Search Results (931)

Landslide displacement prediction is crucial for disaster mitigation, yet traditional methods often fail to capture the complex, non-stationary spatiotemporal dynamics of slope evolution. This study introduces an enhanced prediction framework that integrates multi-scale signal processing with dynamic, geology-aware graph modeling. The proposed methodology first employs the Maximum Overlap Discrete Wavelet Transform (MODWT) to denoise raw Global Navigation Satellite System (GNSS)-monitored displacement time series data, enhancing the underlying deformation features. Subsequently, a geology-aware graph is constructed, using the temporal correlation of displacement series as a practical proxy for physical relatedness between monitoring nodes. The framework’s core innovation lies in a dynamic graph optimization model with low-rank constraints, which adaptively refines the graph topology to reflect time-varying inter-sensor dependencies driven by factors like mining activities. Experiments conducted on a real-world dataset from an active open-pit mine demonstrate the framework’s superior performance. The DCRNN-proposed model achieved the highest accuracy among eight competing models, recording a Root Mean Square Error (RMSE) of 2.773 mm in the Vertical direction, a 39.1% reduction compared to its baseline. This study validates that the proposed dynamic graph optimization approach provides a robust and significantly more accurate solution for landslide prediction in complex, real-world engineering environments. Full article

Traditional 2D optical markers degrade quickly in underwater applications due to sediment accumulation and marine biofouling, becoming undetectable within weeks. This paper presents a Three-Dimensional Binary Marker, a novel passive fiducial marker designed for underwater Long-Term Deployment. The Three-Dimensional Binary Marker addresses the 2D-markers limitation through a 3D design that enhances resilience and maintains contrast for computer vision detection over extended periods. The proposed solution has been validated through simulation, water tank testing, and long-term sea trials for 5 months. In each stage, the marker was compared based on detection per visible frame and the detection distance. In conclusion, the design demonstrated superior performance compared to standard 2D markers. The proposed Three-Dimensional Binary Marker provides compatibility with widely used fiducial markers, such as ArUco and AprilTag, allowing quick adaptation for users. In terms of fabrication, the Three-Dimensional Binary Marker uses additive manufacturing, offering a low-cost and scalable solution for underwater localization tasks. The proposed marker improved the deployment time of fiducial markers from a couple of days to sixty days and with a range up to seven meters, providing robustness and reliability. As the marker survivability and detection range depend on its size, it is still a valuable innovation for Autonomous Underwater Vehicles, as well as for inspection, maintenance, and monitoring tasks in marine robotics and offshore infrastructure applications. Full article

Hydrodynamic cavitation is one of the most promising technologies for sustainable process intensification in the food, nutraceutical, and environmental sectors, due to its ability to generate highly localized and intense implosions. Venturi-type devices, known for their simplicity and efficiency, are widely used for non-thermal extraction, microbial inactivation, and cellular disruption. However, the effectiveness of cavitation critically depends on internal geometry—particularly the perimeter-to-area ratio (P/A), which influences both pressure gradient distribution and the density of nucleation sites. In this context, an innovative configuration based on the Reuleaux triangle is proposed, allowing for a significant increase in the P/A ratio compared to conventional circular-section devices. This theoretical study extends the Navier–Stokes and Rayleigh–Plesset models to describe bubble dynamics and assess the influence of geometric and rotational variants (VRAt) on the localization and intensity of cavitation collapse. The results suggest that optimized internal geometries can reduce treatment times, increase selectivity, and improve the overall energy efficiency of cavitation processes, offering strong potential for advanced and sustainable industrial applications. This work is entirely theoretical and is intended to support the future design and experimental validation of next-generation cavitating devices. Full article

