Search Results (135)

This study develops a novel transverse steel damper (TSD) to enhance the seismic performance of tall-pier girder bridges, featuring superior lateral strength and energy dissipation capacity. The TSD’s design and arrangement are presented, with its hysteretic behavior simulated in ABAQUS. Key parameters (yield strength: 3000 kN; initial gap: 100 mm; post-yield stiffness ratio: 15%) are optimized through seismic analysis under near-fault ground motions, incorporating pulse characteristic investigations. The optimized TSD effectively reduces bearing displacements and results in smaller pier top displacements and internal forces compared to the bridge with fixed bearings. Due to the higher-order mode effects, there is no direct correlation between top displacements and bottom internal forces. As pier height decreases, the S-shaped shear force and bending moment envelopes gradually become linear, reflecting the reduced influence of these modes. Medium- to long-period pulse-like motions amplify seismic responses due to resonance (pulse period ≈ fundamental period) or susceptibility to large low-frequency spectral values. Higher-order mode effects on bending moments and shear forces intensify under prominent high-frequency components. However, the main velocity pulse typically masks the influence of high-order modes by the overwhelming seismic responses due to large spectral values at medium to long periods. Full article

Tay–Sachs disease (TSD) is a rare and severe neurodegenerative disorder inherited in an autosomal recessive manner. It is caused by a deficiency of the enzyme hexosaminidase A, which is responsible for the degradation of GM2 gangliosides—lipids that accumulate in the nerve cells of the central nervous system. The inability to break down these lipids leads to their progressive accumulation, resulting in irreversible brain damage. Mechanistically, TSD is caused by mutations in the HEXA gene, which encodes the alpha subunit of hexosaminidase A. These mutations disrupt enzyme activity and alter cellular pathways involved in lysosomal lipid degradation. Although Tay–Sachs specifically involves the alpha subunit, similar clinical features can be seen in Sandhoff disease, a related disorder caused by mutations in the HEXB gene, which encodes the beta subunit shared by hexosaminidase A and B. Tay–Sachs is classified into three clinical forms according to age of onset and symptom severity: the classic infantile form, which is the most common and severe; a juvenile (subacute) form; and an adult-onset form, which progresses more slowly and tends to present with milder symptoms. Diagnosis is based on enzymatic testing showing reduced or absent hexosaminidase A activity, confirmed by genetic testing. Prenatal diagnosis and genetic counseling play a key role in prevention and reproductive decision-making, especially in high-risk populations. Although no curative treatment currently exists, ongoing research is exploring gene therapy, enzyme replacement, and pharmacological approaches. Certain compounds, such as gemfibrozil, have shown potential to slow symptom progression. Early diagnosis and multidisciplinary care are essential to improving quality of life, although therapeutic options remain limited due to the progressive nature of the disease. Full article

Background/Objectives: Chronic obstructive pulmonary disease (COPD) remains a condition with high morbidity, mortality, and misdiagnosis. The gold standard pulmonary function testing with spirometry has limited availability. This study seeks to test a novel diagnostic test based on auscultatory mapping of pulmonary dynamics. This NIH-funded study aimed to develop a COPD detection technology, using mobile phone auscultation, for situations in which spirometry is not available. Methods: This prospective study collected mobile phone auscultation data on patients presenting for spirometry and evaluation by a pulmonologist. All subjects had same-day or recent (less than 6 months) spirometry in one PFT laboratory. After informed consent, the subjects underwent respiratory auscultation using a selection of mobile phone brands. The auscultation methods included normal breathing observed at the left axillary site and egophony observed at the right supra clavicular fossa. The team created models from the recordings using Time Series Dynamics (TSD), proprietary software that uses computational nonlinear dynamics to characterize the respiratory biofluid dynamics implied by the acoustic data. Results: We enrolled a total of 108 patients (34.3% male), from 19 to 85 years of age (median 61 years). Among the patients, 64 (59.3%) subjects identified as White, 43 (39.8%) as Black, and 1 as Asian. Among the two cohorts with diverse comorbidities, 52 subjects had confirmed COPD and 56 did not. The cohorts differed significantly in age and body mass index, but not in race, number of comorbidities, or COPD assessment test scores. They had significant differences in forced expiratory volume in 1 s (FEV1), the FEV1/FVC (forced vital capacity) ratio, but not FVC. The recordings from the egophonic and axillary sites were initially modeled separately and then combined in a single composite model. The modeling produced excellent results with 90%+ AUC and sensitivity in both the test and train sets relative to the gold standard. Conclusions: Evidence suggests that a mobile phone auscultation device can accurately determine COPD diagnosis. In frontline applications where the availability of gold standard pulmonary function testing is limited, the device could improve the detection of COPD, a condition with significant over- and under-diagnosis. Future trials will investigate the ability of patients to self-record. Success would support remote COPD testing using transmitted telehealth recording data, bringing diagnosis to patients in underserved populations. Full article

