Search Results (64,616)

This paper introduces a digital adaptive control framework for large-scale multivariable systems, integrating matrix linear Diophantine equations with block pole placement. The main innovation lies in adaptively relocating the full eigenstructure using matrix polynomial representations and a recursive identification algorithm for real-time parameter estimation. The proposed method achieves accurate eigenvalue placement, strong disturbance rejection, and fast regulation under model uncertainty. Its effectiveness is demonstrated through simulations on a large-scale winding process, showing precise tracking, low steady-state error, and robust decoupling. Compared with traditional non-adaptive designs, the approach ensures superior performance against parameter variations and noise, highlighting its potential for high-performance industrial applications. Full article

Craters are among the most prominent and significant geomorphological features on the lunar surface. The complex and variable environment of the lunar surface, which is characterized by diverse textures, lighting conditions, and terrain variations, poses significant challenges to existing crater detection methods. To address these challenges, this study introduces DBYOLO, an innovative deep learning framework designed for lunar crater detection, leveraging a dual-backbone feature fusion network, with two key innovations. The first innovation is a lightweight dual-backbone network that processes Lunar Reconnaissance Orbiter Camera (LROC) CCD images and Digital Terrain Model (DTM) data separately, extracting texture and edge features from CCD images and terrain depth features from DTM data. The second innovation is a feature fusion module with attention mechanisms that is used to dynamically integrate multi-source data, enabling the efficient extraction of complementary information from both CCD images and DTM data, enhancing crater detection performance in complex lunar surface environments. Experimental results demonstrate that DBYOLO, with only 3.6 million parameters, achieves a precision of 77.2%, recall of 70.3%, mAP50 of 79.4%, and mAP50-95 of 50.4%, representing improvements of 3.1%, 1.8%, 3.1%, and 2.6%, respectively, over the baseline model before modifications. This showcases an overall performance enhancement, providing a new solution for lunar crater detection and offering significant support for future lunar exploration efforts. Full article

For decades, regeneration of alveolar bone defects has depended on traditional grafting options, such as autogenous/allogenic grafts or allografts. Recently, extracted teeth was introduced as an alternative graft source. Tooth autografts are being used and have gained significant attention due to their biocompatibility, osteoconductivity, osteoinductivity, and osteogenic properties. Furthermore, tooth allografts have potential to act as natural biocomposites for oral regeneration procedures and might be advantageous options in near future. Recent advances in tooth banking, including cryopreservation, can serve to maintain bioactivity and to improve the safety, viability, and regenerative potential of teeth. They might be revolutionary in oral surgery, offering a more sustainable solution to the growing demand for bone regeneration procedures. Nevertheless, challenges such as immunogenic responses, ethical issues, and regulatory constraints persist. Ongoing research and technological innovation continue to address these problems. To date, the success rates of tooth autografts are promising, and they are regarded as a reliable option in clinical practice, with predictable outcomes in alveolar ridge preservation, sinus augmentation, periodontal regeneration, guided bone regeneration (GBR), and endodontic surgery by providing natural scaffolds for cell integration and bone remodeling. However, the scientific literature on tooth allografts is lacking. Therefore, this review aimed to comprehensively evaluate the scientific literature for comparing the properties of tooth grafts with other grafting options, in terms of processing techniques, and various clinical applications, positioning them as versatile biocomposites for the future, bridging material science and regenerative dentistry. Furthermore, possible applications of allogenic tooth grafts and overcoming current limitations are also discussed. Full article

By greatly increasing automation, accuracy, and flexibility at every step of the additive manufacturing process, from design and production to quality assurance, artificial intelligence (AI) is revolutionizing the 3D printing industry. The integration of AI algorithms into 3D printing systems enables real-time optimization of print parameters, accurate prediction of material behavior, and early defect detection using computer vision and sensor data. Machine learning (ML) techniques further streamline the design-to-production pipeline by generating complex geometries, automating slicing processes, and enabling adaptive, self-correcting control during printing—functions that align directly with the principles of Industry 4.0/5.0, where cyber-physical integration, autonomous decision-making, and human–machine collaboration drive intelligent manufacturing systems. Along with improving operational effectiveness and product uniformity, this potent combination of AI and 3D printing also propels the creation of intelligent manufacturing systems that are capable of self-learning. This confluence has the potential to completely transform sectors including consumer products, healthcare, construction, and aerospace as it develops. This comprehensive review explores how AI enhances the capabilities of 3D printing, with a focus on process optimization, defect detection, and intelligent control mechanisms. Moreover, unresolved challenges are highlighted—including data scarcity, limited generalizability across printers and materials, certification barriers in safety-critical domains, computational costs, and the need for explainable AI. Full article

