Image super‐resolution via dynamic network

Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution.However,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction block.The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐resolution.To enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying scenes.To prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained features.Also,a residual learning operation is embedded in the refinement block to prevent long‐term dependency problem.Finally,a construction block is responsible for reconstructing high‐quality images.Designed heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital devices.Experimental results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and complexity.The code of DSRNet can be obtained at https://github.com/hellloxiaotian/DSRNet.

Flexible,high-density,laminated ECoG electrode array for high spatiotemporal resolution foci diagnostic localization of refractory epilepsy

High spatiotemporal resolution brain electrical signals are critical for basic neuroscience research and high-precision focus diagnostic localization,as the spatial scale of some pathologic signals is at the submillimeter or micrometer level.This entails connecting hundreds or thousands of electrode wires on a limited surface.This study reported a class of flexible,ultrathin,highdensity electrocorticogram(ECoG)electrode arrays.The challenge of a large number of wiring arrangements was overcome by a laminated structure design and processing technology improvement.The flexible,ultrathin,high-density ECoG electrode array was conformably attached to the cortex for reliable,high spatial resolution electrophysiologic recordings.The minimum spacing between electrodes was 15μm,comparable to the diameter of a single neuron.Eight hundred electrodes were prepared with an electrode density of 4444 mm^(-2).In focal epilepsy surgery,the flexible,high-density,laminated ECoG electrode array with 36 electrodes was applied to collect epileptic spike waves inrabbits,improving the positioning accuracy of epilepsy lesions from the centimeter to the submillimeter level.The flexible,high-density,laminated ECoG electrode array has potential clinical applications in intractable epilepsy and other neurologic diseases requiring high-precision electroencephalogram acquisition.

Rydberg-Atom Terahertz Heterodyne Receiver with Ultrahigh Spectral Resolution

Terahertz heterodyne receivers with high sensitivity and spectral resolution are crucial for various applications.Here,we present a room-temperature atomic terahertz heterodyne receiver that achieves ultrahigh sensitivity and frequency resolution.At a signal frequency of 338.7 GHz,we obtain a sensitivity of 2.88±0.09V·cm^(−1)·Hz^(−1/2) for electric field measurements.The calibrated linear dynamical range spans approximately 89 dB,ranging from−110 dBV/cm to−21 dBV/cm.We demodulate a 400 symbol stream encoded in 4-state phase-shift keying,demonstrating excellent phase detection capability.By scanning the frequency of the local oscillator,we realize a terahertz spectrometer with Hz level frequency resolution.This resolution is more than two orders of magnitude higher than that of existing terahertz spectrometers.The demonstrated terahertz heterodyne receiver holds promising potential for working across the entire terahertz spectrum,significantly advancing its practical applications.

Virtual source approach for maximizing resolution in high-penetration gamma-ray imaging

High-energy gamma-ray radiography has exceptional penetration ability and has become an indispensable nondestructive testing(NDT)tool in various fields.For high-energy photons,point projection radiography is almost the only feasible imaging method,and its spatial resolution is primarily constrained by the size of the gamma-ray source.In conventional industrial applications,gamma-ray sources are commonly based on electron beams driven by accelerators,utilizing the process of bremsstrahlung radiation.The size of the gamma-ray source is dependent on the dimensional characteristics of the electron beam.Extensive research has been conducted on various advanced accelerator technologies that have the potential to greatly improve spatial resolution in NDT.In our investigation of laser-driven gamma-ray sources,a spatial resolution of about 90μm is achieved when the areal density of the penetrated object is 120 g/cm^(2).A virtual source approach is proposed to optimize the size of the gamma-ray source used for imaging,with the aim of maximizing spatial resolution.In this virtual source approach,the gamma ray can be considered as being emitted from a virtual source within the convertor,where the equivalent gamma-ray source size in imaging is much smaller than the actual emission area.On the basis of Monte Carlo simulations,we derive a set of evaluation formulas for virtual source scale and gamma-ray emission angle.Under optimal conditions,the virtual source size can be as small as 15μm,which can significantly improve the spatial resolution of high-penetration imaging to less than 50μm.

