Search Results (10,278)

Background: Developing reliable and cost-effective decellularization methods is critical for advancing tissue engineering and regenerative medicine, particularly in regions with limited access to specialized perfusion systems. Methods: This study standardized a gravity-assisted perfusion protocol for rat liver decellularization, designed to operate without pumps or pressurized equipment. Adult Wistar rat livers were processed through a gravity-driven vascular flushing method and compared with a conventional immersion-based protocol. The resulting scaffolds were evaluated by macroscopic inspection, histological staining (Masson’s trichrome), and residual DNA quantification. Results: The gravity-assisted perfusion method achieved more efficient cellular removal and superior preservation of extracellular matrix (ECM) integrity compared with immersion. Residual DNA levels were 3.7 ng/mg in perfused samples, 209.47 ng/mg in immersed samples, and 331.97 ng/mg in controls, confirming a statistically significant reduction (p < 0.05). Only the perfused group met the accepted threshold for effective decellularization (<50 ng/mg dry tissue). Histological analysis corroborated these findings, showing the absence of nuclei and the preservation of collagen architecture characteristic of a structurally intact ECM. Conclusions: This low-cost, reproducible, and technically simple system enables the generation of high-quality acellular hepatic scaffolds without mechanical pumps. Its accessibility and scalability make it suitable for laboratories with limited infrastructure and educational settings. Moreover, this gravity-assisted approach provides a foundation for future recellularization and preclinical studies aimed at developing bioengineered liver constructs for regenerative and transplant applications. Full article

With the acceleration of urbanization and the continuous expansion of building stock, building maintenance plays a critical role in ensuring structural safety, extending service life, and promoting sustainable development. In recent years, the application of artificial intelligence (AI) in building maintenance has expanded significantly, markedly improving detection accuracy and decision-making efficiency through predictive maintenance, automated defect recognition, and multi-source data integration. Although existing studies have made progress in predictive maintenance, defect identification, and data fusion, systematic quantitative analyses of the overall knowledge structure, research hotspots, and technological evolution in this field remain limited. To address this gap, this study retrieved 423 relevant publications from the Web of Science Core Collection covering the period 2000–2025 and conducted a systematic bibliometric and scientometric analysis using tools such as bibliometrix and VOSviewer. The results indicate that the field has entered a phase of rapid growth since 2017, forming four major thematic clusters: (1) intelligent construction and digital twin integration; (2) predictive maintenance and health management; (3) algorithmic innovation and performance evaluation; and (4) deep learning-driven structural inspection and automated operation and maintenance. Research hotspots are evolving from passive monitoring to proactive prediction, and further toward system-level intelligent decision-making and multi-technology integration. Emerging directions include digital twins, energy efficiency management, green buildings, cultural heritage preservation, and climate-adaptive architecture. This study constructs, for the first time, a systematic knowledge framework for AI-enabled building maintenance, revealing the research frontiers and future trends, thereby providing both data-driven support and theoretical reference for interdisciplinary collaboration and the practical implementation of intelligent maintenance. Full article

Defect-rate uncertainty creates cascading operational challenges in multi-stage production, often driving inefficiency and misallocation of labor, materials, and capacity. To confront this, we develop a multi-stage Production Integrated Decision (MsPID) framework that unifies quality inspection and shop-floor decision-making within a single computational model. The framework couples a two-stage sampling inspection policy—used to statistically learn and control defect-rate uncertainty via estimation and rejection rules—with a multi-process, multi-part production decision model. Optimization is carried out with an Improved Grey Wolf Optimizer (IGWO) enhanced with Latin hypercube sampling (LHS) for uniformly diverse initialization; an evolutionary factor mechanism that blends simulated binary crossover (SBX) among three leadership-guided parents (Alpha, Beta, Delta) to strengthen global exploration in early iterations and focus exploitation later; and a greedy, mutation-assisted opposition learning step applied to the lowest-performing quartile of the population to effect leader-informed local refinement and accept only fitness-improving moves. Experiments show the method identifies minimum-cost policies across six single-stage benchmark cases and yields a total profit of 43,800 units in a representative multi-stage scenario, demonstrating strong performance in uncertain environments. Sensitivity analysis further clarifies how recommended decisions adapt to shifts in estimated defect rates, finished product prices, and swap/changeover losses. These results highlight how bio-inspired intelligence can enable adaptive, efficient, and resilient integrated production management at scale. Full article