In the digitally saturated hospitality environment, research on digital transformation remains dominated by macro-level adoption trends and user-generated content, while the potential of micro-level web-behavioural data remains largely untapped. Recent systematic reviews highlight a fragmented body of literature and note that hospitality studies seldom address first-party behavioural data or big-data analytics capabilities. To address this gap, we collected clickstream, navigation and booking-funnel data from five luxury hotels in the Mediterranean and employed big-data analytics integrated with simulation modelling—specifically fuzzy cognitive mapping (FCM)—to model causal relationships among digital touchpoints, managerial actions and customer outcomes. FCM is a robust simulation tool that captures stakeholder knowledge and causal influences across complex systems. Using a case-study methodology, we show that first-party behavioural data enable real-time insights, support knowledge-based decision-making and drive digital service innovation. Across a 12-month panel, visitor volume was strongly associated with search traffic and social traffic, with the total-visitors model explaining 99.8% of variance. Our findings extend digital-transformation models by embedding micro-level behavioural data flows and simulation modelling. Practically, this study offers a replicable framework that helps managers integrate web-analytics into decision-making and customer-centric innovation. Overall, embedding micro-level web-behavioural analytics within an FCM framework yields a decision-ready, replicable pipeline that translates behavioural evidence into high-leverage managerial interventions. Full article

Using the “Policy Pressure-Innovation Alignment-Performance Transformation” theory, this paper looks at how ESG ratings, green innovation, and corporate dual-organizational performance are linked. This study uses a multi-period Difference-in-Differences (DID) model in conjunction with a conditional mediation effect model to examine how ESG ratings causally influence substantive green innovation, which in turn improves corporate financial and environmental performance. Regression results show that corporate ESG ratings have a big effect on the performance of both organizations. ESG ratings have a bigger effect on financial performance, while ESG scores have a bigger effect on environmental performance. Looking at the sub-dimensions shows that policy ratings have immediate effects on environmental performance and delayed effects on financial performance. The conclusion that the internalization response of corporate environmental costs is timely, while the market revaluation has a delayed transmission effect, holds true after being tested through parallel trend analysis and synthetic DID testing. More research shows that differences in ESG ratings hurt financial performance but help environmental performance. This means that differences in ESG ratings may lead to more real green innovation activities, which have a direct effect on the environment and, in the end, lead to bigger improvements in environmental performance. The moderating effect test shows that being aware of the environment makes substantive green innovation more focused on quality by making people feel responsible for their actions. Also, environmental management leads to more corporate green patents, which has resource displacement effects and makes green patent innovations less effective. Heterogeneity analysis shows that state-owned businesses use their institutional advantages to improve the “quality-quantity” of substantive green innovation, which helps their corporate green development performance. Declining businesses push for green innovation to fix problems that are already there, but mature businesses don’t like ESG rating policies because they are stuck in their ways, which stops them from making real progress in green innovation. This paper ends with micro-level evidence and theoretical support to solve the “greenwashing” problem of ESG and come up with “harmonious coexistence” policy combinations that work for businesses. Full article

Accurate object detection is fundamental to computer vision, yet detecting small targets in complex backgrounds remains challenging due to feature loss and limited model efficiency. To address this, we propose LCW-YOLO, a lightweight detection framework that integrates three innovations: Wavelet Pooling, a CGBlock-enhanced C3K2 structure, and an improved LDHead detection head. The Wavelet Pooling strategy employs Haar-based multi-frequency reconstruction to preserve fine-grained details while mitigating noise sensitivity. CGBlock introduces dynamic channel interactions within C3K2, facilitating the fusion of shallow visual cues with deep semantic features without excessive computational overhead. LDHead incorporates classification and localization functions, thereby improving target recognition accuracy and spatial precision. Extensive experiments across multiple public datasets demonstrate that LCW-YOLO outperforms mainstream detectors in both accuracy and inference speed, with notable advantages in small-object, sparse, and cluttered scenarios. Here we show that the combination of multi-frequency feature preservation and efficient feature fusion enables stronger representations under complex conditions, advancing the design of resource-efficient detection models for safety-critical and real-time applications. Full article

