Search Results (2,273)

Background/Objectives: Biomechanical research in surfing provides important insights into performance optimization and injury prevention, but the evidence remains fragmented across multiple domains. Methods: This scoping review aimed to systematically organize the existing literature on surfing biomechanics and evaluate the quality of the included studies. Searches were conducted by two independent reviewers in PubMed, Scopus, and Web of Science in accordance with the PRISMA Extension for Scoping Reviews. Systematic searches were performed up to 31 July 2025 using Boolean operators guided by the PECO framework. Methodological quality was assessed using the Downs and Black Quality Assessment Checklist. Results: Of the 195 records identified, 53 duplicates were removed. Following screening and fulltext review, 26 studies were included. Five studies employed randomized controlled designs, while 21 were non-randomized. Publications ranged from 2010 to 2025, with the majority conducted in Australia (65.4%). A total of 490 healthy surfers (mean age: 22.9 ± 16.1 years) were analyzed, with sample sizes ranging from 6 to 42 participants. Research topics included anthropometry, paddling biomechanics, aerial maneuvers, core and trunk strength and mobility, lower-limb function, frontside bottom turns, and pop-up performance. The studies’ methodological quality score was 11.7 points with substantial inter-reviewer agreement (κ = 0.77). Research on surf biomechanics remains limited in volume and exhibits methodological heterogeneity. Conclusions: Although existing studies provide valuable insights into key performance actions, further high-quality and standardized research on performance phases (e.g., paddling, pop-up, turns, aerials) and with different research designs (e.g., longitudinal, sex inclusive, ecological designs integrating lab and in-water measures) is needed. Full article

Aileron fault diagnosis in fixed-wing unmanned aerial vehicles (UAVs) faces significant challenges due to strong noise, multi-modal coupling, and limited fault samples. This paper presents a hybrid fault diagnosis framework that integrates variational mode decomposition (VMD) with a cross-attention-based feature fusion mechanism. First, residual signals are generated from UAV kinematic models and decomposed into multi-scale intrinsic mode functions (IMFs) using VMD to extract multiscale frequency-localized features. An integrated framework is then constructed, where Transformer encoders capture the global features and bidirectional long short-term memory (BiLSTM) networks extract local temporal dynamics. To effectively combine these complementary features, a cross-attention fusion module is designed to focus on the discriminative time-frequency features. Furthermore, a hybrid pooling strategy integrating max pooling and attention pooling is introduced to enhance classification robustness. Experiments on the AirLab failure and anomaly (ALFA) dataset demonstrate that the proposed method achieves 95.12% accuracy with improved fault separability, outperforming VMD + BiLSTM (87.66%), VMD + Transformer (86.89%), Transformer + BiLSTM (84.83%), Transformer (72.24%), CNN + LSTM (94.05%), and HDMTL (94.86%). Full article

Glass ionomer cements (GICs) are considered functionally biomimetic as they participate in ion-exchange processes that partially resemble the behavior of natural enamel and dentin, chemically bond to dental hard tissues, and release fluoride. While GICs are designed to interact with aqueous oral environments, their exposure to dietary beverages may affect their esthetic stability and water-related behavior within the oral environment. For biomimetic restorative materials to perform successfully in the oral environment, they must maintain not only bioactive properties but also esthetic stability and resistance to water-related degradation during exposure to dietary beverages. This study evaluated beverage-induced color changes, water sorption, and water solubility of six GICs following their immersion in coffee, tea, berry juice, cola, and distilled water (n = 5 per material per solution). Color measurements were recorded at baseline and after 2, 4, 6, and 8 weeks using a spectrophotometer, and color change (ΔE) values were calculated using the CIE L*a*b* system. Specimen mass was measured at baseline, after 8 weeks of immersion and then after 4 weeks of desiccation. Data were analyzed using repeated-measures Analysis of Variance (ANOVA) and Fisher’s least significant difference post hoc tests (α = 0.05). The results showed time, material, and solution significantly affected ΔE (p < 0.001). Tea produced the greatest discoloration overall, followed by coffee. ChemFil exhibited the greatest staining susceptibility, while Fuji II showed the lowest staining susceptibility. Water sorption and solubility were material- and solution-dependent. Clinically relevant discoloration of GICs was found when immersed in common beverages over time, with tea showing the strongest staining effect. These findings indicate that although GICs exhibit biomimetic characteristics through their interaction with tooth structures and aqueous environments, their long-term esthetic stability and resistance to environmental challenges should also be considered when selecting restorative materials for clinically visible areas. Full article

