The temporal pole(TP),one of the most expanded cortical regions in humans relative to other primates,plays a crucial role in human language processing.It is also one of the most structurally and functionally asymmetri...The temporal pole(TP),one of the most expanded cortical regions in humans relative to other primates,plays a crucial role in human language processing.It is also one of the most structurally and functionally asymmetric regions.However,whether the functional architecture of the TP is shared by humans and macaques is an open question.We used spectral clustering algorithms to define a cross-species fine-grained TP atlas with different anatomical connectivity patterns.We identified three similar subregions,two ventral and one dorsal,within the TP in both humans and macaques.The parcellation scheme for the TP was validated using functional gradient mapping,anatomical connectivity and resting-state functional connectivity pattern analysis,and functional characterization.Furthermore,in conjunction with the Allen Human Brain Atlas,we revealed the molecular basis for the functional connectivity patterns of each human TP subregion.In addition,we compared the hemispheric asymmetry in mean gray matter volume,anatomical connectivity fingerprints,and whole brain functional connectivity patterns to reveal the evolutionary differences in the TP and found different asymmetric patterns between humans and macaques.In conclusion,our findings reveal that the asymmetry in structure and connectivity may underpin the hemispheric functional specialization of the brain and provide a novel insight into understanding the evolutionary origin of the TP.展开更多
Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-...Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity.On the other hand,fixed-size convolution kernels are often used,which have weak perception ability for emotional regions of different scales.Therefore,this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling.In terms of space,multi-branch large-kernel stripe convolution is used to perceive emotional region features at different scales,and attention weights are generated for each scale feature.In terms of time,multi-layer odd-even down-sampling is performed on the time series,and oddeven sub-sequence interaction is performed to solve the problem of feature similarity,while reducing computational costs due to the linear relationship between sampling and convolution overhead.This paper was tested on CMU-MOSI,CMU-MOSEI,and Hume Reaction.The Acc-2 reached 83.4%,85.2%,and 81.2%,respectively.The experimental results show that the model can significantly improve the accuracy of emotion recognition.展开更多
High-precision optical frequency measurement serves as a cornerstone of modern science and technology,enabling advancements in fields ranging from fundamental physics to quantum information technologies.Obtaining prec...High-precision optical frequency measurement serves as a cornerstone of modern science and technology,enabling advancements in fields ranging from fundamental physics to quantum information technologies.Obtaining precise photon frequencies,especially in the ultraviolet or even extreme ultraviolet regimes,is a key goal in both light–matter interaction experiments and engineering applications.High-order harmonic generation(HHG)is an ideal light source for producing such photons.In this work,we propose an optical temporal interference model(OTIM)that establishes an analogy with multi-slit Fraunhofer diffraction(MSFD)to manipulate fine-frequency photon generation by exploiting the temporal coherence of HHG processes.Our model provides a unified physical framework for three distinct non-integer HHG generation schemes:single-pulse,shaped-pulse,and laser pulse train approaches,which correspond to single-MSFD-like,double-MSFD-like,and multi-MSFD-like processes,respectively.Arbitrary non-integer HHG photons can be obtained using our scheme.Our approach provides a new perspective for accurately measuring and controlling photon frequencies in fields such as frequency comb technology,interferometry,and atomic clocks.展开更多
Central Asia is characterized by an arid climate and widespread desert distribution,with its sustainable development severely constrained by dust events.An objective understanding of the spatiotemporal patterns and dr...Central Asia is characterized by an arid climate and widespread desert distribution,with its sustainable development severely constrained by dust events.An objective understanding of the spatiotemporal patterns and driving forces of dust weather is highly important in this area.Based on the meteorological observations from 2000 to 2020,we examined the spatiotemporal characteristics of dust weather in the five Central Asian countries(Kazakhstan,Uzbekistan,Kyrgyzstan,Turkmenistan,and Tajikistan)via Theil-Sen trend analysis and Geodetector modeling method,quantitatively revealing the influence of environmental factors,such as temperature,precipitation,and vegetation,on the frequency of dust weather.The results showed that:(1)dust weather in Central Asia was mainly distributed in a