Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in us...Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.展开更多
BACKGROUND Internal hernia(IH)is a rare culprit of small bowel obstruction(SBO)with an incidence of<1%.It poses a considerable diagnostic challenge requiring a high index of suspicion to prevent misdiagnosis,improp...BACKGROUND Internal hernia(IH)is a rare culprit of small bowel obstruction(SBO)with an incidence of<1%.It poses a considerable diagnostic challenge requiring a high index of suspicion to prevent misdiagnosis,improper treatment,and subsequent morbidity and mortality.AIM To determine the clinico-demographic profile,radiological and operative findings,and postoperative course of patients with IH and its association with SBO.METHODS Medical records of 586 patients with features of SBO presenting at a tertiary care centre at Lucknow,India between September 2010 and August 2023 were reviewed.RESULTS Out of 586 patients,7(1.2%)were diagnosed with IH.Among these,4 had congenital IH and 3 had acquired IH.The male-to-female ratio was 4:3.The median age at presentation was 32 years.Contrast-enhanced computed tomography(CECT)was the most reliable investigation for preoperative identification,demonstrating mesenteric whirling and clumped-up bowel loops.Left paraduodenal hernia and transmesenteric hernia occurred with an equal frequency(approximately 43%each).Intraoperatively,one patient was found to have bowel ischemia and one had associated malrotation of gut.During follow-up,no recurrences were reported.CONCLUSION IH,being a rare cause,must be considered as a differential diagnosis for SBO,especially in young patients in their 30s or with unexplained abdominal pain or discomfort post-surgery.A rapid imaging evaluation,preferably with CECT,is necessary to aid in an early diagnosis and prompt intervention,thereby reducing financial burden related to unnecessary investigations and preventing the morbidity and mortality associated with closed-loop obstruction and strangulation of the bowel.展开更多
Photon-counting computed tomography(PCCT)represents a significant advancement in pediatric cardiovascular imaging.Traditional CT systems employ energy-integrating detectors that convert X-ray photons into visible ligh...Photon-counting computed tomography(PCCT)represents a significant advancement in pediatric cardiovascular imaging.Traditional CT systems employ energy-integrating detectors that convert X-ray photons into visible light,whereas PCCT utilizes photon-counting detectors that directly transform X-ray photons into electric signals.This direct conversion allows photon-counting detectors to sort photons into discrete energy levels,thereby enhancing image quality through superior noise reduction,improved spatial and contrast resolution,and reduced artifacts.In pediatric applications,PCCT offers substantial benefits,including lower radiation doses,which may help reduce the risk of malignancy in pediatric patients,with perhaps greater potential to benefit those with repeated exposure from a young age.Enhanced spatial resolution facilitates better visualization of small structures,vital for diagnosing congenital heart defects.Additionally,PCCT’s spectral capabilities improve tissue characterization and enable the creation of virtual monoenergetic images,which enhance soft-tissue contrast and potentially reduce contrast media doses.Initial clinical results indicate that PCCT provides superior image quality and diagnostic accuracy compared to conven-tional CT,particularly in challenging pediatric cardiovascular cases.As PCCT technology matures,further research and standardized protocols will be essential to fully integrate it into pediatric imaging practices,ensuring optimized diagnostic outcomes and patient safety.展开更多
Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While suc...Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While such a computing paradigm can only employ a single photonic device as the nonlinear node for data processing,the performance highly relies on the fading memory provided by the delay feedback loop(FL),which sets a restriction on the extensibility of physical implementation,especially for highly integrated chips.Here,we present a simplified photonic scheme for more flexible parameter configurations leveraging the designed quasi-convolution coding(QC),which completely gets rid of the dependence on FL.Unlike delay-based TDRC,encoded data in QC-based RC(QRC)enables temporal feature extraction,facilitating augmented memory capabilities.Thus,our proposed QRC is enabled to deal with time-related tasks or sequential data without the implementation of FL.Furthermore,we can implement this hardware with a low-power,easily integrable vertical-cavity surface-emitting laser for high-performance parallel processing.We illustrate the concept validation through simulation and experimental comparison of QRC and TDRC,wherein the simpler-structured QRC outperforms across various benchmark tasks.Our results may underscore an auspicious solution for the hardware implementation of deep neural networks.展开更多
AIM To analyze the diagnostic performance of surveillance colonoscopy,computed tomography(CT),and tumor markers(TMs)in detecting CRC recurrence or metastasis during follow-up after CRC resection.Secondary objectives i...AIM To analyze the diagnostic performance of surveillance colonoscopy,computed tomography(CT),and tumor markers(TMs)in detecting CRC recurrence or metastasis during follow-up after CRC resection.Secondary objectives included degree of adherence to clinical practice guidelines surveillance recommendations and factors associated with adherence and all-cause and CRC mortality.METHODS The single-center retrospective cohort study including patients undergoing curative resection of stage I-III CRC during 2010-2015.Follow-up was performed using TMs every 6 months,yearly CT for 5 years,and colonoscopy at years 1 and 4.Demographic,primary tumor data,and results at follow-up were collected.RESULTS Of 574 included patients included,153 had recurrences or metastases.Of this group,136(88.9%)were diagnosed by CT,10(6.5%)by CT and colonoscopy,and 7(4.6%)by colonoscopy;only 67.8%showed TMs elevation.Adherence to follow-up recommendations was 68.8%for the first colonoscopy,74%for the first CT scan,and 96.6%for the first blood test;these values declined over time.Younger age at diagnosis[odds ratio(OR)0.93;95%CI:0.91-0.95],CRC stages I-II(OR 0.38;95%CI:0.24-0.61),and adherence to follow-up recommendations(OR 0.30;95%CI:0.20-0.46)were independently associated with lower risk for all-cause death at 5 years.CONCLUSION CT scan had the highest diagnostic yield.Adherence to follow-up recommendations was low and decreased during follow-up.Younger age at diagnosis,stage,and follow-up adherence were associated with lower 5-year mortality.展开更多
