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.

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.

Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm

Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a ground threat prediction-based path planning method is proposed based on artificial bee colony(ABC)algorithm by collaborative thinking strategy.Firstly,a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats.The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time.Then,a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats.By adding the collision warning mechanism to the path planning model,the flight path could be dynamically adjusted according to changing no-fly zones.Furthermore,a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy.The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution,and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy,which makes the optimization performance of ABC algorithm more controllable and efficient.Finally,simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.

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.

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.

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.

Facile electrochemical surface-alloying and etching of Au wires to enable high-performance substrates for surface enhanced Raman scattering

Surface-enhanced Raman Spectroscopy(SERS)is a nondestructive technique for rapid detection of analytes even at the single-molecule level.However,highly sensitive and reliable SERS substrates are mostly fabricated with complex nanofabrication techniques,greatly restricting their practical applications.A convenient electrochemical method for transforming the surface of commercial gold wires/foils into silver-alloyed nanostructures is demonstrated in this report.Au substrates are treated with repetitive anodic and cathodic bias in an electrolyte of thiourea,in a one-pot one-step manner.X-rays absorption fine structure(XAFS)spectroscopy confirms that the AuAg alloy is induced at the surface.The unique AuAg alloyed surface nanostructures are particularly advantageous when served as SERS substrates,enabling a remarkably sensitive detection of Rhodamine B(a detection limit of 10^(-14)M,and uniform strong response throughout the substrates at 10^(-12)M).

Effects of fracture evolution and non-Darcy flow on the thermal performance of enhanced geothermal system in 3D complex fractured rock

In fractured geothermal reservoirs,the fracture networks and internal fluid flow behaviors can significantly impact the thermal performance.In this study,we proposed a non-Darcy rough discrete fracture network(NR-DFN)model that can simultaneously consider the fracture evolution and non-Darcy flow dynamics in studying the thermo-hydro-mechanical(THM)coupling processes for heat extraction in geothermal reservoir.We further employed the model on the Habanero enhanced geothermal systems(EGS)project located in Australia.First,our findings illustrate a clear spatial-temporal variation in the thermal stress and pressure perturbations,as well as uneven spatial distribution of shear failure in 3D fracture networks.Activated shear failure is mainly concentrated in the first fracture cluster.Secondly,channeling flow have also been observed in DFNs during heat extraction and are further intensified by the expansion of fractures driven by thermal stresses.Moreover,the combined effect of non-Darcy flow and fracture evolution triggers a rapid decline in the resulting heat rate and temperature.The NR-DFN model framework and the Habanero EGS's results illustrate the importance of both fracture evolution and non-Darcy flow on the efficiency of EGS production and have the potential to promote the development of more sustainable and efficient EGS operations for stakeholders.

Research advances in enhanced coal seam gas extraction by controllable shock wave fracturing

With the continuous increase of mining in depth,the gas extraction faces the challenges of low permeability,great ground stress,high temperature and large gas pressure in coal seam.The controllable shock wave(CSW),as a new method for enhancing permeability of coal seam to improve gas extraction,features in the advantages of high efficiency,eco-friendly,and low cost.In order to better utilize the CSW into gas extraction in coal mine,the mechanism and feasibility of CSW enhanced extraction need to be studied.In this paper,the basic principles,the experimental tests,the mathematical models,and the on-site tests of CSW fracturing coal seams are reviewed,thereby its future research directions are provided.Based on the different media between electrodes,the CSW can be divided into three categories:hydraulic effect,wire explosion and excitation of energetic materials by detonating wire.During the process of propagation and attenuation of the high-energy shock wave in coal,the shock wave and bubble pulsation work together to produce an enhanced permeability effect on the coal seam.The stronger the strength of the CSW is,the more cracks created in the coal is,and the greater the length,width and area of the cracks being.The repeated shock on the coal seam is conducive to the formation of complex network fracture system as well as the reduction of coal seam strength,but excessive shock frequency will also damage the coal structure,resulting in the limited effect of the enhanced gas extraction.Under the influence of ground stress,the crack propagation in coal seam will be restrained.The difference of horizontal principal stress has a significant impact on the shape,propagation direction and connectivity of the CSW induced cracks.The permeability enhancement effect of CSW is affected by the breakage degree of coal seam.The shock wave is absorbed by the broken coal,which may hinder the propagation of CSW,resulting in a poor effect of permeability enhancement.When arranging two adjacent boreholes for CSW permeability enhancement test,the spacing of boreholes should not be too close,which may lead to negative pressure mutual pulling in the early stage of drainage.At present,the accurate method for effectively predicting the CSW permeability enhanced range should be further investigated.

