Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on p...Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms.To investigate advances in the use of artificial intelligence in the diagnosis,rehabilitation,and scientific examination of peripheral nerve injury,we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994–2023.We identified the following research hotspots in peripheral nerve injury and repair:(1)diagnosis,classification,and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques,such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy;(2)motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms,such as wearable devices and assisted wheelchair systems;(3)improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning,such as implantable peripheral nerve interfaces;(4)the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility,enabling them to control devices such as networked hand prostheses;(5)artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation,thereby reducing surgical risk and complications,and facilitating postoperative recovery.Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair,there are some limitations to this technology,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(e.g.,privacy,data security,research transparency).Future research should address the issue of data collection,as large-scale,high-quality clinical datasets are required to establish effective artificial intelligence models.Multimodal data processing is also necessary,along with interdisciplinary collaboration,medical-industrial integration,and multicenter,large-sample clinical studies.展开更多
The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and ...The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms.These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence.Nevertheless,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images.This research presents an advanced investigation of XAI models to classify cancer images.It describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare applications.In addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging techniques.The proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive explanation.The proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%DOR.It will discuss the potential applications of the proposed XAI models in the smart healthcare environment.It will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.展开更多
Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological concrete.In terms of performance,fly ash and slag are preferredmaterials for pre...Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological concrete.In terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part geopolymer.However,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported.Therefore,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingMATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part geopolymer.The database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing temperature.The performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(MAE),and rootmean square error(RMSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed GEP.Sensitivity and parametric analysis were also performed to identify the significant contributor to strength.According to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 days.The water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.展开更多
Artificial intelligence(AI)is making significant strides in revolutionizing the detection of Barrett's esophagus(BE),a precursor to esophageal adenocarcinoma.In the research article by Tsai et al,researchers utili...Artificial intelligence(AI)is making significant strides in revolutionizing the detection of Barrett's esophagus(BE),a precursor to esophageal adenocarcinoma.In the research article by Tsai et al,researchers utilized endoscopic images to train an AI model,challenging the traditional distinction between endoscopic and histological BE.This approach yielded remarkable results,with the AI system achieving an accuracy of 94.37%,sensitivity of 94.29%,and specificity of 94.44%.The study's extensive dataset enhances the AI model's practicality,offering valuable support to endoscopists by minimizing unnecessary biopsies.However,questions about the applicability to different endoscopic systems remain.The study underscores the potential of AI in BE detection while highlighting the need for further research to assess its adaptability to diverse clinical settings.展开更多
Owing to the rapid development of modern computer technologies,artificial intelligence(AI)has emerged as an essential instrument for intelligent analysis across a range of fields.AI has been proven to be highly effect...Owing to the rapid development of modern computer technologies,artificial intelligence(AI)has emerged as an essential instrument for intelligent analysis across a range of fields.AI has been proven to be highly effective in ophthalmology,where it is frequently used for identifying,diagnosing,and typing retinal diseases.An increasing number of researchers have begun to comprehensively map patients’retinal diseases using AI,which has made individualized clinical prediction and treatment possible.These include prognostic improvement,risk prediction,progression assessment,and interventional therapies for retinal diseases.Researchers have used a range of input data methods to increase the accuracy and dependability of the results,including the use of tabular,textual,or image-based input data.They also combined the analyses of multiple types of input data.To give ophthalmologists access to precise,individualized,and high-quality treatment strategies that will further optimize treatment outcomes,this review summarizes the latest findings in AI research related to the prediction and guidance of clinical diagnosis and treatment of retinal diseases.展开更多
Since ChatGPT emerged on November 30, 2022, Artificial Intelligence (AI) has been increasingly discussed as a radical force that will change our world. People have become used to AI in which such ubiquitous technologi...Since ChatGPT emerged on November 30, 2022, Artificial Intelligence (AI) has been increasingly discussed as a radical force that will change our world. People have become used to AI in which such ubiquitous technologies as Siri, Google, and Netflix deploy AI algorithms to answer questions, impart information, and provide recommendations. However, many individuals including originators and backers of AI have recently expressed grave concerns. In this paper, the authors will assess what is occurring with AI in Visual Arts Education, outline positives and negatives, and provide recommendations addressed specifically for teachers working in the field regarding emerging AI usage from kindergarten to grade twelve levels as well as in higher education.展开更多
