Search Results (768)

Miniaturized-light-emitting diode (mini-LED) backlights have emerged as the state-of-the-art technology for liquid crystal display (LCD), facilitating the improvement in a high dynamic range (HDR) and power saving. The local dimming technology divides the backlight into several dimming zones. Employing mini-LEDs, whose size ranges from 100 to 200 μm, as the light sources can enlarge the number of zones in the local dimming backlight, fulfilling the requirement for HDR. However, the halo effect still acts as one of the primary technological bottlenecks for mini-LED backlights. In this review, packaging technology of LEDs, color conversion, and the driving scheme of mini-LED backlights have been discussed. The strategies to reduce optical crosstalk in adjacent areas by various improved optical structures or to suppress the halo effect of LCDs by mini-LED backlights are summarized. The development trends of mini-LED backlights are also discussed. Full article

Infrared multi-band small and dim target detection is an important research direction in the fields of modern remote sensing and military surveillance. However, achieving high-precision detection remains challenging due to the small scale, low contrast of small and dim targets, and their susceptibility to complex background interference. This paper innovatively proposes a dual-band infrared small and dim target detection method (MM-IRSTD). In this framework, we integrate a convolutional self-attention mechanism module and a self-distillation mechanism to achieve end-to-end dual-band infrared small and dim target detection. The Conv-Based Self-Attention module consists of a convolutional self-attention mechanism and a multilayer perceptron, effectively extracting and integrating input features, thereby enhancing the performance and expressive capability of the model. Additionally, this module incorporates a dynamic weight mechanism to achieve adaptive feature fusion, significantly reducing computational complexity and enhancing the model’s global perception capability. During model training, we use a spatial and channel similarity self-distillation mechanism to drive model updates, addressing the similarity discrepancy between long-wave and mid-wave image features extracted through deep learning, thus improving the model’s performance and generalization capability. Furthermore, to better learn and detect edge features in images, this paper designs an edge extraction method based on Sobel. Finally, comparative experiments and ablation studies validate the advancement and effectiveness of our proposed method. Full article

In extremely dark conditions, low-light imaging may offer spectators a rich visual experience, which is important for both military and civic applications. However, the images taken in ultra-micro light environments usually have inherent defects such as extremely low brightness and contrast, a high noise level, and serious loss of scene details and colors, which leads to great challenges in the research of low-light image and object detection and classification. The low-light night vision image used as the study object in this work has an excessively dim overall picture and very little information about the screen’s features. Three algorithms, HE, AHE, and CLAHE, were used to enhance and highlight the image. The effectiveness of these image enhancement methods is evaluated using metrics such as the peak signal-to-noise ratio and mean square error, and CLAHE was selected after comparison. The target image includes vehicles, people, license plates, and objects. The gray-level co-occurrence matrix (GLCM) was used to extract the texture features of the enhanced images, and the extracted image texture features were used as input to construct a backpropagation (BP) neural network classification model. Then, low-light image classification models were developed based on VGG16 and ResNet50 convolutional neural networks combined with low-light image enhancement algorithms. The experimental results show that the overall classification accuracy of the VGG16 convolutional neural network model is 92.1%. Compared with the BP and ResNet50 neural network models, the classification accuracy was increased by 4.5% and 2.3%, respectively, demonstrating its effectiveness in classifying low-light night vision targets. Full article

Infrared and visible image fusion (IVIF) fully integrates the complementary features of different modal images, and the fused image provides a more comprehensive and objective interpretation of the scene compared to each source image, thus attracting extensive attention in the field of computer vision in recent years. However, current fusion methods usually center their attention on the extraction of prominent features, falling short of adequately safeguarding subtle and diminutive structures. To address this problem, we propose an end-to-end unsupervised IVIF method (ESFuse), which effectively enhances fine edges and small structures. In particular, we introduce a two-branch head interpreter to extract features from source images of different modalities. Subsequently, these features are fed into the edge refinement module with the detail injection module (DIM) to obtain the edge detection results of the source image, improving the network’s ability to capture and retain complex details as well as global information. Finally, we implemented a multiscale feature reconstruction module to obtain the final fusion results by combining the output of the DIM with the output of the head interpreter. Extensive IVIF fusion experiments on existing publicly available datasets show that the proposed ESFuse outperforms the state-of-the-art(SOTA) methods in both subjective vision and objective evaluation, and our fusion results perform well in semantic segmentation, target detection, pose estimation and depth estimation tasks. The source code has been availabled. Full article

