Search Results (5,919)

Industrial defect detection is a fundamental task in intelligent manufacturing, yet existing object detection methods often struggle with the characteristics of industrial defects, such as small size, irregular shapes, and complex visual backgrounds. Moreover, most detection models are designed primarily for natural image datasets, resulting in limited robustness when deployed in real-world industrial environments. To address these challenges, this research focuses on industrial defect detection and presents contributions at both the dataset and method levels. First, two real-world industrial defect datasets collected from actual production lines are introduced, namely, the Stator Housing Defect Dataset and the Cover Plate Silicone Defect Dataset, which cover representative inspection scenarios with distinct defect characteristics. Second, we propose a detection transformer with a mixed encoder for industrial scenarios (IM-DETR). By integrating heterogeneous multi-scale feature representations, the proposed framework jointly enhances local detail sensitivity and global contextual reasoning without relying on complex post-processing. Extensive experiments on the proposed industrial datasets demonstrate that IM-DETR consistently outperforms existing state-of-the-art detection methods, particularly in scenarios involving small defects, complex backgrounds, and appearance ambiguity, validating the effectiveness and robustness of the proposed approach. Full article

Recent advances in enone chemistry have enabled the development of structurally optimized derivatives with improved anticancer selectivity. In this study, the cytotoxic activity and underlying mechanisms of sodium salts of four α,β-unsaturated enones (ES1–ES4), synthesized from vanillin-based scaffolds, were evaluated in human colorectal carcinoma (HCT-116), cervical adenocarcinoma (HeLa), and normal lung fibroblast (MRC-5) cell lines. All compounds exhibited concentration- and time-dependent cytotoxicity, with ES2 showing the highest potency (IC₅₀ = 14.25 μM in HCT-116 and 18.12 μM in HeLa at 72 h) and minimal toxicity toward MRC-5 cells (IC₅₀ > 90 μM). Although cisplatin demonstrated greater overall cytotoxicity, the enone salts displayed significantly higher selectivity indices, indicating a more favorable therapeutic window. Phase-contrast microscopy revealed characteristic morphological features of apoptosis, including cell rounding and membrane blebbing. Mechanistic investigations confirmed mitochondrial-mediated apoptosis, evidenced by increased early and late apoptotic populations, Bax upregulation, Bcl-2 downregulation, and caspase-3 activation. JC-10 staining demonstrated mitochondrial membrane depolarization accompanied by cytochrome c release. In addition, cell cycle analysis revealed pronounced G2/M phase arrest, particularly in HCT-116 cells. Collectively, these findings indicate that vanillin-derived enone sodium salts exert selective anticancer effects through mitochondrial apoptosis and cell cycle disruption, supporting their potential as low-toxicity anticancer candidates. Full article

Background/Objectives: Among cotton species, Gossypium barbadense produces the strongest fibers. Examining cytoskeletal dynamics in single epidermal cells of G. barbadense ovules offers a direct approach to investigating fiber quality. Microtubules are major cytoskeletal components whose organization and dynamics are precisely regulated by microtubule-associated proteins (MAPs). However, information on the TPX2 family remains limited, and characterizing its features in G. barbadense is critical to clarifying the role of TPX2 family members in fiber strength formation. Methods: Using the Arabidopsis thaliana TPX2 sequence as a reference, 40, 49, 26, and 26 TPX2 family members were identified in the genomes of G. barbadense, Gossypium hirsutum, Gossypium arboreum, and Gossypium raimondii, respectively. We further analyzed the expression pattern of GbTPX2-35 and validated its function via virus-induced gene silencing (VIGS). Results: In G. barbadense, GbTPX2-35 (Gbar_D11G59825.1) was significantly upregulated in fiber samples of the parental lines at 25 days post-anthesis, and this expression pattern was further validated in G. barbadense lines with extreme fiber strength phenotypes. Next, VIGS-mediated silencing of GbTPX2-35 downregulated the transcript levels of cellulose synthase and microtubule-related protein genes, a finding further validated by mature fiber strength phenotypic data. Conclusions: This study preliminarily validated a pathway in which GbTPX2-35 regulates fiber strength by coordinating cellulose biosynthesis with microtubule cytoskeleton dynamics, providing valuable candidate genes and theoretical support for molecular breeding of high-strength cotton fibers. Full article

