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Abstract The generation of radiological results from image data represents a pivotal aspect of medical image analysis. The latest iteration of ChatGPT-4, a large multimodal model that integrates both text and image inputs, including dermatoscopy images, histology images, and X-ray images, has attracted considerable attention in the field of radiology. To further investigate the performance of ChatGPT-4 in medical image recognition, we examined the ability of ChatGPT-4 to recognize credible osteosarcoma X-ray images. The results demonstrated that ChatGPT-4 can more accurately diagnose bone with or without significant space-occupying lesions but has a limited ability to differentiate between malignant lesions in bone compared to adjacent normal tissue. Thus far, the current capabilities of ChatGPT-4 are insufficient to make a reliable imaging diagnosis of osteosarcoma. Therefore, users should be aware of the limitations of this technology.

Abstract The viscosity of fracturing fluid and in-situ stress difference are the two important factors that affect the hydraulic fracturing pressure and propagation morphology. In this study, raw coal was used to prepare coal samples for experiments, and clean fracturing fluid samples were prepared using CTAB surfactant. A series of hydraulic fracturing tests were conducted with an in-house developed triaxial hydraulic fracturing simulator and the fracturing process was monitored with an acoustic emission instrument to analyze the influences of fracturing fluid viscosity and horizontal in-situ stress difference on coal fracture propagation. The results show that the number of branched fractures decreased, the fracture pattern became simpler, the fractures width increased obviously, and the distribution of AE event points was concentrated with the increase of the fracturing fluid viscosity or the horizontal in-situ stress difference. The acoustic emission energy decreases with the increase of fracturing fluid viscosity and increases with the increase of horizontal in situ stress difference. The low viscosity clean fracturing fluid has strong elasticity and is easy to be compressed into the tip of fractures, resulting in complex fractures. The high viscosity clean fracturing fluids are the opposite. Our experimental results provide a reference and scientific basis for the design and optimization of field hydraulic fracturing parameters.

Abstract Objective In recent years, cell therapy has emerged as a new research direction in the treatment of diabetes. However, the underlying molecular mechanisms of mesenchymal stem cell (MSC) differentiation necessary to form such treatment have not been clarified. Methods In this study, human umbilical cord mesenchymal stem cells (HUC-MSCs) isolated from newborns were progressively induced into insulin-producing cells (IPCs) using small molecules. HUC-MSC (S0) and four induced stage (S1–S4) samples were prepared. We then performed transcriptome sequencing experiments to obtain the dynamic expression profiles of both mRNAs and long noncoding RNAs (lncRNAs). Results We found that the number of differentially expressed lncRNAs and mRNAs trended downwards during differentiation. Gene Ontology (GO) analysis showed that the target genes of differentially expressed lncRNAs were associated with translation, cell adhesion, and cell connection. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the NF-KB signalling pathway, MAPK signalling pathway, HIPPO signalling pathway, PI3K–Akt signalling pathway, and p53 signalling pathway were enriched in these differentially expressed lncRNA-targeting genes. We also found that the coexpression of the lncRNA CTBP1-AS2 with PROX1 and the lncRNAs AC009014.3 and GS1-72M22.1 with JARID2 mRNA was related to the development of pancreatic beta cells. Moreover, the coexpression of the lncRNAs: XLOC_ 050969, LINC00883, XLOC_050981, XLOC_050925, MAP3K14- AS1, RP11-148K1.12, and CTD2020K17.3 with p53, regulated insulin secretion by pancreatic beta cells. Conclusion In this study, HUC-MSCs combined with small molecule compounds were successfully induced into IPCs. Differentially expressed lncRNAs may regulate the insulin secretion of pancreatic beta cells by regulating multiple signalling pathways. The lncRNAs AC009014.3, Gs1-72m21.1, and CTBP1-AS2 may be involved in the development of pancreatic beta cells, and the lncRNAs: XLOC_050969, LINC00883, XLOC_050981, XLOC_050925, MAP3K14-AS1, RP11-148K1.12, and CTD2020K17.3 may be involved in regulating the insulin secretion of pancreatic beta cells, thus providing a lncRNA catalogue for future research regarding the mechanism of the transdifferentiation of HUC-MSCs into IPCs. It also provides a new theoretical basis for the transplantation of insulin-producing cells into diabetic patients in the future. Graphical Abstract

