基于遗传算法与Hardy-Cross方法的矿井通风机控制

针对矿井通风机系统控制的复杂问题,提出了一种遗传算法与Hardy-Cross方法相结合的矿井通风控制,强化了其在处理离散变量和非凸搜索空间中寻找全局最优解的能力。通过数学形式化定义问题并结合基尔霍夫定律,确立了通风机网络的优化模型。根据实际用风情况智能调节通风机的供风量,以实现能源节约和安全生产的目标。通过对某煤矿的通风案例深入研究和验证,风扇的配置在优化气流方面表现出了卓越性能。该研究不仅为矿山工程领域提供了新的优化思路和方法,也为通风机系统的管理提供了重要参考。

A Simplified Method for the Stress Analysis of Underground Transfer Structures Crossing Multiple Subway Tunnels

According to the design specifications,the construction of extended piles involves traversing the tunnel’s upper region and extending to the underlying rock layer.To address this challenge,a subterranean transfer structure spanning multiple subway tunnels was proposed.Deliberating on the function of piles in the transfer structure as springs with axial and bending stiffness,and taking into account the force balance and deformation coordination conditions of beams and plates within the transfer structure,we established a simplified mechanical model that incorporates soil stratification by combining it with the Winkler elastic foundation beam model.The resolved established simplifiedmechanicalmodel employed finite difference technology and the Newton-Simpsonmethod,elucidating the mechanical mechanism of the transfer structure.The research findings suggest that the load carried by the upper structural columns can be transferred to the pile foundation beneath the beams through the transfer structure,subsequently reaching the deep soil layer and ensuring minimal impact on adjacent tunnels.The established simplified analysis method can be used for stress analysis of the transfer structure,concurrently considering soil stratification,pile foundation behavior,and plate action.The pile length,pile section size,and beam section size within the transfer structure should account for the characteristics of the upper load,ensuring an even distribution of the beam bending moment.

Automatic detection of breast lesions in automated 3D breast ultrasound with cross-organ transfer learning

Background Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications,particularly in visual recognition tasks such as image and video analyses.There is a growing interest in applying this technology to diverse applications in medical image analysis.Automated three dimensional Breast Ultrasound is a vital tool for detecting breast cancer,and computer-assisted diagnosis software,developed based on deep learning,can effectively assist radiologists in diagnosis.However,the network model is prone to overfitting during training,owing to challenges such as insufficient training data.This study attempts to solve the problem caused by small datasets and improve model detection performance.Methods We propose a breast cancer detection framework based on deep learning(a transfer learning method based on cross-organ cancer detection)and a contrastive learning method based on breast imaging reporting and data systems(BI-RADS).Results When using cross organ transfer learning and BIRADS based contrastive learning,the average sensitivity of the model increased by a maximum of 16.05%.Conclusion Our experiments have demonstrated that the parameters and experiences of cross-organ cancer detection can be mutually referenced,and contrastive learning method based on BI-RADS can improve the detection performance of the model.

A sphingolipid message promotes neuronal health across generations

Maternal nutrition can affect brain function of offspring in later life(Fitzgerald et al.,2020).Emerging studies have also shown that the maternal environment can modify susceptibility to neurodegenerative disease(Boots et al.,2023).Transmission of environmental information from mother to offspring depends on maternal nutrient provisioning.In the nematode Caenorhabditis elegans,maternal yolk,which is synthesized in the intestine and transferred to nourish oocytes and early embryos,can provide extranuclear inheritance factors to mediate phenotypic plasticity across generations.However,the molecular mechanisms of regulating such intergenerational effects remain largely unknown.

Synthesis and Charcterization of Silane Crosslinked Hydrogel to pH Sensitive Study

Eco-friendly and biodegradable novel hydrogel were prepared by blending and solution casting method. The designed hydrogel is based on chitosan/ PEG600/Gurgam with carbon nanofiller along silane crosslinked (TEOS) with pH sensitive response to controlled release of drug in biomedical materials and agriculture industry. The various concentration of carbon nanofiller is used to analyze its effect on the fabricated hydrogel characteristics by using FTIR, SEM, TGA, swelling studies (water, buffer and ionic solution). Spectra of FTIR reflected both established and newly developed groups (like hydrogel). COOH group presence is clearly observed in this range in the carbon filler reinforced hydrogel. The SEM micrographs show that CPG0.003 had a collection of polysaccharide chains as thin helices, which is attributed to the increase in the size of porosity. TGA shows to increase concentration of nanofiller enhanced the thermal stability of the designed hydrogels at temperature 25˚C to 550˚C mass loss percentage decrease upto 20% and increase thermal stability. This pH response made these resultant hydrogels as fruitful competitor against the many reported controlled release application.

