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ObjectiveTo observe the clinical efficacy and safety of laparoscopic subtotal colectomy + loop colostomy in adult patients with megacolon. MethodsRetrospective analysis was performed on the clinical data of 22 adult megacolon patients treated by laparoscopic subtotal colectomy + loop colostomy in Affiliated Hospital of Nanjing University of Chinese Medicine from October 2016 to October 2020. All patients underwent preoperative CT, barium enema, anorectal manometry, and postoperative pathological examination to diagnose megacolon. The symptoms of abdominal pain and abdominal distension, gastrointestinal quality of life score index（GIQLI）, Wexner constipation score and surgical complications were analyzed and compared before and after surgery. ResultsThe incidence of preoperative abdominal distension symptoms of 22 patients was 100%, and all improved after surgery. The incidence of abdominal distension at 12 months after surgery was 4.5% （1/22）. The Wexner constipation scores at the 6th and 12th months after surgery were 6.27±2.16 and 4.77±1.11, respectively, significantly better than the preoperative score （20.41±2.48, P＜0.05）. The GIQLI at the 6th and 12th months after surgery were 104.05±12.09 and 102.23±8.20, respectively, significantly better than the preoperative score （68.64±6.04, P＜0.05）. The postoperative complications included 1 infection at the operation site （4.5%）, 1 case of anastomotic fistula （4.5%）, 1 case of abdominal bleeding （4.5%）. ConclusionLaparoscopic subtotal colectomy + loop colostomy is a safe and effective operation for the treatment of adult megacolon, which is worthy of further exploration and application.

Accurate detection and recognition of plant diseases is the key technology to early diagnosis and intelligent monitoring of plant diseases, and is the core of accurate control and information management of plant diseases and insect pests. Deep learning can overcome the disadvantages of traditional diagnosis methods and greatly improve the accuracy of diseases detection and recognition, and has attracted a lot of attention of researchers. This paper collected the main public plant diseases image data sets all over the world, and briefly introduced the basic information of each data set and their websites, which is convenient to download and use. And then, the application of deep learning in plant disease detection and recognition in recent years was systematically reviewed. Plant disease target detection is the premise of accurate classification and recognition of plant disease and evaluation of disease hazard level. It is also the key to accurately locate plant disease area and guide spray device of plant protection equipment to spray drug on target. Plant disease recognition refers to the processing, analysis and understanding of disease images to identify different kinds of disease objects, which is the main basis for the timely and effective prevention and control of plant diseases. The research progress in early disease detection and recognition algorithm was expounded based on depth of learning research, as well as the advantages and existing problems of various algorithms were described. It can be seen from this review that the detection and recognition algorithm based on deep learning is superior to the traditional detection and recognition algorithm in all aspects. Based on the investigation of research results, it was pointed out that the illumination, sheltering, complex background, different disorders with similar symptoms, different changes of disease symptoms in different periods, and overlapping coexistence of multiple diseases were the main challenges for the detection and recognition of plant diseases. At the same time, the establishment of a large-scale and more complex data set that meets the specific research needs is also a difficulty that need to face together. And at further, we point out that the combination of the better performance of the neural network, large-scale data set and agriculture theoretical basis is a major trend of the development of the future. It is also pointed out that multimodal data can be used to identify early plant diseases, which is also one of the future development direction.

This study focuses on the problem of dense object counting. In dense scenes, variations in object scales and uneven distributions greatly hinder counting accuracy. The current methods, whether CNNs with fixed convolutional kernel sizes or Transformers with fixed attention sizes, struggle to handle such variability effectively. Lower-resolution features are more sensitive to larger objects closer to the camera, while higher-resolution features are more efficient for smaller objects further away. Thus, preserving features that carry the most relevant information at each scale is crucial for improving counting precision. Motivated by this, we propose a multi-resolution scale feature fusion-based universal density counting network (MRSNet). It utilizes independent modules to process high- and low-resolution features, adaptively adjusts receptive field sizes, and incorporates dynamic sparse attention mechanisms to optimize feature information at each resolution, by integrating optimal features across multiple scales into density maps for counting evaluation. Our proposed network effectively mitigates issues caused by large variations in object scales, thereby enhancing counting accuracy. Furthermore, extensive quantitative analyses on six public datasets demonstrate the algorithm's strong generalization ability in handling diverse object scale variations. [ABSTRACT FROM AUTHOR]

