Two Stages Segmentation Algorithm of Breast Tumor in DCE-MRI Based on Multi-Scale Feature and Boundary Attention Mechanism

Nuclearmagnetic resonance imaging of breasts often presents complex backgrounds.Breast tumors exhibit varying sizes,uneven intensity,and indistinct boundaries.These characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor segmentation.Thus,we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention mechanisms.Initially,the breast region of interest is extracted to isolate the breast area from surrounding tissues and organs.Subsequently,we devise a fusion network incorporatingmulti-scale features and boundary attentionmechanisms for breast tumor segmentation.We incorporate multi-scale parallel dilated convolution modules into the network,enhancing its capability to segment tumors of various sizes through multi-scale convolution and novel fusion techniques.Additionally,attention and boundary detection modules are included to augment the network’s capacity to locate tumors by capturing nonlocal dependencies in both spatial and channel domains.Furthermore,a hybrid loss function with boundary weight is employed to address sample class imbalance issues and enhance the network’s boundary maintenance capability through additional loss.Themethod was evaluated using breast data from 207 patients at RuijinHospital,resulting in a 6.64%increase in Dice similarity coefficient compared to the benchmarkU-Net.Experimental results demonstrate the superiority of the method over other segmentation techniques,with fewer model parameters.

Structural, Electronic and Optical Properties of ScxAl1-xN alloys within DFT Calculations

Structural, electronic and optical properties of Sc-based aluminum-nitride alloy have been carried out with first-principles methods using both local density approximation (LDA) and Heyd-Scuseria-Ernzerhof (HSE) hybrid functional. This latter provides a more accurate description of the lattice parameters, enthalpy of formation, electronic and optical properties of our alloy than standard DFT. We found the transition from wurtzite to rocksalt structures at 61% of Sc concentration. By increasing the scandium concentration, the lattice parameters and the band gap decrease. The HSE band gap is in good agreement with available experimental data. The existence of the strong hybridization between Sc 3d and N 2p indicates the transport of electrons from Sc to N atoms. Besides, it is shown that the insertion of the Sc atom leads to the redshift of the optical absorption edge. The optical absorption of ScxAl1-xN is found to decrease with increasing Sc concentrations in the low energy range. Because of this, ScxAl1-xN have a great potential for applications in photovoltaics and photocatalysis.

Defects Energetics and Electronic Properties of Li Doped ZnO： A Hybrid Hartree-Fock and Density Functional Study

The electronic properties and stability of Li-doped ZnO with various defects have been stud- ied by calculating the electronic structures and defect formation energies via first-principles calculations using hybrid Hartree-Fock and density functional methods. The results from formation energy calculations show that Li pair complexes have the lowest formation energy in most circumstances and they consume most of the Li content in Li doped ZnO, which make the p-type conductance hard to obtain. The formation of Li pair complexes is the main obstacle to realize p-type conductance in Li doped ZnO. However, the formation energy of Lizn decreases as environment changes from Zn-rich to O-rich and becomes more stable than that of Li-pair complexes at highly O-rich environment. Therefore, p-type conductance can be obtained by Li doped ZnO grown or post annealed in oxygen rich atmosphere.

Hybrid graded element model for transient heat conduction in functionally graded materials

This paper presents a hybrid graded element model for the transient heat conduction problem in functionally graded materials （FGMs）. First, a Laplace transform approach is used to handle the time variable. Then, a fundamental solution in Laplace space for FGMs is constructed. Next, a hybrid graded element is formulated based on the obtained fundamental solution and a frame field. As a result, the graded properties of FGMs are naturally reflected by using the fundamental solution to interpolate the intra-element field. Further, Stefest＇s algorithm is employed to convert the results in Laplace space back into the time-space domain. Finally, the performance of the proposed method is assessed by several benchmark examples. The results demonstrate well the efficiency and accuracy of the proposed method.