Optoelectronic memristor is generating growing research interest for high efficient computing and sensing-memory applications.In this work,an optoelectronic memristor with Au/a-C:Te/Pt structure is developed.Synaptic ...Optoelectronic memristor is generating growing research interest for high efficient computing and sensing-memory applications.In this work,an optoelectronic memristor with Au/a-C:Te/Pt structure is developed.Synaptic functions,i.e.,excita-tory post-synaptic current and pair-pulse facilitation are successfully mimicked with the memristor under electrical and optical stimulations.More importantly,the device exhibited distinguishable response currents by adjusting 4-bit input electrical/opti-cal signals.A multi-mode reservoir computing(RC)system is constructed with the optoelectronic memristors to emulate human tactile-visual fusion recognition and an accuracy of 98.7%is achieved.The optoelectronic memristor provides potential for developing multi-mode RC system.展开更多
The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport ...The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport industry must innovate in key technologies to ensure high-quality transmissions for passengers and railway operations.These systems must function effectively under high mobility conditions while prioritizing safety,ecofriendliness,comfort,transparency,predictability,and reliability.On the other hand,the proposal of 6 G wireless technology introduces new possibilities for innovation in communication technologies,which may truly realize the current vision of HSR.Therefore,this article gives a review of the current advanced 6 G wireless communication technologies for HSR,including random access and switching,channel estimation and beamforming,integrated sensing and communication,and edge computing.The main application scenarios of these technologies are reviewed,as well as their current research status and challenges,followed by an outlook on future development directions.展开更多
As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the...As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)is a common tumor with a poor prognosis.Early intervention is essential;thus,good prognostic markers to identify patients who benefit from first transarterial chemoembolization(...BACKGROUND Hepatocellular carcinoma(HCC)is a common tumor with a poor prognosis.Early intervention is essential;thus,good prognostic markers to identify patients who benefit from first transarterial chemoembolization(TACE)are needed.AIM To investigate the efficacy of computed tomography(CT)radiomics in predicting the success of the first TACE in patients with advanced HCC and to develop an early prediction model based on clinical radiomics features.METHODS Data from 122 patients with advanced HCC treated with TACE were analyzed.Intratumoral and peritumoral areas on arterial and venous CT images were selected to extract radiomic features,which were screened in the training cohort using the minimum redundancy maximum correlation.Then,support vector machines were used to construct the model.To construct a receiver operating characteristic curve,the predictive efficacy of each model was evaluated on the basis of the area under the curve(AUC).RESULTS Among the 122 patients,72 patients were effectively treated via TACE,and in 50 patients,this treatment was ineffective.In the radiomics model,the areas under the curve of the venous phase model were 0.867(95%CI:0.790-0.940)in the training cohort and 0.755(0.600-0.910)in the validation cohort,indicating good predictive efficacy.The multivariate logistic regression results indicated that preoperative alpha-fetoprotein levels(P=0.01)were a risk factor for TACE.The screened clinical features were combined with the radiomic features to construct a combined model.This combined model had an AUC of 0.92(0.87-0.95)in the training cohort and 0.815(0.67-0.95)in the validation cohort.CONCLUSION CT radiomics has good value in predicting the efficacy of the first TACE treatment in patients with HCC.The combined model was a better tool for predicting the first TACE efficacy in patients with advanced HCC and could provide an efficient predictive tool to help with the selection of patients for TACE.展开更多
As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by...As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by combining privacy preserving training with efficient, on device computation. This paper introduces a cutting-edge FL-edge integration framework, achieving a 10% to 15% increase in model accuracy and reducing communication costs by 25% in heterogeneous environments. Blockchain based secure aggregation ensures robust and tamper-proof model updates, while exploratory quantum AI techniques enhance computational efficiency. By addressing key challenges such as device variability and non-IID data, this work sets the stage for the next generation of adaptive, privacy-first AI systems, with applications in IoT, healthcare, and autonomous systems.展开更多