Background: Airway management in patients with obstructive sleep apnea (OSA) presents unique challenges for anesthesiologists and other airway practitioners. This comprehensive review examines current evidence and clinical practices for managing difficult airways in this high-risk population. OSA is characterized by specific anatomical and physiological alterations that increase both the likelihood of encountering difficult intubation and the risk of rapid desaturation during airway manipulation. Methods: Preoperative assessment of OSA patients requires integration of traditional difficult airway evaluation with OSA-specific considerations, including severity indices, oxygen desaturation patterns, and continuous positive airway pressure dependency. Conventional direct laryngoscopy often proves inadequate in these patients, prompting the development and refinement of alternative approaches. Videolaryngoscopy has emerged as a particularly valuable technique in OSA patients, offering improved glottic visualization while maintaining physiologic positioning. Flexible endoscopic techniques, particularly awake flexible bronchoscopic intubation, remain essential for high-risk scenarios, though they require considerable expertise. Results: Recent technological innovations have produced hybrid devices combining multiple modalities to address the specific challenges presented by OSA patients. Adjunctive tools and techniques, including specialized introducers, exchange catheters, and high-flow nasal oxygen, play critical roles in extending safe apnea time and facilitating successful intubation. Professional society guidelines now incorporate OSA-specific recommendations, emphasizing thorough preparation, appropriate device selection, and comprehensive monitoring. Conclusions: Effective management ultimately requires not only appropriate technology but also systematic preparation, strategic device selection, and meticulous execution. As OSA prevalence continues to rise globally, optimizing airway management approaches for this challenging population remains a critical priority for patient safety. Full article

An innovative high-order dimensionality reduction approach, which integrates a condensed finite-difference scheme with proper orthogonal decomposition techniques, has been explored for solving diffusion equations. The difference scheme with forth order accurate in both space and time is introduced through the idea of interpolation approximation. The quartic spline function and $(2,2)$ Padé approximation were utilized in space and time discretization, respectively. The stability and convergence were proven. Moreover, the dimensionality reduction formulas were derived using the proper orthogonal decomposition (POD) method, which is based on the matrix representation of the compact finite-difference scheme. The bases of the POD method were established by cumulative contribution rate of the eigenvalues of snapshot matrix that is different from the traditional ways in which the bases were established by the first eigenvalues. The method of cumulative contribution rate can optimize the degree of freedom. The error analysis of the reduced bases high-order POD finite-difference scheme was provided. Numerical experiments are conducted to validate the soundness and dependability of the reduced-order algorithm. The comparisons between the $(2,2)$ finite-difference method, the traditional POD method, and reduced dimensional method with cumulative contribution rate were discussed. Full article

Iatrogenic urinary tract injury is a known complication of pelvic surgery, most commonly occurring during gynecological procedures. The bladder and ureters are particularly vulnerable due to their close anatomical proximity to the uterus. Urinary tract damage can result from various mechanisms, including laceration, ligation, and thermal injury. Incidence rates vary according to the affected organ and surgical type; bladder injuries occur in 0.24% of benign and 0.4–3.7% of oncologic surgeries, whereas ureteral injuries are reported in 0.08% of benign and 0.39–1.1% of oncologic procedures. Timely diagnosis is essential for effective management. When detected intraoperatively, the injury can often be repaired immediately. Surgical treatment options vary depending on the specific nature and location of the bladder or ureteral damage. Delayed diagnosis can significantly impact the patient’s quality of life, increasing the risk of severe complications such as genitourinary fistulas. This narrative review aims to summarize current evidence on the diagnosis, prevention, and treatment of urinary tract injuries occurring during gynecological surgery. It evaluates risk factors, incidence, management, complications, and prevention strategies for iatrogenic bladder and ureteral injuries. Additionally, it highlights the innovative role of artificial intelligence in preventing urologic damage during gynecological procedures. The relevant literature was identified through a structured search of the PubMed database using predefined keywords related to gynecological surgery and urinary tract injury. Full article