BACKGROUND Artificial intelligence(AI)has potential in the optical diagnosis of colorectal polyps.AIM To evaluate the feasibility of the real-time use of the computer-aided diagnosis system(CADx)AI for ColoRectal Poly...BACKGROUND Artificial intelligence(AI)has potential in the optical diagnosis of colorectal polyps.AIM To evaluate the feasibility of the real-time use of the computer-aided diagnosis system(CADx)AI for ColoRectal Polyps(AI4CRP)for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYE^(TM)(Fujifilm,Tokyo,Japan).CADx influence on the optical diagnosis of an expert endoscopist was also investigated.METHODS AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm.Both CADxsystems exploit convolutional neural networks.Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard.AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value(range 0.0-1.0).A predefined cut-off value of 0.6 was set with values<0.6 indicating benign and values≥0.6 indicating premalignant colorectal polyps.Low confidence characterizations were defined as values 40%around the cut-off value of 0.6(<0.36 and>0.76).Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.RESULTS AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps.Self-critical AI4CRP,excluding 14 low confidence characterizations[27.5%(14/51)],had a diagnostic accuracy of 89.2%,sensitivity of 89.7%,and specificity of 87.5%,which was higher compared to AI4CRP.CAD EYE had a 83.7%diagnostic accuracy,74.2%sensitivity,and 100.0%specificity.Diagnostic performances of the endoscopist alone(before AI)increased nonsignificantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE(AI-assisted endoscopist).Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems,except for specificity for which CAD EYE performed best.CONCLUSION Real-time use of AI4CRP was feasible.Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.展开更多
Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of...Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.展开更多
Spinal cord injury is a serious disease of the central nervous system involving irreversible nerve injury and various organ system injuries.At present,no effective clinical treatment exists.As one of the artificial hi...Spinal cord injury is a serious disease of the central nervous system involving irreversible nerve injury and various organ system injuries.At present,no effective clinical treatment exists.As one of the artificial hibernation techniques,mild hypothermia has preliminarily confirmed its clinical effect on spinal cord injury.However,its technical defects and barriers,along with serious clinical side effects,restrict its clinical application for spinal cord injury.Artificial hibernation is a futureoriented disruptive technology for human life support.It involves endogenous hibernation inducers and hibernation-related central neuromodulation that activate particular neurons,reduce the central constant temperature setting point,disrupt the normal constant body temperature,make the body adapt"to the external cold environment,and reduce the physiological resistance to cold stimulation.Thus,studying the artificial hibernation mechanism may help develop new treatment strategies more suitable for clinical use than the cooling method of mild hypothermia technology.This review introduces artificial hibernation technologies,including mild hypothermia technology,hibernation inducers,and hibernation-related central neuromodulation technology.It summarizes the relevant research on hypothermia and hibernation for organ and nerve protection.These studies show that artificial hibernation technologies have therapeutic significance on nerve injury after spinal co rd injury through inflammatory inhibition,immunosuppression,oxidative defense,and possible central protection.It also promotes the repair and protection of res pirato ry and digestive,cardiovascular,locomoto r,urinary,and endocrine systems.This review provides new insights for the clinical treatment of nerve and multiple organ protection after spinal cord injury thanks to artificial hibernation.At present,artificial hibernation technology is not mature,and research fa ces various challenges.Neve rtheless,the effort is wo rthwhile for the future development of medicine.展开更多
An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and ...An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.展开更多
Controlling intracranial pressure,nerve cell regeneration,and microenvironment regulation are the key issues in reducing mortality and disability in acute brain injury.There is currently a lack of effective treatment ...Controlling intracranial pressure,nerve cell regeneration,and microenvironment regulation are the key issues in reducing mortality and disability in acute brain injury.There is currently a lack of effective treatment methods.Hibernation has the characteristics of low temperature,low metabolism,and hibernation rhythm,as well as protective effects on the nervous,cardiovascular,and motor systems.Artificial hibernation technology is a new technology that can effectively treat acute brain injury by altering the body’s metabolism,lowering the body’s core temperature,and allowing the body to enter a state similar to hibernation.This review introduces artificial hibernation technology,including mild hypothermia treatment technology,central nervous system regulation technology,and artificial hibernation-inducer technology.Upon summarizing the relevant research on artificial hibernation technology in acute brain injury,the research results show that artificial hibernation technology has neuroprotective,anti-inflammatory,and oxidative stress-resistance effects,indicating that it has therapeutic significance in acute brain injury.Furthermore,artificial hibernation technology can alleviate the damage of ischemic stroke,traumatic brain injury,cerebral hemorrhage,cerebral infarction,and other diseases,providing new strategies for treating acute brain injury.However,artificial hibernation technology is currently in its infancy and has some complications,such as electrolyte imbalance and coagulation disorders,which limit its use.Further research is needed for its clinical application.展开更多
Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of ...Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of physics in climate science has occasionally been overlooked.Our perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics,rather than an“either/or”scenario.Scrutinizing controversies around current physical inconsistencies in large AI models,we stress the critical need for detailed dynamic diagnostics and physical constraints.Furthermore,we provide illustrative examples to guide future assessments and constraints for AI models.Regarding AI integration with numerical models,we argue that offline AI parameterization schemes may fall short of achieving global optimality,emphasizing the importance of constructing online schemes.Additionally,we highlight the significance of fostering a community culture and propose the OCR(Open,Comparable,Reproducible)principles.Through a better community culture and a deep integration of physics and AI,we contend that developing a learnable climate model,balancing AI and physics,is an achievable goal.展开更多
