Visual Images Research Articles

Condition assessment of bolted connections in steel structures using deep learning

Steel structure connections are prone to bolt loosening and subsequent loss of strength or stability if not inspected periodically. Use of noncontact sensing and artificial intelligence can substantially increase the safety and efficiency of these inspections; however, the existing defect detection models do not account for variability of defects in both missing and loosened bolt connections. Furthermore, the performance of available deep learning models can be substantially diminished due to the presence of background noise in images of real structures. Finding these defects could be even more difficult in highway steel structures due to the complex and continuously changing nature of background. Additionally, no datasets are available to properly use these models for defect detection. In this paper, Mask RCNN (MRCN), You Only Look Once (YOLO), and Faster RCNN (FRCNN) models were adapted for investigation. These models were trained and tuned on a unique dataset-developed by the Authors comprised of 1100 raw images with connection defects of laboratory-made and in-service structures. A series of image enhancement and augmentation techniques were used to superimpose bolt and nut defects on intact images of in-service ancillary structure and to increase the size in training dataset to 1500. After models achieved satisfactory performance in laboratory images, they were tested on field and lab data (including 200 images with defective bolt). Thermal images were used to develop an efficient way to mask image background in visual images. The result indicated that removing background decreased the false positive rate (FRP) around 65% between the investigated deep learning models on UAS image. Overall accuracy of these models estimated between where 90% and 93%when FRCNN and Mask RCN.

Facile synthesis of olive oil-incorporated oleofilms via high-power ultrasonic emulsification: A sustainable packaging model.

This study aimed to construct oleofilms containing a binary mixture of proteins (soy protein hydrolysate and gelatin) and lipids (olive oil, stearic acid, and lecithin) using various ultrasonic emulsification processes. Initially, oleogels (OG20, OG40, OG60, OG80, and OG100) were fabricated with different sonication powers (20%-100%), along with control (OG) without sonication. Macrostructure, FTIR, DSC, stability coefficient (57.27%-79.52%), oil-binding capacity (68.38%-97.47%), and particle size (1364-3532nm) tests were performed on the oleogels. Oleofilms (OF, OF20, OF40, OF60, OF80, and OF100) were then formulated using the respective oleogels. Their visual, surface, and cross-sectional images were evaluated. The thickness (0.18-0.25mm) and water content (7.32%-11.73%) of oleofilms were investigated. Alterations in color and opacity (3.50-5.49) of the oleofilms were apparent. OF80 exhibited lower water (0.44g.mm/m2.h.kPa)/oxygen permeability (peroxide value: 2.31-14.30meq O2/kg), along with improved mechanical properties (tensile strength: 3.25MPa; elongation at break: 128.23%). OF80-coated pineapples demonstrated the highest resistance to spoilage.

Integrating EnlightenGAN for enhancing car logo detection under challenging lighting conditions

Abstract Detecting small objects in challenging lighting conditions is a critical task with broad applications in computer vision, surveillance, and medical imaging. Small objects are especially vulnerable to detection errors due to their size and the significant impact of varying lighting conditions, such as low-light environments or high glare. Furthermore, the lack of a benchmark dataset that accurately simulates real-world scenarios makes it difficult to evaluate the performance of detection models in such conditions. In the proposed paper, we address these challenges by developing a comprehensive dataset specifically designed for car logo detection under diverse and challenging lighting environments. The dataset incorporates variations in lighting, viewing angles, and object scales to replicate real-world conditions. We also introduce a detection pipeline integrating EnlightenGAN for low-light image enhancement, which preserves fine details and textures, and YOLOv7 and YOLOv8 for improved detection accuracy. By enhancing image quality and refining object detection models, our approach significantly boosts the detection of small car logos, even in challenging lighting scenarios where traditional models tend to fail. Experimental results show that YOLOv8 outperforms YOLOv7 by approximately 7%, with YOLOv8 achieving a mean Average Precision (mAP) of 73.3% on the augmented dataset compared to 68.4% for the original dataset and 67.2% for the processed images. These results validate the effectiveness of GAN-based enhancement techniques in preserving small object details and improving detection performance. Additionally, introducing a new dataset and benchmark provides a valuable resource for future research in small object detection under complex lighting conditions.