To address the challenges of low recognition accuracy and substantial model complexity in crop disease identification models operating in complex field environments, this study proposed a novel hybrid model named ConvTransNet-S, which integrates Convolutional Neural Networks (CNNs) and transformers for crop disease identification tasks. Unlike existing hybrid approaches, ConvTransNet-S uniquely introduces three key innovations: First, a Local Perception Unit (LPU) and Lightweight Multi-Head Self-Attention (LMHSA) modules were introduced to synergistically enhance the extraction of fine-grained plant disease details and model global dependency relationships, respectively. Second, an Inverted Residual Feed-Forward Network (IRFFN) was employed to optimize the feature propagation path, thereby enhancing the model’s robustness against interferences such as lighting variations and leaf occlusions. This novel combination of a LPU, LMHSA, and an IRFFN achieves a dynamic equilibrium between local texture perception and global context modeling—effectively resolving the trade-offs inherent in standalone CNNs or transformers. Finally, through a phased architecture design, efficient fusion of multi-scale disease features is achieved, which enhances feature discriminability while reducing model complexity. The experimental results indicated that ConvTransNet-S achieved a recognition accuracy of 98.85% on the PlantVillage public dataset. This model operates with only 25.14 million parameters, a computational load of 3.762 GFLOPs, and an inference time of 7.56 ms. Testing on a self-built in-field complex scene dataset comprising 10,441 images revealed that ConvTransNet-S achieved an accuracy of 88.53%, which represents improvements of 14.22%, 2.75%, and 0.34% over EfficientNetV2, Vision Transformer, and Swin Transformer, respectively. Furthermore, the ConvTransNet-S model achieved up to 14.22% higher disease recognition accuracy under complex background conditions while reducing the parameter count by 46.8%. This confirms that its unique multi-scale feature mechanism can effectively distinguish disease from background features, providing a novel technical approach for disease diagnosis in complex agricultural scenarios and demonstrating significant application value for intelligent agricultural management. Full article

Poultry feces, a critical biomarker for health assessment, requires timely and accurate pathological identification for food safety. Conventional visual-only methods face limitations due to environmental sensitivity and high visual similarity among feces from different diseases. To address this, we propose MMCD (Multimodal Chicken-feces Diagnosis), a ResNet50-based multimodal fusion model leveraging semantic complementarity between images and descriptive text to enhance diagnostic precision. Key innovations include the following: (1) Integrating MASA(Manhattan self-attention)and DSconv (Depthwise Separable convolution) into the backbone network to mitigate feature confusion. (2) Utilizing a pre-trained BERT to extract textual semantic features, reducing annotation dependency and cost. (3) Designing a lightweight Gated Cross-Attention (GCA) module for dynamic multimodal fusion, achieving a 41% parameter reduction versus cross-modal transformers. Experiments demonstrate that MMCD significantly outperforms single-modal baselines in Accuracy (+8.69%), Recall (+8.72%), Precision (+8.67%), and F1 score (+8.72%). It surpasses simple feature concatenation by 2.51–2.82% and reduces parameters by 7.5M and computations by 1.62 GFLOPs versus the base ResNet50. This work validates multimodal fusion’s efficacy in pathological fecal detection, providing a theoretical and technical foundation for agricultural health monitoring systems. Full article

Rolling element bearings serve as critical transmission components in industrial automation systems, yet their fault signatures are susceptible to interference from strong background noise, complex operating conditions, and nonlinear impact characteristics. Addressing the limitations of conventional methods in adaptive parameter optimization and weak feature enhancement, this paper proposes an innovative diagnostic framework integrating Improved Goose optimized Variational Mode Decomposition (IGOOSE-VMD), RobustICA, and CYCBD. First, to mitigate modal aliasing issues caused by empirical parameter dependency in VMD, we fuse a refraction-guided reverse learning mechanism with a dynamic mutation strategy to develop the IGOOSE. By employing an energy-feature-driven fitness function, this approach achieves synergistic optimization of the mode number and penalty factor. Subsequently, a multi-channel observation model is constructed based on optimal component selection. Noise interference is suppressed through the robust separation capabilities of RobustICA, while CYCBD introduces cyclostationarity-based prior constraints to formulate a blind deconvolution operator with periodic impact enhancement properties. This significantly improves the temporal sparsity of fault-induced impact components. Experimental results demonstrate that, compared to traditional time–frequency analysis techniques (e.g., EMD, EEMD, LMD, ITD) and deconvolution methods (including MCKD, MED, OMEDA), the proposed approach exhibits superior noise immunity and higher fault feature extraction accuracy under high background noise conditions. Full article