Advancing ophthalmic delivery of flurbiprofen via synergistic chiral resolution and ion-pairing strategies

Flurbiprofen(FB),a nonsteroidal anti-inflammatory drug,is widely employed in treating ocular inflammation owing to its remarkable anti-inflammatory effects.However,the racemic nature of its commercially available formulation(Ocufen^(R))limits the full potential of its therapeutic activity,as the(S)-enantiomer is responsible for the desired antiinflammatory effects.Additionally,the limited corneal permeability of FB significantly restricts its bioavailability.In this study,we successfully separated the chiral isomers of FB to obtain the highly active(S)-FB.Subsequently,utilizing ion-pairing technology,we coupled(S)-FB with various counter-ions,such as sodium,diethylamine,trimethamine(TMA),and l-arginine,to enhance its ocular bioavailability.A comprehensive evaluation encompassed balanced solubility,octanol-water partition coefficient,corneal permeability,ocular pharmacokinetics,tissue distribution,and in vivo ocular anti-inflammatory activity of each chiral isomer salt.Among the various formulations,S-FBTMA exhibited superior water solubility(about 1–12 mg/ml),lipid solubility(1<lgP_(ow)<3)and corneal permeability.In comparison to Ocufen^(R),S-FBTMA demonstrated significantly higher in vivo antiinflammatory activity and lower ocular irritability(such as conjunctival congestion and tingling).The findings from this research highlight the potential of chiral separation and ion-pair enhanced permeation techniques in providing pharmaceutical enterprises focused on drug development with a valuable avenue for improving therapeutic outcomes.

Synthesizing high-resolution satellite salinity data based on multi-fractal fusion

The spaceborne platform has unprecedently provided the global eddy-permitting(typically about 0.25°)products of sea surface salinity(SSS),however the existing SSS products can hardly resolve mesoscale motions due to the heavy noises therein and the over-smoothing in denoising processes.By means of the multi-fractal fusion(MFF),the high-resolution SSS product is synthesized with the template of sea surface temperature(SST).Two low-resolution SSS products and four SST products are considered as the source data and the templates respectively to determine the best combination.The fused products are validated by the in situ observations and intercompared via SSS maps,Singularity Exponent maps and wavenumber spectra.The results demonstrate that the MFF can perform a good work in mitigating the noises and improving the resolution.The combination of the climate change initiative SSS and the remote sensing system SST can produce the 0.1°denoised product whose global mean standard derivation of salinity against Argo is 0.21 and the feature resolution can reach 30−40 km.

Fast compressed sensing spectral measurement with adaptive gradient multiscale resolution

We propose a fast,adaptive multiscale resolution spectral measurement method based on compressed sensing.The method can apply variable measurement resolution over the entire spectral range to reduce the measurement time by over 75%compared to a global high-resolution measurement.Mimicking the characteristics of the human retina system,the resolution distribution follows the principle of gradually decreasing.The system allows the spectral peaks of interest to be captured dynamically or to be specified a priori by a user.The system was tested by measuring single and dual spectral peaks,and the results of spectral peaks are consistent with those of global high-resolution measurements.

Lightweight Multi-Resolution Network for Human Pose Estimation

Human pose estimation aims to localize the body joints from image or video data.With the development of deeplearning,pose estimation has become a hot research topic in the field of computer vision.In recent years,humanpose estimation has achieved great success in multiple fields such as animation and sports.However,to obtainaccurate positioning results,existing methods may suffer from large model sizes,a high number of parameters,and increased complexity,leading to high computing costs.In this paper,we propose a new lightweight featureencoder to construct a high-resolution network that reduces the number of parameters and lowers the computingcost.We also introduced a semantic enhancement module that improves global feature extraction and networkperformance by combining channel and spatial dimensions.Furthermore,we propose a dense connected spatialpyramid pooling module to compensate for the decrease in image resolution and information loss in the network.Finally,ourmethod effectively reduces the number of parameters and complexitywhile ensuring high performance.Extensive experiments show that our method achieves a competitive performance while dramatically reducing thenumber of parameters,and operational complexity.Specifically,our method can obtain 89.9%AP score on MPIIVAL,while the number of parameters and the complexity of operations were reduced by 41%and 36%,respectively.

A Unified Model Fusing Region of Interest Detection and Super Resolution for Video Compression

High-resolution video transmission requires a substantial amount of bandwidth.In this paper,we present a novel video processing methodology that innovatively integrates region of interest(ROI)identification and super-resolution enhancement.Our method commences with the accurate detection of ROIs within video sequences,followed by the application of advanced super-resolution techniques to these areas,thereby preserving visual quality while economizing on data transmission.To validate and benchmark our approach,we have curated a new gaming dataset tailored to evaluate the effectiveness of ROI-based super-resolution in practical applications.The proposed model architecture leverages the transformer network framework,guided by a carefully designed multi-task loss function,which facilitates concurrent learning and execution of both ROI identification and resolution enhancement tasks.This unified deep learning model exhibits remarkable performance in achieving super-resolution on our custom dataset.The implications of this research extend to optimizing low-bitrate video streaming scenarios.By selectively enhancing the resolution of critical regions in videos,our solution enables high-quality video delivery under constrained bandwidth conditions.Empirical results demonstrate a 15%reduction in transmission bandwidth compared to traditional super-resolution based compression methods,without any perceivable decline in visual quality.This work thus contributes to the advancement of video compression and enhancement technologies,offering an effective strategy for improving digital media delivery efficiency and user experience,especially in bandwidth-limited environments.The innovative integration of ROI identification and super-resolution presents promising avenues for future research and development in adaptive and intelligent video communication systems.

