Let p be a prime. For any finite p-group G, the deep transfers T H,G ' : H / H ' → G ' / G " from the maximal subgroups H of index (G:H) = p in G to the derived subgroup G ' are introduced as an ...Let p be a prime. For any finite p-group G, the deep transfers T H,G ' : H / H ' → G ' / G " from the maximal subgroups H of index (G:H) = p in G to the derived subgroup G ' are introduced as an innovative tool for identifying G uniquely by means of the family of kernels ùd(G) =(ker(T H,G ')) (G: H) = p. For all finite 3-groups G of coclass cc(G) = 1, the family ùd(G) is determined explicitly. The results are applied to the Galois groups G =Gal(F3 (∞)/ F) of the Hilbert 3-class towers of all real quadratic fields F = Q(√d) with fundamental discriminants d > 1, 3-class group Cl3(F) □ C3 × C3, and total 3-principalization in each of their four unramified cyclic cubic extensions E/F. A systematic statistical evaluation is given for the complete range 1 d 7, and a few exceptional cases are pointed out for 1 d 8.展开更多
讨论了基于M atlab W eb Server的M atlab网络应用开发原理,介绍了M atlab W eb程序处理的一般流程和相关配置文件的详细配置方法,并给出M atlab W eb开发中的两个关键问题:通过输入模块从HTML页面获取输入参数和通过输出模块生成包括...讨论了基于M atlab W eb Server的M atlab网络应用开发原理,介绍了M atlab W eb程序处理的一般流程和相关配置文件的详细配置方法,并给出M atlab W eb开发中的两个关键问题:通过输入模块从HTML页面获取输入参数和通过输出模块生成包括输出数据和图片的HTML文件.利用M atlab W eb Server环境实现了远程控制实验室的控制效果仿真,并以二维图形的输出形式显示仿真结果,为网上控制实验室的建立提供了控制参数选择以及试验结果验证参照.本远程数据处理方法可推广应用到不同的远程数据处理领域,具有很高的推广价值.展开更多
以雅砻江水库群生态调度决策支持系统为背景,研究生态调度优化计算方法,建立了生态调度模型,同时介绍水库群生态调度决策支持系统的体系结构、功能和特点,探讨构建该决策支持系统的解决方案和关键技术;并基于RIA(Rich Internet Applicat...以雅砻江水库群生态调度决策支持系统为背景,研究生态调度优化计算方法,建立了生态调度模型,同时介绍水库群生态调度决策支持系统的体系结构、功能和特点,探讨构建该决策支持系统的解决方案和关键技术;并基于RIA(Rich Internet Applications)和Web Service技术,ASP.NET技术开发出了体系先进、实用可靠和界面友好的分布式、可扩展性系统。展开更多
Over the past 80 years,dozens of underground coal gasification(UCG)mine field tests have been carried out around the world.However,in the early days,only a small number of shallow UCG projects in the former Soviet Uni...Over the past 80 years,dozens of underground coal gasification(UCG)mine field tests have been carried out around the world.However,in the early days,only a small number of shallow UCG projects in the former Soviet Union achieved commercialised production.In this century,a few pilot projects in Australia also achieved short-term small-scale commercialised production using modern UCG technology.However,the commercialisation of UCG,especially medium-deep UCG projects with good development prospects but difficult underground engineering conditions,has not progressed smoothly around the world.Considering investment economy,a single gasifier must realise a high daily output and accumulated output,as well as hold a long gasification tunnel to control a large number of coal resources.However,a long gasification tunnel can easily be affected by blockages and failure,for which the remedial solutions are difficult and expensive,which greatly restricts the investment economy.The design of the underground gasifier determines the success or failure of UCG projects,and it also requires the related petroleum engineering technology.Combining the advantages of the linear horizontal well(L-CRIP)and parallel horizontal well(P-CRIP),this paper proposes a new design scheme for an“inclined ladder”underground gasifier.That is to say,the combination of the main shaft of paired P-CRIP and multiple branch horizontal well gasification tunnels is adopted to realise the control of a large number of coal resources in a single gasifier.The completion of the main shaft by well cementation is beneficial for maintaining the integrity of the main shaft and the stability of the main structure.The branch horizontal well is used as the gasification tunnel,but the length and number of retracting injection points are limited,effectively reducing the probability of blockage or failure.The branch horizontal well spacing can be adjusted flexibly to avoid minor faults and large cracks,which is conducive to increasing the resource utilisation rate.In addition,for multi-layer thin coal seams or ultra-thick coal seams,a multi-layer gasifier sharing vertical well sections can be deployed,thereby saving investment on the vertical well sections.Through preliminary analysis,this gasifier design scheme can be realised in engineering,making it suitable for largescale deployment where it can increase the resource utilisation rate and ensure stable and controllable operations.The new gasifier has outstanding advantages in investment economy,and good prospects for application in the commercial UCG projects of medium-deep coal seams.展开更多