Hybrid density functional study on lattice vibration,thermodynamic properties,and chemical bonding of plutonium monocarbide

Hybrid density functional theory is employed to systematically investigate the structural,magnetic,vibrational,thermodynamic properties of plutonium monocarbide（Pu C and Pu C0.75）.For comparison,the results obtained by DFT,DFT ＋ U are also given.For Pu C and Pu C0.75,Fock-0.25 hybrid functional gives the best lattice constants and predicts the correct ground states of antiferromagnetic（AFM） structure.The calculated phonon spectra suggest that Pu C and Pu C0.75 are dynamically stable.Values of the Helmholtz free energy ？F,internal energy ？E,entropy S,and constant-volume specific heat Cv of Pu C and Pu C0.75 are given.The results are in good agreement with available experimental or theoretical data.As for the chemical bonding nature,the difference charge densities,the partial densities of states and the Bader charge analysis suggest that the Pu–C bonds of Pu C and Pu C0.75 have a mixture of covalent character and ionic character.The effect of carbon vacancy on the chemical bonding is also discussed in detail.We expect that our study can provide some useful reference for further experimental research on the phonon density of states,thermodynamic properties of the plutonium monocarbide.

Liver Tumor Segmentation Based on Multi-Scale and Self-Attention Mechanism

Liver cancer has the second highest incidence rate among all types of malignant tumors,and currently,its diagnosis heavily depends on doctors’manual labeling of CT scan images,a process that is time-consuming and susceptible to subjective errors.To address the aforementioned issues,we propose an automatic segmentation model for liver and tumors called Res2Swin Unet,which is based on the Unet architecture.The model combines Attention-Res2 and Swin Transformer modules for liver and tumor segmentation,respectively.Attention-Res2 merges multiple feature map parts with an Attention gate via skip connections,while Swin Transformer captures long-range dependencies and models the input globally.And the model uses deep supervision and a hybrid loss function for faster convergence.On the LiTS2017 dataset,it achieves better segmentation performance than other models,with an average Dice coefficient of 97.0%for liver segmentation and 81.2%for tumor segmentation.

DT-Net:Joint Dual-Input Transformer and CNN for Retinal Vessel Segmentation

Retinal vessel segmentation in fundus images plays an essential role in the screening,diagnosis,and treatment of many diseases.The acquired fundus images generally have the following problems:uneven illumination,high noise,and complex structure.It makes vessel segmentation very challenging.Previous methods of retinal vascular segmentation mainly use convolutional neural networks on U Network(U-Net)models,and they have many limitations and shortcomings,such as the loss of microvascular details at the end of the vessels.We address the limitations of convolution by introducing the transformer into retinal vessel segmentation.Therefore,we propose a hybrid method for retinal vessel segmentation based on modulated deformable convolution and the transformer,named DT-Net.Firstly,multi-scale image features are extracted by deformable convolution and multi-head selfattention(MHSA).Secondly,image information is recovered,and vessel morphology is refined by the proposed transformer decoder block.Finally,the local prediction results are obtained by the side output layer.The accuracy of the vessel segmentation is improved by the hybrid loss function.Experimental results show that our method obtains good segmentation performance on Specificity(SP),Sensitivity(SE),Accuracy(ACC),Curve(AUC),and F1-score on three publicly available fundus datasets such as DRIVE,STARE,and CHASE_DB1.

Theoretical Study on Reaction Mechanism of Aluminum-Water System

A theoretical study on the reaction of aluminum with water in the gas phase was performed using the hybrid density functional B3LYP and QCISD（T） methods with the 6-311＋G（d,p） and the 6-311＋＋G（d,p） basis sets. The results show that there are three possible reaction pathways that involve four isomers, seven transition structures, and two possible products for the reaction of aluminum with water. The two most favorable reaction pathways were found, whose intermediates and products agreed quite well with experimental results. The enthalpy and Gibbs free energy change of the reaction between A1 and H2O at 298 and 2000 K were calculated. Some results are also in good agreement with the previous calculations or experimental results.

MIA-UNet:Multi-Scale Iterative Aggregation U-Network for Retinal Vessel Segmentation