To address the issues of load aggregation and traffic congestion in faulty electricity–transportation coupled networks (ETCNs), this paper proposes an electric vehicle (EV) charging guidance strategy based on Graph Reinforcement Learning (GRL). First, a graph-structured feature extraction model is developed. The GraphSAGE module is employed to capture the multi-scale spatiotemporal features of the ETCN. The topological changes and energy-information interaction characteristics under fault scenarios are analyzed. Second, a Finite Markov Decision Process (FMDP) framework is established to address the stochastic and dynamic nature of EV charging behavior. The charging station selection and route planning problem is transformed into an agent decision-making process. A reward function is designed by incorporating voltage constraints, traffic flow constraints, and state-of-charge margin penalties. This ensures a balanced consideration of power grid security and traffic efficiency. The FMDP model is then solved using a Deep Q-Network (DQN) to achieve optimal EV charging guidance under fault conditions. Finally, case studies are conducted on a coupled simulation scenario consisting of an IEEE 33-node power distribution system and a 23-node transportation network. Results show that the proposed method reduces the system operation cost to 218,000 CNY, controls the voltage deviation rate of the distribution network at 3.1% in line with the operation standard, and enables the model to achieve stable convergence after only 250 training episodes. It can effectively optimize the charging load distribution and maintain the voltage stability of the power grid under fault conditions. Full article

Plant-derived saponins have attracted significant interest for their potential to promote apoptotic cell death and enhance antitumor immune responses through immunogenic cell death (ICD). Akebia quinata, a saponin-rich medicinal plant, exhibits diverse pharmacological properties; however, studies on its seeds are limited, and their immunomodulatory activity in cancer remains largely unexplored. In this study, A. quinata seeds were extracted using 70% ethanol, and the phytochemical profile was characterized using UHPLC–QTOF MS/MS. We investigated the anticancer properties of A. quinata seed extract (AQSE), focusing on its role in inducing apoptosis and ICD in non-small cell lung cancer (NSCLC). In human NSCLC cell lines (A549 and H460), AQSE exhibited potent cytotoxic effects in a dose-dependent manner. Flow cytometric analysis confirmed the induction of apoptosis, evidenced by a significant increase in Annexin V-positive cells and an elevated sub-G1 population. Mechanistically, AQSE treatment induced cell death by simultaneously inhibiting the survival-promoting MEK/ERK/CREB axis and activating the stress-responsive JNK pathway. Furthermore, AQSE triggered hallmark features of ICD, characterized by surface exposure of calreticulin and the release of extracellular HMGB1 and ATP. Most importantly, an in vivo vaccination assay using a syngeneic mouse model demonstrated that immunization with AQSE-treated dying cells significantly suppressed tumor growth upon rechallenge, confirming the establishment of antitumor immunological memory. Additionally, bioassay-guided fractionation revealed that the anticancer activity was primarily concentrated in the ethyl acetate fraction. These findings suggest that AQSE exerts anticancer effects via the induction of apoptosis and ICD, highlighting its potential as a promising natural candidate for the development of novel therapeutic strategies against NSCLC. Full article

To address the challenges of small targets, severe background clutter, and high deployment cost in UAV-based power-line defect detection, this paper proposes a lightweight defect detection model based on an improved YOLOv8n. In the downsampling stage, we design an improved lightweight adaptive downsampling module (ADownPro) to replace part of conventional convolutions, which uses a dual-branch parallel structure for stronger feature interaction and depthwise separable convolutions (DSConv) for complexity reduction. In the feature extraction stage, an integration of cross-stage partial connections and partial convolution (CSPPC) is proposed to replace the C2F module for efficient multi-scale feature fusion. In the detection head, mixed local channel attention (MLCA), which combines channel-spatial information and local–global contextual features, is introduced to strengthen defect-focused representations under complex backgrounds. For the loss function, a scale-annealed mixed-quality EIoU loss (SAMQ-EIoU) is proposed by combining iso-center scale transformation, scale factor annealing and focal-style quality reweighting to improve localization accuracy at high IoU thresholds. Experiments on a constructed dataset covering six typical defect categories show that the improved YOLOv8n achieves 91.4% mAP@0.50 and 64.5% mAP@0.50:0.95, with only 1.59 M parameters and 4.9 GFLOPs. Compared with mainstream detectors, the proposed model achieves a better balance between detection accuracy and lightweight design. In particular, compared with the recently proposed YOLOv8n-DSN and IDD-YOLO, it improves mAP@0.50 by 0.6% and 0.8%, and mAP@0.50:0.95 by 1.2% and 4.8%, respectively, while further reducing the parameter count by 1.00 M and 1.26 M, and the FLOPs by 1.7 G and 0.2 G. Moreover, the cross-dataset evaluation on the public UPID and SFID datasets further demonstrate the robustness and generalization ability of the proposed method. Full article

