Search Results (70)

Background: Digital twin (DT) technology, integrated with artificial intelligence (AI) and machine learning (ML), holds significant potential to transform oncology care. By creating dynamic virtual replicas of patients, DTs allow clinicians to simulate disease progression and treatment responses, offering a personalized approach to cancer treatment. Aim: This narrative review aimed to synthesize existing review studies on the application of digital twins in oncology, focusing on their potential benefits, challenges, and ethical considerations. Methods: The narrative review of reviews (NRR) followed a structured selection process using a standardized checklist. Searches were conducted in PubMed and Scopus with a predefined query on digital twins in oncology. Reviews were prioritized based on their synthesis of prior studies, with a focus on digital twins in oncology. Studies were evaluated using quality parameters (clear rationale, research design, methodology, results, conclusions, and conflict disclosure). Only studies with scores above a prefixed threshold and disclosed conflicts of interest were included in the final synthesis; seventeen studies were selected. Results and Discussion: DTs in oncology offer advantages such as enhanced decision-making, optimized treatment regimens, and improved clinical trial design. Moreover, economic forecasts suggest that the integration of digital twins into healthcare systems may significantly reduce treatment costs and drive growth in the precision medicine market. However, challenges include data integration issues, the complexity of biological modeling, and the need for robust computational resources. A comparison to cutting-edge research studies contributes to this direction and confirms also that ethical and legal considerations, particularly concerning AI, data privacy, and accountability, remain significant barriers. Conclusions: The integration of digital twins in oncology holds great promise, but requires careful attention to ethical, legal, and operational challenges. Multidisciplinary efforts, supported by evolving regulatory frameworks like those in the EU, are essential for ensuring responsible and effective implementation to improve patient outcomes. Full article

Retention forestry creates anthropogenic ecotones that diversify forest landscapes in terms of age and biomass. Such diversification can have ambiguous ecological impacts, raising uncertainties, particularly for black alder swamp woodlands, which are considered sensitive and are prioritized in EU conservation policy. This study aimed to examine the effects of adjacent clear-cutting on ground cover vegetation in 12 black alder stands in the hemiboreal zone in Latvia 11 to 120 years since the harvest. Ground cover vegetation was recorded by species along 40 m transects. The effects of the time since adjacent stand harvesting and exposure to the edge on species richness and Shannon diversity were assessed using linear mixed-effects models. A detrended correspondence analysis was used to explore the main environmental gradients. A total of 103 species were recorded: 15 in the tree and shrub layer, 66 in the herbaceous layer, and 22 in the moss and lichen layer. The exposure to the adjacent stand had a moderate positive effect on species diversity, while the effects of edge age were complex and varied by stand type. The scale of disturbance (the absolute length of the analyzed edge), rather than edge age or exposure, had the most pronounced effect on ground cover vegetation composition, suggesting persistent secondary edge effects that should be considered in forest management and conservation planning. Full article

Three-dimensional (3D) printing is a cutting-edge technology that is widely used in biomedical fields to construct various commercial products or scaffolds for theoretical research. In this review, 3D printing technologies with different principles are briefly introduced, including selective laser melting (SLM), selective laser sintering (SLS), fused deposition modeling (FDM), stereolithography (SLA), and digital light processing (DLP). In addition, the applications of 3D printing in dentistry, such as dental implantology, prosthodontics, orthodontics, maxillofacial surgery, and dental tissue regeneration, were summarized. Furthermore, the perspective and challenges of 3D printing were also addressed to help the readers obtain a clear map for the development of 3D printing in dentistry. Full article

Aiming to solve the problem of clearing obstacles in narrow and complex sewers, this paper introduces a visually assisted Sewer Cleaning Robot (SCR) for cleaning sewers with diameters ranging from 280 to 780 mm. The main work is carried out as follows: (a) A mobile platform is equipped with a pressing mechanism to press against the pipe walls in different diameters. The arm uses high-load linear actuator structures, enhancing load capacity while maintaining stability. (b) A Detection–Localization–Cleaning mode is proposed for cleaning obstacles. The YOLO detection model is used to identify six types of sewer defects. Target defects are then localized using monocular vision based on edge detection within defect bounding boxes. Finally, cutting is performed according to the localized defect positions. The feasibility of SCR in cleaning operations is validated through a series of experiments conducted under simulated pipeline conditions. These experiments evaluate its mobility, visual detection, and localization capabilities, as well as its ability to clear hard obstacles. This paper provides technical reserves for replacing human labor that use vision algorithms to assist in cleaning tasks within sewers. Full article