Pancreatic cancer is a highly lethal malignant tumor characterized by challenges in early diagnosis and limited therapeutic options, leading to an exceptionally low clinical cure rate. With the advent of novel cancer treatment paradigms, ferroptosis—a form of iron-dependent regulated cell death driven by lipid peroxidation—has emerged as a promising therapeutic strategy, particularly for tumors harboring RAS mutations. However, the poor bioavailability and insufficient tumor-targeting capabilities of conventional drugs constrain the efficacy of ferroptosis-based therapies. Recent advancements in nanotechnology and imaging-guided treatments offer transformative solutions through targeted drug delivery, real-time monitoring of treatment efficacy, and multimodal synergistic strategies. This article aims to elucidate the mechanisms underlying ferroptosis in pancreatic cancer and to summarize the latest identified therapeutic targets for ferroptosis in this context. Furthermore, it reviews the recent progress in nanotechnology-based ferroptosis therapy for pancreatic cancer, encompassing ferroptosis monotherapy, synergistic ferroptosis therapy, and endogenous ferroptosis therapy. Subsequently, the integration of imaging-guided nanotechnology in ferroptosis therapy is summarized. Finally, this paper discusses innovative strategies, such as stroma-targeted ferroptosis therapy, immune-ferroptosis synergy, and AI-driven nanomedicine development, offering new insights and directions for future research in pancreatic cancer treatment. Full article

During the last decade, the blockchain landscape has rapidly evolved, fostering the development of decentralized applications (DApps) that utilize cross-chain interactions. Although existing technologies have enhanced transaction processing and introduced interoperability solutions, scalability challenges persist, undermining their effectiveness. In particular, traditional cross-chain DApp interaction protocols experience performance bottlenecks due to their dependence on on-chain validation mechanisms, resulting in increased latency and computational costs. To address these issues, this paper presents the ScaL2Chain protocol, which is designed to facilitate efficient and secure cross-chain transactions for DApps. ScaL2Chain leverages off-chain technologies, such as payment channels, to enable participants to conduct transactions with a minimal on-chain footprint. By implementing an innovative state verification mechanism, ScaL2Chain guarantees high performance, confidentiality, and transaction integrity. Our empirical evaluations indicate that ScaL2Chain significantly outperforms existing solutions in terms of transaction throughput. Specifically, compared to baseline systems, ScaL2Chain achieves a 7.9-times to 8.4-times improvement in permissionless environments and a 1.9-times to 35.8-times improvement in permissioned environments under workloads with 4-64 DApps and varying cross-chain transaction ratios (0–100%). Full article

Addressing the urgent need for lightweight and reusable energy-absorbing materials in aviation impact resistance, this study introduces an innovative multi-directional braided metamaterial design enabled by 4D printing technology. This approach overcomes the dual challenges of intricate manufacturing processes and the limited functionality inherent to traditional textile preforms. Six distinct braided structural units (types 1–6) were devised based on periodic trigonometric functions (Y = A sin(12πX)), and integrated with shape memory polylactic acid (SMP-PLA), thereby achieving a synergistic combination of topological architecture and adaptive response characteristics. Compression tests reveal that reducing strip density to 50–25% (as in types 1–3) markedly enhances energy absorption performance, achieving a maximum specific energy absorption of 3.3 J/g. Three-point bending tests further demonstrate that the yarn amplitude parameter A is inversely correlated with load-bearing capacity; for instance, the type 1 structure (A = 3) withstands a maximum load stress of 8 MPa, representing a 100% increase compared to the type 2 structure (A = 4.5). A multi-branch viscoelastic constitutive model elucidates the temperature-dependent stress relaxation behavior during the glass–rubber phase transition and clarifies the relaxation time conversion mechanism governed by the Williams–Landel–Ferry (WLF) and Arrhenius equations. Experimental results further confirm the shape memory effect, with the type 3 structure fully recovering its original shape within 3 s under thermal stimulation at 80 °C, thus addressing the non-reusability issue of conventional energy-absorbing structures. This work establishes a new paradigm for the design of impact-resistant aviation components, particularly in the context of anti-collision structures and reusable energy absorption systems for eVTOL aircraft. Future research should further investigate the regulation of multi-stimulus response behaviors and microstructural optimization to advance the engineering application of smart textile metamaterials in aviation protection systems. Full article