The traditional von Neumann architecture faces inherent limitations due to the separation of memory and computa-tion,leading to high energy consumption,significant latency,and reduced operational efficiency.Neuromorph...The traditional von Neumann architecture faces inherent limitations due to the separation of memory and computa-tion,leading to high energy consumption,significant latency,and reduced operational efficiency.Neuromorphic computing,inspired by the architecture of the human brain,offers a promising alternative by integrating memory and computational func-tions,enabling parallel,high-speed,and energy-efficient information processing.Among various neuromorphic technologies,ion-modulated optoelectronic devices have garnered attention due to their excellent ionic tunability and the availability of multi-dimensional control strategies.This review provides a comprehensive overview of recent progress in ion-modulation optoelec-tronic neuromorphic devices.It elucidates the key mechanisms underlying ionic modulation of light fields,including ion migra-tion dynamics and capture and release of charge through ions.Furthermore,the synthesis of active materials and the proper-ties of these devices are analyzed in detail.The review also highlights the application of ion-modulation optoelectronic devices in artificial vision systems,neuromorphic computing,and other bionic fields.Finally,the existing challenges and future direc-tions for the development of optoelectronic neuromorphic devices are discussed,providing critical insights for advancing this promising field.展开更多
Temperature is a critical factor influencing the performance of coal catalytic hydrogasification in bubbling fluidized bed gasifiers.Numerical simulations at various temperatures(1023 K,1073 K,1123 K,and 1173 K)are co...Temperature is a critical factor influencing the performance of coal catalytic hydrogasification in bubbling fluidized bed gasifiers.Numerical simulations at various temperatures(1023 K,1073 K,1123 K,and 1173 K)are conducted to elucidate the mechanisms by which temperature affects bubble size,global reaction performance,and particle-scale reactivity.The simulation results indicate that bubble size increases at elevated temperatures,while H_(2)-char hydrogasification reactivity is enhanced.Particle trajectory analyses reveal that particles sized between 100 and 250μm undergo intense char hydrogasification in the dense phase,contributing to the formation of hot spots.To assess the impact of temperature on the particle-scale flow-transfer-reaction process,the dimensionless quantities of Reynolds,Nusselt,and Sherwood numbers,along with the solids dispersion coefficient,are calculated.It is found that higher temperatures inhibit bubble-induced mass and heat transfer.In general,3 MPa,1123 K,and 3-4 fluidization numbers are identified as the optimal conditions for particles ranging from 0 to350μm.These findings provide valuable insights into the inherent interactions between temperature and gas-particle reaction.展开更多
The traditional von Neumann architecture has demonstrated inefficiencies in parallel computing and adaptive learn-ing,rendering it incapable of meeting the growing demand for efficient and high-speed computing.Neuromo...The traditional von Neumann architecture has demonstrated inefficiencies in parallel computing and adaptive learn-ing,rendering it incapable of meeting the growing demand for efficient and high-speed computing.Neuromorphic comput-ing with significant advantages such as high parallelism and ultra-low power consumption is regarded as a promising pathway to overcome the limitations of conventional computers and achieve the next-generation artificial intelligence.Among various neuromorphic devices,the artificial synapses based on electrolyte-gated transistors stand out due to their low energy consump-tion,multimodal sensing/recording capabilities,and multifunctional integration.Moreover,the emerging optoelectronic neuro-morphic devices which combine the strengths of photonics and electronics have demonstrated substantial potential in the neu-romorphic computing field.Therefore,this article reviews recent advancements in electrolyte-gated optoelectronic neuromor-phic transistors.First,it provides an overview of artificial optoelectronic synapses and neurons,discussing aspects such as device structures,operating mechanisms,and neuromorphic functionalities.Next,the potential applications of optoelectronic synapses in different areas such as artificial visual system,pain system,and tactile perception systems are elaborated.Finally,the current challenges are summarized,and future directions for their developments are proposed.展开更多
The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language proc...The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language processing,image recognition,and real-time decisionmaking.However,these models demand immense computational power and are often centralized,relying on cloud-based architectures with inherent limitations in latency,privacy,and energy efficiency.To address these challenges and bring AI closer to real-world applications,such as wearable health monitoring,robotics,and immersive virtual environments,innovative hardware solutions are urgently needed.This work introduces a near-sensor edge computing(NSEC)system,built on a bilayer AlN/Si waveguide platform,to provide real-time,energy-efficient AI capabilities at the edge.Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction,coupled with Si-based thermo-optic Mach-Zehnder interferometers for neural network computations,the system represents a transformative approach to AI hardware design.Demonstrated through multimodal gesture and gait analysis,the NSEC system achieves high classification accuracies of 96.77%for gestures and 98.31%for gaits,ultra-low latency(<10 ns),and minimal energy consumption(<0.34 pJ).This groundbreaking system bridges the gap between AI models and real-world applications,enabling efficient,privacy-preserving AI solutions for healthcare,robotics,and next-generation human-machine interfaces,marking a pivotal advancement in edge computing and AI deployment.展开更多