To address the challenges posed by the strong uncertainty of high-proportion renewable energy sources (RES) to the secure and stable operation of distributed real-time power dispatch (D-RTPD) in new-type power systems, this paper proposes an integrated solution combining a neighborhood feature aggregation-based graph attention network (NFA-GAT) and multi-agent deep deterministic policy gradient (MADDPG). First, the D-RTPD problem is modeled as a decentralized partially observable Markov decision process (Dec-POMDP), which effectively captures the stochastic game characteristics of multi-regional agents and the partial observability of grid states. Second, the NFA-GAT is designed to enhance agents’ perception of grid operating states: by introducing a spatial discount factor, it realizes rational aggregation of multi-order neighborhood information while modeling the attenuation of electrical quantity influence with topological distance. Third, a prior-guided mechanism is integrated into the MADDPG framework to eliminate constraint-violating actions by setting their actor logits to negative infinity, improving training efficiency and strategy reliability. Simulation validations on the IEEE 118-bus test system (75.2% RES installed capacity ratio) show that the proposed method achieves efficient training convergence. Compared with the multi-layer perceptron (MLP) structure, it attains higher cumulative reward values and scenario win rates. When compared with traditional model-driven (ADMM) and data-driven (Q-MIX) methods, the proposed method balances solution efficiency, operational safety (98.7% maximum line load rate, zero power flow violation rate), and economic performance ($12,845 daily dispatch cost), providing a reliable technical support for D-RTPD under high-proportion RES integration. Full article

To address inverse-velocity amplification and numerical instability of axisymmetric parallel mechanisms near dead-point regions, this paper proposes a low-dimensional feature representation and stable inverse-solving framework based on Fourier-encoded Plücker line fields. The limb axes are first represented by normalized Plücker line vectors, and the discrete rod-axis set is lifted to a circumferential continuous line field. A compact feature vector composed of first-order Fourier coefficients is then constructed, from which the continuous feature coefficients and the corresponding feature Jacobian are derived in closed form. Under constant-length constraints, feasible sensitivity and worst-case gain are introduced to characterize local inverse amplification, and a weighted damped KKT inverse solver is formulated to obtain globally bounded inverse solutions for feature velocities. Numerical results show that, in the ideal axisymmetric model, higher-order harmonics remain at numerical-residual levels and the first-order truncation stays dominant, while the most unfavorable amplification location is governed by the trough of feasible sensitivity. For fully reachable targets, the proposed solver reduces the peak generalized velocity by about 4.32%. For targets containing unreachable components, the damped KKT inverse introduces only a small additional residual while keeping the velocity bounded. Additional tests under mild geometric perturbations show that non-ideal errors mainly affect low-order fitting accuracy and higher-order spectral leakage, whereas the peak worst-case gain and the peak-shaving ratio remain largely stable. These results demonstrate that the proposed framework provides a unified description for inverse velocity mapping of axisymmetric parallel mechanisms with analytical interpretability, global boundedness, and robustness under mild geometric imperfections. Full article

This study uses a process-based technical approach combining X-ray radiography, visible and raking-light examination, and cross-modal image comparison to assess the construction logic of a Cubist-period painting associated with Pablo Picasso. Across the X-ray dataset, the painting shows orientation-dependent structural coherence, hierarchically organized planning seams with mechanically sensible terminations, and a multistage base-layer construction that remains interpretable under grayscale inversion and rotation. Visible and raking-light images reveal physically incised inscriptions, names, places, and numerals with later paint settling into grooves and, in some areas, bridging over them, establishing a clear sequence in which inscriptions precede overpainting. Reduced color and polarity-inversion checks confirm that these features are carried by luminance and surface relief rather than color artifacts. Together, these converging lines of evidence support an interpretation of a multi-campaign, orientation-aware construction process consistent with documented working methods from Picasso’s relevant period and difficult to replicate by superficial imitation. Full article