large''dust belt''extending from west to east from northern part of the Caspian lowland desert,and concentrated in basins,plains,and other low-altitude areas.Strong dust weather mainly occurred in northern areas of the Aral Sea and southern edge of Central Asia,with a maximum annual frequency of 21.9%;(2)strong dust weather in Central Asia has fluctuated and slightly decreased since 2001.The highest frequency(1.1%)occurred in spring(from March to June);(3)from 2000 to 2020,changes such as spot shifting and shrinking occurred in the four main source areas(north of the Aral Sea,Kyzylkum Desert,Karakum Desert,and Garabogazköl Bay region),where sandstorms occurred in Central Asia,and northern Caspian lowland desert became the most important low-emission dust source in Central Asia;and(4)the combined effect of soil moisture and air temperature has the most significant influence on dust weather in Central Asia.This study provides a theoretical basis for sand prevention and sand control in Central Asia.In the future,Central Asia should focus on the rational utilization of land and water resources,and implement human interventions such as vegetation restoration and optimization of irrigation methods to curb further desertification in this area.展开更多
Brain lesions,such as those caused by stroke or traumatic brain injury(TBI),frequently result in persistent motor and cognitive impairments that significantly affect the individual patient's quality of life.Despit...Brain lesions,such as those caused by stroke or traumatic brain injury(TBI),frequently result in persistent motor and cognitive impairments that significantly affect the individual patient's quality of life.Despite differences in the mechanisms of injury,both conditions share a high prevalence of motor and cognitive impairments.These deficits show only limited natural recovery.展开更多
End-to-end Temporal Action Detection(TAD)has achieved remarkable progress in recent years,driven by innovations in model architectures and the emergence of Video Foundation Models(VFMs).However,existing TAD methods th...End-to-end Temporal Action Detection(TAD)has achieved remarkable progress in recent years,driven by innovations in model architectures and the emergence of Video Foundation Models(VFMs).However,existing TAD methods that perform full fine-tuning of pretrained video models often incur substantial computational costs,which become particularly pronounced when processing long video sequences.Moreover,the need for precise temporal boundary annotations makes data labeling extremely expensive.In low-resource settings where annotated samples are scarce,direct fine-tuning tends to cause overfitting.To address these challenges,we introduce Dynamic LowRank Adapter(DyLoRA),a lightweight fine-tuning framework tailored specifically for the TAD task.Built upon the Low-Rank Adaptation(LoRA)architecture,DyLoRA adapts only the key layers of the pretrained model via low-rank decomposition,reducing the number of trainable parameters to less than 5%of full fine-tuning methods.This significantly lowers memory consumption and mitigates overfitting in low-resource settings.Notably,DyLoRA enhances the temporal modeling capability of pretrained models by optimizing temporal dimension weights,thereby alleviating the representation misalignment of temporal features.Experimental results demonstrate that DyLoRA-TAD achieves impressive performance,with 73.9%mAP on THUMOS14,39.52%on ActivityNet-1.3,and 28.2%on Charades,substantially surpassing the best traditional feature-based methods.展开更多
Cerebral ischemia restricts cerebral blood flow(CBF),leading to unstable hemodynamics.Past studies of ischemia mainly focused on cortical CBF reduction.However,its impact on hemodynamic changes,especially temporal var...Cerebral ischemia restricts cerebral blood flow(CBF),leading to unstable hemodynamics.Past studies of ischemia mainly focused on cortical CBF reduction.However,its impact on hemodynamic changes,especially temporal varying characteristics,remains poorly understood.Here,we collected cortical resting-state CBF in rats with left carotid artery blockage during occlusion–reperfusion,and measured the temporal variability and changes in laterality using a novel state-space method.This method was also applied to stroke EEG datasets to validate its effectiveness.After arterial occlusion,the left marginal motor,sensory,auditory,and visual cortices exhibited severe temporal variability impairments.The laterality analysis indicated that affected left regions showed inferior unilateral mean,inter-hemispheric transition probability,time fraction,and laterality duration,while the right side had a higher laterality time fraction and duration.These impairments recovered partially following blood flow restoration.Besides,the ischemic state-space metrics were positively correlated with the pre-occlusion baseline appearance.Stroke patients exhibited impaired temporal variability in the affected ischemic hemisphere.The state-space analysis revealed damaged CBF temporal variability during cerebral ischemia and predicted baseline-ischemia connections.展开更多