To address the increasing demand for massive data storage and processing,brain-inspired neuromorphic comput-ing systems based on artificial synaptic devices have been actively developed in recent years.Among the vario...To address the increasing demand for massive data storage and processing,brain-inspired neuromorphic comput-ing systems based on artificial synaptic devices have been actively developed in recent years.Among the various materials inves-tigated for the fabrication of synaptic devices,silicon carbide(SiC)has emerged as a preferred choices due to its high electron mobility,superior thermal conductivity,and excellent thermal stability,which exhibits promising potential for neuromorphic applications in harsh environments.In this review,the recent progress in SiC-based synaptic devices is summarized.Firstly,an in-depth discussion is conducted regarding the categories,working mechanisms,and structural designs of these devices.Subse-quently,several application scenarios for SiC-based synaptic devices are presented.Finally,a few perspectives and directions for their future development are outlined.展开更多
This paper aims to numerically explore the characteristics of unsteady cavitating flow around a NACA0015 hydrofoil,with a focus on vorticity attributes.The simulation utilizes a homogeneous mixture model coupled with ...This paper aims to numerically explore the characteristics of unsteady cavitating flow around a NACA0015 hydrofoil,with a focus on vorticity attributes.The simulation utilizes a homogeneous mixture model coupled with a filter-based density correction turbulence model and a modified Zwart cavitation model.The study investigates the dynamic cavitation features of the thermal fluid around the hydrofoil at various incoming flow velocities.It systematically elucidates the evolution of cavitation and vortex dynamics corresponding to each velocity condition.The results indicate that with increasing incoming flow velocity,distinct cavitation processes take place in the flow field.展开更多
In cold regions,rock structures will be weakened by freeze-thaw cycles under various water immersion conditions.Determining how water immersion conditions impact rock deterioration under freeze-thaw cycles is critical...In cold regions,rock structures will be weakened by freeze-thaw cycles under various water immersion conditions.Determining how water immersion conditions impact rock deterioration under freeze-thaw cycles is critical to assess accurately the frost resistance of engineered rock.In this paper,freeze-thaw cycles(temperature range of-20℃-20℃)were performed on the sandstones in different water immersion conditions(fully,partially and non-immersed in water).Then,computed tomography(CT)tests were conducted on the sandstones when the freeze-thaw number reached 0,5,10,15,20 and 30.Next,the effects of water immersion conditions on the microstructure deterioration of sandstone under freezethaw cycles were evaluated using CT spatial imaging,porosity and damage factor.Finally,focusing on the partially immersed condition,the immersion volume rate was defined to understand the effects of immersion degree on the freeze-thaw damage of sandstone and to propose a damage model considering the freeze-thaw number and immersion degree.The results show that with increasing freeze-thaw number,the porosities and damage factors under fully and partially immersed conditions increase continuously,while those under non-immersed condition first increase and then remain approximately constant.The most severe freeze-thaw damage occurs in fully immersed condition,followed by partially immersed condition and finally non-immersed condition.Interestingly,the freeze-thaw number and the immersion volume rate both impact the microstructure deterioration of the partially immersed sandstone.For the same freeze-thaw number,the damage factor increases approximately linearly with increasing immersion volume rate,and the increasing immersion degree exacerbates the microstructure deterioration of sandstone.Moreover,the proposed model can effectively estimate the freeze-thaw damage of partially immersed sandstone with different immersion volume rates.展开更多
Recently,for developing neuromorphic visual systems,adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances a...Recently,for developing neuromorphic visual systems,adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances and flexible func-tionalities.In this review,based on a description of the biological adaptive functions that are favorable for dynamically perceiv-ing,filtering,and processing information in the varying environment,we summarize the representative strategies for achiev-ing these adaptabilities in optoelectronic transistors,including the adaptation for detecting information,adaptive synaptic weight change,and history-dependent plasticity.Moreover,the key points of the corresponding strategies are comprehen-sively discussed.And the applications of these adaptive optoelectronic transistors,including the adaptive color detection,sig-nal filtering,extending the response range of light intensity,and improve learning efficiency,are also illustrated separately.Lastly,the challenges faced in developing adaptive optoelectronic transistor for artificial vision system are discussed.The descrip-tion of biological adaptive functions and the corresponding inspired neuromorphic devices are expected to provide insights for the design and application of next-generation artificial visual systems.展开更多
Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power sta...Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid.First,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack problem.Then,solutions are derived by two different algorithms.Furthermore,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems.Finally,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy utilization.The simulation results show the efficiency and superiority of our proposed framework.展开更多
文摘Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.