Plant-based residues generated within the agri-food system represent an abundant resource with significant potential for sustainable valorization. However, they are still underutilized and place a substantial burden on the environment and climate. This review discusses research trends over the past decade, combining bibliometric analysis with an overview of emerging technologies applied to the processing of residues generated from conventional crops and medicinal and aromatic plants. The bibliometric analysis reveals main valorization pathways, ranging from energy production to recovery of high-value bioactive compounds. Recent advances in this field are discussed in detail, with emphasis on low-energy and non-thermal processing (ultrasound, microwave, cold plasma), green solvents (natural deep eutectic solvents, bio-based solvents), biological pretreatments (with ligninolytic microorganisms and enzymes), thermochemical technologies (hydrothermal carbonization, pyrolysis), and emerging cascade strategies applied for multi-product recovery. Published research proves that these approaches have a great potential for sustainable valorization, while process optimization and economic feasibility remain a challenge at industrial scales for wider adoption. By providing an integrated perspective on diverse types of plant-based residues, this review highlights the importance of developing cascade and circular processing strategies, which align with global sustainability goals and encourage innovation in bio-based industries. New knowledge and advances in this field are highly required and will further help the transition of the current agri-food system towards greater circularity and sustainability. Full article

This review provides a comprehensive overview of the therapeutic potential of omaveloxone (OMA) for the treatment of Friedreich’s ataxia (FA), along with an analysis of the historical development and current status of the synthetic strategies for OMA production. OMA activates the nuclear factor-2-(erythroid-2)-related (Nrf2) pathway in vitro and in vivo, in both animal models and humans. The Nrf2 pathway plays a crucial role in the cellular response to oxidative stress. Furthermore, OMA has been shown to mitigate mitochondrial dysfunction, restore redox homeostasis and downregulate nuclear factor-κB (NF-κB), a key mediator of inflammatory responses. Through these mechanisms, OMA contributes to tissue protection and inflammation reduction in patients with FA. The review also highlights future perspective, focusing on the challenges associated with OMA reprofiling through innovative drug delivery approaches and its potential repurposing for diseases beyond FA. Full article

Tourism is a key driver of Portugal’s economy, with the WTTC projecting it to contribute EUR 56.4 billion (21.1% of GDP) by 2033. However, the sector has proven highly vulnerable to external shocks, such as the 2008 financial crisis, Brexit, and the pandemic, which have disrupted demand patterns and exposed structural weaknesses. It is essential to understand these impacts at a regional level in order to design more resilient and sustainable tourism strategies. This study examines how major crises have shaped tourism in Portugal’s NUTS II regions, focusing particularly on overnight stays, and assesses the implications for sustainable development and regional policy. Quarterly data from the National Statistics Institute (INE) covering 2004/2024 are used. We apply ARIMA and SARIMA models to account for seasonality and autocorrelation, and evaluate the accuracy of our forecasts using Mean Absolute Percentage Error (MAPE) and Theil’s U statistics. Structural breaks are considered to capture the effects of crises. The findings show that crises have significantly altered tourism patterns, with a shift towards less crowded and more remote destinations. This reflects vulnerabilities and opportunities for sustainability-oriented tourism. The study offers policymakers actionable guidance by aligning its results with the United Nations Sustainable Development Goals (SDGs), particularly those related to economic resilience (SDG 8), innovation and infrastructure (SDG 9), and partnerships for sustainable governance (SDG 17). This work is original in combining long-term regional data with robust forecasting techniques to provide innovative insights for scientific research and practical policy planning. Full article

To address the technical challenges in the educational domain named entity recognition (NER), such as ambiguous entity boundaries and difficulties with nested entity identification, this study proposes an enhanced semantic BERT model (ES-BERT). The model innovatively adopts an education domain, vocabulary-assisted semantic enhancement strategy that (1) applies the term frequency–inverse document frequency (TF-IDF) algorithm to weight domain-specific terms, and (2) fuses the weighted lexical information with character-level features, enabling BERT to generate enriched, domain-aware, character–word hybrid representations. A complete bidirectional long short-term memory-conditional random field (BiLSTM-CRF) recognition framework was established, and a novel focal loss-based joint training method was introduced to optimize the process. The experimental design employed a three-phase validation protocol, as follows: (1) In a comparative evaluation using 5-fold cross-validation on our proprietary computer-education dataset, the proposed ES-BERT model yielded a precision of 90.38%, which is higher than that of the baseline models; (2) Ablation studies confirmed the contribution of domain-vocabulary enhancement to performance improvement; (3) Cross-domain experiments on the 2016 knowledge base question answering datasets and resume benchmark datasets demonstrated outstanding precision of 98.41% and 96.75%, respectively, verifying the model’s transfer-learning capability. These comprehensive experimental results substantiate that ES-BERT not only effectively resolves domain-specific NER challenges in education but also exhibits remarkable cross-domain adaptability. Full article