Within the hospital sector, digitalization brings smarter, more resilient and more sustainable systems. Advancements in remote sensing technologies and building information modeling (BIM) are revolutionizing infrastructure design and construction. The aim of the study is to investigate the impact of digitalization on the spatial configuration of hospitals and its effects on operational efficiency and environmental sustainability, combining theoretical insights with an empirical survey of fourteen hospitals in Italy. The methodology adopted consisted of the following steps: (i) the conduct of a literature review; (ii) the analysis of international best practice; (iii) the definition of criteria to support the design of digital hospitals; (iv) the investigation on the Italian context through a survey; (v) data collection and analysis to support the formulation of strategies for smart hospital development. The findings highlight how the adoption of innovative solutions related to clinical and management sector can optimize hospital workflow, enhance management efficiency, and create safer and more functional and sustainable environments. However, the persistence of outdated infrastructures and the need for significant adaptation still represent major barriers: only 28.7% of hospitals have a fully centralized logistics hub, and just 7.1% have implemented a Digital Twin. In conclusion, this research provides a reference framework for designers, healthcare administrators, and policymakers, outlining strategies for the development of smart and sustainable hospitals. Full article

To address the low accuracy of traditional freshness detection/grading and poor adaptability to different shell colors in the duck egg industry, this study developed a non-destructive detection model and an integrated device for duck egg freshness based on machine vision combined with eggshell optical property analysis. A four-sided yolk transmission imaging system was designed, and accurate yolk region segmentation was achieved via grayscale conversion, a weighted improved Otsu algorithm for whole-egg segmentation, histogram equalization enhancement, and K-means clustering in the LAB color space. A relational model between the average four-angle yolk projected area ratio and Haugh Units (HU) freshness grades was constructed, with grading thresholds determined by constrained optimization combined with the Youden index to balance food safety and grading accuracy. Experimental results showed the model achieved an overall freshness grade discrimination accuracy of 91.3%, with a sensitivity of 97.1% and specificity of 98.9% for inedible Grade B (HU < 60) duck eggs and below. An automated testing device was further developed, adopting a roller-rotating motor collaborative mechanism for automatic flipping and imaging, and equipped with a 10 W/5500 K LED cool white light source to solve the problem of poor adaptability to different shell colors. The device achieved an overall discrimination accuracy of 88.5% with a detection time of ≤5 s per egg, and its host computer can real-time output the yolk area ratio, predicted HU value, and freshness level. This study provides a high-precision and low-cost technical solution for the refined grading of the poultry egg industry. Full article

Corporate food museums are increasingly recognised as strategic heritage infrastructures capable of mediating between industrial memory, territorial identity, and contemporary societal challenges. This paper proposes a conceptual shift that repositions corporate food museums from static repositories of brand heritage to Living Labs for sustainable, inclusive, and participatory food innovation. Drawing on the EU-funded GNAM project, the study adopts a qualitative methodology combining the mapping of Italian corporate food museums with an analysis of European Living Labs in the food and agri-food domain. The comparative framework informs the development of a heritage-driven Living Lab model articulated around three interconnected dimensions: cultural heritage valorisation, community engagement, and sustainable food system innovation. The model is empirically grounded through a series of design-driven workshops, technology-transfer activities, and digital engagement initiatives conducted within corporate museums and academic laboratories in Southern Italy. These include co-creation processes involving students, citizens, companies, and researchers; experimentation with food waste valorisation, biodegradable and hybrid materials, and 3D food printing; and the deployment of digital platforms and immersive virtual environments. The paper contributes to heritage studies by advancing a replicable framework in which corporate food museums act as active agents of sustainable transformation, linking cultural heritage, technological experimentation, and community participation. Full article

