Super-resolution Restoration of Remote-sensing Images

A novel image restoration scheme, which is super-resolution image restoration algorithm Poisson-maximum-afterword-probability based on Markvo constraint (MPMAP) combined with evaluating image detail parameter D, has been proposed. The advantage of super-resolution algorithm MPMAP incorporated with parameter D lies in the fact that super-resolution algorithm MPMAP model is discrete, which is in accordance with remote-sensing imaging model, and the algorithm MPMAP is proved applicable to linear and non-linear imaging models with a unique solution when noise is not severe. According to simulation experiments for practical images, super-resolution algorithm MPMAP can retain image details better than most of traditional restoration methods; at the same time, the proposed parameter D can help to identify real point spread function (PSF) value of degradation process. Processing result of practical remote-sensing images by MPMAP combined with parameter D are given, it illustrates that MPMAP restoration scheme combined PSF estimation has a better restoration result than that of Photoshop processing, based on the same original images. It is proved that the proposed scheme is helpful to offset the lack of resolution of the original remote-sensing images and has its extensive application foreground.

Ecological problems and ecological restoration zoning of the Aral Sea

The Aral Sea was the fourth largest lake in the world but it has shrunk dramatically as a result of irrational human activities, triggering the "Aral Sea ecological crisis". The ecological problems of the Aral Sea have attracted widespread attention, and the alleviation of the Aral Sea ecological crisis has reached a consensus among the five Central Asian countries(Kazakhstan, Uzbekistan, Tajikistan, Kyrgyzstan, and Turkmenistan). In the past decades, many ecological management measures have been implemented for the ecological restoration of the Aral Sea. However, due to the lack of regional planning and zoning, the results are not ideal. In this study, we mapped the ecological zoning of the Aral Sea from the perspective of ecological restoration based on soil type, soil salinity, surface water, groundwater table, Normalized Difference Vegetation Index(NDVI), land cover, and aerosol optical depth(AOD) data. Soil salinization and salt dust are the most prominent ecological problems in the Aral Sea. We divided the Aral Sea into 7 first-level ecological restoration subregions(North Aral Sea catchment area in the downstream of the Syr Darya River(Subregion Ⅰ);artificial flood overflow area in the downstream of the Aral Sea(Subregion Ⅱ);physical/chemical remediation area of the salt dust source area in the eastern part of the South Aral Sea(Subregion Ⅲ);physical/chemical remediation area of severe salinization in the central part of the South Aral Sea(Subregion Ⅳ);existing water surface and potential restoration area of the South Aral Sea(Subregion Ⅴ);Aral Sea vegetation natural recovery area(Subregion Ⅵ);and vegetation planting area with slight salinization in the South Aral Sea(Subregion Ⅶ)) and 14 second-level ecological restoration subregions according to the ecological zoning principles. Implementable measures are proposed for each ecological restoration subregion. For Subregion Ⅰ and Subregion Ⅱ with lower elevations, artificial flooding should be carried out to restore the surface of the Aral Sea. Subregion Ⅲ and Subregion Ⅳ have severe salinization, making it difficult for vegetation to grow. In these subregions, it is recommended to cover and pave the areas with green biomatrix coverings and environmentally sustainable bonding materials. In Subregion Ⅴ located in the central and western parts of the South Aral Sea, surface water recharge should be increased to ensure that this subregion can maintain normal water levels. In Subregion Ⅵ and Subregion Ⅶ where natural conditions are suitable for vegetation growth, measures such as afforestation and buffer zones should be implemented to protect vegetation. This study could provide a reference basis for future comprehensive ecological management and restoration of the Aral Sea.

Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution

Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution inputs.Super-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance imaging.Super-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater clarity.This study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution image.The shearlet transform is chosen for its excellent sparse approximation capabilities.Initially,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high frequencies.The shearlet coefficients are fed into the EDSR network.The high-resolution image is subsequently reconstructed using the inverse shearlet transform.The incorporation of the EDSR network enhances training stability,leading to improved generated images.The experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image quality.Compared to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.