RGB-guided hyperspectral image super-resolution with deep progressive learning

Due to hardware limitations,existing hyperspectral(HS)camera often suffer from low spatial/temporal resolution.Recently,it has been prevalent to super-resolve a low reso-lution(LR)HS image into a high resolution(HR)HS image with a HR RGB(or mul-tispectral)image guidance.Previous approaches for this guided super-resolution task often model the intrinsic characteristic of the desired HR HS image using hand-crafted priors.Recently,researchers pay more attention to deep learning methods with direct supervised or unsupervised learning,which exploit deep prior only from training dataset or testing data.In this article,an efficient convolutional neural network-based method is presented to progressively super-resolve HS image with RGB image guidance.Specif-ically,a progressive HS image super-resolution network is proposed,which progressively super-resolve the LR HS image with pixel shuffled HR RGB image guidance.Then,the super-resolution network is progressively trained with supervised pre-training and un-supervised adaption,where supervised pre-training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing scenes.The proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral-spatial constraint.It has a good general-isation capability,especially for blind HS image super-resolution.Comprehensive experimental results show that the proposed deep progressive learning method out-performs the existing state-of-the-art methods for HS image super-resolution in non-blind and blind cases.

Ultrahigh spatiotemporal resolution beam signal reconstruction with bunch phase compensation

Various electromagnetic signals are excited by the beam in the acceleration and beam-diagnostic elements of a particle accelerator.It is important to obtain time-domain waveforms of these signals with high temporal resolution for research,such as the study of beam–cavity interactions and bunch-by-bunch parameter measurements.Therefore,a signal reconstruction algorithm with ultrahigh spatiotemporal resolution and bunch phase compensation based on equivalent sampling is proposed in this paper.Compared with traditional equivalent sampling,the use of phase compensation and setting the bunch signal zero-crossing point as the time reference can construct a more accurate reconstructed signal.The basic principles of the method,simulation,and experimental comparison are also introduced.Based on the beam test platform of the Shanghai Synchrotron Radiation Facility(SSRF)and the method of experimental verification,the factors that affect the reconstructed signal quality are analyzed and discussed,including the depth of the sampled data,quantization noise of analog-to-digital converter,beam transverse oscillation,and longitudinal oscillation.The results of the beam experiments show that under the user operation conditions of the SSRF,a beam excitation signal with an amplitude uncertainty of 2%can be reconstructed.

High-resolution neutronics model for ^(238)Pu production in high-flux reactors

We proposed and compared three methods(filter burnup,single energy burnup,and burnup extremum analysis)to build a high-resolution neutronics model for 238Pu production in high-flux reactors.The filter burnup and single energy burnup methods have no theoretical approximation and can achieve a spectrum resolution of up to~1 eV,thereby constructing the importance curve and yield curve of the full energy range.The burnup extreme analysis method combines the importance and yield curves to consider the influence of irradiation time on production efficiency,thereby constructing extreme curves.The three curves,which quantify the transmutation rate of the nuclei in each energy region,are of physical significance because they have similar distributions.A high-resolution neutronics model for ^(238)Pu production was established based on these three curves,and its universality and feasibility were proven.The neutronics model can guide the neutron spectrum optimization and improve the yield of ^(238)Pu by up to 18.81%.The neutronics model revealed the law of nuclei transmutation in all energy regions with high spectrum resolution,thus providing theoretical support for high-flux reactor design and irradiation production of ^(238)Pu.

Large-field objective lens for multi-wavelength microscopy at mesoscale and submicron resolution

Conventional microscopes designed for submicron resolution in biological research are hindered by a limited field of view,typically around 1 mm.This restriction poses a challenge when attempting to simultaneously analyze various parts of a sample,such as different brain areas.In addition,conventional objective lenses struggle to perform consistently across the required range of wavelengths for brain imaging in vivo.Here we present a novel mesoscopic objective lens with an impressive field of view of 8 mm,a numerical aperture of 0.5,and a working wavelength range from 400 to 1000 nm.We achieved a resolution of 0.74μm in fluorescent beads imaging.The versatility of this lens was further demonstrated through high-quality images of mouse brain and kidney sections in a wide-field imaging system,a confocal laser scanning system,and a two-photon imaging system.This mesoscopic objective lens holds immense promise for advancing multi-wavelength imaging of large fields of view at high resolution.