The precise, real-time delineation of rice lodging areas constitutes a fundamental prerequisite for the adaptive operation of unmanned combine harvesters. However, existing deep learning methods struggle to resolve a critical limitation: achieving an optimal equilibrium between robust regional morphological perception—which is crucial for irregular lodging patterns—and the ultra-low computational overhead demanded by resource-constrained edge terminals. To address this specific constraint, StarNet-RiceSeg is proposed as a lightweight semantic segmentation network explicitly tailored for unmanned harvesters. Initially, the architecture incorporates the minimalist StarNet as its backbone. By leveraging the unique “Star Operation,” it implicitly maps features into a high-dimensional nonlinear space, thereby significantly augmenting feature discriminability while drastically curtailing computational overhead. Furthermore, to mitigate the misdetection issues stemming from the textural similarity between lodged and upright rice, the Rice Spatial Attention (RSA) module was designed. By intensifying feature interaction within the spatial dimension, this module steers the network to focus on the cohesive morphology of lodged regions while effectively suppressing background noise. Experiments conducted on a self-constructed high-resolution rice lodging dataset demonstrate that StarNet-RiceSeg achieves a mIoU of 94.42%, significantly outperforming mainstream models such as U-Net, DeepLabV3+, SegNet and HRNet. Notably, the model maintains a compact footprint with only 8.01 million parameters and a computational load as low as 9.32 GFLOPs. Following optimization with TensorRT, the system achieved a real-time inference speed of 32.51 FPS on the NVIDIA Jetson Xavier NX embedded platform. These results indicate that StarNet-RiceSeg provides a high-precision, low-latency solution for perceiving rice lodging areas in complex field environments, facilitating unmanned precision harvesting. Full article

Liposomes, spherical bilayer lipid-containing vesicles, are promising nanocarriers used for constructing drug delivery systems (DDS). Various strategies can be employed to loosen or break the liposome and release drugs as the tumor cells-targeting DDS made of liposomes reach the targeted sites. One of the most commonly used strategies is to heat the liposomal DDS by letting the gold nanoparticles or other light-absorbing substances that partition in various portions (inner water core, lipid bilayer or outside) of the liposome absorb light irradiation. Then, which portion can lead to the largest liposome structure change due to the same temperature variation? The answer is essential to aid the design of liposomal DDS; thus, wet lab experiments were carried out. However, even though irradiation-absorbing substances in different portions were irradiated for the same time and with the same irradiation intensity, it was impossible to ensure the three portions have the same temperature increase in the experiments. Furthermore, it is impossible to learn the related micromechanism and molecular-level details of the effects of temperature changes on the liposome structure with experimental methods. The molecular dynamics (MD) method is extensively employed by researchers to obtain in-depth molecular-level insights. Most researchers tend to simulate only a planar lipid bilayer structure, but Amărandi et al. demonstrated that such simplification strategy may give wrong simulation results contrary to the experimental results. Though Jämbeck et al. and Zhu et al. established whole spherical liposome systems with a diameter of about a dozen nanometers and simulated the systems with MD simulations, they did not simulate temperature-relevant properties of the liposome. Therefore, currently there is a lack of research on simulating the structure change in a whole spherical liposome due to temperature variations. So, we established the whole spherical structure of the liposome, simulated how it changes with temperatures and obtained molecular-level research results. It is observed that the temperature increase in the lipid bilayer causes the largest increase in lipid strand sway amplitude, the largest changes in lipid positions, the largest decrease in the distribution density of lipids and water around a lipid and the largest decrease in the interactions between lipids and lipids and between lipids and water, leading to the largest change in the liposome structure. We also studied how the degree of lipid tail unsaturation affects liposome structure changes with temperatures. Due to the C3 kinks in the unsaturated lipid tails, the distribution density of unsaturated lipids is not as high as saturate ones, leading to smaller attraction interactions and consequently larger liposome structure change with temperature. The obtained results are useful for the liposomal DDS design for the purpose of improving DDS performances and delivery outcomes. Full article