This study constructs a conceptual model based on the relationship between corporate social responsibility (CSR) and organizational resilience based on stakeholder theory, resource dependence theory, information asymmetry theory, and signaling theory, and it uses the panel data of Shanghai and Shenzhen A-share listed enterprises in the period of 2010–2021 to conduct empirical research. The results show that (1) corporate social responsibility helps to reduce financial volatility and promote performance growth, which, in turn, contributes to organizational resilience; (2) CSR shapes the enhancement of organizational resilience mainly through three aspects: improving the corporate information environment, easing corporate financing constraints, and improving technological innovation; (3) the effect of CSR on organizational resilience varies according to the degree of board diversity within the enterprise and the degree of regional marketization outside the enterprise, and the enhancement effect of CSR on organizational resilience is more pronounced when the degree of board diversity and the degree of regional marketization are higher. This study provides theoretical support for CSR-enabled organizational resilience in the era of high-quality development, as well as suggestions for strengthening the level of organizational resilience. Full article

Electronic nose (e-nose) systems have emerged as transformative tools for odor and gas analysis, leveraging advances in nanomaterials, sensor arrays, and machine learning (ML) to mimic biological olfaction. This review synthesizes recent developments in e-nose technology, focusing on innovations in sensor design (e.g., graphene-based nanomaterials, MEMS, and optical sensors), drift compensation techniques, and AI-driven data processing. We highlight key applications across healthcare (e.g., non-invasive disease diagnostics via breath analysis), food quality monitoring (e.g., spoilage detection and authenticity verification), and environmental management (e.g., pollution tracking and wastewater treatment). Despite progress, challenges such as sensor selectivity, long-term stability, and standardization persist. The paper underscores the potential of e-noses to replace conventional analytical methods, offering portability, real-time operation, and cost-effectiveness. Future directions include scalable fabrication, robust ML models, and IoT integration to expand their practical adoption. Full article

Protection of coastal cultural heritage is among the most urgent global priorities, as these sites face increasing threats from climate change, sea level rise, and human activity. This study emphasises the value of innovative geospatial tools and data ecosystems for timely risk assessment. The role of land administration systems, geospatial documentation of coastal cultural heritage sites, and the adoption of innovative techniques that combine various methodologies is crucial for timely action. The coastal management infrastructure in Greece is presented, outlining the key public authorities and national legislation, as well as the land administration and geospatial ecosystems and the various available geospatial ecosystems. We profile the Hellenic Cadastre and the Hellenic Archaeological Cadastre along with open geospatial resources, and introduce TRIQUETRA Decision Support System (DSS), produced through the EU’s Horizon project, and a Digital Twin methodology for hazard identification, quantification, and mitigation. Particular emphasis is given to the role of Digital Twin technology, which acts as a continuously updated virtual replica of coastal cultural heritage sites, integrating heterogeneous geospatial datasets such as cadastral information, photogrammetric 3D models, climate projections, and hazard simulations, allowing for stakeholders to test future scenarios of sea level rise, flooding, and erosion, offering an advanced tool for resilience planning. The approach is validated at the coastal archaeological site of Aegina Kolona, where a UAV-based SfM-MVS survey produced using high-resolution photogrammetric outputs, including a dense point cloud exceeding 60 million points, a 5 cm resolution Digital Surface Model, high-resolution orthomosaics with a ground sampling distance of 1 cm and 2.5 cm, and a textured 3D model using more than 6000 nadir and oblique images. These products provided a geospatial infrastructure for flood risk assessment under extreme rainfall events, following a multi-scale hydrologic–hydraulic modelling framework. Island-scale simulations using a 5 m Digital Elevation Model (DEM) were coupled with site-scale modelling based on the high-resolution UAV-derived DEM, allowing for the nested evaluation of water flow, inundation extents, and velocity patterns. This approach revealed spatially variable flood impacts on individual structures, highlighted the sensitivity of the results to watershed delineation and model resolution, and identified critical intervention windows for temporary protection measures. We conclude that integrating land administration systems, open geospatial data, and Digital Twin technology provides a practical pathway to proactive and efficient management, increasing resilience for coastal heritage against climate change threats. Full article