p53 exerts anticancer effects by regulating enhancer formation and activity

The abnormality of the p53 tumor suppressor is crucial in lung cancer development,because p53 regulates target gene promoters to combat cancer.Recent studies have shown extensive p53 binding to enhancer elements.However,whether p53 exerts a tumor suppressor role by shaping the enhancer landscape remains poorly understood.In the current study,we employed several functional genomics approaches to assess the enhancer activity at p53 binding sites throughout the genome based on our established TP53 knockout(KO)human bronchial epithelial cells(BEAS-2B).A total of 943 active regular enhancers and 370 super-enhancers(SEs)disappeared upon the deletion of p53,indicating that p53 modulates the activity of hundreds of enhancer elements.We found that one p53-dependent SE,located on chromosome 9 and designated as KLF4-SE,regulated the expression of the Krüppel-like factor 4(KLF4)gene.Furthermore,the deletion of p53 significantly decreased the KLF4-SE enhancer activity and the KLF4 expression,but increased colony formation ability in the nitrosamines 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone-induced cell transformation model.Subsequently,in TP53 KO cells,the overexpression of KLF4 partially reversed the increased clonogenic capacity caused by p53 deficiency.Consistently,KLF4 expression also decreased in lung cancer tissues and cell lines.It appeared that overexpression of KLF4 significantly suppressed the proliferation and migration of lung cancer cells.Collectively,our results suggest that the regulation of enhancer formation and activity by p53 is an integral component of the p53 tumor suppressor function.Therefore,our findings offer some novel insights into the regulation mechanism of p53 in lung oncogenesis and introduce a new strategy for screening therapeutic targets.

Strong coupling and catenary field enhancement in the hybrid plasmonic metamaterial cavity and TMDC monolayers

Strong coupling between resonantly matched surface plasmons of metals and excitons of quantum emitters results in the formation of new plasmon-exciton hybridized energy states.In plasmon-exciton strong coupling,plasmonic nanocavities play a significant role due to their ability to confine light in an ultrasmall volume.Additionally,two-dimensional transition metal dichalcogenides(TMDCs) have a significant exciton binding energy and remain stable at ambient conditions,making them an excellent alternative for investigating light-matter interactions.As a result,strong plasmon-exciton coupling has been reported by introducing a single metallic cavity.However,single nanoparticles have lower spatial confinement of electromagnetic fields and limited tunability to match the excitonic resonance.Here,we introduce the concept of catenary-shaped optical fields induced by plasmonic metamaterial cavities to scale the strength of plasmon-exciton coupling.The demonstrated plasmon modes of metallic metamaterial cavities offer high confinement and tunability and can match with the excitons of TMDCs to exhibit a strong coupling regime by tuning either the size of the cavity gap or thickness.The calculated Rabi splitting of Au-MoSe_2 and Au-WSe_2 heterostructures strongly depends on the catenary-like field enhancement induced by the Au cavity,resulting in room-temperature Rabi splitting ranging between 77.86 and 320 me V.These plasmonic metamaterial cavities can pave the way for manipulating excitons in TMDCs and operating active nanophotonic devices at ambient temperature.

Is recovery enhancement after gastric cancer surgery really a safe approach for elderly patients?

BACKGROUND This study aimed to evaluate the safety of enhanced recovery after surgery(ERAS)in elderly patients with gastric cancer(GC).AIM To evaluate the safety of ERAS in elderly patients with GC.METHODS The PubMed,EMBASE,and Cochrane Library databases were used to search for eligible studies from inception to April 1,2023.The mean difference(MD),odds ratio(OR)and 95%confidence interval(95%CI)were pooled for analysis.The quality of the included studies was evaluated using the Newcastle-Ottawa Scale scores.We used Stata(V.16.0)software for data analysis.RESULTS This study consists of six studies involving 878 elderly patients.By analyzing the clinical outcomes,we found that the ERAS group had shorter postoperative hospital stays(MD=-0.51,I2=0.00%,95%CI=-0.72 to-0.30,P=0.00);earlier times to first flatus(defecation;MD=-0.30,I²=0.00%,95%CI=-0.55 to-0.06,P=0.02);less intestinal obstruction(OR=3.24,I2=0.00%,95%CI=1.07 to 9.78,P=0.04);less nausea and vomiting(OR=4.07,I2=0.00%,95%CI=1.29 to 12.84,P=0.02);and less gastric retention(OR=5.69,I2=2.46%,95%CI=2.00 to 16.20,P=0.00).Our results showed that the conventional group had a greater mortality rate than the ERAS group(OR=0.24,I2=0.00%,95%CI=0.07 to 0.84,P=0.03).However,there was no statistically significant difference in major complications between the ERAS group and the conventional group(OR=0.67,I2=0.00%,95%CI=0.38 to 1.18,P=0.16).CONCLUSION Compared to those with conventional recovery,elderly GC patients who received the ERAS protocol after surgery had a lower risk of mortality.