文摘BACKGROUND Internal hernia(IH)is a rare culprit of small bowel obstruction(SBO)with an incidence of<1%.It poses a considerable diagnostic challenge requiring a high index of suspicion to prevent misdiagnosis,improper treatment,and subsequent morbidity and mortality.AIM To determine the clinico-demographic profile,radiological and operative findings,and postoperative course of patients with IH and its association with SBO.METHODS Medical records of 586 patients with features of SBO presenting at a tertiary care centre at Lucknow,India between September 2010 and August 2023 were reviewed.RESULTS Out of 586 patients,7(1.2%)were diagnosed with IH.Among these,4 had congenital IH and 3 had acquired IH.The male-to-female ratio was 4:3.The median age at presentation was 32 years.Contrast-enhanced computed tomography(CECT)was the most reliable investigation for preoperative identification,demonstrating mesenteric whirling and clumped-up bowel loops.Left paraduodenal hernia and transmesenteric hernia occurred with an equal frequency(approximately 43%each).Intraoperatively,one patient was found to have bowel ischemia and one had associated malrotation of gut.During follow-up,no recurrences were reported.CONCLUSION IH,being a rare cause,must be considered as a differential diagnosis for SBO,especially in young patients in their 30s or with unexplained abdominal pain or discomfort post-surgery.A rapid imaging evaluation,preferably with CECT,is necessary to aid in an early diagnosis and prompt intervention,thereby reducing financial burden related to unnecessary investigations and preventing the morbidity and mortality associated with closed-loop obstruction and strangulation of the bowel.
文摘Photon-counting computed tomography(PCCT)represents a significant advancement in pediatric cardiovascular imaging.Traditional CT systems employ energy-integrating detectors that convert X-ray photons into visible light,whereas PCCT utilizes photon-counting detectors that directly transform X-ray photons into electric signals.This direct conversion allows photon-counting detectors to sort photons into discrete energy levels,thereby enhancing image quality through superior noise reduction,improved spatial and contrast resolution,and reduced artifacts.In pediatric applications,PCCT offers substantial benefits,including lower radiation doses,which may help reduce the risk of malignancy in pediatric patients,with perhaps greater potential to benefit those with repeated exposure from a young age.Enhanced spatial resolution facilitates better visualization of small structures,vital for diagnosing congenital heart defects.Additionally,PCCT’s spectral capabilities improve tissue characterization and enable the creation of virtual monoenergetic images,which enhance soft-tissue contrast and potentially reduce contrast media doses.Initial clinical results indicate that PCCT provides superior image quality and diagnostic accuracy compared to conven-tional CT,particularly in challenging pediatric cardiovascular cases.As PCCT technology matures,further research and standardized protocols will be essential to fully integrate it into pediatric imaging practices,ensuring optimized diagnostic outcomes and patient safety.
基金National Natural Science Foundation of China(62171305,62405206,62004135,62001317,62111530301)Natural Science Foundation of Jiangsu Province(BK20240778,BK20241917)+3 种基金State Key Laboratory of Advanced Optical Communication Systems and Networks,China(2023GZKF08)China Postdoctoral Science Foundation(2024M752314)Postdoctoral Fellowship Program of CPSF(GZC20231883)Innovative and Entrepreneurial Talent Program of Jiangsu Province(JSSCRC2021527).
文摘Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While such a computing paradigm can only employ a single photonic device as the nonlinear node for data processing,the performance highly relies on the fading memory provided by the delay feedback loop(FL),which sets a restriction on the extensibility of physical implementation,especially for highly integrated chips.Here,we present a simplified photonic scheme for more flexible parameter configurations leveraging the designed quasi-convolution coding(QC),which completely gets rid of the dependence on FL.Unlike delay-based TDRC,encoded data in QC-based RC(QRC)enables temporal feature extraction,facilitating augmented memory capabilities.Thus,our proposed QRC is enabled to deal with time-related tasks or sequential data without the implementation of FL.Furthermore,we can implement this hardware with a low-power,easily integrable vertical-cavity surface-emitting laser for high-performance parallel processing.We illustrate the concept validation through simulation and experimental comparison of QRC and TDRC,wherein the simpler-structured QRC outperforms across various benchmark tasks.Our results may underscore an auspicious solution for the hardware implementation of deep neural networks.
基金Supported by Instituto de Investigación Sanitaria ISABIAL,No.P42022-0275.
文摘AIM To analyze the diagnostic performance of surveillance colonoscopy,computed tomography(CT),and tumor markers(TMs)in detecting CRC recurrence or metastasis during follow-up after CRC resection.Secondary objectives included degree of adherence to clinical practice guidelines surveillance recommendations and factors associated with adherence and all-cause and CRC mortality.METHODS The single-center retrospective cohort study including patients undergoing curative resection of stage I-III CRC during 2010-2015.Follow-up was performed using TMs every 6 months,yearly CT for 5 years,and colonoscopy at years 1 and 4.Demographic,primary tumor data,and results at follow-up were collected.RESULTS Of 574 included patients included,153 had recurrences or metastases.Of this group,136(88.9%)were diagnosed by CT,10(6.5%)by CT and colonoscopy,and 7(4.6%)by colonoscopy;only 67.8%showed TMs elevation.Adherence to follow-up recommendations was 68.8%for the first colonoscopy,74%for the first CT scan,and 96.6%for the first blood test;these values declined over time.Younger age at diagnosis[odds ratio(OR)0.93;95%CI:0.91-0.95],CRC stages I-II(OR 0.38;95%CI:0.24-0.61),and adherence to follow-up recommendations(OR 0.30;95%CI:0.20-0.46)were independently associated with lower risk for all-cause death at 5 years.CONCLUSION CT scan had the highest diagnostic yield.Adherence to follow-up recommendations was low and decreased during follow-up.Younger age at diagnosis,stage,and follow-up adherence were associated with lower 5-year mortality.