As an important part of the new generation of information technology,the Internet of Things(IoT)has been widely concerned and regarded as an enabling technology of the next generation of health care system.The fundus photography equipment is connected to the cloud platform through the IoT,so as to realize the realtime uploading of fundus images and the rapid issuance of diagnostic suggestions by artificial intelligence.At the same time,important security and privacy issues have emerged.The data uploaded to the cloud platform involves more personal attributes,health status and medical application data of patients.Once leaked,abused or improperly disclosed,personal information security will be violated.Therefore,it is important to address the security and privacy issues of massive medical and healthcare equipment connecting to the infrastructure of IoT healthcare and health systems.To meet this challenge,we propose MIA-UNet,a multi-scale iterative aggregation U-network,which aims to achieve accurate and efficient retinal vessel segmentation for ophthalmic auxiliary diagnosis while ensuring that the network has low computational complexity to adapt to mobile terminals.In this way,users do not need to upload the data to the cloud platform,and can analyze and process the fundus images on their own mobile terminals,thus eliminating the leakage of personal information.Specifically,the interconnection between encoder and decoder,as well as the internal connection between decoder subnetworks in classic U-Net are redefined and redesigned.Furthermore,we propose a hybrid loss function to smooth the gradient and deal with the imbalance between foreground and background.Compared with the UNet,the segmentation performance of the proposed network is significantly improved on the premise that the number of parameters is only increased by 2%.When applied to three publicly available datasets:DRIVE,STARE and CHASE DB1,the proposed network achieves the accuracy/F1-score of 96.33%/84.34%,97.12%/83.17%and 97.06%/84.10%,respectively.The experimental results show that the MIA-UNet is superior to the state-of-the-art methods.

Automatically Eye Detection with Different Gray Intensity Image Conditions

One of the methods for biometric identification is facial features detection, and eye is an important facial feature in the face. In the recent years, automatically detecting eye with different image conditions is attended. This paper proposes a method which can automatically detect eye in extensive range of images with different conditions. In the proposed method, first an image is enhanced by morphological operations then region of face is detected by hybrid projection function. To identify window of eye, vertical edge dominance map is used. The authors＇ method uses elliptical mask on eye image to detect center of pupil. The mask scans eye image to find minimum gray level because pupil is darkest part in eye image compared with 3 well-known methods. The accuracy of 99.53% on this This method has implemented on JAFFE face database and database confirms efficiency of the proposed method.

Defects properties and vacancy diffusion in Cu_(2)MgSnS_(4)

Cu_(2)Mg_(Sn)S_(4)(CZTS)is a promising photovoltaic absorber material,however,efficiency is largely hindered by potential fluctuation and a band tailing problem due to the abundance of defect complexes and low formation energy of an intrinsic Cu_(Zn)defect.Alternatives to CZTS by group I,II,or IV element replacement to circumvent this challenge has grown research in-terest.In this work,using a hybrid(HSE06)functional,we demonstrated the qualitative similarity of defect thermodynamics and electronic properties in Cu_(2)Mg_(Sn)S_(4)(CMTS)to CZTS.We show Sn_(Mg)to be abundant when in Sn-and Cu-rich condition,which can be detrimental,while defect properties are largely similar to CZTS in Sn-and Cu-poor.Under Sn-and Cu-poor chemic-al potential,there is a general increase in formation energy in most defects except Sn_(Mg),Cu_(Mg)remains as the main contribu-tion to p-type carriers,and Sn_(Mg)may be detrimental because of a deep defect level in the mid gap and the possibility of form-ing defect complex Sn_(Mg)+Mg_(Sn).Vacancy diffusion is studied using generalized gradient approximation,and we find similar va-cancy diffusion properties for Cu vacancy and lower diffusion barrier for Mg vacancy,which may reduce possible Cu-Mg dis-order in CMTS.These findings further confirm the feasibility of CMTS as an alternative absorber material to CZTS and suggest the possibility for tuning defect properties of CZTS,which is crucial for high photovoltaic performance.

Electronic Structure and Optical Properties of LiBiO3 Doped with V,Nb,and Ta

The crystal structure, band structure, density of states, Mulliken charge, bond population and optical properties for LiBi_（1-x）M_xO_3（M=V, Nb, and Ta） were investigated using hybrid density functional theory. It was found that LiBiO_3 doped with V, Nb, and Ta presented distinctly stronger covalent interactions in M-O（M=V, Nb, and Ta） than Bi-O, thus resulting in mild distortion of the structure and facilitating the separation of photogenerated carriers. Furthermore, the hybridizations of Bi-6s, M-d（M=V, Nb, and Ta） and O-2p widened the valence and conduction bands, which promoted transmission of photogenerated carriers in the band edge and thus caused better photocatalytic performance.