Enhanced structural damage behavior of liquid-filled tank by reactive material projectile impact

A series of ballistic experiments were performed to investigate the damage behavior of high velocity reactive material projectiles(RMPs) impacting liquid-filled tanks,and the corresponding hydrodynamic ram(HRAM) was studied in detail.PTFE/Al/W RMPs with steel-like and aluminum-like densities were prepared by a pressing/sintering process.The projectiles impacted a liquid-filled steel tank with front aluminum panel at approximately 1250 m/s.The corresponding cavity evolution characteristics and HRAM pressure were recorded by high-speed camera and pressure acquisition system,and further compared to those of steel and aluminum projectiles.Significantly different from the conical cavity formed by the inert metal projectile,the cavity formed by the RMP appeared as an ellipsoid with a conical front.The RMPs were demonstrated to enhance the radial growth velocity of cavity,the global HRAM pressure amplitude and the front panel damage,indicating the enhanced HRAM and structural damage behavior.Furthermore,combining the impact-induced fragmentation and deflagration characteristics,the cavity evolution of RMPs under the combined effect of kinetic energy impact and chemical energy release was analyzed.The mechanism of enhanced HRAM pressure induced by the RMPs was further revealed based on the theoretical model of the initial impact wave and the impulse analysis.Finally,the linear correlation between the deformation-thickness ratio and the non-dimensional impulse for the front panel was obtained and analyzed.It was determined that the enhanced near-field impulse induced by the RMPs was the dominant reason for the enhanced structural damage behavior.

Synergistic anionic/zwitterionic mixed surfactant system with high emulsification efficiency for enhanced oil recovery in low permeability reservoirs

Emulsification is one of the important mechanisms of surfactant flooding. To improve oil recovery for low permeability reservoirs, a highly efficient emulsification oil flooding system consisting of anionic surfactant sodium alkyl glucosyl hydroxypropyl sulfonate(APGSHS) and zwitterionic surfactant octadecyl betaine(BS-18) is proposed. The performance of APGSHS/BS-18 mixed surfactant system was evaluated in terms of interfacial tension, emulsification capability, emulsion size and distribution, wettability alteration, temperature-resistance and salt-resistance. The emulsification speed was used to evaluate the emulsification ability of surfactant systems, and the results show that mixed surfactant systems can completely emulsify the crude oil into emulsions droplets even under low energy conditions. Meanwhile,the system exhibits good temperature and salt resistance. Finally, the best oil recovery of 25.45% is achieved for low permeability core by the mixed surfactant system with a total concentration of 0.3 wt%while the molar ratio of APGSHS:BS-18 is 4:6. The current study indicates that the anionic/zwitterionic mixed surfactant system can improve the oil flooding efficiency and is potential candidate for application in low permeability reservoirs.

Tailoring MXene Thickness and Functionalization for Enhanced Room‑Temperature Trace NO_(2) Sensing

In this study,precise control over the thickness and termination of Ti3C2TX MXene flakes is achieved to enhance their electrical properties,environmental stability,and gas-sensing performance.Utilizing a hybrid method involving high-pressure processing,stirring,and immiscible solutions,sub-100 nm MXene flake thickness is achieved within the MXene film on the Si-wafer.Functionalization control is achieved by defunctionalizing MXene at 650℃ under vacuum and H2 gas in a CVD furnace,followed by refunctionalization with iodine and bromine vaporization from a bubbler attached to the CVD.Notably,the introduction of iodine,which has a larger atomic size,lower electronegativity,reduce shielding effect,and lower hydrophilicity(contact angle:99°),profoundly affecting MXene.It improves the surface area(36.2 cm^(2) g^(-1)),oxidation stability in aqueous/ambient environments(21 days/80 days),and film conductivity(749 S m^(-1)).Additionally,it significantly enhances the gas-sensing performance,including the sensitivity(0.1119Ωppm^(-1)),response(0.2% and 23%to 50 ppb and 200 ppm NO_(2)),and response/recovery times(90/100 s).The reduced shielding effect of the–I-terminals and the metallic characteristics of MXene enhance the selectivity of I-MXene toward NO2.This approach paves the way for the development of stable and high-performance gas-sensing two-dimensional materials with promising prospects for future studies.