Rapid urbanization has promoted socioeconomic growth but has exacerbated spatial-structure imbalances. This study investigates 41 prefecture-level cities in the Yangtze River Delta (YRD) from 2010 to 2022. Using nighttime light data, we compute the Comprehensive Nighttime Light Index (CNLI) to track urbanization dynamics and delineate built-up areas. Furthermore, we apply random-forest recursive feature elimination with cross-validation (RF-RFECV) and a Shapley additive explanations (SHAP)-based interpretation framework to quantify the spatiotemporal evolution of urbanization drivers. The results indicate that urbanization in the YRD increased steadily overall during the study period. Shanghai maintained its core leadership, Jiangsu and Zhejiang advanced steadily, and Anhui rapidly caught up driven by regional integration policies. Although regional disparities generally converged, persistent absolute gaps in small and medium-sized cities and inland areas remain a prominent challenge to balanced development. Spatially, urbanization exhibits a gradient differentiation of “higher in the east and lower in the west, and higher along rivers and coasts than inland.” The regional spatial structure gradually shifted from an early “pole-core–belt” pattern to a polycentric and networked urban agglomeration system, with metropolitan areas and economic belts serving as important carriers for promoting spatial balance. Furthermore, built-up areas exhibit a trajectory of “core agglomeration, corridor-oriented expansion, and intensive transition.” The shrinking coverage of the standard deviational ellipse and a slowdown in expansion rates suggest a shift from extensive outward sprawl to more concentrated development. Regarding driving mechanisms, YRD urbanization has evolved from early-stage factor-scale expansion to a later-stage efficiency- and innovation-driven trajectory. While population density remained the dominant driver, early-stage reliance on transport infrastructure and fiscal decentralization was largely replaced by the strengthening effects of per capita output and green innovation. Overall, these findings provide empirical evidence for optimizing spatial patterns and designing differentiated policies for high-quality urbanization in the YRD. Full article

This research presents a machine learning-based vibration signal acquired from aluminum grinding experiment for potential application in smart and intelligent manufacturing. The study addresses the challenges of traditional surface finishing quality inspection by integrating vibration sensing and support vector machine (SVM). A robot manipulator lab grinding experiment consist of a four-axis DOBOT Magician with a handheld cylindrical grinding tool attached on the end-effector of the DOBOT Magician. This customized lab grinding experiment was designed to perform consistent surface finishing experiment for different aluminum work coupon and time duration. Triaxial accelerometer was used to collect the vibration signal and to investigate the most relevant vibration signal direction (x, y, and z) to the surface quality prediction of the aluminum work coupon. The vibration signal was acquired via LabVIEW and NI data acquisition (DAQ) system. The vibration features were extracted and analyzed using Python programming in Google Colab. The SVM algorithm in Python (3.11 and 3.12) is used to classify surface roughness quality into coarse, medium, and fine categories based on the extracted vibration features. Vibration feature parameters such as root mean square (RMS), Peak to RMS, Skewness, and Kurtosis were also investigated to determined which feature pairs are most critical for effective surface roughness monitoring and prediction using SVM classification. The classification model achieved high accuracy across all three vibration axes (x, y, and z), with the z-axis yielding the most consistent results. The proposed system has potential applications in real-time surface quality prediction within smart manufacturing practices aligned with Industry 4.0 principles. Full article