Prestressing wedges are critical in bridge and road construction, but flaws in wedge threads lead to severe safety hazards, construction delays, and costly maintenance. Traditional manual inspection remains labor-intensive and inconsistent, particularly under variable illumination and complex surface conditions. However, few studies have investigated improving the inspection effectiveness. Therefore, this study aims to propose a lightweight FasterNET-YOLOv5 framework for accurate and robust prestress wedge flaw detection in industrial applications. The framework achieves a detection precision of 96.3%, recall of 96.2, and mAP@0.5 of 96.5 with 18% faster end-to-end inference speed, enabling deployable system configuration on portable or embedded devices, making the approach suitable for real-time industrial inspection. Further practical guidance for workshop inspection illumination conditions was confirmed by robustness evaluations, as white lighting background provides the most balanced performance for incomplete thread and scratch defects. Moreover, a mechanical model-based inverse method was exploited to link the detections from machine vision. The results also demonstrate the potential for broader 3D surface inspection tasks in threaded, machined, and curved components of intelligent, automated, and cost-effective quality control. In general, this research contributes to computational inspection systems by bridging deep learning-based flaw detection with engineering-grade reliability and deployment feasibility. Full article

General aviation (GA), comprised mainly of piston engine airplanes, has an inferior safety history compared with air carriers in the United States. Most studies addressing this safety disparity has focused on pilot deficiencies. Herein, we determined the rates/causes of equipment failure-related GA fatal accidents for type-certificated and experimental-amateur-built airplanes. Aviation accidents/injury severity were per the NTSB Access^R database. Statistical tests employed proportion/binomial tests/a Poisson distribution. The rate of fatal accidents (1990–2019) due to equipment failure was unchanged (p > 0.026), whereas the fatal mishap rate related to other causes declined (p < 0.001). A disproportionate (2× higher) count (p < 0.001) of equipment-related fatal accidents was evident for experimental-amateur-built aircraft with type-certificated references. Propulsion system (67%) and airframe (36%) failures were the most frequent causes of fatal accidents for type-certificated and experimental-amateur-built aircraft, respectively. The components “fatigue/corrosion” and “manufacturer–builder error” resulted in 60% and 55% of powerplant and airframe failures, respectively. Most (>90%) type-certificated aircraft propulsion system failures were within the manufacturer-prescribed engine time-between-overhaul (TBO) and involved components inaccessible for examination during an annual inspection. There is little evidence for a decline in equipment failure-related fatal accident rate over three decades. Considering the fact that powerplant failures mostly occur within the TBO and involve fatigue/corrosion of one or more components inaccessible for examination, GA pilots should avoid operations where a safe off-field landing within glide-range is not assured. Full article

The cloud manufacturing (CMfg) platform serves as a centralized hub for allocating and scheduling tasks to distributed resources. It features a concrete two-agent model that addresses real-world industrial needs: the first agent handles long-term flexible tasks, while the second agent manages urgent short-term tasks, both sharing a common due date. The second agent employs multitasking scheduling, which allows for the flexible suspension and switching of tasks. This paper addresses a novel scheduling problem aimed at minimizing the total weighted completion time of the first agent’s jobs while guaranteeing the second agent’s due date. For single-machine cases, a polynomial algorithm provides an efficient baseline; for parallel machines, an exact branch-and-price approach is developed, where the polynomial method informs the pricing problem and structural properties accelerate convergence. Computational results demonstrate significant improvements: the branch-and-price solves large-sized instances (up to 40 jobs) within 7200 s, outperforming CPLEX, which fails to find solutions for instances with more than 15 jobs. This approach is scalable for industrial cloud manufacturing applications, such as automotive parts production, and is capable of handling both design validation and quality inspection tasks. Full article

Objectives: Colorectal cancer (CRC) is the second-deadliest cancer globally, with an estimated 52,900 additional deaths expected in the United States by 2025. Early detection through colonoscopy significantly reduces CRC mortality by enabling the removal of pre-cancerous polyps. However, manual visual inspection of colonoscopy images is time-consuming, tedious, and prone to human error. This study aims to develop an automated and reliable polyp segmentation and classification method to improve CRC screening. Methods: We propose a novel deep learning architecture called µ-Net for accurate polyp segmentation in colonoscopy images. The model was trained and evaluated using the Kvasir-SEG dataset. To ensure transparency and reliability, we incorporated Explainable AI (XAI) techniques, including saliency maps and Grad-CAM, to highlight regions of interest and interpret the model’s decision-making process. Results: The µ-Net model achieved a Dice coefficient of 94.02%, outperforming other available segmentation models in accuracy, indicating its strong potential for clinical deployment. Integrating XAI provided meaningful visual explanations, enhancing trust in model predictions. Conclusions: The proposed µ-Net framework significantly improves the Precision and efficiency of automated polyp screening. Its ability to segment, classify, and interpret colonoscopy images enables early detection and supports clinical decision-making. This comprehensive approach offers a valuable tool for CRC prevention, ultimately contributing to better patient outcomes. Full article