A Polyvinyl Alcohol/Acrylamide Hydrogel with Enhanced Mechanical Properties Promotes Full-Thickness Skin Defect Healing by Regulating Immunomodulation and Angiogenesis Through Paracrine Secretion

Hydrogel-based tissue-engineered skin has attracted increased attention due to its potential to restore the structural integrity and functionality of skin.However,the mechanical properties of hydrogel scaffolds and natural skin are substantially different.Here,we developed a polyvinyl alcohol(PVA)/acrylamide based interpenetrating network(IPN)hydrogel that was surface modified with polydopamine(PDA)and termed Dopa-gel.The Dopa-gel exhibited mechanical properties similar to native skin tissue and a superior ability to modulate paracrine functions.Furthermore,a tough scaffold with tensile resistance was fabricated using this hydrogel by three-dimensional printing.The results showed that the interpenetration of PVA,alginate,and polyacrylamide networks notably enhanced the mechanical properties of the hydrogel.Surface modification with PDA endowed the hydrogels with increased secretion of immunomodulatory and proangiogenic factors.In an in vivo model,Dopa-gel treatment accelerated wound closure,increased vascularization,and promoted a shift in macrophages from a proinflammatory M1 phenotype to a prohealing and anti-inflammatory M2 phenotype within the wound area.Mechanistically,the focal adhesion kinase(FAK)/extracellular signal-related kinase(ERK)signaling pathway may mediate the promotion of skin defect healing by increasing paracrine secretion via the Dopa-gel.Additionally,proangiogenic factors can be induced through Rho-associated kinase-2(ROCK-2)/vascular endothelial growth factor(VEGF)-mediated paracrine secretion under tensile stress conditions.Taken together,these findings suggest that the multifunctional Dopa-gel,which has good mechanical properties similar to those of native skin tissue and enhanced immunomodulatory and angiogenic properties,is a promising scaffold for skin tissue regeneration.

Enhanced recovery after surgery in elderly patients with non-small cell lung cancer who underwent video-assisted thoracic surgery

BACKGROUND This study was designed to investigate the clinical outcomes of enhanced recovery after surgery(ERAS)in the perioperative period in elderly patients with nonsmall cell lung cancer(NSCLC).AIM To investigate the potential enhancement of video-assisted thoracic surgery(VATS)in postoperative recovery in elderly patients with NSCLC.METHODS We retrospectively analysed the clinical data of 85 elderly NSCLC patients who underwent ERAS(the ERAS group)and 327 elderly NSCLC patients who received routine care(the control group)after VATS at the Department of Thoracic Surgery of Peking University Shenzhen Hospital between May 2015 and April 2017.After propensity score matching of baseline data,we analysed the postoperative stay,total hospital expenses,postoperative 48-h pain score,and postoperative complication rate for the 2 groups of patients who underwent lobectomy or sublobar resection.RESULTS After propensity score matching,ERAS significantly reduced the postoperative hospital stay(6.96±4.16 vs 8.48±4.18 d,P=0.001)and total hospital expenses(48875.27±18437.5 vs 55497.64±21168.63 CNY,P=0.014)and improved the satisfaction score(79.8±7.55 vs 77.35±7.72,P=0.029)relative to those for routine care.No significant between-group difference was observed in postoperative 48-h pain score(4.68±1.69 vs 5.28±2.1,P=0.090)or postoperative complication rate(21.2%vs 27.1%,P=0.371).Subgroup analysis showed that ERAS significantly reduced the postoperative hospital stay and total hospital expenses and increased the satisfaction score of patients who underwent lobectomy but not of patients who underwent sublobar resection.CONCLUSION ERAS effectively reduced the postoperative hospital stay and total hospital expenses and improved the satisfaction score in the perioperative period for elderly NSCLC patients who underwent lobectomy but not for patients who underwent sublobar resection.

