow nasal cannula (HFNC), which additionally allows apneic oxygenation during intubation. Randomized controlled trials, including the recent PREOXY study, demonstrate the superiority of NIV over facemask preoxygenation in reducing peri-intubation desaturation, particularly in hypoxemic patients. HFNC is valuable when NIV is contraindicated, while combined approaches (NIV plus HFNC) may further enhance efficacy. Beyond technique, structured protocols and team organization are crucial to reduce complications. In conclusion, preoxygenation is an essential, patient-specific intervention that mitigates the risks of ICU intubation. Familiarity with available methods enables clinicians to tailor strategies, optimize oxygenation, and improve patient safety during this high-risk procedure. Full article

Ohmic heating (OH) emerged as an alternative processing method for food preservation and has more recently been used to modify the functional properties of proteins. This study aimed to evaluate the effects of OH on the interfacial properties of whey proteins (WPC) and its interactions with hydrophobic and hydrophilic compounds. WPC solutions (8% w/w) were subjected to electric field intensities ranging from 0 to 50 V·cm⁻¹ until reaching 80 °C. Structural and physicochemical parameters, including free sulfhydryl content, zeta potential, surface hydrophobicity, intrinsic fluorescence, and solubility, were analyzed. Protein–ligand interactions were also evaluated using β-carotene and caffeic acid as model compounds. The results indicated that moderate electric field intensities (30 V·cm⁻¹) promoted increased surface hydrophobicity and intrinsic fluorescence, suggesting protein unfolding and exposure of hydrophobic regions. Higher electric field intensities (40–50 V·cm⁻¹) led to aggregation, reducing solubility and binding affinity to β-carotene. Conversely, OH processing increased the interaction of WPC with caffeic acid due to enhanced exposure of hydrophilic binding sites. These findings provide insights into the modulation of whey protein interfacial properties through OH and highlight its potential for tailoring protein functionality in food formulations. Full article

Network slicing (NS) plays a vital role in enabling flexible and efficient resource allocation, tailored to diverse use cases and network domains. This survey paper explores the synergy between NS and Artificial Intelligence (AI), emphasizing how Machine Learning (ML) techniques can address challenges across the slice life-cycle. A key contribution of this work is an in-depth analysis of AI and primarily ML applications in each phase of the slice life-cycle, delving into their specific tasks and discussing the techniques applied to these tasks. Furthermore, we present a taxonomy based on different slicing criteria, offering a structured perspective to enhance understanding and implementation. Full article

Background and Objectives: Accurate assessment of Dental fear and anxiety (DFA) is crucial for effective management, yet the vast array of measurement tools presents a dilemma for clinicians and researchers in selecting appropriate methods. This literature review aims to provide a comprehensive review and comparison of the commonly used DFA measurement tools in the pediatric dentistry literature. Methods: A Comprehensive literature search was conducted in December 2024 using the electronic databases PubMed, MEDLINE, ScienceDirect, and Google Scholar. The search was limited to studies focused on identifying research relevant to DFA in children. For each DFA tool, information on its structure, validity, reliability, strengths, limitations, and target population was recruited and tabulated. A comparison between the DFA tools was then conducted. Quality assessment was performed using Patient-Reported Outcome Measures (PRO). Results: The search identified 15 subjective and 5 objective tools. Subjective tools included self-reported scales and pictorial analogs, while objective measures involved physiological monitoring and behavioral observation. While subjective tools offer valuable insights into a child’s self-perceived anxiety, their applicability is influenced by age and cognitive development. Objective measures provide quantifiable data, but require specialized equipment and trained observers. Tools combining simplicity, visual aids, and robust validation were found to be most practical for clinical use. Conclusions: A wide range of valid and reliable tools are available to assess DFA in children. Selection should be tailored to the child’s age, cognitive abilities, clinical setting, and clinician training. Combining subjective and objective assessments may enhance diagnostic accuracy. Full article

The hospitality industry’s rapid adoption of AI service robots has revealed significant variability in consumer acceptance, highlighting the need for personality-based implementation strategies rather than one-size-fits-all approaches. This study extended the AIDUA (Artificial Intelligence Device Use Acceptance) model by integrating Big Five personality traits and robot design characteristics to understand AI service robot acceptance among South African hospitality consumers. A convergent mixed-methods design combined structural equation modeling of survey data (n = 301) with natural language processing analysis of qualitative responses to examine personality-acceptance pathways and consumer concern themes. Results demonstrated that neuroticism negatively influenced performance expectancy (β = −0.284, p < 0.001), while openness enhanced hedonic motivation and preference for humanoid robots (β = 0.347, p < 0.001). Privacy concerns partially mediated the neuroticism-rejection relationship, while transparency interventions significantly improved acceptance among high-neuroticism consumers (effect size d = 0.98). Four distinct consumer segments emerged: Tech Innovators (23.1%), Pragmatic Adopters (31.7%), Cautious Sceptics (28.4%), and Social Moderates (16.8%), each requiring tailored robot deployment strategies. The extended AIDUA framework explained 68.4% of variance in acceptance intentions, providing hospitality operators with empirically validated guidelines for matching robot types to guest personality profiles, optimizing guest satisfaction while minimizing resistance through culturally sensitive implementation strategies. Full article