Two-dimensional arsenic monolayer sheet predicted from first-principles

Using first-principles calculations, we investigate the two-dimensional arsenic nanosheet isolated from bulk gray arsenic. Its dynamical stability is confirmed by phonon calculations and molecular dynamics analyzing. The arsenic sheet is an indirect band gap semiconductor with a band gap of 2.21 e V in the hybrid HSE06 functional calculations. The valence band maximum（VBM） and the conduction band minimum（CBM） are mainly occupied by the 4p orbitals of arsenic atoms,which is consistent with the partial charge densities of VBM and CBM. The charge density of the VBM G point has the character of a π bond, which originates from p orbitals. Furthermore, tensile and compressive strains are applied in the armchair and zigzag directions, related to the tensile deformations of zigzag and armchair nanotubes, respectively. We find that the ultimate strain in zigzag deformation is 0.13, smaller than 0.18 of armchair deformation. The limit compressive stresses of single-layer arsenic along armchair and zigzag directions are-4.83 GPa and-4.76 GPa with corresponding strains of-0.15 and-0.14, respectively.

Electronic and thermoelectric properties of YbMg_(2)X_(2)(X=P,As,Sb,Bi)zintl compounds by first-principles method

In this work the structural,electronic and thermoelectric properties of YbMg_(2)X_(2)(X=P,As,Sb,Bi)zintl compounds were investigated comprehensively using first principles study.The electronic properties were studied using PBE GGA,TB-mBJ and hybrid(YS-PBE0)potentials.All the structural parameters of optimized structures are in harmonious agreement to the available data.The band structure study illustrates that the titled materials manifest metallic and semi metallic nature using PBE and TB-mBJ potentials while they show wide band gap semiconducting behavior by following hybrid(YS-PBE0)potential.Total density of states(TDOS)and partial density of states(PDOS)were also calculated to glimpse the contribution of orbitals of atoms in the formation of bands.Transport properties were studied by using BoltzTraP2 code employed to WIEN2k.We get enormous values of Seebeck coefficient(S),power factor(PF)and thermoelectric figure of merit(ZT)for all the samples YbMg_(2)X_(2)(X=P,As,Sb,Bi),Moreover,the overwhelming transport properties for the titled compounds indicate the optimum level of carriers’concentration which pinpoints these materials to be better thermoelectrics in the 1-2-2 zintl family.

Hybrid Function Projective Synchronization of Chaotic Systems with Uncertain Time-varying Parameters via Fourier Series Expansion

In this paper, the hybrid function projective synchronization (HFPS) of different chaotic systems with uncertain periodically time-varying parameters is carried out by Fourier series expansion and adaptive bounding technique. Fourier series expansion is used to deal with uncertain periodically time-varying parameters. Adaptive bounding technique is used to compensate the bound of truncation errors. Using the Lyapunov stability theory, an adaptive control law and six parameter updating laws are constructed to make the states of two different chaotic systems asymptotically synchronized. The control strategy does not need to know the parameters thoroughly if the time-varying parameters are periodical functions. Finally, in order to verify the effectiveness of the proposed scheme, the HFPS between Lorenz system and Chen system is completed successfully by using this scheme.

Wavelet-Based Hybrid Thresholding Method for Ultrasonic Liver Image Denoising

This paper presents a wavelet-based hybrid threshold method according to the soft- and hard-threshold functions proposed by Donoho. The wavelet-based hybrid threshold method may help doctors to know more details on the liver disease through denoising the ultrasound image of the liver. First of all, an analytical expression for the hybrid threshold function is discussed. The wavelet-based hybrid threshold method is then investigated for ultrasound image of the liver. Finally, we test the influence of this parameter on the proposed method with the real ultrasound image corrupted by speckle noise with different variances. Moreover, we compare the proposed method under the varying parameters with the soft-threshold function and the hard-threshold function. Three metrics, which are correlation coefficient, edge preservation index and structural similarity index, are measured to quantify the denoised results of ultrasound liver image. Experimental results demonstrate the potential of the proposed method for ultrasound liver image denosing.