Guided-YNet: Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network

Multimodal lung tumor medical images can provide anatomical and functional information for the same lesion.Such as Positron Emission Computed Tomography(PET),Computed Tomography(CT),and PET-CT.How to utilize the lesion anatomical and functional information effectively and improve the network segmentation performance are key questions.To solve the problem,the Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network(Guide-YNet)is proposed in this paper.Firstly,a double-encoder single-decoder U-Net is used as the backbone in this model,a single-coder single-decoder U-Net is used to generate the saliency guided feature using PET image and transmit it into the skip connection of the backbone,and the high sensitivity of PET images to tumors is used to guide the network to accurately locate lesions.Secondly,a Cross Scale Feature Enhancement Module(CSFEM)is designed to extract multi-scale fusion features after downsampling.Thirdly,a Cross-Layer Interactive Feature Enhancement Module(CIFEM)is designed in the encoder to enhance the spatial position information and semantic information.Finally,a Cross-Dimension Cross-Layer Feature Enhancement Module(CCFEM)is proposed in the decoder,which effectively extractsmultimodal image features through global attention and multi-dimension local attention.The proposed method is verified on the lung multimodal medical image datasets,and the results showthat theMean Intersection overUnion(MIoU),Accuracy(Acc),Dice Similarity Coefficient(Dice),Volumetric overlap error(Voe),Relative volume difference(Rvd)of the proposed method on lung lesion segmentation are 87.27%,93.08%,97.77%,95.92%,89.28%,and 88.68%,respectively.It is of great significance for computer-aided diagnosis.

Research on Enhanced Contraband Dataset ACXray Based on ETL

To address the shortage of public datasets for customs X-ray images of contraband and the difficulties in deploying trained models in engineering applications,a method has been proposed that employs the Extract-Transform-Load(ETL)approach to create an X-ray dataset of contraband items.Initially,X-ray scatter image data is collected and cleaned.Using Kafka message queues and the Elasticsearch(ES)distributed search engine,the data is transmitted in real-time to cloud servers.Subsequently,contraband data is annotated using a combination of neural networks and manual methods to improve annotation efficiency and implemented mean hash algorithm for quick image retrieval.The method of integrating targets with backgrounds has enhanced the X-ray contraband image data,increasing the number of positive samples.Finally,an Airport Customs X-ray dataset(ACXray)compatible with customs business scenarios has been constructed,featuring an increased number of positive contraband samples.Experimental tests using three datasets to train the Mask Region-based Convolutional Neural Network(Mask R-CNN)algorithm and tested on 400 real customs images revealed that the recognition accuracy of algorithms trained with Security Inspection X-ray(SIXray)and Occluded Prohibited Items X-ray(OPIXray)decreased by 16.3%and 15.1%,respectively,while the ACXray dataset trained algorithm’s accuracy was almost unaffected.This indicates that the ACXray dataset-trained algorithm possesses strong generalization capabilities and is more suitable for customs detection scenarios.

A Novel Multi-Stream Fusion Network for Underwater Image Enhancement

Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color correction and detail restoration. However,the existing enhancement algorithms cannot achieve the desired results. In order to solve the above problems, this paper proposes a multi-stream feature fusion network. First, an underwater image is preprocessed to obtain potential information from the illumination stream, color stream and structure stream by histogram equalization with contrast limitation, gamma correction and white balance, respectively. Next, these three streams and the original raw stream are sent to the residual blocks to extract the features. The features will be subsequently fused. It can enhance feature representation in underwater images. In the meantime, a composite loss function including three terms is used to ensure the quality of the enhanced image from the three aspects of color balance, structure preservation and image smoothness. Therefore, the enhanced image is more in line with human visual perception.Finally, the effectiveness of the proposed method is verified by comparison experiments with many stateof-the-art underwater image enhancement algorithms. Experimental results show that the proposed method provides superior results over them in terms of MSE,PSNR, SSIM, UIQM and UCIQE, and the enhanced images are more similar to their ground truth images.