A multi-objective pedestrian tracking method based on you only look once-v8 (YOLOv8) and the improved simple online and real time tracking with a deep association metric (DeepSORT) was proposed with the purpose of coping with the issues of local occlusion and ID dynamic transformation that frequently arise when tracking target pedestrians in real complex traffic scenarios. To begin with, in order to enhance the feature extraction network's capacity to learn target feature information in busy traffic situations, the detector implemented the YOLOv8 method with a high level of small-scale feature expression. In addition, the omni-scale network (OSNet) feature extraction network was then put on top of DeepSORT in order to accomplish real-time synchronized target tracking. This increases the effectiveness of picture edge recognition by dynamically fusing the collected feature information at various scales. Furthermore, a new adaptive forgetting smoothing Kalman filtering algorithm (FSA) was created to adapt to the nonlinear condition of the pedestrian trajectory in the traffic scene in order to address the issue of poor prediction attributed to the linear state equation of Kalman filtering once more. Afterward, the original intersection over union (IOU) association matching algorithm of DeepSORT was replaced by the complete-intersection over union (CIOU) association matching algorithm to fundamentally reduce the target pedestrians' omission and misdetection situation and to improve the accuracy of data matching. Eventually, the generalized trajectory feature extractor model (GFModel) was developed to tightly merge the local and global information through the average pooling operation in order to get precise tracking results and further decrease the impact of numerous disturbances on target tracking. The fusion algorithm of YOLOv8 and improved DeepSORT method based on OSNet, FSA and GFModel was named YOFGD. According to the experimental findings, YOFGD's ultimate accuracy can reach 77.9% and its speed can reach 55.8 frames per second (FPS), which is more than enough to fulfill the demands of real-world scenarios.

Abstract Fundus image captures rear of an eye which has been studied for disease identification, classification, segmentation, generation, and biological traits association using handcrafted, conventional, and deep learning methods. In biological traits estimation, most of the studies have been carried out for the age prediction and gender classification with convincing results. The current study utilizes the cutting-edge deep learning (DL) algorithms to estimate biological traits in terms of age and gender together with associating traits to retinal visuals. For the trait’s association, we embed aging as the label information into the proposed DL model to learn knowledge about the effected regions with aging. Our proposed DL models named FAG-Net and FGC-Net, which correspondingly estimates biological traits (age and gender) and generates fundus images. FAG-Net can generate multiple variants of an input fundus image given a list of ages as conditions. In this study, we analyzed fundus images and their corresponding association in terms of aging and gender. Our proposed models outperform randomly selected state-of-the-art DL models.

Abstract Deep resource extraction has been affected by the complex geological environment of "three highs and one disturbance" for a long time. The surrounding rocks experience strong unloading stress disturbance during the underground resource extraction. The creep characteristics of the perimeter rocks are of great theoretical and practical value. Firstly, the triaxial pre-peak loading and unloading tests on the prepared samples are conducted on the intact rock samples using the TAW-200 rock mechanics test system. Then the rock samples with different degrees of pre-peak unloading damage were prepared under the condition of pre-peak yield. Finally, the uniaxial creep tests were carried out to study the uniaxial creep mechanical properties. The results show that the wave velocities of the damaged rock samples are reduced to different degrees compared with those of the intact rock samples, the creep of the pre-peak unloading damaged rock has a time-dependent damage effect, and the study results are similar to those of the conventional uniaxial creep test of the white sandstone. The instantaneous strain at all stress levels of the damaged rock samples increased gradually with the stress level, which is consistent with the non-linear functional relationship; Based on the time-dependent damage effect, the two-parameter Weibull distribution function was introduced into the West Plains viscoelasticity model, and a creep damage model for white sandstone was proposed. The improved Nishihara model can simulate the uniaxial creep characteristics of each damaged rock sample at various stress levels.

To improve the efficiency of coal seam water injection, the influence of nanofluids on coal surface wettability was studied based on the nano drag reduction and injection enhancement technology in the field of tertiary oil recovery. The composition optimization and performance evaluation of nanofluids with nano-silica and sodium lauryl sulfate as the main components were carried out, and the effects of the nanofluid with the optimal ratio on coal wettability were studied through spontaneous upward imbibition experiments. The results show that the composite nanofluid has a lower surface tension, and the lowest value of the interfacial tension is 15.79 mN/m. Therefore, the composite nanofluid can enhance the wettability of coal. However, its effects on coal samples with different metamorphic degrees is different, that is, low rank coal is the largest, middle rank coal is the second, and high rank coal is the least. In addition, a functional relationship between time and imbibition height is found for pulverized coal with different particle sizes. When the particle size of pulverized coal is 60–80 mesh, the wettability of nanofluid to coal is best. The findings in this paper provide a new perspective for improving the water injection efficiency for coal seams with low permeability.