With the rapid development of intelligent transportation systems, traffic sign detection plays a crucial role in ensuring driving safety and preventing accidents. However, detecting small traffic signs in complex road environments remains a significant challenge due to issues such as low resolution, dense distribution, and visually similar background interference. Existing methods face limitations including high computational cost, inconsistent feature alignment, and insufficient resolution in detection heads. To address these challenges, we propose the Traffic Sign Detection Network (TSD-Net), an improved framework designed to enhance the detection performance of small traffic signs in complex backgrounds. TSD-Net integrates a Feature Enhancement Module (FEM) to expand the network’s receptive field and enhance its capability to capture target features. Additionally, we introduce a high-resolution detection branch and an Adaptive Dynamic Feature Fusion (ADFF) detection head to optimize cross-scale feature fusion and preserve critical details of small objects. By incorporating the C3k2 module and dynamic convolution into the network, the framework achieves enhanced feature extraction flexibility while maintaining high computational efficiency. Extensive experiments on the TT100K benchmark dataset demonstrate that TSD-Net outperforms most existing methods in small object detection and complex background handling, achieving 91.4 mAP and 49.7 FPS on 640 × 640 low-resolution images, meeting the requirements of practical applications. Full article

Federated edge learning (FEL) unites the decentralized training capabilities of multiple edge nodes to allow model gradient sharing and parameter aggregation across a peer-to-peer network. However, many intrinsic policy conflicts still exist in FEL, for example, the open accessibility of gradients will lead to the privacy leakage risk during the federal aggregation process. In this paper, we first identify that malicious users weaponized with generative artificial intelligence (GenAI) can generate fake samples that are almost identical to FEL participants’ training data. By analyzing how different configurations of GenAI affect attack effectiveness, we find that an adversary with strong patchwork and reconstruction capabilities can stealthily steal diverse training data from nearly all FEL participants. To thwart such a generative data reconstruction attack (GenDRA) scheme, we propose a novel target semantic dissolution (TSD) mechanism for enhancing the privacy-preserving ability of FEL, which encrypts only a very small number (≤10%) of gradient values in each training round that have a significant impact on human visual formation using format-preserving encryption. With TSD, the speculator cannot obtain a fake sample that is visually similar to the training sample because real gradients are actively concealed. Extensive experiments based on four benchmark datasets are performed to demonstrate the huge threat of GenAI and the effectiveness of TSD in all aspects: compelling accuracy performance, strong visual privacy guarantee, and low computing overhead. Full article

As a vital part of the Eurasian temperate grassland, the Chinese temperate grassland is primarily distributed in the Inner Mongolia Plateau. This paper focuses on mapping temperate grassland dynamics from the 1980s to the 2010s in Inner Mongolia, which was divided into temperate meadow steppe (TMS), temperate typical steppe (TTS), temperate desert steppe (TDS), temperate steppe desert (TSD) and temperate desert (TD). Multi-source features, including multispectral reflectance, vegetation growth, topography, water bodies, meteorological data, and soil characteristics, were selected based on their distinct physical properties and remote sensing variations. Then, we applied deep neural network (DNN) models to classify them, achieving an accuracy of 79.4% in the 1980s and 81.1% in the 2000s. Additionally, validation in the 2010s through field reconnaissance demonstrated an accuracy of 72.7%, which was acceptable, confirming that DNN is an effective method for classifying temperate grasslands. The results revealed that TTS had the highest proportion in the study area (39%), while TMS and TSD had the lowest (8.2% and 8.1%, respectively). Grassland types have the distribution law of aggregation; according to statistics, 61.1% of the grassland area remained unchanged, and the transition zone between adjacent grassland classes was highly easy to change. The area variation mainly came from TTS, TDS, and TSD, but not TD. The mutual transformation of different grassland types occurred mainly in adjacent areas between them. This study demonstrates the potential of DNN for long-term grassland mapping and provides the most comprehensive classification maps of Inner Mongolia grasslands to date, which are invaluable for grassland research and conservation efforts in the area. Full article