Accurate precipitation estimation in semiarid,topographically complicated areas is critical for water resource management and climate risk monitoring.This work provides a detailed,multi-scale evaluation of four major ...Accurate precipitation estimation in semiarid,topographically complicated areas is critical for water resource management and climate risk monitoring.This work provides a detailed,multi-scale evaluation of four major satellite precipitation products(CHIRPS,PERSIANN-CDR,IMERG-F v07,and GSMaP)over Isfahan province,Iran,over a 9-year period(2015-2023).The performance of these products was benchmarked against a dense network of 98 rain gauges using a suite of continuous and categorical statistical metrics,following a two-stage quality control protocol to remove outliers and false alarms.The results revealed that the performance of all products improves with temporal aggregation.At the daily level,GSMaP performed marginally better,although all products were linked with considerable uncertainty.At the monthly and annual levels,the GPM-era products(IMERG and GSMaP)clearly beat the other two,establishing themselves as dependable tools for long-term hydro-climatological studies.Error analysis revealed that topography is the dominant regulating factor,creating a systematic elevationdependent bias,largely characterized by underestimation from most products in high-elevation areas,though the PERSIANN-CDR product exhibited a contrasting overestimation tendency.Finally,the findings highlight the importance of implementing local,elevation-dependent calibration before deploying these products in hydrological modeling.展开更多
Temporal knowledge graph completion(TKGC),which merges temporal information into traditional static knowledge graph completion(SKGC),has garnered increasing attention recently.Among numerous emerging approaches,transl...Temporal knowledge graph completion(TKGC),which merges temporal information into traditional static knowledge graph completion(SKGC),has garnered increasing attention recently.Among numerous emerging approaches,translation-based embedding models constitute a prominent approach in TKGC research.However,existing translation-based methods typically incorporate timestamps into entities or relations,rather than utilizing them independently.This practice fails to fully exploit the rich semantics inherent in temporal information,thereby weakening the expressive capability of models.To address this limitation,we propose embedding timestamps,like entities and relations,in one or more dedicated semantic spaces.After projecting all embeddings into a shared space,we use the relation-timestamp pair instead of the conventional relation embedding as the translation vector between head and tail entities.Our method elevates timestamps to the same representational significance as entities and relations.Based on this strategy,we introduce two novel translation-based embedding models:TE-TransR and TE-TransT.With the independent representation of timestamps,our method not only enhances capabilities in link prediction but also facilitates a relatively underexplored task,namely time prediction.To further bolster the precision and reliability of time prediction,we introduce a granular,time unit-based timestamp setting and a relation-specific evaluation protocol.Extensive experiments demonstrate that our models achieve strong performance on link prediction benchmarks,with TE-TransR outperforming existing baselines in the time prediction task.展开更多
Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating In...Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.展开更多
The accuracy of photovoltaic(PV)power prediction is significantly influenced by meteorological and environmental factors.To enhance ultra-short-term forecasting precision,this paper proposes an interpretable feedback ...The accuracy of photovoltaic(PV)power prediction is significantly influenced by meteorological and environmental factors.To enhance ultra-short-term forecasting precision,this paper proposes an interpretable feedback prediction method based on a parallel dual-stream Temporal Convolutional Network-Bidirectional Long Short-Term Memory(TCN-BiLSTM)architecture incorporating a spatiotemporal attention mechanism.Firstly,during data preprocessing,the optimal historical time window is determined through autocorrelation analysis while highly correlated features are selected as model inputs using Pearson correlation coefficients.Subsequently,a parallel dual-stream TCN-BiLSTM model is constructed where the TCN branch extracts localized transient features and the BiLSTM branch captures long-term periodic patterns,with spatiotemporal attention dynamically weighting spatiotemporal dependencies.Finally,Shapley Additive explanations(SHAP)additive analysis quantifies feature contribution rates and provides optimization feedback to the model.Validation using operational data from a PV power station in Northeast China demonstrates that compared to conventional deep learning models,the proposed method achieves a 17.6%reduction in root mean square error(RMSE),a 5.4%decrease in training time consumption,and a 4.78%improvement in continuous ranked probability score(CRPS),exhibiting significant advantages in both prediction accuracy and generalization capability.This approach enhances the application effectiveness of ultra-short-term PV power forecasting while simultaneously improving prediction accuracy and computational efficiency.展开更多