The concept of Network Centric Therapy represents an amalgamation of wearable and wireless inertial sensor systems and machine learning with access to a Cloud computing environment. The advent of Network Centric Thera...The concept of Network Centric Therapy represents an amalgamation of wearable and wireless inertial sensor systems and machine learning with access to a Cloud computing environment. The advent of Network Centric Therapy is highly relevant to the treatment of Parkinson’s disease through deep brain stimulation. Originally wearable and wireless systems for quantifying Parkinson’s disease involved the use a smartphone to quantify hand tremor. Although originally novel, the smartphone has notable issues as a wearable application for quantifying movement disorder tremor. The smartphone has evolved in a pathway that has made the smartphone progressively more cumbersome to mount about the dorsum of the hand. Furthermore, the smartphone utilizes an inertial sensor package that is not certified for medical analysis, and the trial data access a provisional Cloud computing environment through an email account. These concerns are resolved with the recent development of a conformal wearable and wireless inertial sensor system. This conformal wearable and wireless system mounts to the hand with the profile of a bandage by adhesive and accesses a secure Cloud computing environment through a segmented wireless connectivity strategy involving a smartphone and tablet. Additionally, the conformal wearable and wireless system is certified by the FDA of the United States of America for ascertaining medical grade inertial sensor data. These characteristics make the conformal wearable and wireless system uniquely suited for the quantification of Parkinson’s disease treatment through deep brain stimulation. Preliminary evaluation of the conformal wearable and wireless system is demonstrated through the differentiation of deep brain stimulation set to “On” and “Off” status. Based on the robustness of the acceleration signal, this signal was selected to quantify hand tremor for the prescribed deep brain stimulation settings. Machine learning classification using the Waikato Environment for Knowledge Analysis (WEKA) was applied using the multilayer perceptron neural network. The multilayer perceptron neural network achieved considerable classification accuracy for distinguishing between the deep brain stimulation system set to “On” and “Off” status through the quantified acceleration signal data obtained by this recently developed conformal wearable and wireless system. The research achievement establishes a progressive pathway to the future objective of achieving deep brain stimulation capabilities that promote closed-loop acquisition of configuration parameters that are uniquely optimized to the individual through extrinsic means of a highly conformal wearable and wireless inertial sensor system and machine learning with access to Cloud computing resources.展开更多
Alzheimer's disease is the most common type of cognitive disorder,and there is an urgent need to develop more effective,targeted and safer therapies for patients with this condition.Deep brain stimulation is an in...Alzheimer's disease is the most common type of cognitive disorder,and there is an urgent need to develop more effective,targeted and safer therapies for patients with this condition.Deep brain stimulation is an invasive surgical treatment that modulates abnormal neural activity by implanting electrodes into specific brain areas followed by electrical stimulation.As an emerging therapeutic approach,deep brain stimulation shows significant promise as a potential new therapy for Alzheimer's disease.Here,we review the potential mechanisms and therapeutic effects of deep brain stimulation in the treatment of Alzheimer's disease based on existing clinical and basic research.In clinical studies,the most commonly targeted sites include the fornix,the nucleus basalis of Meynert,and the ventral capsule/ventral striatum.Basic research has found that the most frequently targeted areas include the fornix,nucleus basalis of Meynert,hippocampus,entorhinal cortex,and rostral intralaminar thalamic nucleus.All of these individual targets