Under the background of“artificial intelligence+X”,the development of landscape architecture industry ushers in new opportunities,and professional talents need to be updated to meet the social demand.This paper anal...Under the background of“artificial intelligence+X”,the development of landscape architecture industry ushers in new opportunities,and professional talents need to be updated to meet the social demand.This paper analyzes the cultivation demand of landscape architecture graduate students in the context of the new era,and identifies the problems by comparing the original professional graduate training mode.The new cultivation mode of graduate students in landscape architecture is proposed,including updating the target orientation of the discipline,optimizing the teaching system,building a“dualteacher”tutor team,and improving the“industry-university-research-utilization”integrated cultivation,so as to cultivate high-quality compound talents with disciplinary characteristics.展开更多
Current practice of underground artificial ground freezing(AGF)typically involves huge refrigeration systems of large economic and environmental costs.In this study,a novel AGF technique is proposed deploying availabl...Current practice of underground artificial ground freezing(AGF)typically involves huge refrigeration systems of large economic and environmental costs.In this study,a novel AGF technique is proposed deploying available cold wind in cold regions.This is achieved by a static heat transfer device called thermosyphon equipped with an air insulation layer.A refrigeration unit can be optionally integrated to meet additional cooling requirements.The introduction of air insulation isolates the thermosyphon from ground zones where freezing is not needed,resulting in:(1)steering the cooling resources(cold wind or refrigeration)towards zones of interest;and(2)minimizing refrigeration load.This design is demonstrated using well-validated mathematical models from our previous work based on two-phase enthalpy method of the ground coupled with a thermal resistance network for the thermosyphon.Two Canadian mines are considered:the Cigar Lake Mine and the Giant Mine.The results show that our proposed design can speed the freezing time by 30%at the Giant Mine and by two months at the Cigar Lake Mine.Further,a cooling load of 2.4 GWh can be saved at the Cigar Lake Mine.Overall,this study provides mining practitioners with sustainable solutions of underground AGF.展开更多
Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have rev...Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.展开更多
Plankton are an important component of marine protected areas(MPAs),and its communities would require much smaller interpatch distances to ensure connection among MPAs.According to the survey from MPAs dominated by ar...Plankton are an important component of marine protected areas(MPAs),and its communities would require much smaller interpatch distances to ensure connection among MPAs.According to the survey from MPAs dominated by artificial reefs and adjacent waters(estuary area(EA),aquaculture area(AA),artificial reef area(ARA),natural area(NA)and comprehensive effect area(CEA))in Haizhou Bay in spring and autumn,we analyzed phyto-zooplankton composition,abundance and biomass,and correlation with hydrologic variables to gain information about the forces that structure the plankton.The results showed that the dominant zooplankton were copepods(spring,98.9%;autumn,94.2%),while the phytoplankton were mainly composed of Bacillariophyta(spring,61.8%;autumn,95.6%).The RDA results showed that temperature,salinity and depth highly associated with the distribution and composition of plankton species among the habitats than other factors in spring;temperature,Chla and DO had the strongest influence in autumn.The zooplankton in the ARA and AA ecosystems basically contained the same species as those in other habitats,and each habitat also exhibited a relatively unique combination of plankton species.The structures of the EA zooplankton in spring and the EA phytoplankton in both seasons were much different than other habitats,which may have been caused by factors such as currents and tides.We concluded that there exists similarity of the plankton community between artificial reef area and adjacent waters,whereas the EAs may be relatively independent systems.Therefore,these interaction between plankton community should be considered when designing MPA networks,and ocean circulations should be considered more than the environmental factors.展开更多
Purpose This scoping review aimed to offer researchers and practitioners an understanding of artificial intelligence(AI)applications in physical activity(PA)interventions;introduce them to prevalent machine learning(M...Purpose This scoping review aimed to offer researchers and practitioners an understanding of artificial intelligence(AI)applications in physical activity(PA)interventions;introduce them to prevalent machine learning(ML),deep learning(DL),and reinforcement learning(RL)algorithms;and encourage the adoption of AI methodologies.Methods A scoping review was performed in PubMed,Web of Science,Cochrane Library,and EBSCO focusing on AI applications for promoting PA or predicting related behavioral or health outcomes.AI methodologies were summarized and categorized to identify synergies,patterns,and trends informing future research.Additionally,a concise primer on predominant AI methodologies within the realm of PA was provided to bolster understanding and broader application.Results The review included 24 studies that met the predetermined eligibility criteria.AI models were found effective in detecting significant patterns of PA behavior and associations between specific factors and intervention outcomes.Most studies comparing AI models to traditional statistical approaches reported higher prediction accuracy for AI models on test data.Comparisons of different AI models yielded mixed results,likely due to model performance being highly dependent on the dataset and task.An increasing trend of adopting state-of-the-art DL and RL models over standard ML was observed,addressing complex human–machine communication,behavior modification,and decision-making tasks.Six key areas for future AI adoption in PA interventions emerged:personalized PA interventions,real-time monitoring and adaptation,integration of multimodal data sources,evaluation of intervention effectiveness,expanding access to PA interventions,and predicting and preventing injuries.Conclusion The scoping review highlights the potential of AI methodologies for advancing PA interventions.As the field progresses,staying informed and exploring emerging AI-driven strategies is essential for achieving significant improvements in PA interventions and fostering overall well-being.展开更多