Novel antimicrobial strategies are urgently needed to treat extensively drug‐resistant (XDR) bacterial infections due to the high mortality rate and lack of effective therapeutic agents. Herein, nanoengineered human umbilical cord mesenchymal stem cells (hUC‐MSCs), named PMZMU, are designed as a sonosensitizer for synergistic sonodynamic‐nano‐antimicrobial therapy against gram‐negative XDR bacteria. PMZMU is composed of a bacterial targeting peptide (UBI29‐41) modified hUC‐MSCs membrane (MSCm), a sonosensitizer meso‐tetra(4‐car‐boxyphenyl) porphine doped mesoporous organo‐silica nanoparticle and an acidity‐responsive metal–organic framework ZIF‐8. This innovative formulation enables efficient loading of polymyxin B, reduces off‐target drug release, increases circulation and targeting efficacy, and generates reactive oxygen species upon ultrasound irradiation. PMZMU exhibits remarkable in vitro inhibitory activity against four XDR bacteria: Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa (PA), and Escherichia coli. Taking advantage of the bacterial targeting ability of UBI29‐41 and the inflammatory chemotaxis of hUC‐MSC, PMZMU can be precisely delivered to lung infection sites thereby augmenting polymyxin B concentration. PMZMU‐mediated sonodynamic therapy significantly reduces bacterial burden, relieves inflammatory damage by promoting the polarization of macrophages toward M2 phenotype, and improves survival rates without introducing adverse events. Overall, this study offers promising strategies for treating deep‐tissue XDR bacterial infections, and guides the design and optimization of biomimetic nanomedicine. [ABSTRACT FROM AUTHOR]

Phase change materials have issues in the automotive industry and in‐memory computing due to their limited thermal stability and high power consumption. Consequently, numerous studies are conducted to enhance the performance of these materials. Uncertainty persists regarding the effects and mechanisms of various doping on the parent material. In this study, four metallic doping elements are selected, and their effects on Sb2Te3 are meticulously analyzed through experiments and ab initio calculations. The thermal stability of Sb2Te3 has been somewhat enhanced by doping these four elements, with Ta doping having the most impact. According to the ab initio molecular dynamics, this improvement results from the tetrahedral centered In and the increased coordination numbers of Ta, Ti, and Sc. Sc located at the center of the octahedron can accelerate the crystallization process. Ta, in particular, exerts the greatest influence on the migration rates of Sb and Te, resulting in the slowest grain growth rate. Simultaneously, it maximizes the delay in the transition from the cubic phase to the hexagonal phase. These features are beneficial for reducing power consumption. The elucidation of the distinct effects of doping elements sets the stage for phase change memory to adapt to various application scenarios. [ABSTRACT FROM AUTHOR]

The coupling effects of flexible joints and clearance on the dynamics of a robotic system were investigated. A numerical analysis was undertaken to reveal the coupling effects between flexible joints and clearance. The nonlinear spring-damping model and Coulomb model were applied to characterize the contact characteristics of the clearance, and a model for the flexible joint was formulated using the equivalent spring theory. An accurate robot model was established based on the clearance and joint flexibility characterization. The dynamic equation of a robot was obtained according to the Newton-Euler method. A comparative analysis was performed to assess the impacts of both the joint action of clearance and flexible joints and varying joint clearance values on the performance of the robot. The results showed that the coupling effects of flexible joints and clearance had a negative impact on the system dynamic performance. The amplitudes of the dynamic responses caused by the clearance are weakened by the flexible joint, but it leads to the lag of the system response. This study served as the theoretical foundation for exploring precise control techniques in robotics research. [ABSTRACT FROM AUTHOR]

Most studies on the vibration of variable density paper web are limited to the discussion of small deflection linear problems. There are few researches on nonlinear vibration problems with large deflection in variable density printing web.However,in practice,the density of the web is varied due to the difference in the distribution of the printed image. In this paper,the nonlinear vibration characteristics of moving printing web with variable density are studied. Based on the von Karman plate theory,the vibration equation of the large deflection motion of the axially moving paper is deduced. The differential equation is divided by the Bubnov-Galerkin method,and the differential equation is solved by elliptic integral method. The frequency expression of the nonlinear vibration is obtained. The influence of density coefficient,dimensionless velocity and aspect ratio on the complex frequency of large deflection vibration of moving paper under different initial conditions is analyzed. This study provides a theoretical basis for improving the stability of printing equipment.

To reduce product damage caused by the vibration instability phenomenon associated with the commutation of new material films during long-path transportation, the nonlinear vibration of orthotropic films transported under oblique support is investigated. First, on the basis of von Karman's large deflection theory and D'Alembert's principle, then through coordinate system transformation, the vibration partial differential equations of the system in an oblique coordinate system are established, taking into account the influence of damping. Next, the equations are discretized by the Galerkin method to obtain the system's nonlinear vibration ordinary differential equations. Finally, the fourth-order Runge-Kutta method is used to carry out numerical calculations of the system to analyze the effects of the oblique support angle, orthotopic coefficient, and damping on the nonlinear vibration of films in transportation. The research results show that the selection of these three important parameters has an impact on the vibration characteristics of moving orthotopic films, and reasonable suggestions are made based on the parameter range for the stable motion of the system, which will help in structural adjustment and stability control during actual printing and manufacturing. [ABSTRACT FROM AUTHOR]