A semi-analytical model for coupled flow in stress-sensitive multi-scale shale reservoirs with fractal characteristics

A large number of nanopores and complex fracture structures in shale reservoirs results in multi-scale flow of oil. With the development of shale oil reservoirs, the permeability of multi-scale media undergoes changes due to stress sensitivity, which plays a crucial role in controlling pressure propagation and oil flow. This paper proposes a multi-scale coupled flow mathematical model of matrix nanopores, induced fractures, and hydraulic fractures. In this model, the micro-scale effects of shale oil flow in fractal nanopores, fractal induced fracture network, and stress sensitivity of multi-scale media are considered. We solved the model iteratively using Pedrosa transform, semi-analytic Segmented Bessel function, Laplace transform. The results of this model exhibit good agreement with the numerical solution and field production data, confirming the high accuracy of the model. As well, the influence of stress sensitivity on permeability, pressure and production is analyzed. It is shown that the permeability and production decrease significantly when induced fractures are weakly supported. Closed induced fractures can inhibit interporosity flow in the stimulated reservoir volume (SRV). It has been shown in sensitivity analysis that hydraulic fractures are beneficial to early production, and induced fractures in SRV are beneficial to middle production. The model can characterize multi-scale flow characteristics of shale oil, providing theoretical guidance for rapid productivity evaluation.

Templated synthesis of transition metal phosphide electrocatalysts for oxygen and hydrogen evolution reactions

Transition metal phosphides(TMPs)have been regarded as alternative hydrogen evolution reaction(HER)and oxygen evolution reaction(OER)catalysts owing to their comparable activity to those of noble metal-based catalysts.TMPs have been produced in various morphologies,including hollow and porous nanostructures,which are features deemed desirable for electrocatalytic materials.Templated synthesis routes are often responsible for such morphologies.This paper reviews the latest advances and existing challenges in the synthesis of TMP-based OER and HER catalysts through templated methods.A comprehensive review of the structure-property-performance of TMP-based HER and OER catalysts prepared using different templates is presented.The discussion proceeds according to application,first by HER and further divided among the types of templates used-from hard templates,sacrificial templates,and soft templates to the emerging dynamic hydrogen bubble template.OER catalysts are then reviewed and grouped according to their morphology.Finally,prospective research directions for the synthesis of hollow and porous TMP-based catalysts,such as improvements on both activity and stability of TMPs,design of environmentally benign templates and processes,and analysis of the reaction mechanism through advanced material characterization techniques and theoretical calculations,are suggested.

Recent advances in transition metal phosphide materials:Synthesis and applications in supercapacitors

Supercapacitors(SCs)are considered promising energy storge systems because of their outstanding power density,fast charge and discharge rate and long-term cycling stability.The exploitation of cheap and efficient electrode materials is the key to improve the performance of supercapacitors.As the battery-type materials,transition metal phosphides(TMPs)possess high theoretical specific capacity,good electrical conductivity and superior structural stability,which have been extensively studied to be electrode materials for supercapacitors.In this review,we summarize the up-to-date progress on TMPs materials from diversified synthetic methods,diverse nanostructures and several prominent TMPs and their composites in application of supercapacitors.In the end,we also propose the remaining challenges toward the rational discovery and synthesis of high-performance TMP electrodes materials for energy storage.

New Rural Community Construction or Retention Development:A Comparative Analysis of Rural Settlement Transition Mechanism in Plain Agriculture Area of China Based on Actor Network Theory

It is an important way to realize rural revitalization and sustainable development to guide rural settlement transition(RST)in an appropriate way.This paper uses actor network theory(ANT)to construct a theoretical framework for the study of RST.Taking two typical villages with different transition paths in rural areas of North China Plain as examples,this paper reveals the mechanism of RST and makes a comparative analysis.The results show that:1)after identifying problems and obligatory passage point,key actors recruit heterogeneous actors into the actor network by entrusting them with common interests,and realize RST under the system operation.2)Rural settlements under different transition paths have similarities in the problems to be solved,collective actions and policy factors,but there are differences in the transition process,mechanism and effect.The actor network and mechanism of RST through the path of new rural community construction are more complex and the transition effect is more thorough.In contrast,the degree of RST of retention development path is limited if there is no resource and location advantage.3)Based on the applicable conditions of different paths,this paper designs a logical framework of‘Situation-Structure-Behavior-Result’to scientifically guide the identification of RST paths under the background of rural revitalization.