Spiking neural network(SNN),widely known as the third-generation neural network,has been frequently investigated due to its excellent spatiotemporal information processing capability,high biological plausibility,and l...Spiking neural network(SNN),widely known as the third-generation neural network,has been frequently investigated due to its excellent spatiotemporal information processing capability,high biological plausibility,and low energy consumption characteristics.Analogous to the working mechanism of human brain,the SNN system transmits information through the spiking action of neurons.Therefore,artificial neurons are critical building blocks for constructing SNN in hardware.Memristors are drawing growing attention due to low consumption,high speed,and nonlinearity characteristics,which are recently introduced to mimic the functions of biological neurons.Researchers have proposed multifarious memristive materials including organic materials,inorganic materials,or even two-dimensional materials.Taking advantage of the unique electrical behavior of these materials,several neuron models are successfully implemented,such as Hodgkin–Huxley model,leaky integrate-and-fire model and integrate-and-fire model.In this review,the recent reports of artificial neurons based on memristive devices are discussed.In addition,we highlight the models and applications through combining artificial neuronal devices with sensors or other electronic devices.Finally,the future challenges and outlooks of memristor-based artificial neurons are discussed,and the development of hardware implementation of brain-like intelligence system based on SNN is also prospected.展开更多
Vegetation restoration and reconstruction are effective approaches to desertification control and achieving social and economic sustainability in desert areas.However,the self-succession ability of native plants durin...Vegetation restoration and reconstruction are effective approaches to desertification control and achieving social and economic sustainability in desert areas.However,the self-succession ability of native plants during the later periods of vegetation restoration remains unclear.Therefore,this study was conducted to bridge the knowledge gap by investigating the regeneration dynamics of artificial forest under natural conditions.The information of seed rain and soil seed bank was collected and quantified from an artificial Caragana korshinskii Kom.forest in the Tengger Desert,China.The germination tests were conducted in a laboratory setting.The analysis of species quantity and diversity in seed rain and soil seed bank was conducted to assess the impact of different durations of sand fixation(60,40,and 20 a)on the progress of vegetation restoration and ecological conditions in artificial C.korshinskii forest.The results showed that the top three dominant plant species in seed rain were Echinops gmelinii Turcz.,Eragrostis minor Host.,and Agropyron mongolicum Keng.,and the top three dominant plant species in soil seed bank were E.minor,Chloris virgata Sw.,and E.gmelinii.As restoration period increased,the density of seed rain and soil seed bank increased first and then decreased.While for species richness,as restoration period increased,it gradually increased in seed rain but decreased in soil seed bank.There was a positive correlation between seed rain density and soil seed bank density among all the three restoration periods.The species similarity between seed rain or soil seed bank and aboveground vegetation decreased with the extension of restoration period.The shape of the seeds,specifically those with external appendages such as spines and crown hair,clearly had an effect on their dispersal,then resulting in lower seed density in soil seed bank.In addition,precipitation was a crucial factor in promoting rapid germination,also resulting in lower seed density in soil seed bank.Our findings provide valuable insights for guiding future interventions during the later periods of artificial C.korshinskii forest,such as sowing and restoration efforts using unmanned aerial vehicles.展开更多
With the loss of substantial natural wetlands in coastal zones,artificial wetlands provide alternative habitats for many shorebirds.Scientific management of artificial wetlands used by shorebirds plays an important ro...With the loss of substantial natural wetlands in coastal zones,artificial wetlands provide alternative habitats for many shorebirds.Scientific management of artificial wetlands used by shorebirds plays an important role in maintaining the stability of shorebird population.Satellite tracking technique can obtain high-precision location information of individuals day and night,providing a good technical support for the study of quantitative relationship between waterfowls and their habitats.In this study,satellite tracking method,Remote Sensing(RS)and Geographic Information System(GIS)technology were used to analyze the activity pattern and habitat utilization characteristics of Pied Avocet during breeding period in an artificial wetland complex in the Yellow River Delta(YRD),China.The results showed that the breeding Pied Avocets had a small range of activity,with a total core and main home range of 33.10 km^(2) and 216.30 km^(2),respectively.This species tended to forage in the pond and salt pan during the day and night,respectively,with an unfixed staying time in the breeding ground.The distance between breeding ground and feeding ground was less than 6 km.It is emphasized that in addition to improving the conditions of the remaining natural habitats,effective managing artificial habitats is a priority for shorebird conservation.This research could provide reference for the management of artificial wetlands in coastal zones and supply technique support for the protection of shorebirds and their habitats,and alleviate human-bird conflicts and sustainable development of coastal zones.展开更多
基金supported by the Capital’s Funds for Health Improvement and Research,No.2022-2-2072(to YG).
文摘Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms.To investigate advances in the use of artificial intelligence in the diagnosis,rehabilitation,and scientific examination of peripheral nerve injury,we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994–2023.We identified the following research hotspots in peripheral nerve injury and repair:(1)diagnosis,classification,and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques,such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy;(2)motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms,such as wearable devices and assisted wheelchair systems;(3)improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning,such as implantable peripheral nerve interfaces;(4)the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility,enabling them to control devices such as networked hand prostheses;(5)artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation,thereby reducing surgical risk and complications,and facilitating postoperative recovery.Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair,there are some limitations to this technology,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(e.g.,privacy,data security,research transparency).Future research should address the issue of data collection,as large-scale,high-quality clinical datasets are required to establish effective artificial intelligence models.Multimodal data processing is also necessary,along with interdisciplinary collaboration,medical-industrial integration,and multicenter,large-sample clinical studies.