The environmental analysis and site selection of mussel and large yellow croaker aquaculture areas based on high resolution remote sensing

Mussel aquaculture and large yellow croaker aquaculture areas and their environmental characteristics in Zhoushan were analyzed using satellite data and in-situ surveys.A new two-step remote sensing method was proposed and applied to determine the basic environmental characteristics of the best mussel and large yellow croaker aquaculture areas.This methodology includes the first step of extraction of the location distribution and the second step of the extraction of internal environmental factors.The fishery ranching index(FRI1,FRI2)was established to extract the mussel and the large yellow croaker aquaculture area in Zhoushan,using Gaofen-1(GF-1)and Gaofen-6(GF-6)satellite data with a special resolution of 2 m.In the second step,the environmental factors such as sea surface temperature(SST),chlorophyll a(Chl-a)concentration,current and tide,suspended sediment concentration(SSC)in mussel aquaculture area and large yellow croaker aquaculture area were extracted and analyzed in detail.The results show the following three points.(1)For the extraction of the mussel aquaculture area,FRI1 and FRI2 are complementary,and the combination of FRI1 and FRI2 is suitable to extract the mussel aquaculture area.As for the large yellow croaker aquaculture area extraction,FRI2 is suitable.(2)Mussel aquaculture and the large yellow croaker aquaculture area in Zhoushan are mainly located on the side near the islands that are away from the eastern open waters.The water environment factor template suitable for mussel and large yellow croaker aquaculture was determined.(3)This two-step remote sensing method can be used for the preliminary screening of potential site selection for the mussels and large yellow croaker aquaculture area in the future.the fishery ranching index(FRI1,FRI2)in this paper can be applied to extract the mussel and large yellow croaker aquaculture areas in coastal waters around the world.

Role of Media in Promoting Peace and Conflict Resolution in North Kivu, Democratic Republic of Congo

The research examined the role of media in promoting peace and conflict resolution in North Kivu,Democratic Republic of Congo.The security situation in the Democratic Republic of the Congo(DRC),particularly in the eastern region of North Kivu,has been deteriorating.The study was anchored in social identity theory.The research methodology employed for this study was qualitative,focusing on the analysis of Mr.Edgar Mateso’s contributions to Radio Okapi’s“Dialogue entre Congolais”program during the period from 2019 to 2023.The research was conducted within the broader context of the persistent war and conflict in the Democratic Republic of Congo,providing valuable insights into the dynamics and challenges of addressing the ongoing insecurity and violence in the region.The study found that media,exemplified by Radio Okapi,plays a multifaceted and significant role in promoting peace and conflict resolution in North Kivu,Democratic Republic of Congo(DRC).It serves as a critical source of accurate information,enabling informed decision-making regarding ceasefire agreements,peace talks,and humanitarian aid.Media fosters dialogue and understanding among different stakeholders,contributing to reconciliation efforts.Additionally,it raises awareness about the human cost of the conflict,challenges misinformation,monitors peace agreements,amplifies the concerns of the affected population,and promotes human rights and justice.Media also engages youth,serves as an early warning system,and attracts global attention to the conflict.It is recommended that media organizations in North Kivu continue their efforts to facilitate dialogue and understanding among different stakeholder groups.Additionally,media should maintain its focus on raising awareness about the human cost of the conflict,ensuring that the suffering of civilians remains in the spotlight and generates international empathy and support.Media outlets should also continue their role as watchdogs by closely monitoring the implementation of ceasefire agreements and reporting any violations transparently.To strengthen media’s role in promoting peace,there should be continued advocacy for peace through dedicated radio programs,news reports,and editorial content.Internationally,media should continue to provide comprehensive coverage of the conflict in North Kivu,attracting global attention and shaping the perception of the conflict on the global stage.Lastly,media organizations should preserve a lasting legacy of peace by archiving peace-related content,documentaries,and narratives of reconciliation.