Respiratory syncytial virus (RSV) remains a significant global health threat, especially to infants, the elderly, and immunocompromised individuals. This review comprehensively explores the progress in RSV vaccine development, the immune evaluation methods, and immunological surrogate. The RSV fusion (F) protein, a primary target for vaccine development, has been engineered in prefusion conformation to elicit potent neutralizing antibodies, while the attachment (G) glycoprotein and other immunogens are also being explored to broaden immune responses. Advances in diverse vaccine platforms, ranging from live attenuated and protein subunit vaccines to cutting-edge mRNA- and nanoparticle-based formulations, highlight the field’s progress, yet challenges in balancing safety, immunogenicity, and durability persist. Central to these efforts is the identification and validation of immunological surrogates, which may serve as critical benchmarks for vaccine efficacy. Neutralizing antibody titers, multifunctional T cell responses, and B cell memory have emerged as key correlates of protection. However, the feasibility of these surrogates depends on their ability to predict clinical outcomes across diverse populations and settings. While neutralizing antibodies block the virus directly, T cell responses are essential for clearing infected cells and preventing severe disease, and B cell memory ensures long-term immunity. Integrating these immunological markers into a cohesive framework requires standardized assays, robust clinical validation, and an in-depth understanding of RSV-induced immune response. Full article

The rapid evolution of financial markets and technological advancements has significantly impacted the field of accounting, creating a demand for innovative approaches to financial forecasting and decision making. Our research addresses contemporary socio-economic needs within the accounting domain, particularly the growing reliance on automation and artificial intelligence (AI) to enhance the accuracy of financial projections and improve operational efficiency and proposes a theoretical and empirical framework for applying neural networks to predict corporate profitability, using key accounting variables. The proposed model operates on two distinct levels. At the theoretical level, we defined the conceptual relationship between accounting constructs and profitability, proposing that shifts in financial metrics directly influence the net income. This relationship is grounded in established accounting theory and is operationalized through financial ratios and indicators, creating a clear, semantically linked framework. At the empirical level, these abstract concepts can be reified into measurable variables, where a multi-layered neural network can be deployed to uncover complex, nonlinear relationships between the input data and predicted profit. Through iterative training and testing, the model can provide plausible predictions, validated by historical financial data. We are taking time-honored accounting principles and combining them with cutting-edge technology to predict profitability in ways that have not been possible before. The hope is that by embracing this new approach, we can make financial predictions more accurate, support better strategic decision making, and, ultimately, help businesses navigate the complexities of modern financial markets. This research addresses the growing need for advanced financial forecasting tools by applying neural networks to accounting. By combining theoretical accounting principles with cutting-edge machine learning techniques, we aim to demonstrate that neural networks can bridge the gap between traditional accounting practices and the increasing demands for predictive accuracy and strategic decision making in a rapidly evolving financial environment. Full article

Due to the dense crop residue in the Huang-Huai-Hai region, challenges such as large resistance, increased power consumption, and straw backfilling arise in the process of no-till seeding under the high-speed operations. This paper presents the design of a straw treatment device to address these issues. The cutting edge of a straw-cutting disc is optimized using an involute curve, and the key structural parameters of the device are designed by analyzing the process of stubble cutting and clearing. In this study, the Discrete Element Method (DEM) was employed to construct models of compacted soil and hollow, flexible wheat straw, forming the foundation for a comprehensive interaction model between the tool, soil, and straw. Key experimental variables, including working speed, rotation speed, and installation centre distance, were selected. The power consumption of the straw-cutting disc (PCD) and the straw-clearing rate (SCR) were used as evaluation metrics. Response surface methodology was applied to develop regression models linking the experimental factors with the evaluation indexes using Design-Expert 12 software. Statistical significance was assessed through ANOVA (p < 0.05), and factor interactions were analyzed via response surface analysis. The optimal operational parameters were found to be a working speed of 14 km/h, a rotation speed of 339.2 rpm, and an installation centre distance of 100 cm. Simulation results closely matched the predicted values, with errors of 1.59% for SCR and 9.68% for PCD. Field validation showed an SCR of 86.12%, improved machine passability, and favourable seedling emergence. This research provides valuable insights for further parameter optimization and component development. Full article