Yield maps represent crop production output and are essential for evaluating within-field spatial variability. Managing this yield variability is critical for precision and digital agriculture to facilitate optimized crop yield and reduced environmental impact. This work evaluated the spatial and temporal variability in corn and soybean yield data from three conventionally managed agricultural fields, with nine, three, and four seasons’ data. The data variability was measured through standard deviation (SD) and coefficient of variation (CV%). After separately normalizing each year of the yield data set, the temporal variability (TSD and TCV%) was calculated by grid cell for each field across years. A new index is proposed in this paper, the yield performance index (YPI, the ratio of mean normalized yield (${\overline{Y}}_N$) to the TSD), as an index with a lower value for lower yield and higher temporal variability. Two, three, and four zones were delineated using only YPI. These zones were valuable for identifying areas needing particular attention, with consistently (i) high yields and low variability or (ii) low yields and high variability. Full article

Background: Tay–Sachs disease (TSD) is an autosomal recessive lysosomal storage disorder characterized by the accumulation of GM2 ganglioside due to mutations in the HEXA gene, which encodes the α-subunit of β-Hexosaminidase A. This accumulation leads to significant neuropathological effects and premature death in affected individuals. No effective treatments exist, but enzyme replacement therapies are under investigation. In our previous work, we demonstrated the internalization and efficacy of human recombinant lysosomal β-hexosaminidase A (rhHex-A), produced in the methylotrophic yeast Pichia pastoris, in reducing lipids and lysosomal mass levels in fibroblasts and neural stem cells derived from patient-induced pluripotent stem cells (iPSCs). In this study, we further evaluated the potential of rhHex-A to prevent GM2 accumulation using fibroblast and neuroglia cells from a TSD patient alongside a relevant mouse model. Methods: Fibroblasts and neuroglial cell lines derived from a murine model and TSD patients were treated with 100 nM rhHexA for 72 h. After treatment, cells were stained by anti-GM2 (targeting GM2 ganglioside; KM966) and anti-LAMP1 (lysosomal-associated membrane protein 1) colocalization staining and incubated with 50 nM LysoTracker Red DND-99 to label lysosomes. In addition, GM2AP and HEXB expression were analyzed to assess whether rhHex-A treatment affected the levels of enzymes involved in GM2 ganglioside degradation. Results: Immunofluorescence staining for LysoTracker and colocalization studies of GM2 and Lamp1 indicated reduced lysosomal mass and GM2 levels. Notably, rhHex-A treatment also affected the expression of the HEXB gene, which is involved in GM2 ganglioside metabolism, highlighting a potential regulatory interaction within the metabolic pathway. Conclusions: Here, we report that rhHex-A produced in yeast can efficiently degrade GM2 ganglioside and rescue lysosomal accumulation in TSD cells. Full article

Background/Objectives: Tay–Sachs disease (TSD) is a neurodegenerative disorder caused by a deficiency in β-hexosaminidase A (HexA), which accumulates GM2 gangliosides, primarily in neurons. Currently, therapeutic options are limited, highlighting the need for new strategies such as gene therapy. Despite their effectiveness, viral vectors can elicit adverse immune responses; consequently, non-viral vectors are being explored as an alternative. We have previously investigated the use of CRISPR/Cas9 nickase (nCas9) as a potential tool for treating TSD. Here, we expanded our study by evaluating the PP6D5 polymer as a novel non-viral vector for delivering the CRISPR/nCas9 system to restore HexA activity. Methods: First, we evaluated the PP6D5-mediated CRISPR/nCas9 system’s transfection efficiency in NIH-3T3 fibroblasts, U87MG astrocytoma, SHSY5Y neuroblastoma, and TSD fibroblasts. We then evaluated the potential of PP6D5 to correct the gene defect in TSD fibroblasts. Results: The results showed that PP6D5 exhibited significantly higher transfection efficiency compared to lipofectamine 3000 in all tested cell models. In TSD fibroblasts, transfection with both HEXA and HEXB cDNAs increased the HexA activity levels by up to 7.4-fold, compared to a 3.2-fold increase in cells transfected only with HEXA cDNA after 15 days post-transfection. These levels were up to 4.5-fold higher than those observed in lipofectamine-mediated transfection. Additionally, PP6D5-mediated CRISPR/nCas9-based genome editing led to a significant reduction in the lysosomal mass of TSD fibroblasts. Conclusions: This study provides promising evidence for the use of the PP6D5 polymer as a non-viral vector for delivering CRISPR/nCas9-based gene therapy in TSD. The use of the PP6D5 polymer may offer some advantages that viral vectors cannot, such as a reduction in cytotoxicity and higher TE in difficult-to-transfect cell lines. Furthermore, this type of polymeric vector has not been extensively explored for gene therapy, making this study an important contribution to the development of non-viral delivery systems for the treatment of neurodegenerative diseases. Full article