A novel approach to visual image encryption: 2D hyperchaos, variable Josephus, and 3D diffusion

Abstract With the development of the Internet, image encryption technology has become critical for network security. Traditional methods often suffer from issues such as insufficient chaos, low randomness in key generation, and poor encryption efficiency. To enhance performance, this paper proposes a new encryption algorithm designed to optimize parallel processing and adapt to images of varying sizes and colors. The method begins by using SHA-384 to extract the hash value of the plaintext image, which is then processed to determine the chaotic system’s initial value and block size. The image is padded and divided into blocks for further processing. A novel two-dimensional infinite collapses hyperchaotic map (2D-ICHM) is employed to generate the intra-block scrambling sequence, while n improved variable Joseph traversal sequence is used for inter-block scrambling. After removing the padding, 3D forward and backward shift diffusions, controlled by the 2D-ICHM sequences, are applied to the scrambled image, producing the ciphertext. Simulation results demonstrate that the proposed algorithm outperforms others in terms of entropy, anti-noise resilience, correlation coefficient, robustness, and encryption efficiency.

Sensitization of Lanthanides with Metal Chalcogenides Nanocrystals: Night Vision Imaging and Security Encryption

AbstractThe sensitization of lanthanide ions by semiconductor nanocrystals offers promising potential for developing luminophores with high quantum yields, promising for light harvesting and imaging applications. This study introduces lanthanide‐doped CdS nanocrystals fabricated through a phosphine‐free method. Lanthanide ions such as Yb3+, Tm3+, Ho3+, and Er3+ are successfully doped into the CdS matrix, resulting in tunable NIR emissions ranging from 950 to 1600 nm. These nanocrystals exhibit visible–NIR emission with a total photoluminescence quantum yield of ≈41 ± 3%. Steady‐state and time‐resolved spectroscopic studies confirm the broadband sensitization of lanthanide ions by the CdS host. Binary ions‐doped CdS nanocrystals (CdS:Yb3+, Er3+/Ho3+/Tm3+) demonstrate both upconversion and downshifting processes within a single system. Furthermore, the NIR emission is enhanced under CdS excitation compared to direct lanthanide sensitization. Time‐resolved photoluminescence and transient absorption studies reveal the efficient energy transfer processes from CdS to the lanthanide dopants. Additionally, the NIR emission in CdS:Ln3+ embedded polymethyl methacrylate nanocomposites remains highly stable even months after fabrication, making them suitable for night vision imaging and security encryption. This study, therefore, advances the development of smart visible‐to‐NIR light sources for optoelectronic applications, including enhanced anti‐counterfeiting measures and night vision imaging.

Machine learning demonstrates clinical utility in distinguishing retinoblastoma from pseudo retinoblastoma with RetCam images

ABSTRACT Background Retinoblastoma is diagnosed and treated without biopsy based solely on appearance (with the indirect ophthalmoscope and imaging). More than 20 benign ophthalmic disorders resemble retinoblastoma and errors in diagnosis continue to be made worldwide. A better noninvasive method for distinguishing retinoblastoma from pseudo retinoblastoma is needed. Methods RetCam imaging of retinoblastoma and pseudo retinoblastoma from the largest retinoblastoma center in the U.S. (Memorial Sloan Kettering Cancer Center, New York, NY) were used for this study. We used several neural networks (ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-152, and a Vision Image Transformer, or VIT), using 80% of images for training, 10% for validation, and 10% for testing. Results Two thousand eight hundred eighty-two RetCam images from patients with retinoblastoma at diagnosis, 1,970 images from pseudo retinoblastoma at diagnosis, and 804 normal pediatric fundus images were included. The highest sensitivity (98.6%) was obtained with a ResNet-101 model, as were the highest accuracy and F1 scores of 97.3% and 97.7%. The highest specificity (97.0%) and precision (97.0%) was attained with a ResNet-152 model. Conclusion Our machine learning algorithm successfully distinguished retinoblastoma from retinoblastoma with high specificity and sensitivity and if implemented worldwide will prevent hundreds of eyes from incorrectly being surgically removed yearly.