With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance s...With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.展开更多
The latitudinal diversity gradient(LDG)is one of the most notable biodiversity patterns in biogeography.The metabolic theory of ecology(MTE)explains ecological patterns,including the LDG.However,little is known about ...The latitudinal diversity gradient(LDG)is one of the most notable biodiversity patterns in biogeography.The metabolic theory of ecology(MTE)explains ecological patterns,including the LDG.However,little is known about whether the LDG remains stable over time as climate warming progresses and whether MTE remains applicable to clarify this pattern.In this study,forest data spanning temperate,subtropical,and tropical zones across China were used to analyze long-term changes in the LDG of tree species over 2005-2020.Based on the MTE framework,spatial scales were considered to assess temperature dependence of typical forest trees species.Our results show that species richness decreased with increasing latitude,and that temperature was the primary driver of this change.Although temperature in China has significantly increased over the past two decades,the LDG of tree species has remained stable.However,there was a decrease in species richness in tropical regions over time.With predictions of the MTE,the logarithm of typical forest tree species richness exhibited negative linear relationships with the inverse of ambient temperature,indicating temperature dependence of species richness.However,the relationship remained stable and was strongly influenced by spatial scale,intensifying as spatial scale increased.The findings emphasize the important role of temperature in shaping the LDG.The effects of spatial scale,in particular,should be considered when biodiversity management plans are developed for future climate change.展开更多
Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The prese...Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.展开更多
Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithm...Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.展开更多
Tenebrionid beetles represent a crucial arthropod taxon in the Gobi desert ecosystems owing to their species richness and high biomass,both of which are essential for maintaining ecosystem health and stability.However...Tenebrionid beetles represent a crucial arthropod taxon in the Gobi desert ecosystems owing to their species richness and high biomass,both of which are essential for maintaining ecosystem health and stability.However,the spatiotemporal variations of tenebrionid beetle assemblages in the Gobi desert remain poorly understood.In this study,the monthly dynamics of tenebrionid beetles in the central part of the Hexi Corridor,Northwest China,a representative area of the Gobi desert ecosystems,were monitored using pitfall trapping during 2015-2020.The following results were showed:(1)monthly activity of tenebrionid beetles was observed from March to October,with monthly activity peaking in spring and summer,and monthly activity periods and peak of tenebrionid beetle species exhibited interspecific differences that varied from year to year;(2)spatial distribution of tenebrionid beetle community was influenced by structural factors.Specifically,at a spatial scale of 24.00 m,tenebrionid beetle community was strongly and positively correlated with the dominant species,with distinct spatial distribution patterns observed for Blaps gobiensis and Microdera kraatzi alashanica;(3)abundance of tenebrionid beetles was positively correlated with monthly mean precipitation and monthly mean temperature,whereas monthly abundance of B.gobiensis and M.kraatzi alashanica was positively correlated with monthly mean precipitation;and(4)the cover of Reaumuria soongarica(Pall.)Maxim.and Nitraria sphaerocarpa Maxim.had a positive influence on the number of tenebrionid beetles captured.In conclusion,monthly variation in precipitation significantly influences the community dynamic of tenebrionid beetles,with precipitation and shrub cover jointly determining the spatial distribution pattern of these beetles in the Gobi desert ecosystems.展开更多