This article reframes sustainability transitions by positioning the deliberate deactivation of fossil fuel infrastructures—such as coal plants, oil fields, and pipelines—as a central mechanism of systemic change. While prevailing approaches often emphasize renewable energy and innovation, they tend to neglect how existing fossil systems are actively maintained by powerful networks. We argue that sustainability transitions require not only building alternatives but also deactivating entrenched fossil infrastructures. To address this gap, we propose an analytical framework that conceptualizes deactivation as a contested socio-political process shaped by antagonistic interactions between fossil blocs—coalitions of incumbent agents defending fossil infrastructures—and emerging deactivation networks working to disable and dismantle them. Drawing on six illustrative cases from diverse contexts, we examine the legal, institutional, narrative, and spatial mechanisms through which deactivation is either enabled or obstructed. We also introduce an interdisciplinary methodology that combines path tracing, social network analysis, and qualitative comparison to analyze how these dynamics between fossil blocs and deactivation networks evolve over time. This article contributes to the sustainability transition literature by demonstrating that the deactivation of fossil infrastructures is a political, material, and justice-oriented process, one that is essential to ending fossil fuel dependency and enabling sustainable futures. Full article

The need for organs suitable for transplantation has continued to rise as need outweighs availability. Increased demand has driven innovation in the field. Over the past ten years, donation after circulatory death (DCD) donors have become a greater portion of the donor pool. This method of donation includes a period of warm ischemia time to the organs. Thus, its use is dependent on recovery methods. Historically, extracorporeal membrane oxygenation (ECMO) was one of the first pumping technologies to enhance organ preservation in the potential donor. Subsequently, the adoption of normothermic regional perfusion (NRP) technology has also shown promise in organ transplantation. These technologies have increased utilization of organs and enhanced the pool of donor organs. This review seeks to summarize the literature supporting in situ technologies (ECMO and NRP) utilized in procurement of solid organs from DCD donors. The benefit of in situ perfusion in DCD organ recovery is that these technologies increase the number of organs available for transplantation by reducing ischemic injury. The disadvantages include the added technical aspect, added operating room time, and the increased ethical concerns surrounding these technologies compared to conventional methods of organ recovery. Full article

In recent years, the traditional orchard sprayer has had problems, such as waste of liquid agrochemicals, low target coverage, high manual dependence, and environmental pollution. In this study, an automatic swing-arm sprayer for orchards was developed based on the standardized pear orchard in Pinggu, Beijing. Firstly, the structural principles of a crawler-type traveling system and swing-arm sprayer were simulated using finite element software design. The combination of a diffuse reflection photoelectric sensor and Arduino single-chip microcomputer was used to realize real-time detection and dynamic spray control in the pear canopy, and the sensor delay compensation algorithm was used to optimize target recognition accuracy and improve the utilization rate of liquid agrochemicals. Through the integration of innovative structural design and intelligent control technology, a vertical droplet distribution test was carried out, and the optimal working distance of the spray was determined to be 1 m; the nozzle angle for the upper layer was 45°, that for the lower layer was 15°, and the optimal speed of the swing-arm motor was 75 r/min. Finally, a particle size test and field test of the orchard sprayer were completed, and it was concluded that the swing-arm mode increased the pear tree canopy droplet coverage by 74%, the overall droplet density by 21.4%, and the deposition amount by 23% compared with the non-swing-arm mode, which verified the practicability and reliability of the swing-arm spray and achieved the goal of on-demand pesticide application in pear orchards. Full article