exhibit therapeutic potential for patients with Alzheimer's disease and associated mechanisms of action have been investigated.Deep brain stimulation may exert therapeutic effects on Alzheimer's disease through various mechanisms,including reducing the deposition of amyloid-β,activation of the cholinergic system,increasing the levels of neurotrophic factors,enhancing synaptic activity and plasticity,promoting neurogenesis,and improving glucose metabolism.Currently,clinical trials investigating deep brain stimulation for Alzheimer's disease remain insufficient.In the future,it is essential to focus on translating preclinical mechanisms into clinical trials.Furthermore,consecutive follow-up studies are needed to evaluate the long-term safety and efficacy of deep brain stimulation for Alzheimer's disease,including cognitive function,neuropsychiatric symptoms,quality of life and changes in Alzheimer's disease biomarkers.Researchers must also prioritize the initiation of multi-center clinical trials of deep brain stimulation with large sample sizes and target earlier therapeutic windows,such as the prodromal and even the preclinical stages of Alzheimer's disease.Adopting these approaches will permit the efficient exploration of more effective and safer deep brain stimulation therapies for patients with Alzheimer's disease.展开更多
On the basis of the analysis of field thermogeochemical data along abnormal zones of a thermal stream in the Bukhara-Khiva, oil-and-gas region of the Turan (Tegermen, Chagakul, Shimoly Alat, Beshtepa) was succeeded to...On the basis of the analysis of field thermogeochemical data along abnormal zones of a thermal stream in the Bukhara-Khiva, oil-and-gas region of the Turan (Tegermen, Chagakul, Shimoly Alat, Beshtepa) was succeeded to obtain important data on a deep structure of sites. Data of gas-chemical and geothermal observations show about confinedness of abnormal concentration of methane to zones of the increased values of the temperature field the measured values of temperatures (Tegermen Square and others). On geoelectric section mines 2-D of inversion of the MT-field depth of 4000 m are lower, among very high-resistance the chemogenic and carbonate deposits of the Paleozoic is traced the subvertical carrying-out abnormal zone. This zone is identified as the channel of a deep heat and mass transfer with which hydrocarbon (HC) deposits are connected. It is shown that electro-investigation when using a geophysical complex can and has to become “advancing” at exploration by oil and gas.展开更多
Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone t...Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.展开更多
Splenic rupture is a rare complication of diagnostic and therapeutic gastrointestinal endoscopy procedures.Herein,we report for the first time a case of splenic rupture following therapeutic retrograde double-balloon ...Splenic rupture is a rare complication of diagnostic and therapeutic gastrointestinal endoscopy procedures.Herein,we report for the first time a case of splenic rupture following therapeutic retrograde double-balloon enteroscopy,which occurred in an 85-year-old man who was treated for recurrent mid-intestinal bleeding that resulted from ileal angioectasia.This patient promptly underwent an operation and eventually recovered.展开更多
文摘Let p be a prime. For any finite p-group G, the deep transfers T H,G ' : H / H ' → G ' / G " from the maximal subgroups H of index (G:H) = p in G to the derived subgroup G ' are introduced as an innovative tool for identifying G uniquely by means of the family of kernels ùd(G) =(ker(T H,G ')) (G: H) = p. For all finite 3-groups G of coclass cc(G) = 1, the family ùd(G) is determined explicitly. The results are applied to the Galois groups G =Gal(F3 (∞)/ F) of the Hilbert 3-class towers of all real quadratic fields F = Q(√d) with fundamental discriminants d > 1, 3-class group Cl3(F) □ C3 × C3, and total 3-principalization in each of their four unramified cyclic cubic extensions E/F. A systematic statistical evaluation is given for the complete range 1 d 7, and a few exceptional cases are pointed out for 1 d 8.
文摘讨论了基于M atlab W eb Server的M atlab网络应用开发原理,介绍了M atlab W eb程序处理的一般流程和相关配置文件的详细配置方法,并给出M atlab W eb开发中的两个关键问题:通过输入模块从HTML页面获取输入参数和通过输出模块生成包括输出数据和图片的HTML文件.利用M atlab W eb Server环境实现了远程控制实验室的控制效果仿真,并以二维图形的输出形式显示仿真结果,为网上控制实验室的建立提供了控制参数选择以及试验结果验证参照.本远程数据处理方法可推广应用到不同的远程数据处理领域,具有很高的推广价值.