Only some subpopulations of natural killer (NK) cells have cytotoxic functionality, and the effects of anesthetics on these subpopulations are unknown. This study aimed to evaluate the in vitro effects of various anesthetics, both alone and in combination, on the distribution and cytotoxic function of NK cells and their subpopulations. Peripheral blood mononuclear cells (PBMCs) from eight healthy volunteers were treated for 4 h in vitro with dexmedetomidine, remifentanil, lidocaine, propofol, sevoflurane, and combinations in clinically relevant concentrations or left untreated. Flow cytometry was used to quantify the percentage of sampled NK cells and evaluate their distribution (CD56^brightCD16^neg, CD56^brightCD16^dim, CD56^dimCD16^neg, CD56^dimCD16^bright, and CD56^negCD16^bright) and cytotoxicity (Granzyme B (GrzB) and perforin) of NK cell subpopulations. Although the percentage of total NK cells did not change following exposure to anesthesia, the most important cytotoxic subpopulation (CD56^dimCD16^bright NK cells) decreased after exposure to both propofol (−3.58%, p = 0.045) and sevoflurane (−16.10%, p = 0.008) alone, and most combinations, especially in combination with lidocaine (propofol with lidocaine (−9.66%, p = 0.002) and sevoflurane with lidocaine (−21.90%, p < 0.001)). Dexmedetomidine and remifentanil had no effect on CD56^dimCD16^bright NK cells. Furthermore, no anesthetic regimen or combination altered the expression of GrzB and perforin in NK cells or NK cell subpopulations. In short, propofol and sevoflurane suppressed the highly cytotoxic phenotype (CD56^dimCD16^bright) of NK cells, with those exposed to sevoflurane combinations showing greater reductions. Immunosuppression was intensified with the inclusion of lidocaine in the anesthetic regimen. Full article

This study was conducted at a commercial dairy farm in Queensland, Australia to evaluate the effects of feeding a lactic acid bacteria-based direct-fed microbial (DFM) during gestation on the metabolic profile of periparturient dairy cows and its effects on milk production and body weight. A total of 150 multiparous Holstein cows were randomly selected based on parity (2.3) and days in milk (130 DIM) and divided into two groups of 75 cows each (control and DFM). The control cows were assigned to a basal diet consisting of a silage-based partial mixed ration (PMR), concentrate fed in the dairy twice a day, and ad libitum pasture. The DFM group received the same basal diet supplemented with three strains of Lactobacillus top-dressed in the feed. The DFM supplementation continued during both the dry period and the subsequent lactation. A subset of 82 cows (40 control and 42 DFM) were monitored during the calving season (March to July 2022) to assess the metabolic profile and postpartum performance. Blood samples were collected during the periparturient period (−4 to −2 w prepartum, around calving, and at weeks 1, 3, and 6 postpartum) to measure the levels of metabolites, enzymes, and minerals. Overall, the serum glucose, NEFA, and chloride levels were higher, while protein and urea were lower in cows supplemented with the DFM (p < 0.005). The pre-calving levels of glucose were higher and the total bilirubin, urea, and BHB were lower in cows supplemented with DFM than in the control (p < 0.05). The post-calving levels of glucose and Mg were also higher in the DFM cows than in the control cows (p < 0.05). Average milk production at 110 DIM was significantly higher in the DFM cows compared to control cows (p = 0.03). Although the total milk production over 305 days was numerically greater in the DFM cows, the difference was not statistically significant (p = 0.3), whereas the milk protein percentage was higher in the control cows (p = 0.03). The body weight of the DFM cows was greater during the periparturient period (p = 0.001) than that of the control cows. In the DFM cows, glucose levels had a positive correlation (r = 0.16) with milk yield, at 110 DIM, while serum total protein had a positive correlation with body weight (r = 0.32) (p < 0.05). In conclusion, feeding Lactobacillus-based DFM during gestation can positively influence the metabolic profile of periparturient cows, which, in turn, may affect the milk production and body weight of postpartum dairy cows. Full article