Asymmetric expression of CA2 and CA13 linked to calcification in the bilateral mandibular condyles cause crossed beaks in chickens

Crossed beak is a complex mode of inheritance with prevalence ranging from 0.2 to 7.4% in at least 12 chicken strains worldwide.To reveal the intrinsic factors causing crossed beaks,genes expression patterns in bilateral mandibular condyle between affected and normal birds were characterized by RNA sequencing analysis in the present studies.Crossed beak was induced by short length of unilateral mandibular ramus,and a total of 110differentially expressed genes were up-or down-regulated in the affected(short)mandibular condyle side as compared to the normal side.Carbonic anhydrase 2(CA2)and Carbonic anhydrase 13(CA13)were enriched in the carbonate dehydratase activity,and high-expressed in mandibular condyle and osteoblasts(P<0.05).However,both were low-expressed in short mandibular condyle side of affected birds(P<0.05).The carbonate dehydratase inhibitor experiments confirmed that there is positive association between the calcification and carbonic anhydrase isoenzymes.Quantitative analysis with cetylpyridinium chloride showed a decrease in calcification when the cells were transfected with an anti-CA13 shRNA.Our research suggested that CA2 and CA13 are down-calcified in shortside mandibular condyle,and caused mandibular ramus to grow slowly.CA2 and CA13 have the critical role in crossed beaks by regulating calcification of mandibular condyle.

Atomic transport dynamics in crossed optical dipole trap

We study the dynamical evolution of cold atoms in crossed optical dipole trap theoretically and experimentally. The atomic transport process is accompanied by two competitive kinds of physical mechanics, atomic loading and atomic loss.The loading process normally is negligible in the evaporative cooling experiment on the ground, while it is significant in preparation of ultra-cold atoms in the space station. Normally, the atomic loading process is much weaker than the atomic loss process, and the atomic number in the central region of the trap decreases monotonically, as reported in previous research. However, when the atomic loading process is comparable to the atomic loss process, the atomic number in the central region of the trap will initially increase to a maximum value and then slowly decrease, and we have observed the phenomenon first. The increase of atomic number in the central region of the trap shows the presence of the loading process, and this will be significant especially under microgravity conditions. We build a theoretical model to analyze the competitive relationship, which coincides with the experimental results well. Furthermore, we have also given the predicted evolutionary behaviors under different conditions. This research provides a solid foundation for further understanding of the atomic transport process in traps. The analysis of loading process is of significant importance for preparation of ultra-cold atoms in a crossed optical dipole trap under microgravity conditions.

State-selective charge exchange cross sections in collisions of highly-charged sulfur ions with helium and molecular hydrogen

The state-selective cross section data are useful for understanding and modeling the x-ray emission in celestial observations.In the present work,using the cold target recoil ion momentum spectroscopy,for the first time we investigated the state-selective single electron capture processes for S^(q+)–He and H_(2)(q=11–15)collision systems at an impact energy of q×20 keV and obtained the relative state-selective cross sections.The results indicate that only a few principal quantum states of the projectile energy level are populated in a single electron capture process.In particular,the increase of the projectile charge state leads to the population of the states with higher principal quantum numbers.It is also shown that the experimental averaged n-shell populations are reproduced well by the over-barrier model.The database is openly available in Science Data Bank at 10.57760/sciencedb.j00113.00091.