Recently, oral decitabine/cedazuridine has been approved for the treatment of AML patients who are not eligible for intensive chemotherapy. Although efficacy data on phenotypic features and the prognostic impact of molecular aberrations at diagnosis were reported in the registration study, serial determinations of the mutational landscape during therapy were not reported. In this study, we present data on a subset of five patients in whom molecular markers were monitored during treatment with oral decitabine/cedazuridine within the registration study. The following observations were made in individual patients. Regarding the changes in the molecular landscape during therapy in four/five patients, there was no major (>50%) reduction in mutated AML clones. There was only one patient with CRi and more than 50% reduction in the VAF of clones with molecular aberrations, including RAS pathway mutations. We observed a marked drop of blast cells (>50%) in two other patients without changes in the molecular profile. The overall survival was significantly longer in patients with CRi and PR, respectively, as compared to patients with no response. Finally, four/five (80%) of patients had druggable molecular aberrations at diagnosis, including mutations in IDH2 (2/5), NPM1 (2/5), and FLT3 (1/5). Our results show that in the majority of patients, changes in the genetic profiles are not seen despite decreases in blast cells in some patients. Disease-modifying activity with decreases in mutated clones is rare. Although the exact mechanism behind our findings remains undetermined, they are in line with the proposed effects of HMA on epigenetics in leukemia cells. Full article

Ensuring the structural reliability of power transmission networks is a fundamental prerequisite for the stable operation of modern energy systems. To address the challenges posed by complex weather interference and the small scale of insulator defects during power line inspections, this paper proposes LID-YOLO, a lightweight insulator defect detection network. First, to mitigate image feature degradation caused by weather interference, we design the C3k2-CDGC module. By leveraging the input-adaptive characteristics of dynamic convolution and the spatial preservation properties of coordinate attention, this module enhances feature extraction capabilities and robustness in complex weather scenarios. Second, to address the detection challenges arising from the significant scale disparity between insulators and defects, we propose Detect-LSEAM, a detection head featuring an asymmetric decoupled architecture. This design facilitates multi-scale feature fusion while minimizing computational redundancy. Subsequently, we develop the NWD-MPDIoU hybrid loss function to balance the weights between distribution metrics and geometric constraints dynamically. This effectively mitigates gradient instability arising from boundary ambiguity and the minute size of insulator defects. Finally, we construct a synthetic multi-weather condition insulator defect dataset for training and validation. Compared to the baseline, LID-YOLO improves precision, recall, and mAP@0.5 by 1.7%, 3.6%, and 4.2%, respectively. With only 2.76 M parameters and 6.2 G FLOPs, it effectively maintains the lightweight advantage of the baseline, achieving an optimal balance between detection accuracy and computational efficiency for insulator inspections under complex weather conditions. This lightweight and robust framework provides a reliable algorithmic foundation for automated grid monitoring, supporting the continuous and resilient operation of modern energy systems. Full article

The brain’s primary sensory processing areas often present a topographical organization and are distributed following hierarchical architecture, permitting the integration of the information in higher levels of its hierarchy: a process referred to as multisensory integration. A system with such characteristics naturally computes in a parallel and distributed manner and is based in associations between the different symbols built from our perceptions of the environment. In this work, we take inspiration from the sensory processing areas of the brain and propose proof-of-concept of a multi-layered neuromorphic system with parallel and distributed computing capabilities by means of simulation. The proposed neuromorphic architecture is constituted by identical self-organizing modules which are trained with on-line unsupervised-friendly learning rules, such as the spike-timing-dependent plasticity (STDP). These self-organizing modules are constituted by oxide-based resistive random access memory (OxRAM) devices, which play the analog synaptic role. The different modules display a topographical organization according to the input dataset features they have been trained with and are organized following a hierarchical system. The system exhibits conceptual associative behavior between inputs with clustering capabilities, able to classify inputs which have never been seen before by the system, according to their similarity with the ones it has been trained with. Full article