基金supported by theCONAHCYT(Consejo Nacional deHumanidades,Ciencias y Tecnologias).
文摘The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms.These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence.Nevertheless,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images.This research presents an advanced investigation of XAI models to classify cancer images.It describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare applications.In addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging techniques.The proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive explanation.The proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%DOR.It will discuss the potential applications of the proposed XAI models in the smart healthcare environment.It will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2023-02-02385).
文摘Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological concrete.In terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part geopolymer.However,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported.Therefore,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingMATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part geopolymer.The database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing temperature.The performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(MAE),and rootmean square error(RMSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed GEP.Sensitivity and parametric analysis were also performed to identify the significant contributor to strength.According to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 days.The water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.
文摘Artificial intelligence(AI)is making significant strides in revolutionizing the detection of Barrett's esophagus(BE),a precursor to esophageal adenocarcinoma.In the research article by Tsai et al,researchers utilized endoscopic images to train an AI model,challenging the traditional distinction between endoscopic and histological BE.This approach yielded remarkable results,with the AI system achieving an accuracy of 94.37%,sensitivity of 94.29%,and specificity of 94.44%.The study's extensive dataset enhances the AI model's practicality,offering valuable support to endoscopists by minimizing unnecessary biopsies.However,questions about the applicability to different endoscopic systems remain.The study underscores the potential of AI in BE detection while highlighting the need for further research to assess its adaptability to diverse clinical settings.
基金Supported by the National Natural Science Foundation of China (No.82171080)Nanjing Health Science and Technology Development Special Fund (No.YKK23264).
文摘Owing to the rapid development of modern computer technologies,artificial intelligence(AI)has emerged as an essential instrument for intelligent analysis across a range of fields.AI has been proven to be highly effective in ophthalmology,where it is frequently used for identifying,diagnosing,and typing retinal diseases.An increasing number of researchers have begun to comprehensively map patients’retinal diseases using AI,which has made individualized clinical prediction and treatment possible.These include prognostic improvement,risk prediction,progression assessment,and interventional therapies for retinal diseases.Researchers have used a range of input data methods to increase the accuracy and dependability of the results,including the use of tabular,textual,or image-based input data.They also combined the analyses of multiple types of input data.To give ophthalmologists access to precise,individualized,and high-quality treatment strategies that will further optimize treatment outcomes,this review summarizes the latest findings in AI research related to the prediction and guidance of clinical diagnosis and treatment of retinal diseases.
文摘Since ChatGPT emerged on November 30, 2022, Artificial Intelligence (AI) has been increasingly discussed as a radical force that will change our world. People have become used to AI in which such ubiquitous technologies as Siri, Google, and Netflix deploy AI algorithms to answer questions, impart information, and provide recommendations. However, many individuals including originators and backers of AI have recently expressed grave concerns. In this paper, the authors will assess what is occurring with AI in Visual Arts Education, outline positives and negatives, and provide recommendations addressed specifically for teachers working in the field regarding emerging AI usage from kindergarten to grade twelve levels as well as in higher education.
文摘BACKGROUND Artificial intelligence(AI)has potential in the optical diagnosis of colorectal polyps.AIM To evaluate the feasibility of the real-time use of the computer-aided diagnosis system(CADx)AI for ColoRectal Polyps(AI4CRP)for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYE^(TM)(Fujifilm,Tokyo,Japan).CADx influence on the optical diagnosis of an expert endoscopist was also investigated.METHODS AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm.Both CADxsystems exploit convolutional neural networks.Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard.AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value(range 0.0-1.0).A predefined cut-off value of 0.6 was set with values<0.6 indicating benign and values≥0.6 indicating premalignant colorectal polyps.Low confidence characterizations were defined as values 40%around the cut-off value of 0.6(<0.36 and>0.76).Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.RESULTS AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps.Self-critical AI4CRP,excluding 14 low confidence characterizations[27.5%(14/51)],had a diagnostic accuracy of 89.2%,sensitivity of 89.7%,and specificity of 87.5%,which was higher compared to AI4CRP.CAD EYE had a 83.7%diagnostic accuracy,74.2%sensitivity,and 100.0%specificity.Diagnostic performances of the endoscopist alone(before AI)increased nonsignificantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE(AI-assisted endoscopist).Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems,except for specificity for which CAD EYE performed best.CONCLUSION Real-time use of AI4CRP was feasible.Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.
基金supported in part by the National Natural Science Foundation of China(82072019)the Shenzhen Basic Research Program(JCYJ20210324130209023)+5 种基金the Shenzhen-Hong Kong-Macao S&T Program(Category C)(SGDX20201103095002019)the Mainland-Hong Kong Joint Funding Scheme(MHKJFS)(MHP/005/20),the Project of Strategic Importance Fund(P0035421)the Projects of RISA(P0043001)from the Hong Kong Polytechnic University,the Natural Science Foundation of Jiangsu Province(BK20201441)the Provincial and Ministry Co-constructed Project of Henan Province Medical Science and Technology Research(SBGJ202103038,SBGJ202102056)the Henan Province Key R&D and Promotion Project(Science and Technology Research)(222102310015)the Natural Science Foundation of Henan Province(222300420575),and the Henan Province Science and Technology Research(222102310322).