Tailoring the texture and mechanical properties of 3%Y_(2)O_(3)p/ZGK200 composites fabricated by unidirectional and cross rolling followed by annealing

3%Y_(2)O_(3)p/ZGK200 composites were subjected to unidirectional rolling(UR)and cross rolling(CR)at 400℃and 350℃followed by annealing at 300℃for 1 h.The microstructure,texture and mechanical properties of rolled and annealed composites were systematically studied.The rolled composites exhibited a heterogeneous microstructure,consisting of deformed grains elongated along rolling direction(RD)and Y_(2)O_(3)particles bands distributed along RD.After annealing,static recrystallization(SRX)occurred and most deformed grains transformed into equiaxed grains.A non-basal texture with two strong T-texture components was obtained after UR while a non-basal elliptical/circle texture with circle multi-peaks was obtained after CR,indicating that rolling path had great influences on texture of the composites.After annealing process,R-texture component disappeared or weakened,as results,a non-basal texture with double peaks tilting from normal direction(ND)to transverse direction(TD)and a more random non-basal texture with circle multi-peaks were obtained for UR and CR composites,respectively.The yield strength of rolled composites after UR showed obvious anisotropy along RD and TD while a low anisotropic yield strength was obtained after CR.Some Y_(2)O_(3)particles broke during rolling.The fracture of the composites was attributed to the existence of Y_(2)O_(3)clusters and interfacial debonding between particles and matrix during tension,as a result,the ductility was not as superior as matrix alloy.

Nonlinear Registration of Brain Magnetic Resonance Images with Cross Constraints of Intensity and Structure

Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes.However,these methods often lack constraint information and overlook semantic consistency,limiting their performance.To address these issues,we present a novel approach for medical image registration called theDual-VoxelMorph,featuring a dual-channel cross-constraint network.This innovative network utilizes both intensity and segmentation images,which share identical semantic information and feature representations.Two encoder-decoder structures calculate deformation fields for intensity and segmentation images,as generated by the dual-channel cross-constraint network.This design facilitates bidirectional communication between grayscale and segmentation information,enabling the model to better learn the corresponding grayscale and segmentation details of the same anatomical structures.To ensure semantic and directional consistency,we introduce constraints and apply the cosine similarity function to enhance semantic consistency.Evaluation on four public datasets demonstrates superior performance compared to the baselinemethod,achieving Dice scores of 79.9%,64.5%,69.9%,and 63.5%for OASIS-1,OASIS-3,LPBA40,and ADNI,respectively.

Absolute partial and total ionization cross sections of carbon monoxide with electron collision from 350 eV to 8000 eV

The absolute partial and total cross sections for electron impact ionization of carbon monoxide are reported for electron energies from 350 eV to 8000 eV.The product ions(CO^(+),C^(+),O^(+),CO^(2+),C^(2+),and O^(2+))are measured by employing an ion imaging mass spectrometer and two ion-pair dissociation channels(C^(+)+O^(+)and C^(2+)+O^(+))are identified.The absolute cross sections for producing individual ions and their total,as well as for the ion-pair dissociation channels are obtained by normalizing the data of CO^(+)to that of Ar^(+)from CO-Ar mixture target with a fixed 1:1 ratio.The overall errors are evaluated by considering various kinds of uncertainties.A comprehensive comparison is made with the available data,which shows a good agreement with each other over the energy ranges that are overlapped.This work presents new cross-section data with electron energies above 1000 eV.

Three‑dimensional numerical simulation of dynamic strength and failure mode of a rock mass with cross joints

To study the dynamic mechanical properties and failure characteristics of intersecting jointed rock masses with different joint distributions under confining pressure,considering the cross angleαand joint persistence ratioη,a numerical model of the biaxial Hopkinson bar test system was established using the finite element method–discrete-element model coupling method.The validity of the model was verified by comparing and analyzing it in conjunction with laboratory test results.Dynamics-static combined impact tests were conducted on specimens under various conditions to investigate the strength characteristics and patterns of crack initiation and expansion.The study revealed the predominant factors influencing intersecting joints with different angles and penetrations under impact loading.The results show that the peak stress of the specimens decreases first and then increases with the increase of the cross angle.Whenα<60°,regardless of the value ofη,the dynamic stress of the specimens is controlled by the main joint.Whenα≥60°,the peak stress borne by the specimens decreases with increasingη.Whenα<60°,the initiation and propagation of cracks in the cross-jointed specimens are mainly controlled by the main joint,and the final failure surface of the specimens is composed of the main joint and wing cracks.Whenα≥60°orη≥0.67,the secondary joint guides the expansion of the wing cracks,and multiple failure surfaces composed of main and secondary joints,wing cracks,and co-planar cracks are formed.Increasing lateral confinement significantly increases the dynamic peak stress able to be borne by the specimens.Under triaxial conditions,the degree of failure of the intersecting jointed specimens is much lower than that under uniaxial and biaxial conditions.