Vein visualization enhancement by dual-wavelength phase-locked denoising technology

Visual near-infrared imaging equipment has broad applications in various fields such as venipuncture,facial injections,and safety verification due to its noncontact,compact,and portable design.Currently,most studies utilize near-infrared single-wavelength for image acquisition of veins.However,many substances in the skin,including water,protein,and melanin can create significant background noise,which hinders accurate detection.In this paper,we developed a dual-wavelength imaging system with phase-locked denoising technology to acquire vein image.The signals in the effective region are compared by using the absorption valley and peak of hemoglobin at 700nm and 940nm,respectively.The phase-locked denoising algorithm is applied to decrease the noise and interference of complex surroundings from the images.The imaging results of the vein are successfully extracted in complex noise environment.It is demonstrated that the denoising effect on hand veins imaging can be improved with 57.3%by using our dual-wavelength phase-locked denoising technology.Consequently,this work proposes a novel approach for venous imaging with dual-wavelengths and phase-locked denoising algorithm to extract venous imaging results in complex noisy environment better.

Does enhanced recovery after surgery programs improve clinical outcomes in liver cancer surgery?

Enhanced recovery after surgery(ERAS)programs have been widely applied in liver surgery since the publication of the first ERAS guidelines in 2016 and the new recommendations in 2022.Liver surgery is usually performed in oncological patients(liver metastasis,hepatocellular carcinoma,cholangiocarcinoma,etc.),but the real impact of liver surgery ERAS programs in oncological outcomes is not clearly defined.Theoretical advantages of ERAS programs are:ERAS decreases postoperative complication rates and has been demonstrated a clear relationship between complications and oncological outcomes;a better and faster posto-perative recovery should let oncologic teams begin chemotherapeutic regimens on time;prehabilitation and nutrition actions before surgery should also improve the performance status of the patients receiving chemotherapy.So,ERAS could be another way to improve our oncological results.We will discuss the literature about liver surgery ERAS focusing on its oncological implications and future investigations projects.

Unsupervised Multi-Expert Learning Model for Underwater Image Enhancement

Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ignored that the R,G and B channels of underwater degraded images present varied degrees of degradation,due to the selective absorption for the light.To address this issue,we propose an unsupervised multi-expert learning model by considering the enhancement of each color channel.Specifically,an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater images.Based on this,we design a generator,including a multi-expert encoder,a feature fusion module and a feature fusion-guided decoder,to generate the clear underwater image.Accordingly,a multi-expert discriminator is proposed to verify the authenticity of the R,G and B channels,respectively.In addition,content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced images.Extensive experiments on public datasets demonstrate that our method achieves more pleasing results in vision quality.Various metrics(PSNR,SSIM,UIQM and UCIQE) evaluated on our enhanced images have been improved obviously.

Colorectal Cancer Segmentation Algorithm Based on Deep Features from Enhanced CT Images

Colorectal cancer,a malignant lesion of the intestines,significantly affects human health and life,emphasizing the necessity of early detection and treatment.Accurate segmentation of colorectal cancer regions directly impacts subsequent staging,treatment methods,and prognostic outcomes.While colonoscopy is an effective method for detecting colorectal cancer,its data collection approach can cause patient discomfort.To address this,current research utilizes Computed Tomography(CT)imaging;however,conventional CT images only capture transient states,lacking sufficient representational capability to precisely locate colorectal cancer.This study utilizes enhanced CT images,constructing a deep feature network from the arterial,portal venous,and delay phases to simulate the physician’s diagnostic process and achieve accurate cancer segmentation.The innovations include:1)Utilizing portal venous phase CT images to introduce a context-aware multi-scale aggregation module for preliminary shape extraction of colorectal cancer.2)Building an image sequence based on arterial and delay phases,transforming the cancer segmentation issue into an anomaly detection problem,establishing a pixel-pairing strategy,and proposing a colorectal cancer segmentation algorithm using a Siamese network.Experiments with 84 clinical cases of colorectal cancer enhanced CT data demonstrated an Area Overlap Measure of 0.90,significantly better than Fully Convolutional Networks(FCNs)at 0.20.Future research will explore the relationship between conventional and enhanced CT to further reduce segmentation time and improve accuracy.

Physics-Constrained Robustness Enhancement for Tree Ensembles Applied in Smart Grid

With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and intelligence.However,tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks,making it urgent to enhance their robustness.To address this,we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles.Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws,ensuring training data accurately reflects possible attack scenarios while adhering to physical rules.In our experiments,the proposed method increased robustness against adversarial attacks by 100%when applied to real grid data under physical constraints.These results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions.