Field-effect transistor (FET) biosensors using graphene have become one of the most promising biosensing platforms for the early diagnosis of diseases with features such as high sensitivity, label-free detection and application compatibility with point-of-care systems. Herein, we critically discuss recent advances in graphene FET (GFET) biosensor development toward clinically relevant biomarkers associated with representative diseases including cancer, neurodegenerative disease, infectious disease, and inflammatory conditions. Recent progress was reviewed to evaluate GFET architectures, surface functionalization methods, and detection quality. The biomarkers explored were clusterin in Alzheimer’s disease, thrombin in coagulopathy, estrogen receptor α (ER-α) in breast cancer, Carcinoembryonic antigen in lung cancer, microRNAs for malignant tumors, exosomes derived from HepG2 for the hepatocellular carcinoma (HCC) cell line, interleukin-6 (IL-6) for chronic obstructive pulmonary disease (COPD), Polyclonal antibodies and antigens (P24) for HIV and prostate-specific antigen for prostate cancer. The developed devices demonstrate ultralow detection limits at femtomolar to attomolar concentrations with the aid of designed antibodies, aptamers and nanomaterials. Herein, this review presents the sensing mechanisms and biomedical application of various GFET platforms, focusing on their emerging potential as next-generation platforms for rapid, non-invasive and point-of-care diagnostics. Full article

Map conflation is essential for integrating heterogeneous road datasets, but it often requires region- and data-specific algorithm design to automate the complex identification of feature-to-feature correspondences. This effort is increased when only cartographic products are available instead of GIS-ready vectors since both digitization or matching corresponding features manually are labor-intensive. In this study, we assess the performance of a multimodal LLM, GPT-5 “thinking” mode for map conflation directly on a PDF map where road networks from TomTom and OpenStreetMap are embedded as colored polylines. We instruct the LLM to interpret the PDF, extract road geometries and their identifiers, and generate both strict 1:1 and flexible M:N matches. In any hybrid-patterned network cases located around Bosphorus, Istanbul, while M:N matching process increased the number of matches, it also increased false positives and lowered overall F1 scores. In contrast, 1:1 matching produced more balanced correctness-completeness results. The model achieves its highest performance in the cellular-patterned networks. The results show that LLM-based matching can detect a substantial share of true correspondences in such a challenging hybrid setting, but performance clearly depends on the matching strategy: strict or flexible. It highlights both the potential promise and the current limitations of matching embedded road lines. Full article

Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by immune dysregulation and systemic inflammation, with the cardiovascular (CV) system representing a major yet frequently under-recognized target. Cardiac involvement spans from subclinical myocardial inflammation to overt pericardial disease, myocarditis, valvular abnormalities, coronary microvascular dysfunction, and accelerated atherosclerosis. Given that CV disease remains a leading cause of morbidity and mortality in SLE, early detection of silent cardiac injury is crucial. Aim: This review aims to provide a comprehensive and clinically oriented overview of CV involvement in SLE, focusing on the role of multimodal cardiac imaging in the detection, characterization, and risk stratification of cardiac abnormalities, as well as its potential implications for clinical management and preventive strategies. Methods: This narrative review is based on a structured, non-systematic search of PubMed (2013–2026), combining the term “systemic lupus erythematosus” with imaging-related keywords including “transthoracic echocardiography,” “cardiac magnetic resonance,” and “cardiac computed tomography.” English-language studies in adult populations were screened and selected according to clinical relevance, methodological robustness, and contribution to understanding SLE-related cardiac involvement. Discussion: Multimodal cardiac imaging plays a central role in the evaluation of SLE-related cardiac disease. Transthoracic echocardiography (TTE) represents the first-line modality for the assessment of ventricular function, pericardial disease, and valvular abnormalities, while deformation imaging enables the detection of subtle myocardial dysfunction. Cardiac magnetic resonance (CMR) provides comprehensive tissue characterization, allowing differentiation between active inflammation and chronic fibrosis. Cardiac computed tomography (cCT) identifies subclinical coronary atherosclerosis and high-risk plaque features, whereas nuclear imaging techniques offer insight into inflammatory activity and microvascular dysfunction. Conclusions: An integrated, imaging-based approach enables early diagnosis, refined CV risk stratification, longitudinal monitoring, and personalized therapeutic strategies. Multimodal imaging thus represents a key pillar of precision medicine in lupus-associated CV disease. Full article