As the demand for advanced computational systems capable of handling large data volumes rises,nano-electronic devices,such as memristors,are being developed for efficient data processing,especially in reservoir comput...As the demand for advanced computational systems capable of handling large data volumes rises,nano-electronic devices,such as memristors,are being developed for efficient data processing,especially in reservoir computing(RC).RC enables the processing of temporal information with minimal training costs,making it a promising approach for neuromorphic computing.However,current memristor devices of-ten suffer from limitations in dynamic conductance and temporal behavior,which affects their perfor-mance in these applications.In this study,we present a multilayered indium-tin-oxide(ITO)/ZnO/indium-gallium-zinc oxide(IGZO)/ZnO/ITO memristor fabricated via radiofrequency sputtering to explore its fil-amentary and nonfilamentary resistive switching(RS)characteristics.High-resolution transmission elec-tron microscopy confirmed the polycrystalline structure of the ZnO/IGZO/ZnO active layer.Dual-switching modes were demonstrated by controlling the current compliance(I_(CC)).In the filamentary mode,the memristor exhibited a large memory window(10^(3)),low-operating voltages(±2 V),excellent cycle-to-cycle stability,and multilevel switching with controlled reset-stop voltages,making it suitable for high-density memory applications.Nonfilamentary switching demonstrated stable on/off ratios above 10,en-durance up to 102 cycles,and retention suited for short-term memory.Key synaptic behaviors,such as paired-pulse facilitation(PPF),post-tetanic potentiation(PTP),and spike-rate dependent plasticity(SRDP)were successfully emulated by modulating pulse amplitude,width,and interval.Experience-dependent plasticity(EDP)was also demonstrated,further replicating biological synaptic functions.These tempo-ral properties were utilized to develop a 4-bit reservoir computing system with 16 distinct conductance states,enabling efficient information encoding.For image recognition tasks,convolutional neural net-work(CNN)simulations achieved a high accuracy of 98.45%after 25 training epochs,outperforming the accuracy achieved following artificial neural network(ANN)simulations(87.79%).These findings demon-strate that the multilayered memristor exhibits high performance in neuromorphic systems,particularly for complex pattern recognition tasks,such as digit and letter classification.展开更多
Objective:Critically appraise the current state of alternate temporal bone training techniques(virtual reality(VR)simulation,3D-printed models,and mental practice(MP))compared to traditional and cadaver methods.Databa...Objective:Critically appraise the current state of alternate temporal bone training techniques(virtual reality(VR)simulation,3D-printed models,and mental practice(MP))compared to traditional and cadaver methods.Databases Reviewed:PubMed,Cochrane,Web of Science.Methods:Search terms utilized“temporal bone training”,“temporal bone surgical modalities”,and“training modalities temporal bone surgery”with“3D”,“rapid prototyp*”,“stereolithography”,“additive manufact*”,“plaster”,“VR”,“virtual reality”,“animal model”,“animal temporal bone”,and“synthetic”with“AND”for all literature.Exclusion criteria:non-ENT,non-English,and did not compare against alternative/traditional methods.Results:10 studies were included with 322 participants(83.9%ENT residents and 16.1%medical students).Costs include the FDM printer($300),materials($5/3D model),and<$5,000 for freeware simulator hardware.The Welling scale was used in 50%of studies.Alternate methods produced comparable or improved assessment scores to traditional and cadaver methods.Injuries were reported in three VR studies,with two reported significantly lower injury scores in the intervention groups.Time to completion was not significantly different in four VR studies,except for one finding that the time to visualize the incus was significantly lower in the intervention group.Performance after MP was not statistically different.Conclusion:More data are needed to assess whether the alternate methods are comparable to cadaveric dissection in temporal bone training.3D models and VR simulation demonstrate promising potential for novel trainees to acquire the basic skills and produce performance comparable to or significantly better than traditional methods of lectures,textbooks,CT images,and operative videos.展开更多
Temporal interference(TI)is a form of stimulation that epitomizes an innovative and non-invasive approach for profound neuromodulation of the brain,a technique that has been validated in mice.Yet,the thin cranial bone...Temporal interference(TI)is a form of stimulation that epitomizes an innovative and non-invasive approach for profound neuromodulation of the brain,a technique that has been validated in mice.Yet,the thin cranial bone structure of mice has a marginal influence on the effect of the TI technique and may not effectively showcase its effectiveness in larger animals.Based on this,we carried out TI stimulation experiments on rats.Following the TI intervention,analysis of electrophysiological data and immunofluorescence staining indicated the generation of a stimulation focus within the nucleus accumbens(depth,8.5 mm)in rats.Our findings affirm the viability of the TI methodology in the presence of thick cranial bones,furnishing efficacious parameters for profound stimulation with TI administered under such conditions.This experiment not only sheds light on the intervention effects of TI deep in the brain but also furnishes robust evidence in support of its prospective clinical utility.展开更多
基金supported by the Yunnan Fundamental Research Projects(202501AV070005 and 202201BE070001-004).