文摘Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.
基金supported by the Key Projects of the National Natural Science Foundation of China,No.11932013(to XC)Key Military Logistics Research Projects,No.B WJ21J002(to XC)+4 种基金the Key projects of the Special Zone for National Defence Innovation,No.21-163-12-ZT006002-13(to XC)the National Nature Science Foundation of China No.82272255(to XC)the National Defense Science and Technology Outstanding Youth Science Fund Program,No.2021-JCIQ-ZQ-035(to XC)the Scientific Research Innovation Team Project of Armed Police Characteristic Medical Center,No.KYCXTD0104(to ZL)the National Natural Science Foundation of China Youth Fund,No.82004467(to BC)。
文摘Spinal cord injury is a serious disease of the central nervous system involving irreversible nerve injury and various organ system injuries.At present,no effective clinical treatment exists.As one of the artificial hibernation techniques,mild hypothermia has preliminarily confirmed its clinical effect on spinal cord injury.However,its technical defects and barriers,along with serious clinical side effects,restrict its clinical application for spinal cord injury.Artificial hibernation is a futureoriented disruptive technology for human life support.It involves endogenous hibernation inducers and hibernation-related central neuromodulation that activate particular neurons,reduce the central constant temperature setting point,disrupt the normal constant body temperature,make the body adapt"to the external cold environment,and reduce the physiological resistance to cold stimulation.Thus,studying the artificial hibernation mechanism may help develop new treatment strategies more suitable for clinical use than the cooling method of mild hypothermia technology.This review introduces artificial hibernation technologies,including mild hypothermia technology,hibernation inducers,and hibernation-related central neuromodulation technology.It summarizes the relevant research on hypothermia and hibernation for organ and nerve protection.These studies show that artificial hibernation technologies have therapeutic significance on nerve injury after spinal co rd injury through inflammatory inhibition,immunosuppression,oxidative defense,and possible central protection.It also promotes the repair and protection of res pirato ry and digestive,cardiovascular,locomoto r,urinary,and endocrine systems.This review provides new insights for the clinical treatment of nerve and multiple organ protection after spinal cord injury thanks to artificial hibernation.At present,artificial hibernation technology is not mature,and research fa ces various challenges.Neve rtheless,the effort is wo rthwhile for the future development of medicine.
基金the support of the National Natural Science Foundation of China(22278234,21776151)。
文摘An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.
基金supported by the National Defense Science and Technology Outstanding Youth Science Fund Project,No.2021-JCJQ-ZQ-035National Defense Innovation Special Zone Project,No.21-163-12-ZT-006-002-13Key Program of the National Natural Science Foundation of China,No.11932013(all to XuC).
文摘Controlling intracranial pressure,nerve cell regeneration,and microenvironment regulation are the key issues in reducing mortality and disability in acute brain injury.There is currently a lack of effective treatment methods.Hibernation has the characteristics of low temperature,low metabolism,and hibernation rhythm,as well as protective effects on the nervous,cardiovascular,and motor systems.Artificial hibernation technology is a new technology that can effectively treat acute brain injury by altering the body’s metabolism,lowering the body’s core temperature,and allowing the body to enter a state similar to hibernation.This review introduces artificial hibernation technology,including mild hypothermia treatment technology,central nervous system regulation technology,and artificial hibernation-inducer technology.Upon summarizing the relevant research on artificial hibernation technology in acute brain injury,the research results show that artificial hibernation technology has neuroprotective,anti-inflammatory,and oxidative stress-resistance effects,indicating that it has therapeutic significance in acute brain injury.Furthermore,artificial hibernation technology can alleviate the damage of ischemic stroke,traumatic brain injury,cerebral hemorrhage,cerebral infarction,and other diseases,providing new strategies for treating acute brain injury.However,artificial hibernation technology is currently in its infancy and has some complications,such as electrolyte imbalance and coagulation disorders,which limit its use.Further research is needed for its clinical application.
基金supported by the National Natural Science Foundation of China(Grant Nos.42141019 and 42261144687)and STEP(Grant No.2019QZKK0102)supported by the Korea Environmental Industry&Technology Institute(KEITI)through the“Project for developing an observation-based GHG emissions geospatial information map”,funded by the Korea Ministry of Environment(MOE)(Grant No.RS-2023-00232066).
文摘Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of physics in climate science has occasionally been overlooked.Our perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics,rather than an“either/or”scenario.Scrutinizing controversies around current physical inconsistencies in large AI models,we stress the critical need for detailed dynamic diagnostics and physical constraints.Furthermore,we provide illustrative examples to guide future assessments and constraints for AI models.Regarding AI integration with numerical models,we argue that offline AI parameterization schemes may fall short of achieving global optimality,emphasizing the importance of constructing online schemes.Additionally,we highlight the significance of fostering a community culture and propose the OCR(Open,Comparable,Reproducible)principles.Through a better community culture and a deep integration of physics and AI,we contend that developing a learnable climate model,balancing AI and physics,is an achievable goal.
基金University-level Graduate Education Reform Project of Yangtze University(YJY202329).