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.

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.

Optimal decision-making method for equipment maintenance to enhance the resilience of power digital twin system under extreme disaster

Digital twins and the physical assets of electric power systems face the potential risk of data loss and monitoring failures owing to catastrophic events,causing surveillance and energy loss.This study aims to refine maintenance strategies for the monitoring of an electric power digital twin system post disasters.Initially,the research delineates the physical electric power system along with its digital counterpart and post-disaster restoration processes.Subsequently,it delves into communication and data processing mechanisms,specifically focusing on central data processing(CDP),communication routers(CRs),and phasor measurement units(PMUs),to re-establish an equipment recovery model based on these data transmission methodologies.Furthermore,it introduces a mathematical optimization model designed to enhance the digital twin system’s post-disaster monitoring efficacy by employing the branch-and-bound method for its resolution.The efficacy of the proposed model was corroborated by analyzing an IEEE-14 system.The findings suggest that the proposed branch-and-bound algorithm significantly augments the observational capabilities of a power system with limited resources,thereby bolstering its stability and emergency response mechanisms.

Carbon enhanced nucleophilicity of Na_(3)V_(2)(PO_(4))_(3):A general approach for dendrite-free zinc metal anodes

Zincophilic property and high electrical conductivity are both very important parameters to design novel Zn anode for aqueous Zn-ion batteries(AZIBs).However,single material is difficult to exhibit zincophilic property and high electrical conductivity at the same time.Herein,originating from theoretical calculation,a zincophilic particle regulation strategy is proposed to address these limitations and carbon coated Na_(3)V_(2)(PO_(4))_(3)is taken as an example to be a protective layer on zinc metal(NVPC@Zn).Na_(3)V_(2)(PO_(4))_(3)(NVP)is a common cathode material for Zn-ion batteries,which is zincophilic.Carbon materials not only offer an electron pathway to help Zn deposition onto NVPC surface,but also enhance the zinc nucleophilicity of Na_(3)V_(2)(PO_(4))_(3).Hence,this hybrid coating layer can tune zinc deposition and resist side reactions such as hydrogen generation and Zn metal corrosion.Experimentally,a symmetrical battery with NVPC@Zn electrode displays highly reversible plating/stripping behavior with a long cycle lifespan over 1800 h at2 mA cm^(-2),much better than carbon and Na_(3)V_(2)(PO_(4))_(3)solely modified Zn electrodes.When the Na_(3)V_(2)(PO_(4))_(3)is replaced with zincophobic Al2O3or zincophilic V2O3,the stability of the modified zinc anodes is also prolonged.This strategy expands the option of zincophilic materials and provides a general and effective way to stabilize the Zn electrode.

CO_(2)flooding in shale oil reservoir with radial borehole fracturing for CO_(2)storage and enhanced oil recovery

This study introduces a novel method integrating CO_(2)flooding with radial borehole fracturing for enhanced oil recovery and CO_(2)underground storage,a solution to the limited vertical stimulation reservoir volume in horizontal well fracturing.A numerical model is established to investigate the production rate,reservoir pressure field,and CO_(2)saturation distribution corresponding to changing time of CO_(2)flooding with radial borehole fracturing.A sensitivity analysis on the influence of CO_(2)injection location,layer spacing,pressure difference,borehole number,and hydraulic fractures on oil production and CO_(2)storage is conducted.The CO_(2)flooding process is divided into four stages.Reductions in layer spacing will significantly improve oil production rate and gas storage capacity.However,serious gas channeling can occur when the spacing is lower than 20 m.Increasing the pressure difference between the producer and injector,the borehole number,the hydraulic fracture height,and the fracture width can also increase the oil production rate and gas storage rate.Sensitivity analysis shows that layer spacing and fracture height greatly influence gas storage and oil production.Research outcomes are expected to provide a theoretical basis for the efficient development of shale oil reservoirs in the vertical direction.

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.

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.

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.

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.

Enhanced Temporal Correlation for Universal Lesion Detection

Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks.However,3D CT blocks necessitate significantly higher hardware resources during the learning phase.Therefore,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks.In this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named TCE-Net.The designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT slices.Besides,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various sizes.Extensive experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.