Structural Health Monitoring (SHM) of steel bridges is vital for ensuring the longevity, safety, and reliability of critical transportation infrastructure. This review synthesizes recent advancements in SHM technologies and methodologies for steel bridges, highlighting the shift from traditional vibration-based monitoring to data-driven, intelligent systems. It covers core technological themes, including various sensing systems such as wireless sensor networks, fiber optics, and piezoelectric transducers, along with the impact of machine learning, artificial intelligence, and statistical pattern recognition. The paper explores applications for damage detection, such as fatigue life assessment and monitoring of components like expansion joints. Persistent challenges, including deployment costs, data management complexities, and the need for real-world validation, are addressed. The future of SHM lies in integrating diverse sensing technologies with computational analytics, advancing from periodic inspections to continuous, predictive infrastructure management, which enhances bridge safety, resilience, and economic sustainability. Full article

Maritime transport plays a pivotal role in global trade, where efficiency and accuracy in port operations are crucial. Among the various tasks carried out in ports, container code recognition is essential for tracking and handling cargo. Manual inspections of container codes are becoming increasingly impractical, as they induce delays and raise the risk of human error. To address these issues, this work proposes a hybrid Optical Character Recognition system that integrates YOLOv7 for text detection with the transformer-based TrOCR for recognition of the container codes, enabling accurate and efficient automated recognition. This design addresses the real-world challenges, such as varying light, distortions, and multi-orientation of container codes. To evaluate the system, we conducted a comprehensive evaluation on datasets that simulate the conditions found in port environments. The results demonstrate that the proposed hybrid model delivers significant improvements in detection and recognition accuracy and robustness compared to traditional OCR methods. In particular, the reliability in recognizing multi-oriented codes marks a notable advancement compared to existing solutions. Overall, this study presents an approach to automating container code recognition, contributing to the efficiency and modernization of port operations, with the potential to streamline port operations, reduce human error, and enhance the overall logistics workflow. Full article

The durability and management of reinforced concrete structures and infrastructures are a central issue in contemporary civil engineering. Efficient structural maintenance has become strategically critical to sustainable land and community management due to aging infrastructure, increasing operational stress, and limited financial resources. This study focuses specifically on reinforced concrete bridge piers, whose fundamental structural role influences road infrastructure management strategies. The objective of this study is to develop and use a system based on convolutional neural networks to visually, rapidly, and automatically identify degraded portions of the reinforcement, based on images acquired on-site or from visual inspections, and classify their level of degradation. The topic addressed is highly innovative. The need to define and calibrate reliable degradation classification criteria, and the difficulty of obtaining images and classifying them correctly for database construction, have influenced the development of the study and make the results interesting and promising, but absolutely preliminary. Full article

Conventionally, tomography is an inspection technique in which tomographic images are intended for human perception and interpretation. In this work, we shift this paradigm by transforming tomography into an autonomous estimator of industrial reactor states, enabling fully automated process control. Alcoholic fermentation was employed as an example of a controlled process in the current study. The work presents an original concept utilizing transfer learning in conjunction with a ResNet-type artificial neural network, which converts electrical measurements into a sequence of values correlated with the conductivity of pixels constituting the cross-section of the examined biochemical reactor. The conductivity vector is transformed into a parameter determining substrate concentration, enabling dynamic process regulation in response to signals generated from EIT (Electrical Impedance Tomography). Within the scope of the described research, calibration of the conductivity vector against substrate concentrations was performed, and a Matlab/Simulink-based dynamic Monod kinetics model was developed. The obtained results demonstrate high accuracy in substrate concentration estimation relative to reference values throughout a forty-six-hour process. The same signals enable energy-efficient process control, in which cooling and mixing intensity are regulated according to energy prices and renewable energy availability. This strategy may possess particular application in facilities where fermentation installations are co-located with bioenergy production units. Full article