With advancements in autonomous driving and intelligent transportation, the need for responsive, stable braking systems in low-speed vehicles (LSVs) has risen, especially in complex conditions where traditional systems fall short. Brake-by-Wire (BBW) systems, known for their efficiency, energy savings, and safety, are becoming increasingly popular. This paper provides a systematic review of BBW technology for low-speed vehicles (LSV-BBW), aiming to offer valuable insights for researchers, engineers, and decision-makers in related fields. This comprehensive review covers the application of BBW and its associated technologies in LSVs. First, the current state of research on BBW systems is assessed, both domestically and internationally. Next, the fundamental principles and components of LSV-BBW technology are detailed. Following this, the control strategies of the LSV-BBW system are elaborated, with a clear definition of its performance metrics and identification of the key technologies involved. By analyzing the current trends in LSV-BBW technology development, this paper highlights cutting-edge advancements in the field. Finally, the significance and application prospects of LSV-BBW technology in promoting the intelligent, safe, and efficient development of LSVs are emphasized. Full article

The natural resilience of the forests to face impacts of blowdown damages was affected by harvesting operations. Variable retention harvesting (VRH) increases forest structure heterogeneity in managed stands and decreases blowdown damages. The objective of this study was to characterize blowdown in Nothofagus pumilio forests managed with VRH in Southern Patagonia (Argentina). We analyzed long-term plots and one area affected by a windstorm after harvesting (exposure to winds and influence of retention patches) using univariate analyses. We found a differential impact in retention patches compared to dispersed retention after a windstorm considering aspect and distance to edge (e.g., blowdown trees: F = 6.64, p < 0.001). The aspect in retention patches presented few structural differences before the windstorm (e.g., tree diameter: F = 3.92, p = 0.014) but was not greatly influenced by the received damage after the windstorm. In long-term plots, we found that aspect and location in patches (distance to edge) determined the tree stability. We also found differences in wind damage considering retention level and design (e.g., aggregates and dispersed retention vs. aggregates and clear-cuts). We conclude that VRH increased the heterogeneity in harvested areas, where retention patches presented greater resilience in confronting extreme climate events and decreased recurrent wind exposure impacts in the long term. We found the marginal influence of aspect in the retention patches despite dominant winds and damages received by remnant trees during harvesting. Full article

Off-site construction is well-known technology that facilitates parallel processes of manufacturing and construction processes. This method enhances productivity while reducing accident, cost, and environmental impact. Many studies have highlighted its benefits, prompting further encouragement of off-site construction. This study consolidates current research and charts future directions by reviewing the existing literature. However, reviewing papers is time-intensive and laborious. Consequently, generative AI models, particularly Large Language Models (LLMs), are increasingly employed for document summarization. Specifically, LangChain influences LLMs through chaining data, demonstrating notable potential for research paper reviews. This study aims to evaluate the well-documented advantages of off-site construction through LangChain integrated with an LLM. It follows a streamlined process from the collection of research papers to conducting network analysis, examining 47 papers to uncover that current research primarily demonstrates off-site construction’s superiority through cutting-edge technologies. Yet, a data deficiency remains a challenge. The findings demonstrate that LangChain can rapidly and effectively summarize research, making it a valuable tool for literature reviews. This study advocates the broader application of LangChain in reviewing research papers, emphasizing its potential to streamline the literature review process and provide clear insights into off-site construction’s evolving landscape. Full article

Despite the increasing availability of vast amounts of data, the challenge of acquiring labeled data persists. This issue is particularly serious in supervised learning scenarios, where labeled data are essential for model training. In addition, the rapid growth in data required by cutting-edge technologies such as deep learning makes the task of labeling large datasets impractical. Active learning methods offer a powerful solution by iteratively selecting the most informative unlabeled instances, thereby reducing the amount of labeled data required. However, active learning faces some limitations with imbalanced datasets, where majority class over-representation can bias sample selection. To address this, combining active learning with data augmentation techniques emerges as a promising strategy. Nonetheless, the best way to combine these techniques is not yet clear. Our research addresses this question by analyzing the effectiveness of combining both active learning and data augmentation techniques under different scenarios. Moreover, we focus on improving the generalization capabilities for minority classes, which tend to be overshadowed by the improvement seen in majority classes. For this purpose, we generate synthetic data using multiple data augmentation methods and evaluate the results considering two active learning strategies across three imbalanced datasets. Our study shows that data augmentation enhances prediction accuracy for minority classes, with approaches based on CTGANs obtaining improvements of nearly 50% in some cases. Moreover, we show that combining data augmentation techniques with active learning can reduce the amount of real data required. Full article