Towards adaptable synchrotron image restoration pipeline

Synchrotron microscopic data commonly suffer from poor image quality with degraded resolution incurred by instrumentation defects or experimental conditions.Image restoration methods are often applied to recover the reduced resolution,providing improved image details that can greatly facilitate scientific discovery.Among these methods,deconvolution techniques are straightforward,yet either require known prior information or struggle to tackle large experimental data.Deep learning(DL)-based super-resolution(SR)methods handle large data well,however data scarcity and model generalizability are problematic.In addition,current image restoration methods are mostly offline and inefficient for many beamlines where high data volumes and data complexity issues are encountered.To overcome these limitations,an online image-restoration pipeline that adaptably selects suitable algorithms and models from a method repertoire is promising.In this study,using both deconvolution and pretrained DL-based SR models,we show that different restoration efficacies can be achieved on different types of synchrotron experimental data.We describe the necessity,feasibility,and significance of constructing such an image-restoration pipeline for future synchrotron experiments.

Stepwise Ecological Restoration:A framework for improving restoration outcomes

Ecosystem degradation is one of the critical constraints for the sustainable development of our planet.However,recovering an ecosystem to a pre-impairment condition is often not practical.The International Restoration Standards provide the first framework for practical guidance on what constitutes the process of ecological repair and how this repair process can be influenced to improve net ecological benefits.In these Standards,Restorative Continuum is highlighted and it recognises that many do not,yet there is still value in aspiring to improvements to the highest extent possible,with some sites potentially being able to be improved in a stepwise manner.Here we elaborate on these Standards by providing a cross-ecosystem theoretical framework of Stepwise Ecological Restoration(STERE)for promoting higher environmental benefits.STERE allows the selection of suitable restorative modes by considering the degree of degradation while encouraging a transition to a higher state.These models include environmental remediation for completely modified and degraded ecosystems,ecological rehabilitation for highly modified and degraded ecosystems,and ecological restoration for degraded native ecosystems.STERE requires selecting tailored restorative modes,setting clear restorative targets and reference ecosystems,applying a systematic-thinking approach,and implementing a continuous monitoring program at all process stages to achieve a resilient trajectory.STERE allows adaptive management in the context of climate change,and when the evidence is available,to“adapt to the future”to ensure climate resilience.The STERE framework could assist in initiating and implementing restoration projects worldwide,especially in developing countries.

AFBNet: A Lightweight Adaptive Feature Fusion Module for Super-Resolution Algorithms

At present,super-resolution algorithms are employed to tackle the challenge of low image resolution,but it is difficult to extract differentiated feature details based on various inputs,resulting in poor generalization ability.Given this situation,this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block(AFB)for feature extraction.This module mainly comprises dynamic convolution,attention mechanism,and pixel-based gating mechanism.Combined with dynamic convolution with scale information,the network can extract more differentiated feature information.The introduction of a channel spatial attention mechanism combined with multi-feature fusion further enables the network to retain more important feature information.Dynamic convolution and pixel-based gating mechanisms enhance the module’s adaptability.Finally,a comparative experiment of a super-resolution algorithm based on the AFB module is designed to substantiate the efficiency of the AFB module.The results revealed that the network combined with the AFB module has stronger generalization ability and expression ability.

Lactate is a potentially harmful substitute for brain glucose fuel:consequences for metabolic restoration of neurotransmission

The metabolite lactate (L-lactate) can be generated and released by diverse brain cells,including neurons,astrocytes,and oligodendrocytes (Kann,2023;Rae et al.,2024).Lactate production usually requires the degradation of glucose (D-glucose)-and glycogen in astrocytes-to pyruvate by glycolysis and subsequent conversion of pyruvate to lactate by the enzyme lactate dehydrogenase(Figure 1A;Dienel,2019;Rae et al.,2024).

Restoration or Rehabilitation of the Faleme River Affected by Mining Activities: What Methods?