Deep learning techniques have shown great potential in medical image processing, particularly through accurate and reliable image segmentation on magnetic resonance imaging (MRI) scans or computed tomography (CT) scans, which allow the localization and diagnosis of lesions. However, training these segmentation models requires a large number of manually annotated pixel-level labels, which are time-consuming and labor-intensive, in contrast to image-level labels that are easier to obtain. It is imperative to resolve this problem through weakly-supervised semantic segmentation models using image-level labels as supervision since it can significantly reduce human annotation efforts. Most of the advanced solutions exploit class activation mapping (CAM). However, the original CAMs rarely capture the precise boundaries of lesions. In this study, we propose the strategy of multi-scale inference to refine CAMs by reducing the detail loss in single-scale reasoning. For segmentation, we develop a novel model named Mixed-UNet, which has two parallel branches in the decoding phase. The results can be obtained after fusing the extracted features from two branches. We evaluate the designed Mixed-UNet against several prevalent deep learning-based segmentation approaches on our dataset collected from the local hospital and public datasets. The validation results demonstrate that our model surpasses available methods under the same supervision level in the segmentation of various lesions from brain imaging.

Bone age is the chronological age of human bones, which serves as a key indicator of the maturity of bone development and can more objectively reflect the extent of human growth and development. The prevalent viewpoint and research development direction now favor the employment of deep learning-based bone age detection algorithms to determine bone age. Although bone age detection accuracy has increased when compared to more established methods, more work needs to be conducted to raise it because bone age detection is primarily used in clinical medicine, forensic identification, and other critical and rigorous fields. Due to the symmetry of human hand bones, bone age detection can be performed on either the left hand or the right hand, and the results are the same. In other words, the bone age detection results of both hands are universal. In this regard, the left hand is chosen as the target of bone age detection in this paper. To accomplish this, the You Only Look Once-v5 (YOLOv5) and Residual Network-34 (ResNet34) integration techniques are combined in this paper to create an innovative bone age detection model (YARN), which is then combined with the RUS-CHN scoring method that applies to Chinese adolescent children to comprehensively assess bone age at multiple levels. In this study, the images in the hand bone dataset are first preprocessed with number enhancement, then YOLOv5 is used to train the hand bone dataset to identify and filter out the main 13 joints in the hand bone, and finally, ResNet34 is used to complete the classification of local joints and achieve the determination of the developmental level of the detected region, followed by the calculation of the bone age by combining with the RUS-CHN method. The bone age detection model based on YOLOv5 and ResNet34 can significantly improve the accuracy and efficiency of bone age detection, and the model has significant advantages in the deep feature extraction of key regions of hand bone joints, which can efficiently complete the task of bone age detection. This was discovered through experiments on the public dataset of Flying Paddle AI Studio.

Coal fire remains one of the main hazards of underground work. Spontaneous coal fires cause serious casualties and property losses. At present, most of the studies on coal spontaneous combustion have been conducted on working faces shorter than 200 m. However, the ultra-long working face gob of shallow buried coal seam is much larger, the distribution of its flow field is more complex, and, thus, risk of spontaneous combustion in the gob is higher. Exploring the evolution law of the gob flow field of ultra-long working face to quickly determine the range of the coal spontaneous combustion hazardous zone is of great significance to the safe production of similar mines. In this study, the gas flow field distribution in the gob of an ultra-long working face was measured by buried pipeline method and oxygen concentration was used as the index. It is found that the oxygen concentration decreases with the advance of the working face. Based on the flow field distribution, the oxidation zone of the gob was determined. Meanwhile, a three-dimensional (3D) numerical model of the working face was established, and the overlying stratum collapse and porosity evolution in the gob were simulated using the particle flow software, PFC3D discrete element software, for the porosity distribution law of the gob. The obtained porosity data were then imported into FLUENT using the custom function UDF to construct a 3D grid model. The flow field distribution in the gob was then numerically simulated for the seepage and migration law of the wind flow in the gob. The results reveal an arch-shaped wind flow field distribution with a swirl shape on the intake airway side. In the strike direction, the wind flow gradually becomes weaker with the advance of the working face. In the dip direction, the wind flow seepage range on the return airway side is obviously higher than that on the intake airway side. In the vertical direction, the wind flow range in the upper gob is larger than that in the middle and lower gob. The spontaneous combustion and oxidation zone of the gob is determined to be at 140.4–313.3 m on the intake airway side, 201.2–351.6 m in the middle of the gob, and 153.2–328.1 m on the return airway side. Finally, the residual coal distribution was superimposed onto the oxygen concentration distribution to obtain the spontaneous residual coal combustion hazardous zone in the gob.