The increasing global demand for sustainable water purification technologies has accelerated research on catalytic degradation and advanced oxidation processes for the removal of refractory pollutants. This study provides a comprehensive bibliometric analysis of global research trends in catalytic water and wastewater treatment from 2010 to 2025, combining quantitative mapping with a qualitative synthesis of emerging technological directions. Bibliographic data were retrieved from the Scopus database and screened using the PRISMA framework, followed by analysis using VOSviewer (v1.6.20) and OriginPro (version 2023, OriginLab Corporation, Northampton, MA, USA) to examine publication growth, citation patterns, international collaboration networks, and thematic evolution. A total of 1550 publications, including 1265 research articles and 285 review papers, were analyzed. The results show a significant increase in research output after 2015, reflecting growing global attention to water sustainability and environmental remediation. China, the United States, and India were identified as the leading contributors, with strong international collaboration networks. Keyword co-occurrence analysis revealed three dominant research themes: photocatalytic degradation and semiconductor engineering, Fenton and Fenton-like advanced oxidation processes, and emerging hybrid catalytic systems involving carbon-based materials and metal–organic frameworks. The analysis also indicates a recent shift toward multifunctional hybrid catalysts designed to improve efficiency, stability, and performance in complex wastewater systems. These findings highlight key scientific developments and suggest future research priorities, including green catalyst synthesis, reactor and process scale-up, AI-assisted catalyst design, and life-cycle sustainability assessment to support the transition from laboratory research to practical water treatment applications. Full article

City–port thresholds are increasingly exposed to multi-risk, including climate change impacts, pollution, and obsolescence of buildings and infrastructure as well as socio-economic marginalization. This paper aims to understand what role co-design—and more generally collaborative planning processes—can play in enabling communities and institutions to learn how to live with risk when managing water, city–port interfaces, and coastal public spaces. To do so, this paper analyses the experience of a co-design workshop held in Castellammare di Stabia, in the Metropolitan Area of Naples, organized within the framework of the research MIRACLE and SPArTaCHus. The results of the workshop show that co-design can act as an effective instrument for developing strategies aimed at the regeneration and valorization of underused, abandoned, or polluted spaces in the coastal thresholds of City–Port areas—wastescapes—that are exposed to multiple risks. In these complex territories new methods are needed to understand, describe and interpret the fuzzy boundaries between the city and the port to collaboratively envision sustainable strategies for urban regeneration of coastal wastescapes. Full article

Lactic acid bacteria (LAB) are pivotal microorganisms in the food industry. Current approaches for functional gene validation and trait improvement in LAB primarily rely on traditional gene editing and homologous recombination techniques. These methods are often cumbersome, inefficient, and time-consuming, hindering the rapid and precise customization of strains. This limitation has, to some extent, constrained the rapid selection and industrial application of functional LAB strains. The engineering of LAB through gene editing technologies has significantly advanced both fundamental and applied research. Among these, CRISPR/Cas gene editing has successfully achieved precise modification of multiple genes in various LAB species. Compared to conventional methods, it offers superior editing efficiency and lower operational costs, opening new avenues for functional gene identification and genetic improvement in LAB. However, the application of exogenous CRISPR/Cas systems in LAB faces technical challenges such as high off-target rates, chromosomal abnormalities, and cytotoxicity. The development of endogenous CRISPR/Cas-based editing tools for LAB provides novel pathways for precise regulation, rational design, and flexible application. This paper first outlines the structural components and mechanistic principles of CRISPR/Cas gene editing tools. It then explores the research progress and applications of both endogenous and exogenous CRISPR/Cas systems in LAB. Finally, it provides an outlook on the future application of CRISPR/Cas gene editing technology in LAB, offering a reference for its implementation in this field. The advent of gene editing technologies has significantly propelled functional gene validation and trait improvement in lactic acid bacteria (LAB), thereby advancing both fundamental research and industrial applications. Notably, the CRISPR/Cas system has emerged as a transformative tool enabling precise genetic modification in diverse LAB species, offering marked improvements in editing efficiency and cost reduction relative to conventional approaches. CRISPR/Cas-based editing strategies in LAB are broadly classified into exogenous and endogenous systems. Exogenous systems operate independently of the host’s native immune repertoire, conferring the advantages of broad strain applicability and high editing efficiency. These systems have been successfully deployed for functional gene characterization, metabolic pathway engineering, such as augmenting antimicrobial production, and probiotic safety enhancement via virulence gene deletion. Conversely, endogenous systems leverage the intrinsic CRISPR/Cas machinery of LAB, offering superior biocompatibility and minimized off-target risks. Notable applications include precise gene knockout and integration using the native Type I-E system in Lacticaseibacillus paracasei. This review provides a concise overview of CRISPR/Cas system architecture and mechanisms, followed by a systematic synthesis of research progress and applications for both exogenous and endogenous systems in LAB. Finally, future directions are outlined to guide the continued development and application of CRISPR/Cas technologies in this field. Full article