文摘以雅砻江水库群生态调度决策支持系统为背景,研究生态调度优化计算方法,建立了生态调度模型,同时介绍水库群生态调度决策支持系统的体系结构、功能和特点,探讨构建该决策支持系统的解决方案和关键技术;并基于RIA(Rich Internet Applications)和Web Service技术,ASP.NET技术开发出了体系先进、实用可靠和界面友好的分布式、可扩展性系统。
文摘Over the past 80 years,dozens of underground coal gasification(UCG)mine field tests have been carried out around the world.However,in the early days,only a small number of shallow UCG projects in the former Soviet Union achieved commercialised production.In this century,a few pilot projects in Australia also achieved short-term small-scale commercialised production using modern UCG technology.However,the commercialisation of UCG,especially medium-deep UCG projects with good development prospects but difficult underground engineering conditions,has not progressed smoothly around the world.Considering investment economy,a single gasifier must realise a high daily output and accumulated output,as well as hold a long gasification tunnel to control a large number of coal resources.However,a long gasification tunnel can easily be affected by blockages and failure,for which the remedial solutions are difficult and expensive,which greatly restricts the investment economy.The design of the underground gasifier determines the success or failure of UCG projects,and it also requires the related petroleum engineering technology.Combining the advantages of the linear horizontal well(L-CRIP)and parallel horizontal well(P-CRIP),this paper proposes a new design scheme for an“inclined ladder”underground gasifier.That is to say,the combination of the main shaft of paired P-CRIP and multiple branch horizontal well gasification tunnels is adopted to realise the control of a large number of coal resources in a single gasifier.The completion of the main shaft by well cementation is beneficial for maintaining the integrity of the main shaft and the stability of the main structure.The branch horizontal well is used as the gasification tunnel,but the length and number of retracting injection points are limited,effectively reducing the probability of blockage or failure.The branch horizontal well spacing can be adjusted flexibly to avoid minor faults and large cracks,which is conducive to increasing the resource utilisation rate.In addition,for multi-layer thin coal seams or ultra-thick coal seams,a multi-layer gasifier sharing vertical well sections can be deployed,thereby saving investment on the vertical well sections.Through preliminary analysis,this gasifier design scheme can be realised in engineering,making it suitable for largescale deployment where it can increase the resource utilisation rate and ensure stable and controllable operations.The new gasifier has outstanding advantages in investment economy,and good prospects for application in the commercial UCG projects of medium-deep coal seams.
文摘The concept of Network Centric Therapy represents an amalgamation of wearable and wireless inertial sensor systems and machine learning with access to a Cloud computing environment. The advent of Network Centric Therapy is highly relevant to the treatment of Parkinson’s disease through deep brain stimulation. Originally wearable and wireless systems for quantifying Parkinson’s disease involved the use a smartphone to quantify hand tremor. Although originally novel, the smartphone has notable issues as a wearable application for quantifying movement disorder tremor. The smartphone has evolved in a pathway that has made the smartphone progressively more cumbersome to mount about the dorsum of the hand. Furthermore, the smartphone utilizes an inertial sensor package that is not certified for medical analysis, and the trial data access a provisional Cloud computing environment through an email account. These concerns are resolved with the recent development of a conformal wearable and wireless inertial sensor system. This conformal wearable and wireless system mounts to the hand with the profile of a bandage by adhesive and accesses a secure Cloud computing environment through a segmented wireless connectivity strategy involving a smartphone and tablet. Additionally, the conformal wearable and wireless system is certified by the FDA of the United States of America for ascertaining medical grade inertial sensor data. These characteristics make the conformal wearable and wireless system uniquely suited for the quantification of Parkinson’s disease treatment through deep brain stimulation. Preliminary evaluation of the conformal wearable and wireless system is demonstrated through the differentiation of deep brain stimulation set to “On” and “Off” status. Based on the robustness of the acceleration signal, this signal was selected to quantify hand tremor for the prescribed deep brain stimulation settings. Machine learning classification using the Waikato Environment for Knowledge Analysis (WEKA) was applied using the multilayer perceptron neural network. The multilayer perceptron neural network achieved considerable classification accuracy for distinguishing between the deep brain stimulation system set to “On” and “Off” status through the quantified acceleration signal data obtained by this recently developed conformal wearable and wireless system. The research achievement establishes a progressive pathway to the future objective of achieving deep brain stimulation capabilities that promote closed-loop acquisition of configuration parameters that are uniquely optimized to the individual through extrinsic means of a highly conformal wearable and wireless inertial sensor system and machine learning with access to Cloud computing resources.