Releasing Wolbachia-infected mosquitoes to suppress or replace wild vector mosquitoes has been carried out in 24 countries worldwide, showing great promise in controlling mosquitoes and mosquito-borne diseases. To face the instability of Wolbachia infection in different environments during the area-wide application, we should consider the overlapping of two Wolbachia strains. In this case, bidirectional cytoplasmic incompatibility occurs, which results in mating partners infected with exclusive Wolbachia strains producing inviable offspring. To determine the better Wolbachia candidate for release, we develop an ordinary differential equation model to study the global dynamics for competition between two Wolbachia strains. Our theoretical results on the sharp estimate of stable curves completely determine the fate of the two Wolbachia strains, which help choose appropriate strains for release.

With the increase of mine mining intensity, the scope of goaf continues to expand, a large amount of coal remains in goaf, and the spontaneous combustion fire of coal is particularly serious. In addition, the spontaneous combustion of coal under the condition of gas mining is not clear. At once, spontaneous combustion can induce gas explosion, resulting in the waste of manpower and material resources, bringing hidden dangers to the mine safety production. In order to prevent and control the fire caused by spontaneous combustion of left coal under the condition of gas mining, in this paper by means of laboratory test, field measurement, and numerical simulation, the law of spontaneous combustion of residual coal under gas mining is studied. The index gas of coal spontaneous combustion was determined by temperature-programmed experimental device. Secondly, the distribution of the stope working face, goaf, and O2 is discussed. It is of great significance to study the spontaneous combustion rule of three belts in goaf under acid leaching condition and then to discuss the best position of injection idling opening and put forward targeted precontrol measures, which is of great significance to guarantee the safety of mine production. The results show that CO begins to appear when the temperature rises to 40 °C, and its concentration increases exponentially with the increase of coal temperature, which can reflect the degree of coal oxidation in goaf. Therefore, CO should be selected as the indicator gas in the working face. Under the influence of negative drainage pressure, the dangerous area will be enlarged. The optimal drainage volume of high drainage roadway is 90 m3/min, and the optimal air intake volume of working face is 1400 m3/min. The area 0–34 m away from the working face is scattered zone, 34–135 m away is spontaneous combustion zone, and 135 m away is asphyxiation zone. When the position of the idler is 50 m behind the working face, the reduction of the oxidation zone is the largest.

Deep mines are greatly affected by changes in natural wind pressure because of their large buried depths and long ventilation paths. Changes in natural wind pressure do affect the air flow of the underground ventilation system, and even change the direction of individual branches. If the dynamic changes of natural wind pressure are not monitored constantly, it is very likely to cause disasters such as gas overrun and may even lead to heavy casualties. In this paper, the changes of natural wind pressure and the air volume entering the mine are measured on-site in the 630 mining area in the south wing of Tangkou Coal Mine, Then, compare the change law of natural wind pressure with the change law of ventilation air volume. Finally, through numerical simulation by FLUENT, the change of internal flow in the gob where there is a loosely closed condition is simulated. Through research, the annual natural wind pressure change and the change of air intake in the 630 mining area of the south wing of Tangkou Coal Mine were obtained; The influence of changes in external conditions on the ventilation air volume of deep mines is obtained; The importance of the influence of natural wind pressure on the stability of the deep mine ventilation system is verified.

The Cahn–Hilliard–Navier–Stokes model is extensively used for simulating two-phase incompressible fluid flows. With the absence of exterior force, this model satisfies the energy dissipation law. The present work focuses on developing a linear, decoupled, and energy dissipation-preserving time-marching scheme for the hydrodynamics coupled Cahn–Hilliard model. An efficient time-dependent auxiliary variable approach is first introduced to design equivalent equations. Based on equivalent forms, a BDF2-type linear scheme is constructed. In each time step, the unique solvability and the energy dissipation law can be analytically estimated. To enhance the energy stability and the consistency, we correct the modified energy by a practical relaxation technique. Using the finite difference method in space, the fully discrete scheme is described, and the numerical solutions can be separately implemented. Numerical results indicate that the proposed scheme has desired accuracy, consistency, and energy stability. Moreover, the flow-coupled phase separation, the falling droplet, and the dripping droplet are well simulated.