The Faleme River, a West Africa long transboundary stream (625 km) and abundant flow (>1100 million m3) is affected by severe erosion because of mining activities that takes place throughout the riverbed. To preserve this important watercourse and ensure the sustainability of its services, selecting and implementing appropriates restorations techniques is vital. In this context, the purpose of this paper was to present an overview of the actions and techniques that can be implemented for the restoration/rehabilitation of the Faleme. The methodological approach includes field investigation, water sampling, literature review with cases studies and SWOT analysis of the four methods presented: river dredging, constructed wetlands, floating treatment wetlands and chemical precipitation (coagulation and flocculation). The study confirmed the pollution of the river by suspended solids (TSS > 1100 mg/L) and heavy metals such as iron, zinc, aluminium, and arsenic. For the restoration methods, it was illustrated through description of their mode of operation and through some case studies presented, that all the four methods have proven their effectiveness in treating rivers but have differences in their costs, their sustainability (detrimental to living organisms or causing a second pollution) and social acceptance. They also have weaknesses and issues that must be addressed to ensure success of rehabilitation. For the case of the Faleme river, after analysis, floating treatment wetlands are highly recommended for their low cost, good removal efficiency if the vulnerability of the raft and buoyancy to strong waves and flow is under control.

PSMFNet:Lightweight Partial Separation and Multiscale Fusion Network for Image Super-Resolution

The employment of deep convolutional neural networks has recently contributed to significant progress in single image super-resolution(SISR)research.However,the high computational demands of most SR techniques hinder their applicability to edge devices,despite their satisfactory reconstruction performance.These methods commonly use standard convolutions,which increase the convolutional operation cost of the model.In this paper,a lightweight Partial Separation and Multiscale Fusion Network(PSMFNet)is proposed to alleviate this problem.Specifically,this paper introduces partial convolution(PConv),which reduces the redundant convolution operations throughout the model by separating some of the features of an image while retaining features useful for image reconstruction.Additionally,it is worth noting that the existing methods have not fully utilized the rich feature information,leading to information loss,which reduces the ability to learn feature representations.Inspired by self-attention,this paper develops a multiscale feature fusion block(MFFB),which can better utilize the non-local features of an image.MFFB can learn long-range dependencies from the spatial dimension and extract features from the channel dimension,thereby obtaining more comprehensive and rich feature information.As the role of the MFFB is to capture rich global features,this paper further introduces an efficient inverted residual block(EIRB)to supplement the local feature extraction ability of PSMFNet.A comprehensive analysis of the experimental results shows that PSMFNet maintains a better performance with fewer parameters than the state-of-the-art models.

Efficient 2-D MUSIC algorithm for super-resolution moving target tracking based on an FMCW radar

Frequency modulated continuous wave(FMCW)radar is an advantageous sensor scheme for target estimation and environmental perception.However,existing algorithms based on discrete Fourier transform(DFT),multiple signal classification(MUSIC)and compressed sensing,etc.,cannot achieve both low complexity and high resolution simultaneously.This paper proposes an efficient 2-D MUSIC algorithm for super-resolution target estimation/tracking based on FMCW radar.Firstly,we enhance the efficiency of 2-D MUSIC azimuth-range spectrum estimation by incorporating 2-D DFT and multi-level resolution searching strategy.Secondly,we apply the gradient descent method to tightly integrate the spatial continuity of object motion into spectrum estimation when processing multi-epoch radar data,which improves the efficiency of continuous target tracking.These two approaches have improved the algorithm efficiency by nearly 2-4 orders of magnitude without losing accuracy and resolution.Simulation experiments are conducted to validate the effectiveness of the algorithm in both single-epoch estimation and multi-epoch tracking scenarios.

Pyramid Separable Channel Attention Network for Single Image Super-Resolution

Single Image Super-Resolution(SISR)technology aims to reconstruct a clear,high-resolution image with more information from an input low-resolution image that is blurry and contains less information.This technology has significant research value and is widely used in fields such as medical imaging,satellite image processing,and security surveillance.Despite significant progress in existing research,challenges remain in reconstructing clear and complex texture details,with issues such as edge blurring and artifacts still present.The visual perception effect still needs further enhancement.Therefore,this study proposes a Pyramid Separable Channel Attention Network(PSCAN)for the SISR task.Thismethod designs a convolutional backbone network composed of Pyramid Separable Channel Attention blocks to effectively extract and fuse multi-scale features.This expands the model’s receptive field,reduces resolution loss,and enhances the model’s ability to reconstruct texture details.Additionally,an innovative artifact loss function is designed to better distinguish between artifacts and real edge details,reducing artifacts in the reconstructed images.We conducted comprehensive ablation and comparative experiments on the Arabidopsis root image dataset and several public datasets.The experimental results show that the proposed PSCAN method achieves the best-known performance in both subjective visual effects and objective evaluation metrics,with improvements of 0.84 in Peak Signal-to-Noise Ratio(PSNR)and 0.017 in Structural Similarity Index(SSIM).This demonstrates that the method can effectively preserve high-frequency texture details,reduce artifacts,and have good generalization performance.