文摘Under the background of“artificial intelligence+X”,the development of landscape architecture industry ushers in new opportunities,and professional talents need to be updated to meet the social demand.This paper analyzes the cultivation demand of landscape architecture graduate students in the context of the new era,and identifies the problems by comparing the original professional graduate training mode.The new cultivation mode of graduate students in landscape architecture is proposed,including updating the target orientation of the discipline,optimizing the teaching system,building a“dualteacher”tutor team,and improving the“industry-university-research-utilization”integrated cultivation,so as to cultivate high-quality compound talents with disciplinary characteristics.
文摘Current practice of underground artificial ground freezing(AGF)typically involves huge refrigeration systems of large economic and environmental costs.In this study,a novel AGF technique is proposed deploying available cold wind in cold regions.This is achieved by a static heat transfer device called thermosyphon equipped with an air insulation layer.A refrigeration unit can be optionally integrated to meet additional cooling requirements.The introduction of air insulation isolates the thermosyphon from ground zones where freezing is not needed,resulting in:(1)steering the cooling resources(cold wind or refrigeration)towards zones of interest;and(2)minimizing refrigeration load.This design is demonstrated using well-validated mathematical models from our previous work based on two-phase enthalpy method of the ground coupled with a thermal resistance network for the thermosyphon.Two Canadian mines are considered:the Cigar Lake Mine and the Giant Mine.The results show that our proposed design can speed the freezing time by 30%at the Giant Mine and by two months at the Cigar Lake Mine.Further,a cooling load of 2.4 GWh can be saved at the Cigar Lake Mine.Overall,this study provides mining practitioners with sustainable solutions of underground AGF.
基金supported by a grant from the Standardization and Integration of Resources Information for Seed-cluster in Hub-Spoke Material Bank Program,Rural Development Administration,Republic of Korea(PJ01587004).
文摘Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.
基金financed by the Jiangsu Haizhou Bay National Sea Ranching Demonstration Project(No.D-8005-18-0188)the Shanghai Municipal Science and Technology Commission Local Capacity Construction Project(No.21010502200).
文摘Plankton are an important component of marine protected areas(MPAs),and its communities would require much smaller interpatch distances to ensure connection among MPAs.According to the survey from MPAs dominated by artificial reefs and adjacent waters(estuary area(EA),aquaculture area(AA),artificial reef area(ARA),natural area(NA)and comprehensive effect area(CEA))in Haizhou Bay in spring and autumn,we analyzed phyto-zooplankton composition,abundance and biomass,and correlation with hydrologic variables to gain information about the forces that structure the plankton.The results showed that the dominant zooplankton were copepods(spring,98.9%;autumn,94.2%),while the phytoplankton were mainly composed of Bacillariophyta(spring,61.8%;autumn,95.6%).The RDA results showed that temperature,salinity and depth highly associated with the distribution and composition of plankton species among the habitats than other factors in spring;temperature,Chla and DO had the strongest influence in autumn.The zooplankton in the ARA and AA ecosystems basically contained the same species as those in other habitats,and each habitat also exhibited a relatively unique combination of plankton species.The structures of the EA zooplankton in spring and the EA phytoplankton in both seasons were much different than other habitats,which may have been caused by factors such as currents and tides.We concluded that there exists similarity of the plankton community between artificial reef area and adjacent waters,whereas the EAs may be relatively independent systems.Therefore,these interaction between plankton community should be considered when designing MPA networks,and ocean circulations should be considered more than the environmental factors.
文摘Purpose This scoping review aimed to offer researchers and practitioners an understanding of artificial intelligence(AI)applications in physical activity(PA)interventions;introduce them to prevalent machine learning(ML),deep learning(DL),and reinforcement learning(RL)algorithms;and encourage the adoption of AI methodologies.Methods A scoping review was performed in PubMed,Web of Science,Cochrane Library,and EBSCO focusing on AI applications for promoting PA or predicting related behavioral or health outcomes.AI methodologies were summarized and categorized to identify synergies,patterns,and trends informing future research.Additionally,a concise primer on predominant AI methodologies within the realm of PA was provided to bolster understanding and broader application.Results The review included 24 studies that met the predetermined eligibility criteria.AI models were found effective in detecting significant patterns of PA behavior and associations between specific factors and intervention outcomes.Most studies comparing AI models to traditional statistical approaches reported higher prediction accuracy for AI models on test data.Comparisons of different AI models yielded mixed results,likely due to model performance being highly dependent on the dataset and task.An increasing trend of adopting state-of-the-art DL and RL models over standard ML was observed,addressing complex human–machine communication,behavior modification,and decision-making tasks.Six key areas for future AI adoption in PA interventions emerged:personalized PA interventions,real-time monitoring and adaptation,integration of multimodal data sources,evaluation of intervention effectiveness,expanding access to PA interventions,and predicting and preventing injuries.Conclusion The scoping review highlights the potential of AI methodologies for advancing PA interventions.As the field progresses,staying informed and exploring emerging AI-driven strategies is essential for achieving significant improvements in PA interventions and fostering overall well-being.