This study presents a transmitarray antenna design operating at a center frequency of 9 GHz, addressing the need for high-gain and broad bandwidth antennas in modern data communication. The proposed design is structured as a quad-layer configuration using FR-4 dielectric substrates. The transmitarray’s phase profile is tailored to deliver a wideband flat response with low Side Lobe Level (SLL) and stable aperture efficiency across the operating band. It achieves a 1-dB gain bandwidth of 12.12% (from 8.56 GHz to 9.67 GHz) and a 3-dB gain bandwidth of 49.43% (from 8.45 GHz to 13.46 GHz) with an aperture efficiency of 21.3%. A prototype of the proposed design with 11 × 11 elements was fabricated and measured, and its measurement results closely aligned with simulation results, validating its performance. The proposed simple design realizes reduced complexity and fabrication costs while expanding operational gain bandwidth, thereby demonstrating substantial promise for next-generation X-band communication systems. Full article

This study focuses on the development, characterization, and numerical simulation of novel composite materials based on natural vegetable fibers for applications in civil engineering. Three different bio-based composites were formulated using Alfa plant fibers, wood waste, and an equal mixture of both (50% Alfa, 50% wood), with polyvinyl acetate (PVAc), a non-polluting polymer matrix, as the binder. The performance of these composites is strongly influenced by the fiber morphology, structural characteristics, and the nature of the matrix. Thus, understanding and optimizing these parameters is crucial for tailoring materials to meet specific design requirements. The experimental approach began with the morphological and structural characterization of the raw and treated fibers, followed by the evaluation of the thermal a properties of the resulting composites. Furthermore, thermal conductivity simulations were performed using COMSOL Multiphysics to validate the experimental results and gain deeper insights into heat transfer behavior within the composites. A comparative analysis with conventional synthetic insulation materials revealed that the developed bio-composites offer competitive thermal performance while being more environmentally sustainable. These findings highlight the potential of Alfa and wood waste fibers as effective, eco-friendly alternatives for thermal insulation in building applications. Full article

You Only Look Once (YOLO) is a convolutional neural network-based object detection algorithm widely used in real-time vision applications. However, its high computational demand leads to significant power consumption and cost when deployed in graphics processing units. Field-programmable gate arrays offer a low-power alternative. However, their efficient implementation requires architecture-level optimization tailored to limited device resources. This study presents an optimized YOLOv2 accelerator for the Zynq-7000 system-on-chip (SoC). The design employs 16-bit integer quantization, a filter reuse structure, an input feature map reuse scheme using a line buffer, and tiling parameter optimization for the convolution and max pooling layers to maximize resource efficiency. In addition, a stall-based control mechanism is introduced to prevent structural hazards in the pipeline. The proposed accelerator was implemented on the Zynq-7000 SoC board, and a system-level evaluation confirmed a negligible accuracy drop of only 0.2% compared with the 32-bit floating-point baseline. Compared with previous YOLO accelerators on the same SoC, the design achieved up to 26% and 15% reductions in flip-flop and digital signal processor usage, respectively. This result demonstrates feasible deployment on XC7Z020 with DSP 57.27% and FF 16.55% utilization. Full article

Smart and sustainable cities are often assessed using indicator-based models. However, most existing systems evaluate cities as a whole, offering limited support for project-level decision-making, particularly in small and medium-sized cities with scarce resources. This study aims to fill this gap by developing a comprehensive indicator framework tailored to the evaluation of smart city projects, designed to guide investment choices and support evidence-based planning. To build this framework, a systematic review of international indicator systems was conducted, compiling and refining over 1200 indicators into a unified taxonomy. The analysis revealed structural imbalances, with environmental and social dimensions prevailing over economic and governance aspects, and confirmed substantial redundancies, with nearly one-third of indicators overlapping. Using project actions as an analytical lens, gaps were detected and 73 evaluation areas defined. From these, anticipated impact indicators were developed and linked to corresponding performance metrics. Beyond consolidating fragmented systems, the framework provides a practical and balanced tool for multidimensional project assessment. An initial empirical pre-validation demonstrated its coverage and usability, reinforcing its potential to support planners and policymakers in comparing investment alternatives. Unlike traditional ranking or maturity models, it directly bridges the gap between abstract smart city strategies and tangible, project-level outcomes. Full article