Combining energy storage with base-load power sources offers an effective way to cover the fluctuation of renewable energy. This study proposes a nuclear–solar hybrid energy system (NSHES), which integrates a small modular thorium molten salt reactor (smTMSR), concentrating solar power (CSP), and thermal energy storage (TES). Two operation modes are designed and analyzed: constant nuclear power (mode 1) and adjusted nuclear power (mode 2). The nondominated sorting genetic algorithm II (NSGA-II) is applied to minimize both the deficiency of power supply probability (DPSP) and the levelized cost of energy (LCOE). The decision variables used are the solar multiple (SM) of CSP and the theoretical storage duration (TSD) of TES. The criteria importance through inter-criteria correlation (CRITIC) method and the technique for order preference by similarity to ideal solution (TOPSIS) are utilized to derive the optimal compromise solution. The electricity curtailment probability (ECP) is calculated, and the results show that mode 2 has a lower ECP compared with mode 1. Furthermore, the configuration with an installed capacity of nuclear and CSP (100:100) has the lowest LCOE and ECP when the DPSP is satisfied with certain conditions. Optimizing the NSHES offers an effective approach to mitigating the mismatch between energy supply and demand. Full article

Polymer blending enhances material properties by combining different polymers, which requires careful consideration of both thermodynamic and technological compatibility. This study investigates the compatibility of polyvinyl chloride (PVC), thermoplastic polyurethane (TPU), and a bio-plasticizer in blends produced via roll milling at various mixing ratios. Compatibility and morphology were analyzed using thermally stimulated discharge (TSD), dynamic mechanical analysis (DMA), and scanning electron microscopy (SEM), while mechanical and thermal properties were assessed by mechanical testing and thermogravimetric analysis (TGA). The PVC/TPU (100/30) blend exhibited superior phase compatibility over PVC/TPU (100/50), as indicated by a single relaxation peak in TSD and DMA, along with a more homogeneous morphology and enhanced tensile properties. The PVC/TPU/bio-plasticizer (100/20/50) blend showed a well-balanced mechanical performance and improved phase homogeneity. The TSD peak maxima trends for the TPU/bio-plasticizer blend highlighted the bio-plasticizer’s dual role in enhancing flexibility at low concentrations while restricting molecular mobility at higher concentrations. TGA revealed TPU’s positive effect on PVC’s degradation profile, while the bio-plasticizer reduced thermal stability. These findings demonstrate that blending PVC, TPU, and bio-plasticizer creates compatible materials with enhanced and diverse properties, making them suitable for industrial applications. Full article

This study developed a dynamic model for the Traffic Speed Deflectometer (TSD) on inverted asphalt pavement structures. It is aimed at evaluating the deflection slope and quantifying measurement errors. First, the reliability of the ABAQUS model in simulating the dynamic response of asphalt pavements was validated by comparing with previous studies. The deflection slope curves of inverted and semi-rigid base pavements with varying thicknesses were compared, revealing that the inverted pavement exhibited complex deflection slope trends in TSD measurements. A significant decrease in peak deflection was observed at 0.15 m from the load gap center with increasing surface thickness. The deflection velocity measurement value of the TSD calibration sensor (S3500) on the inverted asphalt pavement is not zero, which causes the road surface deflection to be lower than the actual deflection, with an error as high as 80.1%, which overestimated the pavement’s structural capacity. These findings suggest that the sensor configuration and measurement strategy of TSD should be reconsidered when applied to inverted asphalt pavement structures. The results provide useful insights that may support the refinement of TSD application strategies. Full article