基金supported financially by the fund from the Ministry of Science and Technology of China(Grant No.2019YFB2205100)the National Science Fund for Distinguished Young Scholars(No.52025022)+3 种基金the National Nature Science Foundation of China(Grant Nos.U19A2091,62004016,51732003,52072065,1197407252272140 and 52372137)the‘111’Project(Grant No.B13013)the Fundamental Research Funds for the Central Universities(Nos.2412023YQ004 and 2412022QD036)the funding from Jilin Province(Grant Nos.20210201062GX,20220502002GH,20230402072GH,20230101017JC and 20210509045RQ)。
文摘Spiking neural network(SNN),widely known as the third-generation neural network,has been frequently investigated due to its excellent spatiotemporal information processing capability,high biological plausibility,and low energy consumption characteristics.Analogous to the working mechanism of human brain,the SNN system transmits information through the spiking action of neurons.Therefore,artificial neurons are critical building blocks for constructing SNN in hardware.Memristors are drawing growing attention due to low consumption,high speed,and nonlinearity characteristics,which are recently introduced to mimic the functions of biological neurons.Researchers have proposed multifarious memristive materials including organic materials,inorganic materials,or even two-dimensional materials.Taking advantage of the unique electrical behavior of these materials,several neuron models are successfully implemented,such as Hodgkin–Huxley model,leaky integrate-and-fire model and integrate-and-fire model.In this review,the recent reports of artificial neurons based on memristive devices are discussed.In addition,we highlight the models and applications through combining artificial neuronal devices with sensors or other electronic devices.Finally,the future challenges and outlooks of memristor-based artificial neurons are discussed,and the development of hardware implementation of brain-like intelligence system based on SNN is also prospected.
基金funded by the General Project of Key R&D Plan of Ningxia Hui Autonomous Region,China(2021BEG03008,2022BEG02012)the Science and Technology Innovation Leading Talent Project of Ningxia Hui Autonomous Region(2021GKLRLX13)the National Natural Science Foundation of China(31760707).
文摘Vegetation restoration and reconstruction are effective approaches to desertification control and achieving social and economic sustainability in desert areas.However,the self-succession ability of native plants during the later periods of vegetation restoration remains unclear.Therefore,this study was conducted to bridge the knowledge gap by investigating the regeneration dynamics of artificial forest under natural conditions.The information of seed rain and soil seed bank was collected and quantified from an artificial Caragana korshinskii Kom.forest in the Tengger Desert,China.The germination tests were conducted in a laboratory setting.The analysis of species quantity and diversity in seed rain and soil seed bank was conducted to assess the impact of different durations of sand fixation(60,40,and 20 a)on the progress of vegetation restoration and ecological conditions in artificial C.korshinskii forest.The results showed that the top three dominant plant species in seed rain were Echinops gmelinii Turcz.,Eragrostis minor Host.,and Agropyron mongolicum Keng.,and the top three dominant plant species in soil seed bank were E.minor,Chloris virgata Sw.,and E.gmelinii.As restoration period increased,the density of seed rain and soil seed bank increased first and then decreased.While for species richness,as restoration period increased,it gradually increased in seed rain but decreased in soil seed bank.There was a positive correlation between seed rain density and soil seed bank density among all the three restoration periods.The species similarity between seed rain or soil seed bank and aboveground vegetation decreased with the extension of restoration period.The shape of the seeds,specifically those with external appendages such as spines and crown hair,clearly had an effect on their dispersal,then resulting in lower seed density in soil seed bank.In addition,precipitation was a crucial factor in promoting rapid germination,also resulting in lower seed density in soil seed bank.Our findings provide valuable insights for guiding future interventions during the later periods of artificial C.korshinskii forest,such as sowing and restoration efforts using unmanned aerial vehicles.
基金Under the auscpices of Shandong Provincial Natural Science Foundation (No.ZR2020QD090)Research Funds of Beijing VMinFull Limted (No.VMF2021RS)+1 种基金National Natural Science Foundation of China (No.42176221)Seed Project of Yantai Institute of Coastal Zone Research,Chinese Academy of Sciences (No.YICE351030601)。
文摘With the loss of substantial natural wetlands in coastal zones,artificial wetlands provide alternative habitats for many shorebirds.Scientific management of artificial wetlands used by shorebirds plays an important role in maintaining the stability of shorebird population.Satellite tracking technique can obtain high-precision location information of individuals day and night,providing a good technical support for the study of quantitative relationship between waterfowls and their habitats.In this study,satellite tracking method,Remote Sensing(RS)and Geographic Information System(GIS)technology were used to analyze the activity pattern and habitat utilization characteristics of Pied Avocet during breeding period in an artificial wetland complex in the Yellow River Delta(YRD),China.The results showed that the breeding Pied Avocets had a small range of activity,with a total core and main home range of 33.10 km^(2) and 216.30 km^(2),respectively.This species tended to forage in the pond and salt pan during the day and night,respectively,with an unfixed staying time in the breeding ground.The distance between breeding ground and feeding ground was less than 6 km.It is emphasized that in addition to improving the conditions of the remaining natural habitats,effective managing artificial habitats is a priority for shorebird conservation.This research could provide reference for the management of artificial wetlands in coastal zones and supply technique support for the protection of shorebirds and their habitats,and alleviate human-bird conflicts and sustainable development of coastal zones.