Evaluation of Landscapes and Soundscapes in Traditional Villages in the Hakka Region of Guangdong Province Based on Audio-Visual Interactions

Traditional villages in the Hakka region of Guangdong Province have attracted significant attention for their unique cultural heritage and traditional lifestyles. Their favourable audio-visual environments offer immersive and realistic experiences for both residents and visitors. Thus, we selected four representative villages and used semantic segmentation to extract the core visual elements (sky, vegetation, construction, and dynamic) from visual landscape images. Audio-visual interaction experiments and subjective surveys were conducted to investigate the participants’ evaluations of the visual landscape and soundscape to explore the mechanisms of audio-visual interaction. The results revealed that different audio-visual combinations significantly influenced the participants’ visual landscape satisfaction, acoustic comfort, and audio-visual harmony evaluations. Specifically, visual images of natural spaces with a high proportion of sky (24.54%) and vegetation (72.56%), matched with natural sounds (with a sound pressure level of approximately 55 dB) such as birdsong, wind, and flowing water, received excellent ratings for both visual landscape satisfaction and acoustic comfort evaluations. Moreover, the findings further revealed that coordination between visual and audio materials was crucial for enhancing the participants’ perceptions and assessments, highlighting the importance of audio-visual coordination in creating harmonious environments. These findings provide recommendations for spatial planning, landscape design, and soundscape optimisation in traditional villages.

Topological importance of CD8+ T-cell enrichment in the tumor microenvironment of classic Hodgkin lymphoma.

Classic Hodgkin lymphoma (CHL) histologically consists of Hodgkin Reed-Sternberg (HRS) cells and the tumor microenvironment (TME), but the relationship between TME characteristics and clinical features of CHL remains unclear. We aimed to investigate the effects of the TME structure on the outcomes of patients with CHL. We performed a high-throughput analysis of HRS cells and their topological relationship with the reactive immune cells in the TME. After multiplexed immunofluorescence labeling against CD4, CD8, CD30, CD68, CD163, PD-1, and PD-L1, visual images were analyzed. Phenotypes were assigned to all reactive cells, such as CD4+ and CD8+ T-cells and macrophages. Since the densities of PD1+/CD4+ T-cells, CD8+ T-cells, and PD-L1+ macrophages were significantly higher in the area < 60μm than in the area < 120μm from each HRS cell in 45 tissue samples from 34 patients with CHL, we further analyzed the TME-component cells by focusing on the 60μm radius in the initial samples. TMEs containing > 15 CD8+ T-cells were associated with a significantly better 3-year progression-free survival than those with ≤ 15 CD8+ T-cells (100% vs. 53%, p = 0.006). In comparison with TMEs containing ≤ 15 CD8+ T-cells, TMEs containing > 15 CD8+ T-cells had significantly more PD-L1- macrophages (mean 3 vs. 1 cell, p = 0.015) and fewer PD-1+/CD4+ T-cells (mean 16 vs. 28 cells, p = 0.036). Epstein-Barr virus positivity in HRS cells was significantly associated with a higher number of macrophages in the 60μm radius area. In conclusion, the TME structure in patients with CHL can differ, enabling precision therapies.

A Multimodal Social Semiotic Analysis of Typography and Layout in ‘’Alexander Master’s Novel Stuart: A Life Backwards’’

The output of multimodal texts has increased over the past 20 years due to advances in technology and culture, which has accelerated the demand for tackling multimodal additional semiotic modes for meaning making, such as typography, layout ,visual images, colour, etc. The theory of multimodality, as supposed by Kress and Van Leeuwen (2001–2006), Baldry and Thibaut (2006), is applied to a potential framework for the analysis of multimodal novels that use not only wording structure but also many semiotic modes in the formation of meaning. The social approach to multimodality, in brief, is based on Halliday's linguistic description and adhered to the multimodal assumption that "mutual propositions are employed and worked together in various modes" (Kress and Leeuwen,2002:3). Thus, Kress and Van Leeuwen (1996:39), in their book ‘’Reading Images’’ investigate the level for which the underlying theories of Halliday's framework to language are feasible and applicative to visual communicative management, and they create a visual system that mostly makes use of Halliday's principles and concepts. The study is concerned with the investigation of a multimodal analysis of the multiple elements of various modes of typographic devices, and layout design used in the selected novel. This selected text ‘’Stuart: A Life Backwards’’ is a multimodal novel since the writer Masters does not rely on linguistic verbal tools, he extends them to incorporate a variety of visual semiotic modes and graphic communicative elements that will intensify his understanding of storytelling and narrative development. The study has shown that an extension to the mode of wording formation is performed in the multimodal analysis by incorporating more multimodal tackling features of typographic principles and layout framing devices in creating an appropriate comprehensive meaning construction in Masters’ selected narration.