In the intricate and dynamic infrared imaging environment, the detection of infrared dim and small targets becomes notably challenging due to their feeble radiation intensity, intricate background noise, and high interference characteristics. To tackle this issue, this paper introduces a lightweight detection and recognition algorithm, named YOLOv5-IR, and further presents an even more lightweight version, YOLOv5-IRL. Firstly, a lightweight network structure incorporating spatial and channel attention mechanisms is proposed. Secondly, a detection head equipped with an attention mechanism is designed to intensify focus on small target information. Lastly, an adaptive weighted loss function is devised to improve detection performance for low-quality samples. Building upon these advancements, the network size can be further compressed to create the more lightweight YOLOv5-IRL version, which is better suited for deployment on resource-constrained mobile platforms. Experimental results on infrared dim and small target detection datasets with complex backgrounds indicate that, compared to the baseline model YOLOv5, the proposed YOLOv5-IR and YOLOv5-IRL detection algorithms reduce model parameter counts by 42.9% and 45.6%, shorten detection time by 13.6% and 16.9%, and enhance mAP0.5 by 2.4% and 1.8%, respectively. These findings demonstrate that the proposed algorithms effectively elevate detection efficiency, meeting future demands for infrared dim and small target detection. Full article

The significance of detecting faint and diminutive space targets cannot be overstated, as it underpins the preservation of Earth’s orbital environment’s safety and long-term sustainability. Founded by the different response characteristics between targets and backgrounds to aberrations, this paper proposes a novel aberration modulation correlation method (AMCM) for dim and small space target detection. By meticulously manipulating the light path using a wavefront corrector via a modulation signal, the target brightness will fluctuate periodically, while the background brightness remains essentially constant. Benefited by the strong correlation between targets’ characteristic changes and the modulation signal, dim and small targets can be effectively detected. Rigorous simulations and practical experiments have validated the remarkable efficacy of AMCM. Compared to conventional algorithms, AMCM boasts a substantial enhancement in the signal-to-noise ratio (SNR) detection limit from 5 to approximately 2, with an area under the precision–recall curve of 0.9396, underscoring its ability to accurately identify targets while minimizing false positives. In essence, AMCM offers an effective method for detecting dim and small space targets and is also conveniently integrated into other passive target detection systems. Full article

For all integers $n\ge 1$ and $k\ge 2$, the Hadamard product $v_1★\cdots ★v_k$ of k elements of $K^{n+1}$ (with K being the complex numbers or real numbers) is the element $v\in K^{n+1}$ which is the coordinate-wise product of $v_1,\dots ,v_k$ (introduced by Cueto, Morton, and Sturmfels for a model in Algebraic Statistics). This product induces a rational map $h:{\mathbb{P}}^n{\left(K\right)}^k⤏{\mathbb{P}}^n\left(K\right)$. When $K=\mathbb{C}$, $k=2$ and $X_i\left(\mathbb{C}\right)\subset {\mathbb{P}}^n\left(\mathbb{C}\right)$, $i=1,2$ are irreducible, we prove four theorems for the case $dim{X}_2\left(\mathbb{C}\right)=1$, three of them with $X_2\left(\mathbb{C}\right)$ as a line. We discuss the existence (non-existence) of a cancellation law for ★-products and use the symmetry group of the Hadamard product. In the second part, we work over $\mathbb{R}$. Under mild assumptions, we prove that by knowing $X_1\left(\mathbb{R}\right)★\cdots ★X_k\left(\mathbb{R}\right)$, we know $X_1\left(\mathbb{C}\right)★\cdots ★X_k\left(\mathbb{C}\right)$. The opposite, i.e., taking and multiplying a set of complex entries that are invariant for the complex conjugation and then seeing what appears in the screen ${\mathbb{P}}^n\left(\mathbb{R}\right)$, very often provides real ghosts, i.e., images that do not come from a point of $X_1\left(\mathbb{R}\right)\times \cdots \times X_k\left(\mathbb{R}\right)$. We discuss a case in which we certify the existence of real ghosts as well as a few cases in which we certify the non-existence of these ghosts, and ask several open questions. We also provide a scenario in which ghosts are not a problem, where the Hadamard data are used to test whether the images cover the full screen. Full article