Using Cross Entropy as a Performance Metric for Quantifying Uncertainty in DNN Image Classifiers: An Application to Classification of Lung Cancer on CT Images

Cross entropy is a measure in machine learning and deep learning that assesses the difference between predicted and actual probability distributions. In this study, we propose cross entropy as a performance evaluation metric for image classifier models and apply it to the CT image classification of lung cancer. A convolutional neural network is employed as the deep neural network (DNN) image classifier, with the residual network (ResNet) 50 chosen as the DNN archi-tecture. The image data used comprise a lung CT image set. Two classification models are built from datasets with varying amounts of data, and lung cancer is categorized into four classes using 10-fold cross-validation. Furthermore, we employ t-distributed stochastic neighbor embedding to visually explain the data distribution after classification. Experimental results demonstrate that cross en-tropy is a highly useful metric for evaluating the reliability of image classifier models. It is noted that for a more comprehensive evaluation of model perfor-mance, combining with other evaluation metrics is considered essential. .

Measurement of the neutron-induced total cross sections of ^(nat)Pb from 0.3 eV to 20 MeV on the Back-n at CSNS

The neutron-induced total cross sections of natural lead have been measured in a wide energy range(0.3 eV-20 MeV)on the back-streaming white neutron beamline(Back-n)at the China Spallation Neutron Source.Neutron energy was determined by the neutron total cross-section spectrometer using the time-of-flight technique.A fast multi-cell fission chamber was used as the neutron detector,and a 10-mm-thick high-purity natural lead sample was employed for the neutron transmission measurements.The on-beam background was determined using Co,In,Ag,and Cd filters.The excitation function of ^(nat)Pb(n,tot)reaction below 20 MeV was calculated using the TALYS-1.96 nuclear-reaction modeling program.The present results were compared with previous results,the evaluated data available in the five major evaluated nuclear data libraries(i.e.,ENDF/B-VIII.0,JEFF-3.3,JENDL-5,CENDL-3.2,and BROND-3.1),and the theoretical calculation curve.Good agreement was found between the new results and those of previous experiments and with the theoretical curves in the corresponding region.This measurement obtained the neutron total cross section of natural lead with good accuracy over a wide energy range and added experimental data in the resonance energy range.This provides more reliable experimental data for nuclear engineering design and nuclear data evaluation of lead.

DCFNet:An Effective Dual-Branch Cross-Attention Fusion Network for Medical Image Segmentation

Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis.Notably,most existing methods that combine the strengths of convolutional neural networks(CNNs)and Transformers have made significant progress.However,there are some limitations in the current integration of CNN and Transformer technology in two key aspects.Firstly,most methods either overlook or fail to fully incorporate the complementary nature between local and global features.Secondly,the significance of integrating the multiscale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine CNN and Transformer.To address this issue,we present a groundbreaking dual-branch cross-attention fusion network(DCFNet),which efficiently combines the power of Swin Transformer and CNN to generate complementary global and local features.We then designed the Feature Cross-Fusion(FCF)module to efficiently fuse local and global features.In the FCF,the utilization of the Channel-wise Cross-fusion Transformer(CCT)serves the purpose of aggregatingmulti-scale features,and the Feature FusionModule(FFM)is employed to effectively aggregate dual-branch prominent feature regions from the spatial perspective.Furthermore,within the decoding phase of the dual-branch network,our proposed Channel Attention Block(CAB)aims to emphasize the significance of the channel features between the up-sampled features and the features generated by the FCFmodule to enhance the details of the decoding.Experimental results demonstrate that DCFNet exhibits enhanced accuracy in segmentation performance.Compared to other state-of-the-art(SOTA)methods,our segmentation framework exhibits a superior level of competitiveness.DCFNet’s accurate segmentation of medical images can greatly assist medical professionals in making crucial diagnoses of lesion areas in advance.