文摘The temporal pole(TP),one of the most expanded cortical regions in humans relative to other primates,plays a crucial role in human language processing.It is also one of the most structurally and functionally asymmetric regions.However,whether the functional architecture of the TP is shared by humans and macaques is an open question.We used spectral clustering algorithms to define a cross-species fine-grained TP atlas with different anatomical connectivity patterns.We identified three similar subregions,two ventral and one dorsal,within the TP in both humans and macaques.The parcellation scheme for the TP was validated using functional gradient mapping,anatomical connectivity and resting-state functional connectivity pattern analysis,and functional characterization.Furthermore,in conjunction with the Allen Human Brain Atlas,we revealed the molecular basis for the functional connectivity patterns of each human TP subregion.In addition,we compared the hemispheric asymmetry in mean gray matter volume,anatomical connectivity fingerprints,and whole brain functional connectivity patterns to reveal the evolutionary differences in the TP and found different asymmetric patterns between humans and macaques.In conclusion,our findings reveal that the asymmetry in structure and connectivity may underpin the hemispheric functional specialization of the brain and provide a novel insight into understanding the evolutionary origin of the TP.
基金supported,in part,by the National Nature Science Foundation of China under Grant 62272236,62376128in part,by the Natural Science Foundation of Jiangsu Province under Grant BK20201136,BK20191401.
文摘Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity.On the other hand,fixed-size convolution kernels are often used,which have weak perception ability for emotional regions of different scales.Therefore,this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling.In terms of space,multi-branch large-kernel stripe convolution is used to perceive emotional region features at different scales,and attention weights are generated for each scale feature.In terms of time,multi-layer odd-even down-sampling is performed on the time series,and oddeven sub-sequence interaction is performed to solve the problem of feature similarity,while reducing computational costs due to the linear relationship between sampling and convolution overhead.This paper was tested on CMU-MOSI,CMU-MOSEI,and Hume Reaction.The Acc-2 reached 83.4%,85.2%,and 81.2%,respectively.The experimental results show that the model can significantly improve the accuracy of emotion recognition.
基金supported by the National Natural Science Foundation of China(Grant No.12304379)the Natural Science Foundation of Liaoning Province(Grant No.2024BS-269)the Guangdong Basic and Applied Basic Research Foundation(Grant No.025A1515011117)。
文摘High-precision optical frequency measurement serves as a cornerstone of modern science and technology,enabling advancements in fields ranging from fundamental physics to quantum information technologies.Obtaining precise photon frequencies,especially in the ultraviolet or even extreme ultraviolet regimes,is a key goal in both light–matter interaction experiments and engineering applications.High-order harmonic generation(HHG)is an ideal light source for producing such photons.In this work,we propose an optical temporal interference model(OTIM)that establishes an analogy with multi-slit Fraunhofer diffraction(MSFD)to manipulate fine-frequency photon generation by exploiting the temporal coherence of HHG processes.Our model provides a unified physical framework for three distinct non-integer HHG generation schemes:single-pulse,shaped-pulse,and laser pulse train approaches,which correspond to single-MSFD-like,double-MSFD-like,and multi-MSFD-like processes,respectively.Arbitrary non-integer HHG photons can be obtained using our scheme.Our approach provides a new perspective for accurately measuring and controlling photon frequencies in fields such as frequency comb technology,interferometry,and atomic clocks.
基金funded by the National Natural Science Foundation of China(42571311).