Background/Objectives: Morphological defects such as limb malformations, cranial asymmetries, loin deviations, jaw misalignments, and navel irregularities are associated with early culling and reduced productivity in beef cattle. In Bos taurus indicus such as Nellore, the genetic basis of these traits remains poorly characterized. This study aimed to investigate the genetic architecture of six morphological defects in Nellore cattle, namely feet and legs malformation, chamfer asymmetry, fallen hump, loin deviation, jaw misalignment, and navel irregularities, via a genome-wide association study (GWAS) approach tailored for binary traits. Methods: Depending on the trait, the number of genotyped animals analyzed ranged from 3369 to 23,206, using 385,079 SNPs (after quality control). Analyses were conducted using a linear mixed model framework adapted for binary outcomes. Results: Significant associations were identified for four traits: feet and legs, chamfer, hump, and loin. No significant markers were detected for jaw or navel defects, likely due to lower sample sizes and trait incidence. Gene annotation revealed 49 candidate genes related to feet and legs, 4 for chamfer, 4 for hump, and 6 for loin. Conclusions: Candidate genes were enriched for biological functions, including bone remodeling, muscle development, lipid metabolism, and epithelial organization. Overlaps with QTL related to conformation, feed intake, reproductive traits, and carcass quality were also observed. These findings provide novel insights into the genetic control of morphological defects in Nellore cattle and may inform breeding strategies aimed at improving structural soundness. Full article

This study reports on the preparation and comprehensive characterisation of new brominated hydrogen-bonded liquid crystalline (HBLC) materials. Two distinct series of supramolecular complexes were prepared by hydrogen-bond formation between 3-bromo-4-pentyloxybenzoic acid as the proton donor and non-fluorinated and fluorinated azopyridines with variable terminal chains as proton acceptors. The successful formation of a hydrogen bond was confirmed by FTIR spectroscopy. The impact of alkyl chain length and fluorination on the mesomorphic properties of the HBLCs was systematically investigated. The molecular self-assembly was thoroughly examined using polarised optical microscopy (POM) and differential scanning calorimetry (DSC), revealing the presence of smectic C (SmC), smectic A (SmA), and nematic (N) phases, with thermal stability being highly dependent on the molecular architecture. Notably, the introduction of fluorine atoms significantly influenced the phase transition temperatures and the overall mesophase range. Using bromine as a lateral substituent induces the formation of SmC phases in these HBLCs, a feature absent in their non-brominated analogues. Further structural insights were obtained through X-ray diffraction (XRD) investigations, confirming the nature of the observed LC phases. Additionally, the photo-responsive characteristics of these HBLCs were explored via UV-Vis spectroscopy, demonstrating their ability to undergo reversible photoisomerisation upon light irradiation. These findings underscore the critical role of precise molecular design in tailoring the properties of HBLCs for potential applications such as optical storage devices. Full article

Shape memory polymer composites (SMPCs) are an intelligent class of materials capable of self-actuation, offering promising applications in diverse stimuli-responsive material systems. This study developed epoxy-based SMPCs reinforced with carbon–aramid fibers at a 15:85 ratio, with their glass transition temperature (T_g) tailored by incorporating 5 wt.% (SMPC-5) and 10 wt.% (SMPC-10) polyethylene glycol (PEG-600). Dynamic mechanical analysis (DMA) confirmed that PEG addition effectively reduced the T_g from 89.79 °C in the neat composite (SMPC-P) to 70.28 °C in SMPC-5 and 59.34 °C in SMPC-10. Incorporating 5 wt.% PEG enhanced storage and loss moduli, whereas excessive plasticization at 10 wt.% reduced stiffness. Infrared spectroscopy analysis revealed shifts and increased intensities in hydroxyl (OH), aliphatic C-H, and carbonyl (C=O) groups, indicating enhanced intermolecular interactions and bond formation. Tensile testing showed that the carbon–aramid filler significantly improved tensile strength and stiffness, with SMPC-10 achieving the highest tensile strength (233.59 MPa) and SMPC-5 the highest Young’s modulus (14.081 GPa). These results highlight the complementary role of carbon–aramid reinforcement and PEG plasticization in tuning thermomechanical behavior, providing baseline insights for designing SMPCs with tailored actuation and reliable structural performance. Full article

The vitality of areas around tourist attractions plays a crucial role in promoting the sustainable development of both tourism and the regional economy. However, there is a lack of comprehensive studies on the methods for mining vitality around attraction perimeters, and existing approaches are often inadequate to meet the evolving needs of contemporary tourism development. To address this gap, we proposed a method for inferring vitality around attractions based on a knowledge graph. Our approach began by analyzing the functional and morphological characteristics of the areas surrounding the attractions, followed by the design of a vitality calculation model for these regions. Next, we developed a knowledge graph structure tailored for vitality reasoning around the attractions and established reasoning rules based on this graph. Finally, we conducted experiments to apply the vitality inference method to the main urban area of Kaifeng City as a case study. The results indicated that our method could effectively reason about vitality around the attractions. Notably, the vitality levels around the attractions in Kaifeng’s main urban area exhibited clear spatial differentiation. Attractions such as the Yang Family’s Tianbo Mansion, the Millennium City Park, and Lord Bao’s Memorial Temple showed higher vitality values, largely due to their advantageous functional integration and synergistic morphological characteristics. Full article