Autonomous Landing Guidance for Quad-UAVs Based on Visual Image and Altitude Estimation

In this paper, an autonomous landing guidance strategy is proposed for quad-UAVs, including landing marker detection, altitude estimation, and adaptive landing commands generation. A double-layered nested marker is designed to ensure that the marker can be captured both in high and low altitudes. A deep learning-based marker detection method is designed where the intersection of union is replaced by the normalized Wasserstein distance in the computation of non-maximum suppression to improve the detection accuracy. The UAV altitude measured by inertial measurement unit is fused with vision-based altitude estimation data to improve the accuracy during the landing process. An image-based visual servoing method is designed to guide the UAV approach to the landing marker. Both simulation and flight experiments are conducted to verify the proposed strategy.

Relationship Between Intraocular Pressure and the True Rate of Functional and Structural Progression in the United Kingdom Glaucoma Treatment Study.

To investigate the effect of average intraocular pressure (IOP) on the true rate of glaucoma progression (RoP) in the United Kingdom Glaucoma Treatment Study (UKGTS). UKGTS participants were randomized to placebo or Latanoprost drops and monitored for up to two years with visual field tests (VF, 24-2 SITA standard), IOP measurements, and optic nerve imaging. We included eyes with at least three structural or functional assessments (VF with <15% false-positive errors). Structural tests measured rim area (RA) with Heidelberg retina tomography (HRT) and average peripapillary retinal nerve fiber layer (pRNFL) thickness with optical coherence tomography (OCT). One eye of 436 patients (222 on Latanoprost) was analyzed. A Bayesian hierarchical model estimated the true RoP of VF and structural metrics, and their correlations, using sign-reversed multivariable exponential distribution. RA and pRNFL measurements were converted to a dB scale, matching the VF metric (mean deviation [MD]). The effect of average IOP on the true RoPs was estimated. True RoP at the mean average IOP (17 mm Hg) was faster (P < 0.001) for VF-MD (-0.59 [-0.73, -0.48] dB/year) than HRT-RA (-0.05 [-0.07, -0.03] dB/year) and OCT-pRNFL (-0.08 [-0.11, -0.06] dB/year). The proportional acceleration of RoP per mm Hg increase was, however, not significantly different (smallest P = 0.15). Accounting for the structural floor-effect largely eliminated the differences in RoPs (smallest P = 0.25). VF appeared to deteriorate at a faster rate than structural measurements. However, this could be explained by the floor-effect from nonfunctional tissue. IOP induced a similar acceleration in RoP per mm Hg increase.

Postcards of "Cape girls": Telling an Edwardian story of Cape Town

Picture postcards originated in the nineteenth century as an efficient, cheap, and democratic form of mass communication that encompassed many functions, including entertainment. As bimodal texts, comprising a visual image, anchoring textual caption, and (sometimes) the written message by the sender, postcards assumed the power to communicate complex ideas and ideologies in a compact format. Under the influence of cultural studies in the 1960s, which stated that culture itself is the site of struggle for social meanings expressed in class, race, and gender relations, postcard studies (deltiology) has become an important interdisciplinary field since the 1980s. The postcard exposed millions of people to visual culture and predated the functions of mobile phones, the Internet, and social media platforms such as Instagram. In this article, I focus on a series of artist-drawn, lithographic postcards by Dennis Santry (1879-1960) in Cape Town in 1904. They depict six so-called "Cape Girls" engaged in leisure activities against the backdrop of iconic Capetonian sites. My interpretation of the postcards suggests that a selective story privileges the tastes of a white, middle-class, English-speaking, imperial audience.

Total variational noise reduction method for EBAPS image based on weighted nuclear norm minimization.

The electron-bombarded active pixel sensor (EBAPS) is a highly sensitive vacuum-solid hybrid low-light imaging device capable of functioning in ultra-low illumination environments as low as 10-4 lx. However, this high sensitivity also causes problems, such as a low signal-to-noise ratio and complex noise. To enhance the quality of low-light night vision images captured by EBAPS and achieve effective imaging in ultra-low illumination, this study proposes a noise reduction algorithm based on the noise characteristics of EBAPS images. By utilizing the weighted nuclear norm minimization (WNNM) as the fundamental framework, several enhanced methods to address the multi-source noise of the EBAPS under ultra-low illumination have been proposed. These methods include outlier removal and variance-stabilizing transformation. To address the lack of edge preservation, a four-directional total variational regularization term has been incorporated into the WNNM model to maintain the image edge. Experimental results demonstrated that the improved method effectively eliminates various types of EBAPS noise while considerably returning the edges, ultimately enhancing the quality of low-light images. This study greatly facilitates subsequent applications of the low-light night vision technology.