基金supported by the Capital Fund for Health Improvement and Research,No.2022-2-2048(to WZ)the National Natural Science Foundation of China,No.81970992(to WZ)+3 种基金Capital Clinical Characteristic Application Research,No.Z121107001012161(to WZ)the Natural Science Foundation of Beijing,No.7082032(to WZ)the Key Technology R&D Program of Beijing Municipal Education Commission,No.KZ201610025030(to WZ)Project of Scientific and Technological Development of Traditional Chinese Medicine in Beijing,No.JJ2018-48(to WZ)。
文摘Alzheimer's disease is the most common type of cognitive disorder,and there is an urgent need to develop more effective,targeted and safer therapies for patients with this condition.Deep brain stimulation is an invasive surgical treatment that modulates abnormal neural activity by implanting electrodes into specific brain areas followed by electrical stimulation.As an emerging therapeutic approach,deep brain stimulation shows significant promise as a potential new therapy for Alzheimer's disease.Here,we review the potential mechanisms and therapeutic effects of deep brain stimulation in the treatment of Alzheimer's disease based on existing clinical and basic research.In clinical studies,the most commonly targeted sites include the fornix,the nucleus basalis of Meynert,and the ventral capsule/ventral striatum.Basic research has found that the most frequently targeted areas include the fornix,nucleus basalis of Meynert,hippocampus,entorhinal cortex,and rostral intralaminar thalamic nucleus.All of these individual targets exhibit therapeutic potential for patients with Alzheimer's disease and associated mechanisms of action have been investigated.Deep brain stimulation may exert therapeutic effects on Alzheimer's disease through various mechanisms,including reducing the deposition of amyloid-β,activation of the cholinergic system,increasing the levels of neurotrophic factors,enhancing synaptic activity and plasticity,promoting neurogenesis,and improving glucose metabolism.Currently,clinical trials investigating deep brain stimulation for Alzheimer's disease remain insufficient.In the future,it is essential to focus on translating preclinical mechanisms into clinical trials.Furthermore,consecutive follow-up studies are needed to evaluate the long-term safety and efficacy of deep brain stimulation for Alzheimer's disease,including cognitive function,neuropsychiatric symptoms,quality of life and changes in Alzheimer's disease biomarkers.Researchers must also prioritize the initiation of multi-center clinical trials of deep brain stimulation with large sample sizes and target earlier therapeutic windows,such as the prodromal and even the preclinical stages of Alzheimer's disease.Adopting these approaches will permit the efficient exploration of more effective and safer deep brain stimulation therapies for patients with Alzheimer's disease.
文摘On the basis of the analysis of field thermogeochemical data along abnormal zones of a thermal stream in the Bukhara-Khiva, oil-and-gas region of the Turan (Tegermen, Chagakul, Shimoly Alat, Beshtepa) was succeeded to obtain important data on a deep structure of sites. Data of gas-chemical and geothermal observations show about confinedness of abnormal concentration of methane to zones of the increased values of the temperature field the measured values of temperatures (Tegermen Square and others). On geoelectric section mines 2-D of inversion of the MT-field depth of 4000 m are lower, among very high-resistance the chemogenic and carbonate deposits of the Paleozoic is traced the subvertical carrying-out abnormal zone. This zone is identified as the channel of a deep heat and mass transfer with which hydrocarbon (HC) deposits are connected. It is shown that electro-investigation when using a geophysical complex can and has to become “advancing” at exploration by oil and gas.
文摘Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.
文摘Splenic rupture is a rare complication of diagnostic and therapeutic gastrointestinal endoscopy procedures.Herein,we report for the first time a case of splenic rupture following therapeutic retrograde double-balloon enteroscopy,which occurred in an 85-year-old man who was treated for recurrent mid-intestinal bleeding that resulted from ileal angioectasia.This patient promptly underwent an operation and eventually recovered.