The fabrication of a steel structure facility in the aerospace sector was executed through the implementation of welding techniques. In order to reduce the effects of environmental corrosion and extend its service life, it is typically coated with a protective layer. Nevertheless, conventional non-destructive testing (NDT) techniques generally necessitate preliminary procedures, such as coating removal and surface grinding, prior to inspection, leading to elevated costs and diminished efficiency. Consequently, the investigation into NDT methodologies for welds encased under coatings is of considerable practical significance. The objective of this paper is to comprehensively review and thoroughly analyze the latest research progress in NDT techniques for detecting defects in coated steel welds, seeking feasible approaches for achieving NDT on coated steel structures. Firstly, the paper examines the hazards of common weld defects and the challenges coatings pose to NDT operations. The text then proceeds to expound upon the principles, research advancements, and application scenarios of multiple NDT methods currently available for detecting defects beneath coatings. A comparative summary of these methods is provided, focusing on detection capabilities, coating penetration abilities, key advantages, and limitations. In conclusion, the paper provides insights into future development trends. Full article

Microrobot navigation in constrained environments requires path planning methods that ensure both efficiency and collision avoidance. Conventional approaches, which typically combine graph-based path finding with geometric path simplification, effectively reduce path complexity but often generate collision-prone paths because wall boundaries are not considered during simplification. Therefore, to overcome this limitation, we present Secure Angle-based Geometric Elimination (SAGE), a single-pass path-simplification algorithm that converts pixel-level shortest paths into low-complexity trajectories suitable for real-time collision-free navigation of microrobots. SAGE inspects consecutive triplets (p_i, p_i+1, p_i+2) and removes the middle point when the turning angle is smaller than threshold (∠p_ip_i+1p_i+2 ≤ θ_th) or the direct segment (p_i → p_i+2) is collision-free. Quantitative analysis shows that SAGE achieves approximately 5% shorter path length, 20% lower turning cost and 0% collision rate, while maintaining computation comparable to the Ramer–Douglas–Peucker algorithm. Integration with Dijkstra and RRT planners confirms scalability across complex maze and vascular environments. Experimental microrobot demonstrations show navigation with complete collision avoidance, establishing SAGE as an efficient and reliable framework for high-speed microrobot navigation and automation in lab-on-a-chip, chemical-reaction and molecular-diagnostic systems. Full article

Accurate prediction of bridge deterioration is essential for optimizing inspection schedules, prioritizing maintenance activities, and ensuring infrastructure safety. This study developed machine learning-based predictive models to estimate the deterioration states of bridge decks, superstructures, and substructures using a comprehensive dataset from the Colorado National Highway System spanning 2014 to 2024. Structural, operational, and environmental parameters including freeze–thaw cycles, precipitation, condensation risk, and extreme temperatures were incorporated to capture both design-driven and climate-driven deterioration mechanisms. Decision Tree, Random Forest, and Gradient Boosting classifiers were trained and evaluated using Balanced Accuracy, Matthews Correlation Coefficient, Cohen’s Kappa, and macro-averaged F1-scores, with class imbalance addressed via SMOTETomek resampling. The Gradient Boosting classifier achieved the highest predictive performance, with balanced accuracy exceeding 97% across all components. Feature importance analysis revealed that sufficiency rating, year of construction, and environmental stressors were among the most influential predictors. Incorporating environmental variables improved predictive accuracy by up to 4.5 percentage points, underscoring their critical role in deterioration modeling. These findings demonstrate that integrating environmental factors into machine learning frameworks enhances the reliability of deterioration forecasts and supports the development of climate-aware asset management strategies, enabling transportation agencies to proactively plan maintenance interventions and improve infrastructure resilience. Full article

Subsea pipelines are extensively utilized in transportation systems. Conducting regular and effective internal inspections of these pipelines to promptly identify internal defects and potential risks is of paramount importance to ensure safe operational practices. In response to the practical engineering requirements for the internal inspection of subsea pipelines, this paper presents the design of an inspection device capable of capturing point cloud data from pipelines with internal diameters of 100 mm and above and performing three-dimensional reconstruction through coding. This device clearly reveals internal pipeline defects and enables both qualitative and quantitative analyses. Upon designing the motion module, control system, and LiDAR-based detection module of the internal pipeline inspection device, the capacity to collect internal point cloud data and perform 3D reconstruction was achieved. An experimental prototype of the inspection device was manufactured and tested using a simulated pipeline constructed to replicate real-world conditions. An analysis of the inspection results demonstrates that the device can travel steadily inside the pipeline, and the collected point cloud data can be used for 3D reconstruction via coding, accurately and clearly displaying the internal 3D structure of the pipeline and its defects. This device provides a basis for the prediction of pipelines’ service lives. Full article