To reveal the evolution law of coal skeleton deformation during the process of CO2 flooding and displacing CH4 in coal seam, a fluid-solid coupling mathematical model of CO2 injection enhanced CH4 drainage was established based on Fick’s law, Darcy’s law, ideal gas state equation, and Langmuir equation. Meanwhile, numerical simulations were carried out by implementing the mathematical model in the COMSOL Multiphysics. Results show that the CH4 content of both regular gas drainage and CO2 enhanced gas drainage gradually decreases with time, and the decreasing rate is high between 10 and 60 days. Compared with regular gas drainage, the efficiency of CO2 enhanced gas drainage is more obvious with greater amount of CH4 extracted out. When coal seam gas is extracted for 10, 60, 120, and 180 days, CH4 content in coal seam is reduced by 5.2, 17.2, 23.6, and 26.7%, respectively. For regular gas drainage, the deformation of coal skeleton is dominated by the shrink of coal matrix induced by gas desorption, and the strain curve shows a continuous downward trend. For CO2 enhanced gas drainage, the strain curve of coal skeleton showed a decrease—rapid increase—slow increase trend. The evolution of permeability is opposite to the evolution of coal skeleton strain. Higher gas injection pressure will lead to a greater coal skeleton strain. The pumping pressure affects the deformation of coal skeleton slightly compared with that of initial water saturation and initial temperature. Greater initial water saturation leads to larger deformation of coal skeleton in the early stage. The strain value of coal skeleton gradually tends to be consistent as gas injection prolongs. Higher initial temperature leads to greater reduction in coal skeleton strain when the gas injection continues. Research achievements provide a basis for the field application of CO2 injection enhanced CH4 drainage in underground coal mines.

Abstract A novel bismuth–carbon composite, in which bismuth nanoparticles were anchored in a nitrogen-doped carbon matrix (Bi@NC), is proposed as anode for high volumetric energy density lithium ion batteries (LIBs). Bi@NC composite was synthesized via carbonization of Zn-containing zeolitic imidazolate (ZIF-8) and replacement of Zn with Bi, resulting in the N-doped carbon that was hierarchically porous and anchored with Bi nanoparticles. The matrix provides a highly electronic conductive network that facilitates the lithiation/delithiation of Bi. Additionally, it restrains aggregation of Bi nanoparticles and serves as a buffer layer to alleviate the mechanical strain of Bi nanoparticles upon Li insertion/extraction. With these contributions, Bi@NC exhibits excellent cycling stability and rate capacity compared to bare Bi nanoparticles or their simple composites with carbon. This study provides a new approach for fabricating high volumetric energy density LIBs.

In this study, we selected 9 typical coal samples with different metamorphic grades as the study subjects, measured their initial 30-min gas desorption at 30 °C and different pressure using a self-developed gas adsorption/desorption device. Based on the characteristics of gas desorption from coal samples, we proposed a direct fitting method for measurement of gas content in coalbed, analyzed the effects of sampling time on the measurement results and determined the reasonable sampling time of coal samples with different metamorphic grades at different gas adsorption pressure at equilibrium. The results show that (1) the error of gas contents obtained using the direct fitting method relative to that obtained using indirect method is less than 10%, which meets the actual on-site requirements and verifies the feasibility of the direct fitting method; (2) when the relative error is controlled within ±10%, the reasonable sampling time of coal samples is linearly related to the gas adsorption pressure at equilibrium; (3) the reasonable sampling time of coal samples with the same metamorphic grade exhibits a shortening trend with increasing gas adsorption pressure at equilibrium; (4) for coal samples with similar gas adsorption pressure at equilibrium, the reasonable sampling time of coal samples displays a shortening trend with increasing metamorphic grade. Overall, the study provides a basis for improving the measurement accuracy of gas content in coalbed. Keywords: Gas content, Fitting, Desorption, Metamorphic grade, Sampling time

Abstract All‐solid‐state zinc–air batteries are characterized as low cost and have high energy density, providing wearable devices with an ideal power source. However, the sluggish oxygen reduction and evolution reactions in air cathodes are obstacles to its flexible and rechargeable application. Herein, a strategy called MOF‐on‐MOF (MOF, metal‐organic framework) is presented for the structural design of air cathodes, which creatively develops an efficient oxygen catalyst comprising hierarchical Co3O4 nanoparticles anchored in nitrogen‐doped carbon nano‐micro arrays on flexible carbon cloth (Co3O4@N‐CNMAs/CC). This hierarchical and free‐standing structure design guarantees high catalyst loading on air cathodes with multiple electrocatalytic activity sites, undoubtedly boosting reaction kinetics, and energy density of an all‐solid‐state zinc–air battery. The integrated Co3O4@N‐CNMAs/CC cathode in an all‐solid‐state zinc–air battery exhibits a high open circuit potential of 1.461 V, a high capacity of 815 mAh g−1 Zn at 1 mA cm−2, a high energy density of 1010 Wh kg−1 Zn, excellent cycling stability as well as outstanding mechanical flexibility, significantly outperforming the Pt/C‐based cathode. This work opens a new door for the practical applications of rechargeable zinc–air batteries in wearable electronic devices.