Industrial Control Systems (ICSs) are the backbone of modern critical infrastructure, such as electric power, water treatment, oil and gas distribution, and manufacturing operations. While the convergence of IT and OT has greatly increased efficiency and observability, it has also greatly expanded the attack surface of these once-isolated systems. High-profile cyber-physical attacks, including Stuxnet (2010), TRITON (2017), and the Colonial Pipeline ransomware attack (2021), have shown that ICS-targeted cyberattacks can cause physical damage, disrupt economic stability, and put public safety at risk. Despite the growing prevalence and intensity of such threats, ICS-based cybersecurity education remains largely under-resourced and underfunded. Traditional ICS training laboratories require highly specialized hardware, vendor-specific tools, and expensive licensing that significantly raise barriers to entry. Traditional labs typically require on-site participation and pose physical safety concerns when cyber-physical attack scenarios are performed. These barriers leave students unable to get necessary security training for ICSs. Therefore, this paper introduces ${\mathrm{AE}}^3$GIS: Agile Emulated Educational Environment for Guided Industrial Security—a fully virtual, lightweight, open-source platform designed to democratize ICS cybersecurity education. Based on the GNS3 network simulation tool, ${\mathrm{AE}}^3$GIS enables rapid deployment of comprehensive ICS environments containing IT and OT systems, industrial communication protocols, control logic, and diverse security tools. ${\mathrm{AE}}^3$GIS is designed to provide practical training for students using realistic ICS cybersecurity scenarios through a local or remote training platform without the cost, safety, or accessibility limitations of hardware-based labs. Full article

Building extraction is a core task in the semantic segmentation of satellite remote sensing imagery. Conventional pixel-level segmentation methods often prioritize texture over geometric structure, resulting in suboptimal performance in complex scenes affected by illumination variations, shadows, and scale changes. In this article, an innovative object-level building extraction approach is introduced to better capture the geometric structure of buildings, which incorporates superpixel segmentation to represent images as a set of adjacent regions. The proposed model consists of a cascade multi-scale fusion module (CMSFM) that progressively integrates contextual information across different receptive fields, along with a boundary-assisted loss function designed to enhance edge delineation and improve object-level accuracy. The experimental results on the WHU building dataset and the Massachusetts Buildings Dataset show that the proposed method notably outperforms other representative semantic segmentation approaches, such as FCN, UNet, DeepLab V3, and SETR. On the WHU dataset, MRLNet achieves the largest MIoU of 90.14% and the highest F1 score of 92.47%. On the Massachusetts Buildings Dataset, MRLNet attains the best MIoU of 83.14% and the highest F1 score of 90.46%. In addition, our building extraction model achieves a substantial performance improvement after the addition of the CMSFM module and the boundary-assisted loss function, demonstrating the effectiveness of these two enhancements used in our proposed model. It is expected that this research can provide a promising tool for the accurate extraction of buildings using satellite remote sensing images, which is indispensable in urban planning, disaster assessment, and other fields. Full article