Photodoping-Modified Charge Density Wave Phase Transition in WS_(2)/1T-TaS_(2) Heterostructure

Controlling collective electronic states hold great promise for development of innovative devices. Here, we experimentally detect the modification of the charge density wave(CDW) phase transition within a 1T-TaS_(2) layer in a WS_(2)/1T-TaS_(2) heterostructure using time-resolved ultrafast spectroscopy. Laser-induced charge transfer doping strongly suppresses the commensurate CDW phase, which results in a significant decrease in both the phase transition temperature(T_(c)) and phase transition stiffness. We interpret the phenomenon that photoinduced hole doping, when surpassing a critical threshold value of ~ 10^(18)cm^(-3), sharply decreases the phase transition energy barrier. Our results provide new insights into controlling the CDW phase transition, paving the way for optical-controlled novel devices based on CDW materials.

Transfer learning framework for multi-scale crack type classification with sparse microseismic networks

Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting for underground mines where the microseismic stations often lack azimuthal coverage.Thus,there is a need for a method to invert fracture mechanisms using waveforms acquired by a sparse microseismic network.Here,we present a novel,multi-scale framework to classify whether a rock crack contracts or dilates based on a single waveform.The framework consists of a deep learning model that is initially trained on 2400000+manually labelled field-scale seismic and microseismic waveforms acquired across 692 stations.Transfer learning is then applied to fine-tune the model on 300000+MT-labelled labscale acoustic emission waveforms from 39 individual experiments instrumented with different sensor layouts,loading,and rock types in training.The optimal model achieves over 86%F-score on unseen waveforms at both the lab-and field-scale.This model outperforms existing empirical methods in classification of rock fracture mechanisms monitored by a sparse microseismic network.This facilitates rapid assessment of,and early warning against,various rock engineering hazard such as induced earthquakes and rock bursts.

MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot.However,there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images.A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper.The main innovations are as follows:Firstly,the Multi-scale Residual Feature Extraction Module(MRFEM)is designed to effectively extract multi-scale features.The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively.Secondly,the Multi-scale Directional Feature Perception Module(MDFPM)is designed,which uses a three-branch structure of different sizes convolution to transmit direction feature layer by layer,and focuses on the target region to enhance the feature information.Thirdly,the Axial Compression Former Module(ACFM)is designed to perform global calculations to enhance the perception ability of global features in different directions.To verify the effectiveness of the MSD-Net,comparative experiments and ablation experiments are carried out.In the COVID-19 RADIOGRAPHY DATABASE,the Accuracy,Recall,Precision,F1 Score,and Specificity of MSD-Net are 97.76%,95.57%,95.52%,95.52%,and 98.51%,respectively.In the chest X-ray dataset,the Accuracy,Recall,Precision,F1 Score and Specificity of MSD-Net are 97.78%,95.22%,96.49%,95.58%,and 98.11%,respectively.This model improves the accuracy of lung image recognition effectively and provides an important clinical reference to pneumonia Computer-Aided Diagnosis.

Interconnection Gridlock Threatens Transition to Renewable Energy

In October 2023,the International Energy Agency(IEA)released a report identifying inadequate grid infrastructure as a key threat to meeting the climate goals laid out by the Paris Agreement[1],the landmark 2015 international treaty in which 196 parties agreed to limit global warming to well below 2℃ above preindustrial levels[2].Importantly,funding for the treaty’s calledfor transition from fossil fuels to clean energy has accelerated[3],with recently legislated massive governmental subsidies and other incentives(e.g.,the Inflation Reduction Act[4])aimed specifically at promoting renewable energy.However,a commensurate surge in investment in electrical grids has not materialized;instead,it has stagnated for the past decade at around 300 billion USD per year worldwide[1].

YOLO-MFD:Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery.Additionally,these complexities contribute to inaccuracies in target localization and hinder precise target categorization.This paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery analysis.Specifically,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex backgrounds.To resolve these issues,we introduce a novel approach.First,we propose the implementation of a lightweight multi-scale module called CEF.This module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature information.It effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing imagery.Second,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone section.This allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed images.Finally,a dynamic head attentionmechanism is introduced.This allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different sizes.Consequently,the precision of object detection is significantly improved.The trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 and map@0.5:0.95,separately.These results illustrate the clear advantages of the method.

Unveiling the pressure-driven metal–semiconductor–metal transition in the doped TiS_(2)

Conventional theories expect that materials under pressure exhibit expanded valence and conduction bands,leading to increased electrical conductivity.Here,we report the electrical properties of the doped 1T-TiS_(2) under high pressure by electrical resistance investigations,synchrotron x-ray diffraction,Raman scattering and theoretical calculations.Up to 70 GPa,an unusual metal-semiconductor-metal transition occurs.Our first-principles calculations suggest that the observed anti-Wilson transition from metal to semiconductor at 17 GPa is due to the electron localization induced by the intercalated Ti atoms.This electron localization is attributed to the strengthened coupling between the doped Ti atoms and S atoms,and the Anderson localization arising from the disordered intercalation.At pressures exceeding 30.5 GPa,the doped TiS_(2) undergoes a re-metallization transition initiated by a crystal structure phase transition.We assign the most probable space group as P2_(1)2_(1)2_(1).Our findings suggest that materials probably will eventually undergo the Wilson transition when subjected to sufficient pressure.