This study proposes a novel approach for detecting weak small infrared (IR) targets, called double-image and double-local contrast measurement (DDLCM), designed to overcome challenges of low contrast and complex backgrounds in images. In this approach, the original image is decomposed into odd and even images, and the gray difference contrast is determined using a dual-neighborhood sliding window structure, enhancing target saliency and contrast by increasing the distinction between the target and the local background. A central unit is then constructed to capture relationships between neighboring and non-neighboring units, aiding in clutter suppression and eliminating bright non-target interference. Lastly, the output value is derived by extracting the lowest contrast value of the weak small targets from the saliency map in each direction. Experimental results on two datasets demonstrate that the DDLCM algorithm significantly enhances real-time IR dim target detection, achieving an average performance improvement of 32.83%. The area under the ROC curve (AUC) decline is effectively controlled, with a maximum reduction limited to 3%. Certain algorithms demonstrate a notable AUC improvement of up to 43.96%. To advance infrared dim target detection research, we introduce the IFWS dataset for benchmarking and validating algorithm performance. Full article

Background: The effect of the expression of the newly identified immune checkpoint, T cell immunoglobulin and immunoreceptor tyrosine-based inhibition motif domain (TIGIT) on NK cells in core binding factor-acute myeloid leukemia (CBF-AML) remains to be investigated. Methods: Fresh bone marrow samples from a total of 39 newly diagnosed CBF-AML patients and 25 healthy donors (HDs) were collected for testing the phenotype and function state of total NK, CD56^bright, and CD56^dim NK cell subsets after in vitro stimulation. Results: The frequencies of TIGIT⁺ cells in total NK, CD56^bright, and CD56^dim NK cell subsets had no significant difference between patients and HDs. TNF-α and INF-γ levels were uniformly lower in TIGIT⁺ cells than the corresponding TIGIT⁻ cells in all HDs, whereas those for TIGIT⁺ to TIGIT⁻ cells in patients were highly heterogenous; TIGIT expression was not related to PFP and GZMB expression in HDs, whereas it was related to higher intracellular PFP and GZMB levels in patients. Patients’ TIGIT⁺ NK cells displayed lower K562 cell-killing activity than their TIGIT⁻ NK cells. In addition, high frequencies of TIGIT⁺ cells in total NK and CD56^dim NK cells were associated with poor RFS. Conclusions: TIGIT expression affected the diagnostic bone marrow-sited NK cell function and had prognostic significance in CBF-AML patients. Full article

Fatty acid is an important factor affecting the nutritional quality of milk. In this study, we collected and assessed 78,086 milk samples from 12,065 Chinese Holstein cows from 11 farms in Northern China from November 2019 to September 2022. The contents of eight fatty acid groups were predicted using FT-MIRS-based models. The contents of TFAs, SFAs, UFAs, MUFAs, PUFAs, and LCFAs in milk reached the highest at 96–125 DIM, and SCFA and MCFA contents reached the highest at 276–305 DIM. With the increase in somatic cell score, the contents of various fatty acid groups in milk gradually decreased, and the nutritional value of milk and flavor of dairy products gradually deteriorated. The contents of high-quality fatty acids in milk, particularly UFAs and MUFAs, were significantly higher in the non-pregnant state than in the pregnant state. However, SCFA and MCFA contents exhibited the opposite pattern. Our findings provided valuable information on the content and distribution range of fatty acid groups in milk from Chinese Holstein cows. Further analysis is warranted to explore the breeding of Chinese Holstein cows providing milk with abundant beneficial fatty acids. Full article