Finesse measurement for high-power optical enhancement cavity

Finesse is a critical parameter for describing the characteristics of an optical enhancement cavity(OEC). This paper first presents a review of finesse measurement techniques, including a comparative analysis of the advantages, disadvantages, and potential limitations of several main methods from both theoretical and practical perspectives. A variant of the existing method called the free spectral range(FSR) modulation method is proposed and compared with three other finesse measurement methods, i.e., the fast-switching cavity ring-down(CRD) method, the rapidly swept-frequency(SF) CRD method, and the ringing effect method. A high-power OEC platform with a high finesse of approximately 16000 is built and measured with the four methods. The performance of these methods is compared, and the results show that the FSR modulation method and the fast-switching CRD method are more suitable and accurate than the other two methods for high-finesse OEC measurements. The CRD method and the ringing effect method can be implemented in open loop using simple equipment and are easy to perform. Additionally, recommendations for selecting finesse measurement methods under different conditions are proposed, which benefit the development of OEC and its applications.

Image enhancement with intensity transformation on embedding space

In recent times,an image enhancement approach,which learns the global transformation function using deep neural networks,has gained attention.However,many existing methods based on this approach have a limitation:their transformation functions are too simple to imitate complex colour transformations between low-quality images and manually retouched high-quality images.In order to address this limitation,a simple yet effective approach for image enhancement is proposed.The proposed algorithm based on the channel-wise intensity transformation is designed.However,this transformation is applied to the learnt embedding space instead of specific colour spaces and then return enhanced features to colours.To this end,the authors define the continuous intensity transformation(CIT)to describe the mapping between input and output intensities on the embedding space.Then,the enhancement network is developed,which produces multi-scale feature maps from input images,derives the set of transformation functions,and performs the CIT to obtain enhanced images.Extensive experiments on the MIT-Adobe 5K dataset demonstrate that the authors’approach improves the performance of conventional intensity transforms on colour space metrics.Specifically,the authors achieved a 3.8%improvement in peak signal-to-noise ratio,a 1.8%improvement in structual similarity index measure,and a 27.5%improvement in learned perceptual image patch similarity.Also,the authors’algorithm outperforms state-of-the-art alternatives on three image enhancement datasets:MIT-Adobe 5K,Low-Light,and Google HDRþ.

More Than Lightening:A Self-Supervised Low-Light Image Enhancement Method Capable for Multiple Degradations

Low-light images suffer from low quality due to poor lighting conditions,noise pollution,and improper settings of cameras.To enhance low-light images,most existing methods rely on normal-light images for guidance but the collection of suitable normal-light images is difficult.In contrast,a self-supervised method breaks free from the reliance on normal-light data,resulting in more convenience and better generalization.Existing self-supervised methods primarily focus on illumination adjustment and design pixel-based adjustment methods,resulting in remnants of other degradations,uneven brightness and artifacts.In response,this paper proposes a self-supervised enhancement method,termed as SLIE.It can handle multiple degradations including illumination attenuation,noise pollution,and color shift,all in a self-supervised manner.Illumination attenuation is estimated based on physical principles and local neighborhood information.The removal and correction of noise and color shift removal are solely realized with noisy images and images with color shifts.Finally,the comprehensive and fully self-supervised approach can achieve better adaptability and generalization.It is applicable to various low light conditions,and can reproduce the original color of scenes in natural light.Extensive experiments conducted on four public datasets demonstrate the superiority of SLIE to thirteen state-of-the-art methods.Our code is available at https://github.com/hanna-xu/SLIE.

Non-dimensional analysis on blast wave propagation in foam concrete:Minimum thickness to avoid stress enhancement

Foam concrete is a prospective material in defense engineering to protect structures due to its high energy absorption capability resulted from the long plateau stage.However,stress enhancement rather than stress mitigation may happen when foam concrete is used as sacrificial claddings placed in the path of an incoming blast load.To investigate this interesting phenomenon,a one-dimensional difference model for blast wave propagation in foam concrete is firstly proposed and numerically solved by improving the second-order Godunov method.The difference model and numerical algorithm are validated against experimental results including both the stress mitigation and the stress enhancement.The difference model is then used to numerically analyze the blast wave propagation and deformation of material in which the effects of blast loads,stress-strain relation and length of foam concrete are considered.In particular,the concept of minimum thickness of foam concrete to avoid stress enhancement is proposed.Finally,non-dimensional analysis on the minimum thickness is conducted and an empirical formula is proposed by curve-fitting the numerical data,which can provide a reference for the application of foam concrete in defense engineering.