Traffic safety remains a pressing global concern, with traffic signs playing a vital role in regulating and guiding drivers. However, environmental factors like lighting and weather often compromise their visibility, impacting human drivers and autonomous vehicle (AV) systems. This study addresses critical traffic sign detection (TSD) and classification (TSC) gaps by leveraging the YOLOv8 algorithm to evaluate the detection accuracy and sign quality under diverse lighting conditions. The model achieved robust performance metrics across day and night scenarios using the novel ZND dataset, comprising 16,500 labeled images sourced from the GTSRB, GitHub repositories, and real-world own photographs. Complementary retroreflectivity assessments using handheld retroreflectometers revealed correlations between the material properties of the signs and their detection performance, emphasizing the importance of the retroreflective quality, especially under night-time conditions. Additionally, video analysis highlighted the influence of sharpness, brightness, and contrast on detection rates. Human evaluations further provided insights into subjective perceptions of visibility and their relationship with algorithmic detection, underscoring areas for potential improvement. The findings emphasize the need for using various assessment methods, advanced algorithms, enhanced sign materials, and regular maintenance to improve detection reliability and road safety. This research bridges the theoretical and practical aspects of TSD, offering recommendations that could advance AV systems and inform future traffic sign design and evaluation standards. Full article

Background: Cardiogenic shock (CS), characterized by inadequate tissue perfusion due to cardiac dysfunction, has a high mortality rate despite advances in treatment. Systemic inflammation and organ failure exacerbate the severity of CS. Extracorporeal hemadsorption techniques such as CytoSorb^® have been introduced to control inflammation. However, evidence of their efficacy, particularly in patients on various mechanical circulatory support (MCS) systems, remains limited. Methods: This retrospective study analyzed data from 129 CS patients treated with CytoSorb^® at the University Hospital of Marburg between August 2019 and December 2023. Those patients receiving MCS were grouped according to MCS type: (1) Impella, (2) VA-ECMO, and (3) ECMELLA. The hemodynamic parameters of circulatory support (e.g., MCS flow rates and vasoactive inotropic score, VIS) and laboratory and ventilation parameters were assessed 24 h before start of CytoSorb^® therapy (T1) and 24 h after completion of CytoSorb^® therapy (T2). Results: Of 129 CS patients (mean age: 64.7 ± 13.1 years), 103 (79.8%) received MCS. Comparing T1 and T2, there was a significant reduction in VIS in the entire cohort (T1: 38.0, T2: 16.3; p = 0.002), with a concomitant significant reduction in the level of MCS support in all subgroups, indicating successful weaning. Analysis of laboratory parameters showed significant reductions in lactate (T1: 2.1, T2: 1.3 mmol/L; p = 0.014), myoglobin (T1: 1549.0, T2: 618.0 µg/L; p < 0.01), lactate dehydrogenase (T1: 872.0, T2: 632.0 U/L; p = 0.048), and procalcitonin (T1: 2.9, T2: 1.6 µg/L; p < 0.001). However, a significant decrease in platelets (T1: 140.0, T2: 54.0 tsd/µL; p < 0.001) and albumin (T1: 25.0, T2: 22.0 g/dL; p < 0.001) was also documented. The median SOFA score of the entire cohort was 15.0 (IQR 12.0–16.0), predicting a mortality rate of >80%, which could be reduced to 60.5% in the present study. Conclusions: During CytoSorb^® therapy in CS, a significant reduction in VIS was demonstrated, resulting in improved organ perfusion. Therefore, the results of this study underline that CytoSorb^® therapy can be considered a useful “component” in the complex management of CS, especially when combined with MCS. To refine and optimize treatment strategies in CS, prospective studies are needed to better define the role of hemadsorption. Full article

Maritime behavior detection is vital for maritime surveillance and management, ensuring safe ship navigation, normal port operations, marine environmental protection, and the prevention of illegal activities on water. Current methods for detecting anomalous vessel behaviors primarily rely on single time series data or feature point analysis, which struggle to capture the relationships between vessel behaviors, limiting anomaly identification accuracy. To address this challenge, we proposed a novel vessel anomaly detection framework, which is called the BPEF-TSD framework. It integrates a ship behavior pattern recognition algorithm, Smith–Waterman, and text similarity measurement methods. Specifically, we first introduced the BPEF mining framework to extract vessel behavior events from AIS data, then generated complete vessel behavior sequence chains through temporal combinations. Simultaneously, we employed the Smith–Waterman algorithm to achieve local alignment between the test vessel and known anomalous vessel behavior sequences. Finally, we evaluated the overall similarity between behavior chains based on the text similarity measure strategy, with vessels exceeding a predefined threshold being flagged as anomalous. The results demonstrate that the BPEF-TSD framework achieves over 90% accuracy in detecting abnormal trajectories in the waters of Xiamen Port, outperforming alternative methods such as LSTM, iForest, and HDBSCAN. This study contributes valuable insights for enhancing maritime safety and advancing intelligent supervision while introducing a novel research perspective on detecting anomalous vessel behavior through maritime big data mining. Full article