Visualization of Two-dimensional Single Chains of Hybrid Polyelectrolytes on Solid Surface

The polyacidic character of polyoxometalate(POM)clusters endows high ionic conductivity,making these clusters good candidates for solar and fuel cells.Covalent bonding of clusters to polymer chains creates poly(POM)s that are polyelectrolytes with both cluster functions and polymer performance.Thus,solution-processable poly(POM)s are expected to be used as key materials in advanced devices.Further understanding of poly(POM)s will optimize the preparation process and improve device performance.Herein,we report a study of the first linear poly(POM)s by directly visualizing the chains using scanning transmission electron microscopy.Compared with traditional polymers,individual clusters of poly(POM)s can be directly visualized because of the resistance to electron-beam damage and the high contrast of the tungsten POM pendants.Thus,cluster aggregates with diverse shapes were observed.Counting the number of clusters in the aggregates allowed the degree of polymerization and molecular weight distribution to be determined,and studying the aggregate shapes revealed the presence of a curved semirigid chain in solution.Further study of shape diversity revealed that strong interactions between clusters determine the diverse chain shapes formed during solution processing.Fundamental insight is critical to understanding the formation of poly(POM)films from solutions as key functional materials,especially for fuel and solar cells.

Band gap bowing and crossing of B_xGa_(1-x)N alloy investigated by hybrid functional method

The electronic properties of zinc-blende BxGal-xN alloys are comparatively investigated by employ- ing both the Perdewe-Burkee-Ernzerhof generalized-gradient approximation （PBE-GGA） and the Heyd-Scuseria- Ernzerhof screened hybrid functional methods （HSE06）. HSE06 reproduced much closer ground-state properties to experiments. Large and composition-dependent bowing parameters br for the direct band gaps were obtained from both PBE and HSE06. The crossover composition where alloy switches from direct to indirect was predicted to occur at very similar x from PBE and HSE06. We can obtain direct gap BxGal-xN with a gap value much larger than that of GaN by alloying x 〈 0.557 boron into GaN.

Hybrid functional microfibers for textile electronics and biosensors

Fibers are low-cost substrates that are abundantly used in our daily lives. This review highlights recent advances in the fabrication and application of multifunctional fibers to achieve fibers with unique functions for specific applications ranging from textile electronics to biomedical applications. By incorporating various nanomaterials such as carbon nanomaterials, metallic nanomaterials, and hydrogel-based biomaterials, the functions of fibers can be precisely engineered. This review also highlights the performance of the functional fibers and electronic materials incorporated with textiles and demonstrates their practical application in pressure/tensile sensors,chemical/biosensors, and drug delivery. Textile technologies in which fibers containing biological factors and cells are formed and assembled into constructions with biomimetic properties have attracted substantial attention in the field of tissue engineering. We also discuss the current limitations of functional textile-based devices and their prospects for use in various future applications.

Carrier-driven magnetic and topological phase transitions in twodimensional III-V semiconductors

III-V semiconductors such as GaAs are widely studied as promising candidates for high-speed integrated circuit.Despite these applications for conventional bulk structures,their fundamental physical properties in the nanoscale limit are still in scarcity,which is of great importance for the development of nanoelectronics.In this work,we demonstrate that the III-V semiconductor MX(M=Al,Ga,In;X=P,As,Sb)in its two-dimensional(2D)limit could exhibit double layer honeycomb(DLHC)configuration and distorted tetrahedral coordination,according to our first-principles calculations with HSE06 hybrid functional.It is found that surface reconstruction endows 2D III-V DLHCs with pronouncedly different electronic and magnetic properties from their bulk counterparts due to strong interlayer coupling.Mexican-hat-shape bands emerge at the top valence bands of pristine AlP,GaP,InP,AlAs,and InAs DLHCs,inducing the density of states showing a sharp van Hove singularity near the Fermi level.As a result,these DLHCs exhibit itinerant magnetism upon moderate hole doping,while the rest GaAs,AlSb,GaSb,and InSb DLHCs become magnetic under tensile strain with hole doping.With an exchange splitting of the localized pz states at the top valence bands,the hole-doped III-V DLHCs become half-metals with 100%spin-polarization.Remarkably,the InSb DLHC shows inverted band structure near the Fermi level,bringing about nontrivial topological band structures in stacked InSb DLHC due to the strong spin-orbital coupling.These III-V DLHCs expand the members of 2D material family and their exotic magnetic and topological properties may offer great potential for applications in the novel electronic and spintronic devices.