Multi-prior physics-enhanced neural network enables pixel super-resolution and twin-imagefree phase retrieval from single-shot hologram

Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact setup.However,the need for a larger pixel size of detector to improve imaging photosensitivity,field-of-view,and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution.Additionally,the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image.The deep learning(DL)approach has emerged as a powerful tool for phase retrieval in DIHM,effectively addressing these challenges.However,most DL-based strategies are datadriven or end-to-end net approaches,suffering from excessive data dependency and limited generalization ability.Herein,a novel multi-prior physics-enhanced neural network with pixel super-resolution(MPPN-PSR)for phase retrieval of DIHM is proposed.It encapsulates the physical model prior,sparsity prior and deep image prior in an untrained deep neural network.The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods.With the capabilities of pixel super-resolution,twin-image elimination and high-throughput jointly from a single-shot intensity measurement,the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.

Phytoremediation Strategies for Heavy Metal Contamination: A Review on Sustainable Approach for Environmental Restoration

Current globalization trends and important breakthroughs globally need a complete study of heavy metal contamination, its causes, its impacts on human and environmental health, and different remediation strategies. Heavy metal pollution is mostly produced by urbanization and industry, which threatens ecosystems and human health. Herein, we discuss a sustainable environmental restoration strategy employing phytoremediation for heavy metal pollution, the carcinogenic, mutagenic, and cytotoxic effects of heavy metals such as cadmium, copper, mercury, selenium, zinc, arsenic, chromium, lead, nickel, and silver, which may be fatal. Phytoremediation, which was prioritized, uses plants to remove, accumulate, and depollute pollutants. This eco-friendly method may safely collect, accumulate, and detoxify toxins using plants, making it popular. This study covers phytostabilization, phytodegradation, rhizodegradation, phytoextraction, phytovolatilization, and rhizofiltration. A phytoremediation process’s efficiency in varied environmental circumstances depends on these components’ complex interplay. This paper also introduces developing phytoremediation approaches including microbe-assisted, chemical-assisted, and organic or bio-char use. These advancements attempt to overcome conventional phytoremediation’s limitations, such as limited suitable plant species, location problems, and sluggish remediation. Current research includes machine learning techniques and computer modeling, biostimulation, genetic engineering, bioaugmentation, and hybrid remediation. These front-line solutions show that phytoremediation research is developing towards transdisciplinary efficiency enhancement. We acknowledge phytoremediation’s promise but also its drawbacks, such as site-specific variables, biomass buildup, and sluggish remediation, as well as ongoing research to address them. In conclusion, heavy metal pollution threatens the ecology and public health and must be reduced. Phytoremediation treats heavy metal pollution in different ways. Over time, phytoremediation systems have developed unique ways that improve efficiency. Despite difficulties like site-specificity, sluggish remediation, and biomass buildup potential, phytoremediation is still a vital tool for environmental sustainability.

Restoration of the JPEG Maximum Lossy Compressed Face Images with Hourglass Block-GAN

In the context of high compression rates applied to Joint Photographic Experts Group(JPEG)images through lossy compression techniques,image-blocking artifacts may manifest.This necessitates the restoration of the image to its original quality.The challenge lies in regenerating significantly compressed images into a state in which these become identifiable.Therefore,this study focuses on the restoration of JPEG images subjected to substantial degradation caused by maximum lossy compression using Generative Adversarial Networks(GAN).The generator in this network is based on theU-Net architecture.It features a newhourglass structure that preserves the characteristics of the deep layers.In addition,the network incorporates two loss functions to generate natural and high-quality images:Low Frequency(LF)loss and High Frequency(HF)loss.HF loss uses a pretrained VGG-16 network and is configured using a specific layer that best represents features.This can enhance the performance in the high-frequency region.In contrast,LF loss is used to handle the low-frequency region.The two loss functions facilitate the generation of images by the generator,which can mislead the discriminator while accurately generating high-and low-frequency regions.Consequently,by removing the blocking effects frommaximum lossy compressed images,images inwhich identities could be recognized are generated.This study represents a significant improvement over previous research in terms of the image resolution performance.