基金supported by the"Science and Technology Development Plan Project of Jilin Province,China"(Grant No.20240101018JJ)the Fundamental Research Funds for the Central Universities(Grant No.2412023YQ004)the National Natural Science Foundation of China(Grant Nos.52072065,52272140,52372137,and U23A20568).
文摘Optoelectronic memristor is generating growing research interest for high efficient computing and sensing-memory applications.In this work,an optoelectronic memristor with Au/a-C:Te/Pt structure is developed.Synaptic functions,i.e.,excita-tory post-synaptic current and pair-pulse facilitation are successfully mimicked with the memristor under electrical and optical stimulations.More importantly,the device exhibited distinguishable response currents by adjusting 4-bit input electrical/opti-cal signals.A multi-mode reservoir computing(RC)system is constructed with the optoelectronic memristors to emulate human tactile-visual fusion recognition and an accuracy of 98.7%is achieved.The optoelectronic memristor provides potential for developing multi-mode RC system.
基金National Natural Science Foundation of China(U2468201,62122012,62221001).
文摘The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport industry must innovate in key technologies to ensure high-quality transmissions for passengers and railway operations.These systems must function effectively under high mobility conditions while prioritizing safety,ecofriendliness,comfort,transparency,predictability,and reliability.On the other hand,the proposal of 6 G wireless technology introduces new possibilities for innovation in communication technologies,which may truly realize the current vision of HSR.Therefore,this article gives a review of the current advanced 6 G wireless communication technologies for HSR,including random access and switching,channel estimation and beamforming,integrated sensing and communication,and edge computing.The main application scenarios of these technologies are reviewed,as well as their current research status and challenges,followed by an outlook on future development directions.
基金funded by the Fundamental Research Funds for the Central Universities(J2023-024,J2023-027).
文摘As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.
文摘BACKGROUND Hepatocellular carcinoma(HCC)is a common tumor with a poor prognosis.Early intervention is essential;thus,good prognostic markers to identify patients who benefit from first transarterial chemoembolization(TACE)are needed.AIM To investigate the efficacy of computed tomography(CT)radiomics in predicting the success of the first TACE in patients with advanced HCC and to develop an early prediction model based on clinical radiomics features.METHODS Data from 122 patients with advanced HCC treated with TACE were analyzed.Intratumoral and peritumoral areas on arterial and venous CT images were selected to extract radiomic features,which were screened in the training cohort using the minimum redundancy maximum correlation.Then,support vector machines were used to construct the model.To construct a receiver operating characteristic curve,the predictive efficacy of each model was evaluated on the basis of the area under the curve(AUC).RESULTS Among the 122 patients,72 patients were effectively treated via TACE,and in 50 patients,this treatment was ineffective.In the radiomics model,the areas under the curve of the venous phase model were 0.867(95%CI:0.790-0.940)in the training cohort and 0.755(0.600-0.910)in the validation cohort,indicating good predictive efficacy.The multivariate logistic regression results indicated that preoperative alpha-fetoprotein levels(P=0.01)were a risk factor for TACE.The screened clinical features were combined with the radiomic features to construct a combined model.This combined model had an AUC of 0.92(0.87-0.95)in the training cohort and 0.815(0.67-0.95)in the validation cohort.CONCLUSION CT radiomics has good value in predicting the efficacy of the first TACE treatment in patients with HCC.The combined model was a better tool for predicting the first TACE efficacy in patients with advanced HCC and could provide an efficient predictive tool to help with the selection of patients for TACE.
文摘As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by combining privacy preserving training with efficient, on device computation. This paper introduces a cutting-edge FL-edge integration framework, achieving a 10% to 15% increase in model accuracy and reducing communication costs by 25% in heterogeneous environments. Blockchain based secure aggregation ensures robust and tamper-proof model updates, while exploratory quantum AI techniques enhance computational efficiency. By addressing key challenges such as device variability and non-IID data, this work sets the stage for the next generation of adaptive, privacy-first AI systems, with applications in IoT, healthcare, and autonomous systems.
基金supported by National Natural Science Foundation of China(62174164,U23A20568,and U22A2075)National Key Research and Development Project(2021YFA1202600)+2 种基金Talent Plan of Shanghai Branch,Chinese Academy of Sciences(CASSHB-QNPD-2023-022)Ningbo Technology Project(2022A-007-C)Ningbo Key Research and Development Project(2023Z021).