Ultrafast Carrier Dynamics in Ba_(6)Cr_(2)S_(10)Modified by Toroidal Magnetic Phase Transition

Ba_(6)Cr_(2)S_(10)is a recently discovered magnetic material,in which the spins are aligned ferromagnetically in the ab-plane and anti-parallelly in a paired form along the c-axis.It is characterized as a quasi-one dimensional(1D)dimerized structure with a ferrotoroidic order,forming the simplest candidate toroidal magnetic(TM)order and exhibiting an anti-ferromagnetic-like transition at around 10 K.Time-resolved ultrafast dynamics investigation of the novel A-Cr-S(A:metal elements)family of quantum materials has rarely been reported.Here,we investigate the time-resolved pump-probe ultrafast dynamics of a Ba6Cr2S10 single crystal.A prominent change in the photo-excited carrier dynamics is observed at T_(c)=10 K,corresponding to the reported TM-paramagnetic phase transition.A potential unknown magnetic transition is also found at T^(*)=29 K.Our results provide new evidence for the TM magnetic transition in Ba_(6)Cr_(2)S_(10),and shed light on phase transitions in TM quantum materials.

Publisher Correction to:Strongly Coupled 2D Transition Metal Chalcogenide‑MXene‑Carbonaceous Nanoribbon Heterostructures with Ultrafast Ion Transport for Boosting Sodium/Potassium Ions Storage

Due to the technical fault,a wrong version of the paper was uploaded.The content of the article was not affected,but the layout of the article was affected.The original article has been corrected.

Advancing neuroscience through real-time processing of big data:Transition from open-loop to closed-loop paradigms

The brain functions as a closed-loop system that continuously generates behavior in response to the external environment and adjusts actions based on the outcomes.Traditional research methodologies in neuroscience,especially those employed in brain imaging experiments,have mainly adopted an open-loop paradigm(Grosenick et al.,2015).Functional neural circuits are analyzed offline and subsequently tested through manipulation of neuronal activities within specific regions or with genetic markers.By establishing a closed-loop research paradigm,functional ensembles can be detected and tested in real time with temporal sequences.These functional ensembles,rather than brain regions or genetically labeled neural populations,serve as fundamental units of neural networks,offering valuable insights into the dissection of neural circuits.The closed-loop research paradigm also enables the capture of high-dimensional activities of internal brain dynamics and precise elucidation of physiological processes such as learning,decision-making,and sleep.

Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions

Multi-scale system remains a classical scientific problem in fluid dynamics,biology,etc.In the present study,a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at high Reynolds numbers without any data.The flow is divided into several regions with different scales based on Prandtl's boundary theory.Different regions are solved with governing equations in different scales.The method of matched asymptotic expansions is used to make the flow field continuously.A flow on a semi infinite flat plate at a high Reynolds number is considered a multi-scale problem because the boundary layer scale is much smaller than the outer flow scale.The results are compared with the reference numerical solutions,which show that the msPINNs can solve the multi-scale problem of the boundary layer in high Reynolds number flows.This scheme can be developed for more multi-scale problems in the future.

Dissipation-Driven Superradiant Phase Transition of a Two-Dimensional Bose-Einstein Condensate in a Double Cavity

We study superradiant phase transitions in a hybrid system of a two-dimensional Bose–Einstein condensate of atoms and two cavities arranged with a tilt angle.By adjusting the loss rate of cavities,we map out the phase diagram of steady states within a mean field framework.It is found that when the loss rates of the two cavities are different,superradiant transitions may not occur at the same time in the two cavities.A first-order phase transition is observed between the states with only one cavity in superradiance and both in superradiance.In the case that both cavities are superradiant,a net photon current is observed flowing from the cavity with small decay rate to the one with large decay rate.The photon current shows a non-monotonic dependence on the loss rate difference,owing to the competition of photon number difference and cavity field phase difference.Our findings can be realized and detected in experiments.

MSC-YOLO:Improved YOLOv7 Based on Multi-Scale Spatial Context for Small Object Detection in UAV-View

Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.