This narrative review synthesizes recent basic and clinical research on visual disturbances in low-light environments, highlighting the evaluation techniques for these conditions. It focuses on the degradation of visual acuity under dim lighting, exacerbated by pupil dilation, known as night vision disturbance (NVD). Key contributors to NVD include optical scattering, intraocular diffraction, ocular aberrations, and uncorrected refractive errors, all significantly impacting quality of life and functional abilities. This review also examines the effects of aging, eye disorders, surgical interventions, and corneal irregularities on NVD. It details the definitions, distinctions, and measurement methodologies for various optical phenomena, using both objective and subjective approaches, such as visual function questionnaires, simulators, and the light disturbance analyzer (LDA). The LDA is validated for clinical characterization and quantification of light distortion, proving useful in both clinical and research settings. This review advocates for continued innovation in therapeutic interventions to improve patient outcomes and alleviate the impact of visual disturbances. Full article

This paper addresses image enhancement and 3D reconstruction techniques for dim scenes inside the vacuum chamber of a nuclear fusion reactor. First, an improved multi-scale Retinex low-light image enhancement algorithm with adaptive weights is designed. It can recover image detail information that is not visible in low-light environments, maintaining image clarity and contrast for easy observation. Second, according to the actual needs of target plate defect detection and 3D reconstruction inside the vacuum chamber, a defect reconstruction algorithm based on photometric stereo vision is proposed. To optimize the position of the light source, a light source illumination profile simulation system is designed in this paper to provide an optimized light array for crack detection inside vacuum chambers without the need for extensive experimental testing. Finally, a robotic platform mounted with a binocular stereo-vision camera is constructed and image enhancement and defect reconstruction experiments are performed separately. The results show that the above method can broaden the gray level of low-illumination images and improve the brightness value and contrast. The maximum depth error is less than 24.0% and the maximum width error is less than 15.3%, which achieves the goal of detecting and reconstructing the defects inside the vacuum chamber. Full article

Accreting stellar-mass black holes represent unique laboratories for studying matter and radiation under the influence of extreme gravity. They are highly variable sources going through different accretion states, showing various components in their X-ray spectra from the thermal emission of the accretion disc dominating in the soft state to the up-scattered Comptonisation component from an X-ray corona in the hard state. X-ray polarisation measurements are particularly sensitive to the geometry of the X-ray scatterings and can thus constrain the orientation and relative positions of the innermost components of these systems. The IXPE mission has observed about a dozen stellar-mass black holes with masses up to 20 solar masses in X-ray binaries with different orientations and in various accretion states. The low-inclination sources in soft states have shown a low fraction of polarisation. On the other hand, several sources in soft and hard states have revealed X-ray polarisation higher than expected, which poses significant challenges for theoretical interpretation, with 4U 1630–47 being one of the most puzzling sources. IXPE has measured the spin of three black holes via the measurement of their polarisation properties in the soft emission state. In each of the three cases, the new results agree with the constraints from the spectral observations. The polarisation observations of the black hole X-ray transient Swift J1727.8–1613 across its entire outburst has revealed that the soft-state polarisation is much weaker than the hard-state polarisation. Remarkably, the observations furthermore show that the polarisation of the bright hard state and that of the 100 times less luminous dim hard state are identical within the accuracy of the measurement. For sources with a radio jet, the electric field polarisation tends to align with the radio jet, indicating the equatorial geometry of the X-ray corona, e.g., in the case of Cyg X–1. In the unique case of Cyg X–3, where the polarisation is perpendicular to the radio jet, the IXPE observations reveal the presence and geometry of obscuring material hiding this object from our direct view. The polarisation measurements acquired by the IXPE mission during its first 2.5 years have provided unprecedented insights into the geometry and physical processes of accreting stellar-mass black holes, challenging existing theoretical models and offering new avenues for understanding these extreme systems. Full article