Portrait of Historical Figures in Engraving Graphic Printing Techniques as A Means of Visual Communication to Spread National Identity

The portrayal of historical figures through graphic print techniques plays an important role in spreading national identity. This technique involves slaughtering fine lines on metal plates that produce detailed images. Images produced with this technique can be found in banknotes, stamps, coins, and various educational media. The visual image not only strengthens respect for historical figures but also acts as a symbol of national identity. Grafir utilizes and strengthens the symbol of nationalism by including several elements of local culture, such as batik and flora motifs. In addition, graphir is also used as a security feature on money and official documents. The results of this study helped prove the perception that a strong graphir in creating a national symbol that is eternal, iconic, and easily recognized by all levels of society.

Visual Performance and Predictive OCT Biomarkers in Epiretinal Membrane Assessment: Beyond Distance Visual Acuity.

This study aimed to comprehensively assess visual performance in eyes with idiopathic epiretinal membrane (iERM). Additionally, it sought to explore the associations between optical coherence tomography (OCT) imaging biomarkers and visual performance in patients with iERM. In this prospective, non-interventional study, 57 participants with treatment-naïve iERM from the University of Turin, between September 2023 and March 2024 were enrolled. Visual performance was measured using distance best-corrected visual acuity (BCVA), near BCVA, and maximum reading speed (MaxRS). Structural retinal imaging biomarkers were obtained from OCT, focusing on retinal layer thicknesses and epiretinal membrane characteristics. Statistical analyses, including linear regression and multivariate analysis, were used to determine relationships between visual function and imaging metrics. Monocular distance BCVA (0.37 ± 0.23 LogMAR), near BCVA (0.59 ± 0.18 LogMAR), and MaxRS (108.88 [68.38] words perminute [wpm]) in patients with iERM were significantly reduced compared with reference values. Both near BCVA and reading speed exhibited a greater percentage reduction than distance visual acuity. Patients with phakic showed worse visual acuity than patients with pseudophakia, although their reading performance was similar. Higher outer plexiform layers thickness and inner retinal thickness were associated with decreased distance and near visual acuity and reduced reading speed (beta, P value). The iERM predominantly impacts near visual performance, with near visual acuity and reading speed being more affected than distance visual acuity. Structural OCT biomarkers, particularly retinal thickness in specific regions, correlate with worse functional impairments. This highlights the importance of near vision assessments and imaging biomarkers for a comprehensive evaluation of visual impairment in iERM.

Staging of prostate Cancer with ultra-fast PSMA-PET scans enhanced by AI.

PSMA-PET is a reference standard examination for patients with prostate cancer, but even using recently introduced digital PET detectors image acquisition with standard field-of-view scanners is still in the range of 20min. This may cause limited access to examination slots because of the growing demand for PSMA-PET. Ultra-fast PSMA-PET may enhance throughput but comes at the cost of poor image quality. The aim of this manuscript is to evaluate the accuracy of AI-enhanced ultra-fast PSMA-PET for staging of patients with prostate cancer. A total number of 357 whole-body [68Ga]Ga-PSMA-11 PET datasets were included. Patients underwent two digital PET scans, one at standard and one at ultra-fast speed (table speed: 0.6-1.2mm/s vs. 50mm/s). A modified pix2pixHD generative adversarial network to enhance the ultra-fast images was trained with 286 datasets and evaluated with the remaining 71 datasets. The staging accuracy of ultra-fast PSMA-PET and AI-enhanced ultra-fast PET was compared with the reference standard PET separately for miTNM regions proposed by PROMISE V2.0. The AI-network significantly improved the visual image quality and detection rate in most miTNM regions compared with the non-enhanced image data (T: 69.6% vs. 43.5%, p < 0.05; N: 46.3% vs. 27.8%, p < 0.01; M1a 64.4% vs. 47.5%, p < 0.01; M1b: 85.7% vs. 72.1%, p < 0.01). However, improvement was not significant for the M1c category (42.9 vs. 28.6%, p > 0.05). Missed lesions had a smaller SUVmax and lesion size compared with detected lesions (exemplary for N: 9.5 vs. 26.5 SUVmax; 4 vs. 10mm). SUVmax values of lesions were significantly different in all miTNM regions between the ultra-fast and reference standard PET, but only in the T-region between the AI-enhanced and reference standard PET. The AI-based image enhancement improved image quality and region detection rates by a mean of 17.9%. As the sensitivity of synthetic PET for small and low-uptake lesions was limited, a potential clinical use case could be disease monitoring in patients with high tumor volume and PSMA uptake undergoing PSMA radioligand therapy. The improvement in detection rate of distant metastases was not significant. This indicates that more training data is needed to ensure robust results also for lesions that have lower appearance frequency. Future studies on accelerated PSMA-PET seem warranted.