文摘The traditional von Neumann architecture faces inherent limitations due to the separation of memory and computa-tion,leading to high energy consumption,significant latency,and reduced operational efficiency.Neuromorphic computing,inspired by the architecture of the human brain,offers a promising alternative by integrating memory and computational func-tions,enabling parallel,high-speed,and energy-efficient information processing.Among various neuromorphic technologies,ion-modulated optoelectronic devices have garnered attention due to their excellent ionic tunability and the availability of multi-dimensional control strategies.This review provides a comprehensive overview of recent progress in ion-modulation optoelec-tronic neuromorphic devices.It elucidates the key mechanisms underlying ionic modulation of light fields,including ion migra-tion dynamics and capture and release of charge through ions.Furthermore,the synthesis of active materials and the proper-ties of these devices are analyzed in detail.The review also highlights the application of ion-modulation optoelectronic devices in artificial vision systems,neuromorphic computing,and other bionic fields.Finally,the existing challenges and future direc-tions for the development of optoelectronic neuromorphic devices are discussed,providing critical insights for advancing this promising field.
基金supported by the National Natural Science Foundation of China(22308170).
文摘Temperature is a critical factor influencing the performance of coal catalytic hydrogasification in bubbling fluidized bed gasifiers.Numerical simulations at various temperatures(1023 K,1073 K,1123 K,and 1173 K)are conducted to elucidate the mechanisms by which temperature affects bubble size,global reaction performance,and particle-scale reactivity.The simulation results indicate that bubble size increases at elevated temperatures,while H_(2)-char hydrogasification reactivity is enhanced.Particle trajectory analyses reveal that particles sized between 100 and 250μm undergo intense char hydrogasification in the dense phase,contributing to the formation of hot spots.To assess the impact of temperature on the particle-scale flow-transfer-reaction process,the dimensionless quantities of Reynolds,Nusselt,and Sherwood numbers,along with the solids dispersion coefficient,are calculated.It is found that higher temperatures inhibit bubble-induced mass and heat transfer.In general,3 MPa,1123 K,and 3-4 fluidization numbers are identified as the optimal conditions for particles ranging from 0 to350μm.These findings provide valuable insights into the inherent interactions between temperature and gas-particle reaction.
基金supported by the Hunan Science Fund for Distinguished Young Scholars(2023JJ10069)the National Natural Science Foundation of China(52172169)the Project of State Key Laboratory of Precision Manufacturing for Extreme Service Performance,Central South University(ZZYJKT2024-02).
文摘The traditional von Neumann architecture has demonstrated inefficiencies in parallel computing and adaptive learn-ing,rendering it incapable of meeting the growing demand for efficient and high-speed computing.Neuromorphic comput-ing with significant advantages such as high parallelism and ultra-low power consumption is regarded as a promising pathway to overcome the limitations of conventional computers and achieve the next-generation artificial intelligence.Among various neuromorphic devices,the artificial synapses based on electrolyte-gated transistors stand out due to their low energy consump-tion,multimodal sensing/recording capabilities,and multifunctional integration.Moreover,the emerging optoelectronic neuro-morphic devices which combine the strengths of photonics and electronics have demonstrated substantial potential in the neu-romorphic computing field.Therefore,this article reviews recent advancements in electrolyte-gated optoelectronic neuromor-phic transistors.First,it provides an overview of artificial optoelectronic synapses and neurons,discussing aspects such as device structures,operating mechanisms,and neuromorphic functionalities.Next,the potential applications of optoelectronic synapses in different areas such as artificial visual system,pain system,and tactile perception systems are elaborated.Finally,the current challenges are summarized,and future directions for their developments are proposed.
基金the National Research Foundation(NRF)Singapore mid-sized center grant(NRF-MSG-2023-0002)FrontierCRP grant(NRF-F-CRP-2024-0006)+2 种基金A*STAR Singapore MTC RIE2025 project(M24W1NS005)IAF-PP project(M23M5a0069)Ministry of Education(MOE)Singapore Tier 2 project(MOE-T2EP50220-0014).
文摘The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language processing,image recognition,and real-time decisionmaking.However,these models demand immense computational power and are often centralized,relying on cloud-based architectures with inherent limitations in latency,privacy,and energy efficiency.To address these challenges and bring AI closer to real-world applications,such as wearable health monitoring,robotics,and immersive virtual environments,innovative hardware solutions are urgently needed.This work introduces a near-sensor edge computing(NSEC)system,built on a bilayer AlN/Si waveguide platform,to provide real-time,energy-efficient AI capabilities at the edge.Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction,coupled with Si-based thermo-optic Mach-Zehnder interferometers for neural network computations,the system represents a transformative approach to AI hardware design.Demonstrated through multimodal gesture and gait analysis,the NSEC system achieves high classification accuracies of 96.77%for gestures and 98.31%for gaits,ultra-low latency(<10 ns),and minimal energy consumption(<0.34 pJ).This groundbreaking system bridges the gap between AI models and real-world applications,enabling efficient,privacy-preserving AI solutions for healthcare,robotics,and next-generation human-machine interfaces,marking a pivotal advancement in edge computing and AI deployment.