High-Resolution Remote Sensing Imagery for the Recognition of Traditional Villages

Traditional Chinese villages,vital carriers of traditional culture,have faced significant alterations due to urbanization in recent years,urgently necessitating artificial intelligence data updates.This study integrates high spatial resolution remote sensing imagery with deep learning techniques,proposing a novel method for identifying rooftops of traditional Chinese village buildings using high-definition remote sensing images.Using 0.54 m spatial resolution imagery of traditional village areas as the data source,this method analyzes the geometric and spectral image characteristics of village building rooftops.It constructs a deep learning feature sample library tailored to the target types.Employing a semantically enhanced version of the improved Mask R-CNN(Mask Region-based Convolutional Neural Network)for building recognition,the study conducts experiments on localized imagery from different regions.The results demonstrated that the modified Mask R-CNN effectively identifies traditional village building rooftops,achieving an of 0.7520 and an of 0.7400.It improves the current problem of misidentification and missed detection caused by feature heterogeneity.This method offers a viable and effective approach for industrialized data monitoring of traditional villages,contributing to their sustainable development.

Research on Risk Prevention and Resolution Strategies of College Network Ideology in the Context of the Internet

With the rapid development of the Internet,the network ideology of colleges and universities is facing severe challenges.This paper deeply analyzes the root of the risk of network ideology and makes a specific investigation of the status quo of network public opinion in colleges and universities.On this basis,the study explores and puts forward a series of targeted risk prevention and resolution strategies,aiming at providing a systematic solution for the network ideology security of colleges and universities.In this paper,with the combination of theory and practice as the path,we verify the effectiveness and applicability of the proposed strategy through the analysis of the implementation effect of the strategy.This study also provides theoretical support and practical guidance for the prevention and control of ideological risks and public opinion guidance in universities under the network environment,which has important practical significance.With the continuous progress of network technology,the threats to the network ideology of colleges and universities are increasing.For example,the spread of false information has become a serious problem affecting the security of college network ideology.

Resolution analysis of thermal neutron radiography based on accelerator-driven compact neutron source

Owing to the immobility of traditional reactors and spallation neutron sources,the demand for compact thermal neutron radiography(CTNR)based on accelerator neutron sources has rapidly increased in industrial applications.Recently,thermal neutron radiography experiments based on a D-T neutron generator performed by Hefei Institutes of Physical Science indicated a significant resolution deviation between the experimental results and the values calculated using the traditional resolution model.The experimental result was up to 23%lower than the calculated result,which hinders the achievement of the design goal of a compact neutron radiography system.A GEANT4 Monte Carlo code was developed to simulate the CTNR process,aiming to identify the key factors leading to resolution deviation.The effects of a low collimation ratio and high-energy neutrons were analyzed based on the neutron beam environment of the CTNR system.The results showed that the deviation was primarily caused by geometric distortion at low collimation ratios and radiation noise induced by highenergy neutrons.Additionally,the theoretical model was modified by considering the imaging position and radiation noise factors.The modified theoretical model was in good agreement with the experimental results,and the maximum deviation was reduced to 4.22%.This can be useful for the high-precision design of CTNR systems.

Improved spatiotemporal resolution of anti-scattering super-resolution label-free microscopy via synthetic wave 3D metalens imaging

Super-resolution(SR)microscopy has dramatically enhanced our understanding of biological processes.However,scattering media in thick specimens severely limits the spatial resolution,often rendering the images unclear or indistinguishable.Additionally,live-cell imaging faces challenges in achieving high temporal resolution for fast-moving subcellular structures.Here,we present the principles of a synthetic wave microscopy(SWM)to extract three-dimensional information from thick unlabeled specimens,where photobleaching and phototoxicity are avoided.SWM exploits multiple-wave interferometry to reveal the specimen’s phase information in the area of interest,which is not affected by the scattering media in the optical path.SWM achieves~0.42λ/NA resolution at an imaging speed of up to 106 pixels/s.SWM proves better temporal resolution and sensitivity than the most conventional microscopes currently available while maintaining exceptional SR and anti-scattering capabilities.Penetrating through the scattering media is challenging for conventional imaging techniques.Remarkably,SWM retains its efficacy even in conditions of low signal-to-noise ratios.It facilitates the visualization of dynamic subcellular structures in live cells,encompassing tubular endoplasmic reticulum(ER),lipid droplets,mitochondria,and lysosomes.

Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images

The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images,we present an effective YOLOv3 framework,corner regression-based YOLOv3(Correg-YOLOv3),to localize dense building accurately.This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box.By extending output dimensions,the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile.Finally,we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively.The experimental results achieve high performance in precision(96.45%),recall rate(95.75%),F1 score(96.10%)and average precision(98.05%),which were 2.73%,5.4%,4.1%and 4.73%higher than that of YOLOv3.Therefore,our proposed algorithm effectively tackles the problem of dense building detection in high resolution images.