A vision-language foundation model for precision oncology.

Clinical decision-making is driven by multimodal data, including clinical notes and pathological characteristics. Artificial intelligence approaches that can effectively integrate multimodal data hold significant promise in advancing clinical care1,2. However, the scarcity of well-annotated multimodal datasets in clinical settings has hindered the development of useful models. In this study, we developed the Multimodal transformer with Unified maSKed modeling (MUSK), a vision-language foundation model designed to leverage large-scale, unlabelled, unpaired image and text data. MUSK was pretrained on 50 million pathology images from 11,577 patients and one billion pathology-related text tokens using unified masked modelling. It was further pretrained on one million pathology image-text pairs to efficiently align the vision and language features. With minimal or no further training, MUSK was tested in a wide range of applications and demonstrated superior performance across 23 patch-level and slide-level benchmarks, including image-to-text and text-to-image retrieval, visual question answering, image classification and molecular biomarker prediction. Furthermore, MUSK showed strong performance in outcome prediction, including melanoma relapse prediction, pan-cancer prognosis prediction and immunotherapy response prediction in lung and gastro-oesophageal cancers. MUSK effectively combined complementary information from pathology images and clinical reports and could potentially improve diagnosis and precision in cancer therapy.

Salience maps for judgments of frontal plane distance, centroids, numerosity, and letter identity inferred from substance-invariant processing.

A salience map is a topographic map that has inputs at each x,y location from many different feature maps and summarizes the combined salience of all those inputs as a real number, salience, which is represented in the map. Of the more than 1 million Google references to salience maps, nearly all use the map for computing the relative priority of visual image components for subsequent processing. We observe that salience processing is an instance of substance-invariant processing, analogous to household measuring cups, weight scales, and measuring tapes, all of which make single-number substance-invariant measurements. Like these devices, the brain also collects material for substance-invariant measurements but by a different mechanism: salience maps that collect visual substances for subsequent measurement. Each salience map can be used by many different measurements. The instruction to attend is implemented by increasing the salience of the to-be-attended items so they can be collected in a salience map and then further processed. Here we show that, beyond processing priority, the following measurement tasks are substance invariant and therefore use salience maps: computing distance in the frontal plane, computing centroids (center of a cluster of items), computing the numerosity of a collection of items, and identifying alphabetic letters. We painstakingly demonstrate that defining items exclusively by color or texture not only is sufficient for these tasks, but that light-dark luminance information significantly improves performance only for letter recognition. Obviously, visual features are represented in the brain but their salience alone is sufficient for these four judgments.

PdSe2/NbSe2 Heterojunction Photodetector with Broadband Detection and Polarization Sensitivity.

Polarized photodetectors based on anisotropic two-dimensional (2D) materials display great potential applications in communications and optoelectronics. However, the existence of high dark current, low anisotropic ratio, and response speed limits their development. In this paper, a broadband polarization angle-dependent photodetector based on the PdSe2/NbSe2 van der Waals (vdW) heterojunction has been constructed. Characterization results show that the PdSe2/NbSe2 heterojunction photodetector can suppress the dark current effectively (NbSe2 ∼4 orders of magnitude and PdSe2 ∼1 order of magnitude compared with individual material). Meanwhile, the device exhibits a broadband detection capability ranging from 405 to 980 nm. The device shows a superior responsivity of 27 mA/W, a considerable detectivity of 9.8 × 107 Jones, a large external quantum efficiency of 528%, and an ultrafast rise/decay time of 1.6/1.9 μs at 1 V bias under 638 nm laser wavelength. The photodetector also achieves a high polarization-sensitive anisotropic ratio of ∼2.62 under 638 nm laser irradiation. In addition, the heterojunction device shows outstanding polarization imaging capabilities, which can be used as the image sensor. This work proposed a PdSe2/NbSe2 vdW heterojunction device with low dark current, broadband polarized detection, and polarized visual imaging, which will promote the development and practicality of polarization-sensitive photodetectors.

Multimodal identity work: The power of visual images for identity construction in the gig economy

Multimodal identity work: The power of visual images for identity construction in the gig economy