基金supported by the Natural Science Foundation of Zhejiang Province(Grant No.LQ24F040007)the National Natural Science Foundation of China(Grant No.U22A2075)the Opening Project of State Key Laboratory of Polymer Materials Engineering(Sichuan University)(Grant No.sklpme2024-1-21).
文摘To address the increasing demand for massive data storage and processing,brain-inspired neuromorphic comput-ing systems based on artificial synaptic devices have been actively developed in recent years.Among the various materials inves-tigated for the fabrication of synaptic devices,silicon carbide(SiC)has emerged as a preferred choices due to its high electron mobility,superior thermal conductivity,and excellent thermal stability,which exhibits promising potential for neuromorphic applications in harsh environments.In this review,the recent progress in SiC-based synaptic devices is summarized.Firstly,an in-depth discussion is conducted regarding the categories,working mechanisms,and structural designs of these devices.Subse-quently,several application scenarios for SiC-based synaptic devices are presented.Finally,a few perspectives and directions for their future development are outlined.
文摘This paper aims to numerically explore the characteristics of unsteady cavitating flow around a NACA0015 hydrofoil,with a focus on vorticity attributes.The simulation utilizes a homogeneous mixture model coupled with a filter-based density correction turbulence model and a modified Zwart cavitation model.The study investigates the dynamic cavitation features of the thermal fluid around the hydrofoil at various incoming flow velocities.It systematically elucidates the evolution of cavitation and vortex dynamics corresponding to each velocity condition.The results indicate that with increasing incoming flow velocity,distinct cavitation processes take place in the flow field.
基金funding support from the National Natural Science Foundation of China(Grant No.12172019).
文摘In cold regions,rock structures will be weakened by freeze-thaw cycles under various water immersion conditions.Determining how water immersion conditions impact rock deterioration under freeze-thaw cycles is critical to assess accurately the frost resistance of engineered rock.In this paper,freeze-thaw cycles(temperature range of-20℃-20℃)were performed on the sandstones in different water immersion conditions(fully,partially and non-immersed in water).Then,computed tomography(CT)tests were conducted on the sandstones when the freeze-thaw number reached 0,5,10,15,20 and 30.Next,the effects of water immersion conditions on the microstructure deterioration of sandstone under freezethaw cycles were evaluated using CT spatial imaging,porosity and damage factor.Finally,focusing on the partially immersed condition,the immersion volume rate was defined to understand the effects of immersion degree on the freeze-thaw damage of sandstone and to propose a damage model considering the freeze-thaw number and immersion degree.The results show that with increasing freeze-thaw number,the porosities and damage factors under fully and partially immersed conditions increase continuously,while those under non-immersed condition first increase and then remain approximately constant.The most severe freeze-thaw damage occurs in fully immersed condition,followed by partially immersed condition and finally non-immersed condition.Interestingly,the freeze-thaw number and the immersion volume rate both impact the microstructure deterioration of the partially immersed sandstone.For the same freeze-thaw number,the damage factor increases approximately linearly with increasing immersion volume rate,and the increasing immersion degree exacerbates the microstructure deterioration of sandstone.Moreover,the proposed model can effectively estimate the freeze-thaw damage of partially immersed sandstone with different immersion volume rates.
基金the National Key Research and Development Program of China(2021YFA0717900)National Natural Science Foundation of China(62471251,62405144,62288102,22275098,and 62174089)+1 种基金Basic Research Program of Jiangsu(BK20240033,BK20243057)Jiangsu Funding Program for Excellent Postdoctoral Talent(2022ZB402).
文摘Recently,for developing neuromorphic visual systems,adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances and flexible func-tionalities.In this review,based on a description of the biological adaptive functions that are favorable for dynamically perceiv-ing,filtering,and processing information in the varying environment,we summarize the representative strategies for achiev-ing these adaptabilities in optoelectronic transistors,including the adaptation for detecting information,adaptive synaptic weight change,and history-dependent plasticity.Moreover,the key points of the corresponding strategies are comprehen-sively discussed.And the applications of these adaptive optoelectronic transistors,including the adaptive color detection,sig-nal filtering,extending the response range of light intensity,and improve learning efficiency,are also illustrated separately.Lastly,the challenges faced in developing adaptive optoelectronic transistor for artificial vision system are discussed.The descrip-tion of biological adaptive functions and the corresponding inspired neuromorphic devices are expected to provide insights for the design and application of next-generation artificial visual systems.
基金supported in part by the National Natural Science Foundation of China under Grant No.61473066in part by the Natural Science Foundation of Hebei Province under Grant No.F2021501020+2 种基金in part by the S&T Program of Qinhuangdao under Grant No.202401A195in part by the Science Research Project of Hebei Education Department under Grant No.QN2025008in part by the Innovation Capability Improvement Plan Project of Hebei Province under Grant No.22567637H
文摘Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid.First,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack problem.Then,solutions are derived by two different algorithms.Furthermore,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems.Finally,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy utilization.The simulation results show the efficiency and superiority of our proposed framework.