Strategies for obtaining deep slots in soft materials can vary significantly. Conventionally, the tool travels along the slot, removing material mainly with the side cutting edges. However, a “plunge milling” strategy is also possible, performing the cut vertically, taking advantage of the tip cutting edges that almost reach the center of the tool. Although both strategies are already commonly used, there is a clear gap in the literature in studies that compare tool wear, surface roughness, and productivity in each case. This paper describes an experimental study comparing the milling of deep slots in AA7050-T7451 aluminum alloy, coated with a novel DLCSiO500W3.5O₂ layer to minimize the aluminum adhesion to the tool, using conventional and plunge milling strategies. The main novelty of this paper is to present a broad study regarding different factors involved in machining operations and comparing two distinct strategies using a novel tool coating in the milling of aeronautical aluminum alloy. Tool wear is correlated with the vibrations of the tools in each situation, the cycle time is compared between the cases studied, and the surface roughness of the machined surfaces is analyzed. This study concludes that the cycle time of plunge milling can be about 20% less than that of conventional milling procedures, favoring economic sustainability and modifying the wear observed on the tools. Plunge milling can increase productivity, does not increase tool tip wear, and avoids damaging the side edges of the tool, which can eventually be used for final finishing operations. Therefore, it can be said that the plunge milling strategy improves economic and environmental sustainability as it uses all the cutting edges of the tools in a more balanced way, with less global wear. Full article

Full waveform inversion (FWI) is recognized as a leading data-fitting methodology, leveraging the detailed information contained in physical waveform data to construct accurate, high-resolution velocity models essential for crosshole surveys. Despite its effectiveness, FWI is often challenged by its sensitivity to data quality and inherent nonlinearity, which can lead to instability and the inadvertent incorporation of noise and extraneous data into inversion models. To address these challenges, we introduce the scale-aware edge-preserving FWI (SAEP-FWI) technique, which integrates a cutting-edge nonlinear anisotropic hybrid diffusion (NAHD) filter within the gradient computation process. This innovative filter effectively reduces noise while simultaneously enhancing critical small-scale structures and edges, significantly improving the fidelity and convergence of the FWI inversion results. The application of SAEP-FWI across a variety of experimental and authentic crosshole datasets clearly demonstrates its effectiveness in suppressing noise and preserving key scale-aware and edge-delineating features, ultimately leading to clear inversion outcomes. Comparative analyses with other FWI methods highlight the performance of our technique, showcasing its ability to produce images of notably higher quality. This improvement offers a robust solution that enhances the accuracy of subsurface imaging. Full article

European Union (EU) policy is clear in its ambition to deliver a sustainable urban future for Europe. In this paper, we consider the role of urban geoscience to help achieve these ambitions. We highlight the relevance of geology to urban subsurface planning and wider EU policy and strategy. Despite the lack of explicit mention of urban underground space in key policy documents, we identify a significant number of priority urban issues for which geological characterisation is a pre-requisite and for which the geological system forms part of the solution, such as mitigation of climate impacts, delivering net zero energy, and implementing nature-based solutions. We reflect on the paradigm shift of urban geoscience as a geological discipline, rooted initially in engineering geology but which has moved towards an interdisciplinary, solution-focused science operating at the inter-section of environmental–social–built systems. In this regard, we highlight cutting-edge urban geoscience research aligned to current urban challenges and note, in particular, the significance of digital technologies to enable 3D urban characterisation, support data-driven decision-making for planning and development, and serve as a means to communicate geology to urban practitioners. The role of the urban geoscientist as an agent of change to enhance integrated science, improve the accessibility of geological issues, and accelerate the translation of national–regional geology to local settings and to urban policy drivers should not be underestimated. Full article

Photoacoustic imaging (PAI) is a cutting-edge biomedical imaging modality, providing detailed anatomical and functional information about the area beneath the skin surface. Its light energy deposition is such that PAI typically provides clear images of the skin with high signal-to-noise ratios. Specifically, the rich optical contrast of PAI allows biological information related to lesion growth, malignancy, treatment response, and prognosis to be seen. Given its significant advantages and emerging role in imaging skin lesions, we summarize and comment on representative studies of skin PAI, such as the guidance of skin cancer biopsies and surgical excisions, and the accurate diagnosis of psoriasis. We conclude with our insights about the clinical significance of skin PAI, showing how its use to identify biological characteristics in lesion microenvironments allows early diagnosis and prognosis of disease. Full article