A method for Improving the Efficiency of Pear Tree Crossbreeding

In view of the short blooming period of pear tree crossbreeding and the complexity of pollination process, a method that can improve the efficiency of crossbreeding of pear trees was provided. Meanwhile, this method can also be applied to the study of pollen xenia effect, pollination tree selection and pure pollen collection in pear tree cultivation.

Crossed renal ectopia with rectal cancer:A case report

BACKGROUND Crossed renal ectopia(CRE)occurs when one kidney crosses the midline from the primary side to the contralateral side while the ureter remains on the primary side.Rectal cancer,one of the most common malignant tumors of the digestive tract,refers to cancer from the dentate line to the rectosigmoid junction.The concurrent presentation of CRE alongside rectal cancer is an uncommon clinical observation.CASE SUMMARY Herein,we report a 69-year-old male patient with rectal cancer who was diagnosed with CRE via computed tomography during hospitalization.Following thorough preoperative evaluations,the patient underwent Dixon surgery.CONCLUSION We performed laparoscopic radical resection of rectal cancer and adequate lymph node removal in a patient with CRE with no postoperative discomfort.

Corneal collagen cross-linking in patients with keratoconus from the Dresden protocol to customized solutions:theoretical basis

Keratoconus is an ectatic condition characterized by gradual corneal thinning,corneal protrusion,progressive irregular astigmatism,corneal fibrosis,and visual impairment.The therapeutic options regarding improvement of visual function include glasses or soft contact lenses correction for initial stages,gas-permeable rigid contact lenses,scleral lenses,implantation of intrastromal corneal ring or corneal transplants for most advanced stages.In keratoconus cases showing disease progression corneal collagen crosslinking(CXL)has been proven to be an effective,minimally invasive and safe procedure.CXL consists of a photochemical reaction of corneal collagen by riboflavin stimulation with ultraviolet A radiation,resulting in stromal crosslinks formation.The aim of this review is to carry out an examination of CXL methods based on theoretical basis and mathematical models,from the original Dresden protocol to the most recent developments in the technique,reporting the changes proposed in the last 15y and examining the advantages and disadvantages of the various treatment protocols.Finally,the limits of non-standardized methods and the perspectives offered by a customization of the treatment are highlighted.

Oscillatory Dynamics of a Spherical Solid in a Liquid in an Axisymmetric Variable Cross Section Channel

The dynamics of a solid spherical body in an oscillating liquid flow in a vertical axisymmetric channel of variable cross section is experimentally studied.It is shown that the oscillating liquid leads to the generation of intense averaged flows in each of the channel segments.The intensity and direction of these flows depend on the dimensionless oscillating frequency.In the region of studied frequencies,the dynamics of the considered body is examined when the primary vortices emerging in the flow occupy the whole region in each segment.For a fixed frequency,an increase in the oscillation amplitude leads to a phase-inclusion holding effect,i.e.,the body occupies a quasi-stationary position in one of the cells of the vertical channel,while oscillating around its average position.It is also shown that the oscillating motion of a liquid column generates an averaged force acting on the body,the magnitude of which depends on the properties of the body and its position in the channel.The quasi-stationary position is determined by the relative density and size of the body,as well as the dimensionless frequency.The behavior of the body as a function of the amplitude and frequency of fluid oscillation and relative size is discussed in detail.Such findings may be used in the future to control the position of a phase inclusion and/or to strengthen mass transfer effects in a channel of variable cross section by means of fluid oscillations.

Intelligent Power Grid Load Transferring Based on Safe Action-Correction Reinforcement Learning

When a line failure occurs in a power grid, a load transfer is implemented to reconfigure the network by changingthe states of tie-switches and load demands. Computation speed is one of the major performance indicators inpower grid load transfer, as a fast load transfer model can greatly reduce the economic loss of post-fault powergrids. In this study, a reinforcement learning method is developed based on a deep deterministic policy gradient.The tedious training process of the reinforcement learning model can be conducted offline, so the model showssatisfactory performance in real-time operation, indicating that it is suitable for fast load transfer. Consideringthat the reinforcement learning model performs poorly in satisfying safety constraints, a safe action-correctionframework is proposed to modify the learning model. In the framework, the action of load shedding is correctedaccording to sensitivity analysis results under a small discrete increment so as to match the constraints of line flowlimits. The results of case studies indicate that the proposed method is practical for fast and safe power grid loadtransfer.