Research on Restoration of Murals Based on Diffusion Model and Transformer

Due to the limitations of a priori knowledge and convolution operation,the existing image restoration techniques cannot be directly applied to the cultural relics mural restoration,in order to more accurately restore the original appearance of the cultural relics mural images,an image restoration based on the denoising diffusion probability model(Denoising Diffusion Probability Model(DDPM))and the Transformer method.The process involves two steps:in the first step,the damaged mural image is firstly utilized as the condition to generate the noise image,using the time,condition and noise image patch as the inputs to the noise prediction network,capturing the global dependencies in the input sequence through the multi-attentionmechanismof the input sequence and feedforward neural network processing,and designing a long skip connection between the shallow and deep layers in the Transformer blocks between the shallow and deep layers using long skip connections to fuse the feature information of global and local outputs to maintain the overall consistency of the restoration results;In the second step,taking the noisy image as a condition to direct the diffusion model to back sample to generate the restored image.Experiment results show that the PSNR and SSIM of the proposedmethod are improved by 2%to 9%and 1%to 3.3%,respectively,which are compared to the comparison methods.This study proposed synthesizes the advantages of the diffusionmodel and deep learningmodel to make themural restoration results more accurate.

Characterisation of the Bacteria and Archaea Community Associated with Wild Oysters, At Three Possible Restoration Sites in the North Sea

With 85% of the global oyster reefs destroyed, there is an urgent need for large scale restoration to benefit from the ecosystem services provided by biogenic oyster reefs and their associated biodiversity, including microorganisms that drive marine biogeochemical cycles. This experiment established a baseline for the monitoring of the bacterial and archaeal community associated with wild oysters, using samples from their immediate environment of the Voordelta, with cohabiting Crassostrea gigas and Ostrea edulis, Duikplaats with only C. gigas attached to rocks, and the Dansk Skaldyrcentre, with no onsite oysters. The microbial profiling was carried out through DNA analysis of samples collected from the surfaces of oyster shells and their substrate, the sediment and seawater. Following 16S rRNA amplicon sequencing and bioinformatics, alpha indices implied high species abundance and diversity in sediment but low abundance in seawater. As expected, Proteobacteria, Bacteroidetes, Firmicutes and Thaumarchaeota dominated the top 20 OTUs. In the Voordelta, OTUs related to Colwellia, Shewanella and Psychrobium differentiated the oysters collected from a reef with those attached to rocks. Duikplaats were distinct for sulfur-oxidizers Sulfurimonas and sulfate-reducers from the Sva 0081 sediment group. Archaea were found mainly in sediments and the oyster associated microbiome, with greater abundance at the reef site, consisting mostly of Thaumarchaeota from the family Nitrosopumilaceae. The oyster free site displayed archaea in sediments only, and algal bloom indicator microorganisms from the Rhodobacteraceae, Flavobacteriaceae family and genus [Polaribacter] huanghezhanensis, in addition to the ascidian symbiotic partner, Synechococcus. This study suggests site specific microbiome shifts, influenced by the presence of oysters and the type of substrate.

Feature extraction and analysis of reclaimed vegetation in ecological restoration area of abandoned mines based on hyperspectral remote sensing images