Evolution of Superconducting-Transition Temperature with Superfluid Density and Conductivity in Pressurized Cuprate Superconductors

What factors fundamentally determine the value of superconducting transition temperature Tc in high temperature superconductors has been the subject of intense debate.Following the establishment of an empirical law known as Homes'law,there is a growing consensus in the community that the Tc value of the cuprate superconductors is closely linked to the superfluid density(ρ_(s))of its ground state and the conductivity(σ)of its normal state.However,all the data supporting this empirical law(ρ_(s)=AσT_(c))have been obtained from the ambientpressure superconductors.In this study,we present the first high-pressure results about the connection of the quantities of ρ_(s) and σ with T_(c),through the studies on the Bi_(1.74)Pb_(0.38)Sr_(1.88)CuO_(6+δ)and Bi_(2)Sr_(2)CaCu_(2)O_(8+δ),in which the value of their high-pressure resistivity(ρ=1/σ)is achieved by adopting our newly established method,while the quantity ofρs is extracted using Homes'law.We highlight that the Tc values are strongly linked to the joint response factors of magnetic field and electric field,i.e.,ρ_(s) and σ,respectively,implying that the physics determining T_(c) is governed by the intrinsic electromagnetic fields of the system.

Pressure-induced magnetic phase and structural transition in SmSb_(2)

Motivated by the recent discovery of unconventional superconductivity around a magnetic quantum critical point in pressurized CeSb_(2),here we present a high-pressure study of an isostructural antiferromagnetic(AFM) SmSb_(2) through electrical transport and synchrotron x-ray diffraction measurements.At P_(C)~2.5 GPa,we found a pressure-induced magnetic phase transition accompanied by a Cmca→P4/nmm structural phase transition.In the pristine AFM phase below P_(C),the AFM transition temperature of SmSb_(2) is insensitive to pressure;in the emergent magnetic phase above P_(C),however,the magnetic critical temperature increases rapidly with increasing pressure.In addition,at ambient pressure,the magnetoresistivity(MR) of SmSb_(2) increases suddenly upon cooling below the AFM transition temperature and presents linear nonsaturating behavior under high field at 2 K.With increasing pressure above P_(C),the MR behavior remains similar to that observed at ambient pressure,both in terms of temperature-and field-dependent MR.This leads us to argue an AFM-like state for SmSb_(2) above P_(C).Within the investigated pressure of up to 45.3 GPa and the temperature of down to 1.8 K,we found no signature of superconductivity in SmSb_(2).

Improved multi-scale inverse bottleneck residual network based on triplet parallel attention for apple leaf disease identification

Accurate diagnosis of apple leaf diseases is crucial for improving the quality of apple production and promoting the development of the apple industry. However, apple leaf diseases do not differ significantly from image texture and structural information. The difficulties in disease feature extraction in complex backgrounds slow the related research progress. To address the problems, this paper proposes an improved multi-scale inverse bottleneck residual network model based on a triplet parallel attention mechanism, which is built upon ResNet-50, while improving and combining the inception module and ResNext inverse bottleneck blocks, to recognize seven types of apple leaf(including six diseases of alternaria leaf spot, brown spot, grey spot, mosaic, rust, scab, and one healthy). First, the 3×3 convolutions in some of the residual modules are replaced by multi-scale residual convolutions, the convolution kernels of different sizes contained in each branch of the multi-scale convolution are applied to extract feature maps of different sizes, and the outputs of these branches are multi-scale fused by summing to enrich the output features of the images. Second, the global layer-wise dynamic coordinated inverse bottleneck structure is used to reduce the network feature loss. The inverse bottleneck structure makes the image information less lossy when transforming from different dimensional feature spaces. The fusion of multi-scale and layer-wise dynamic coordinated inverse bottlenecks makes the model effectively balances computational efficiency and feature representation capability, and more robust with a combination of horizontal and vertical features in the fine identification of apple leaf diseases. Finally, after each improved module, a triplet parallel attention module is integrated with cross-dimensional interactions among channels through rotations and residual transformations, which improves the parallel search efficiency of important features and the recognition rate of the network with relatively small computational costs while the dimensional dependencies are improved. To verify the validity of the model in this paper, we uniformly enhance apple leaf disease images screened from the public data sets of Plant Village, Baidu Flying Paddle, and the Internet. The final processed image count is 14,000. The ablation study, pre-processing comparison, and method comparison are conducted on the processed datasets. The experimental results demonstrate that the proposed method reaches 98.73% accuracy on the adopted datasets, which is 1.82% higher than the classical ResNet-50 model, and 0.29% better than the apple leaf disease datasets before preprocessing. It also achieves competitive results in apple leaf disease identification compared to some state-ofthe-art methods.

Multi-Scale Mixed Attention Tea Shoot Instance Segmentation Model

Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.