The vegetation growth status largely represents the ecosystem function and environmental quality.Hyperspectral remote sensing data can effectively eliminate the effects of surface spectral reflectance and atmospheric scattering and directly reflect the vegetation parameter information.In this study,the abandoned mining area in the Helan Mountains,China was taken as the study area.Based on hyperspectral remote sensing images of Zhuhai No.1 hyperspectral satellite,we used the pixel dichotomy model,which was constructed using the normalized difference vegetation index(NDVI),to estimate the vegetation coverage of the study area,and evaluated the vegetation growth status by five vegetation indices(NDVI,ratio vegetation index(RVI),photochemical vegetation index(PVI),red-green ratio index(RGI),and anthocyanin reflectance index 1(ARI1)).According to the results,the reclaimed vegetation growth status in the study area can be divided into four levels(unhealthy,low healthy,healthy,and very healthy).The overall vegetation growth status in the study area was generally at low healthy level,indicating that the vegetation growth status in the study area was not good due to short-time period restoration and harsh damaged environment such as high and steep rock slopes.Furthermore,the unhealthy areas were mainly located in Dawukougou where abandoned mines were concentrated,indicating that the original mining activities have had a large effect on vegetation ecology.After ecological restoration of abandoned mines,the vegetation coverage in the study area has increased to a certain extent,but the amplitude was not large.The situation of vegetation coverage in the northern part of the study area was worse than that in the southern part,due to abandoned mines mainly concentrating in the northern part of the Helan Mountains.The combination of hyperspectral remote sensing data and vegetation indices can comprehensively extract the characteristics of vegetation,accurately analyze the plant growth status,and provide technical support for vegetation health evaluation.

Faster split-based feedback network for image super-resolution

Although most of the existing image super-resolution(SR)methods have achieved superior performance,contrastive learning for high-level tasks has not been fully utilized in the existing image SR methods based on deep learning.This work focuses on two well-known strategies developed for lightweight and robust SR,i.e.,contrastive learning and feedback mechanism,and proposes an integrated solution called a split-based feedback network(SPFBN).The proposed SPFBN is based on a feedback mechanism to learn abstract representations and uses contrastive learning to explore high information in the representation space.Specifically,this work first uses hidden states and constraints in recurrent neural network(RNN)to implement a feedback mechanism.Then,use contrastive learning to perform representation learning to obtain high-level information by pushing the final image to the intermediate images and pulling the final SR image to the high-resolution image.Besides,a split-based feedback block(SPFB)is proposed to reduce model redundancy,which tolerates features with similar patterns but requires fewer parameters.Extensive experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-art methods.Moreover,this work extends the experiment to prove the effectiveness of this method and shows better overall reconstruction quality.

Offset restoration in total hip arthroplasty:Important:A current review

Normal vertical and horizontal offset is essential for hip biomechanics,muscle functioning and gait pattern.Total hip arthroplasty(THA)should aim to restore normal offset with implantation of femoral and acetabular components.This would be possible with proper preoperative planning,templating and ensuring implant options are available for offset restoration.Templating is essential for understanding the vertical and horizontal offset change,especially in hip arthritis presenting late with significant limb length discrepancy at THA.Planning should include appropriate soft tissue releases and the use of ideal implants to achieve restoration of horizontal and vertical offset.Under correction of horizontal offset at THA for fracture neck of femur could result in abductor fatigue,limp and increased wear.Restoration of horizontal offset is imperative at THA for a fractured neck of the femur to achieve optimal abductor function.Horizontal offset is necessary for optimal abductor muscle tension and function.Revision THA for acetabular bone loss would require hip center restoration with the acetabular and femoral offset correction to achieve limb length correction and abductor length.The inability to achieve vertical and horizontal offset correction could lead to dislocation or signs of abductor fatigue.Careful vertical and horizontal femur offset restoration is required for normal hip biomechanics,decreased wear and increased longevity.

Resilience-Oriented Load Restoration Method and Repair Strategies for Regional Integrated Electricity-Natural Gas System

The rising frequency of extreme disaster events seriously threatens the safe and secure operation of the regional integrated electricity-natural gas system(RIENGS).With the growing level of coupling between electric and natural gas systems,it is critical to enhance the load restoration capability of both systems.This paper proposes a coordinated optimization strategy for resilience-enhanced RIENGS load restoration and repair scheduling and transforms it into a mixed integer second-order cone programming(MISOCP)model.The proposed model considers the distribution network reconfiguration and the coordinated repair strategy between the two systems,minimizing the total system load loss cost and repair time.In addition,a bi-directional gas flow model is used to describe the natural gas system,which can provide the RIENGS with more flexibility for load restoration during natural gas system failure.Finally,the effectiveness of the proposed approach is verified by conducting case studies on the test systems RIENGS E13-G7 and RIENGS E123-G20.