文摘Central Asia is characterized by an arid climate and widespread desert distribution,with its sustainable development severely constrained by dust events.An objective understanding of the spatiotemporal patterns and driving forces of dust weather is highly important in this area.Based on the meteorological observations from 2000 to 2020,we examined the spatiotemporal characteristics of dust weather in the five Central Asian countries(Kazakhstan,Uzbekistan,Kyrgyzstan,Turkmenistan,and Tajikistan)via Theil-Sen trend analysis and Geodetector modeling method,quantitatively revealing the influence of environmental factors,such as temperature,precipitation,and vegetation,on the frequency of dust weather.The results showed that:(1)dust weather in Central Asia was mainly distributed in a large''dust belt''extending from west to east from northern part of the Caspian lowland desert,and concentrated in basins,plains,and other low-altitude areas.Strong dust weather mainly occurred in northern areas of the Aral Sea and southern edge of Central Asia,with a maximum annual frequency of 21.9%;(2)strong dust weather in Central Asia has fluctuated and slightly decreased since 2001.The highest frequency(1.1%)occurred in spring(from March to June);(3)from 2000 to 2020,changes such as spot shifting and shrinking occurred in the four main source areas(north of the Aral Sea,Kyzylkum Desert,Karakum Desert,and Garabogazköl Bay region),where sandstorms occurred in Central Asia,and northern Caspian lowland desert became the most important low-emission dust source in Central Asia;and(4)the combined effect of soil moisture and air temperature has the most significant influence on dust weather in Central Asia.This study provides a theoretical basis for sand prevention and sand control in Central Asia.In the future,Central Asia should focus on the rational utilization of land and water resources,and implement human interventions such as vegetation restoration and optimization of irrigation methods to curb further desertification in this area.
基金supported by the Defitech Foundation(Morges,CH)to FCHthe Bertarelli Foundation-Catalyst program(Gstaad,CH)to FCH+2 种基金the Wyss Center for Bio and Neuroengineering the Lighthouse Partnership for AI-guided Neuromodulation to FCHthe Fonds de recherche du Quebec-Sante(FRQS#342969)to CEPthe Neuro X Postdoctoral Fellowship Program to CEP。
文摘Brain lesions,such as those caused by stroke or traumatic brain injury(TBI),frequently result in persistent motor and cognitive impairments that significantly affect the individual patient's quality of life.Despite differences in the mechanisms of injury,both conditions share a high prevalence of motor and cognitive impairments.These deficits show only limited natural recovery.
基金supported by the National Natural Science Foundation of China(Grant No.62266054)the Major Science and Technology Project of Yunnan Province(Grant No.202402AD080002)the Scientific Research Fund of the Yunnan Provincial Department of Education(Grant No.2025Y0302).
文摘End-to-end Temporal Action Detection(TAD)has achieved remarkable progress in recent years,driven by innovations in model architectures and the emergence of Video Foundation Models(VFMs).However,existing TAD methods that perform full fine-tuning of pretrained video models often incur substantial computational costs,which become particularly pronounced when processing long video sequences.Moreover,the need for precise temporal boundary annotations makes data labeling extremely expensive.In low-resource settings where annotated samples are scarce,direct fine-tuning tends to cause overfitting.To address these challenges,we introduce Dynamic LowRank Adapter(DyLoRA),a lightweight fine-tuning framework tailored specifically for the TAD task.Built upon the Low-Rank Adaptation(LoRA)architecture,DyLoRA adapts only the key layers of the pretrained model via low-rank decomposition,reducing the number of trainable parameters to less than 5%of full fine-tuning methods.This significantly lowers memory consumption and mitigates overfitting in low-resource settings.Notably,DyLoRA enhances the temporal modeling capability of pretrained models by optimizing temporal dimension weights,thereby alleviating the representation misalignment of temporal features.Experimental results demonstrate that DyLoRA-TAD achieves impressive performance,with 73.9%mAP on THUMOS14,39.52%on ActivityNet-1.3,and 28.2%on Charades,substantially surpassing the best traditional feature-based methods.
基金supported by the National Natural Science Foundation of China(82250410380 and 62171101)the Natural Science Foundation of Sichuan Province(24NSFSC6257)the China MOST2030 Brain Project(2022ZD0208500).
文摘Cerebral ischemia restricts cerebral blood flow(CBF),leading to unstable hemodynamics.Past studies of ischemia mainly focused on cortical CBF reduction.However,its impact on hemodynamic changes,especially temporal varying characteristics,remains poorly understood.Here,we collected cortical resting-state CBF in rats with left carotid artery blockage during occlusion–reperfusion,and measured the temporal variability and changes in laterality using a novel state-space method.This method was also applied to stroke EEG datasets to validate its effectiveness.After arterial occlusion,the left marginal motor,sensory,auditory,and visual cortices exhibited severe temporal variability impairments.The laterality analysis indicated that affected left regions showed inferior unilateral mean,inter-hemispheric transition probability,time fraction,and laterality duration,while the right side had a higher laterality time fraction and duration.These impairments recovered partially following blood flow restoration.Besides,the ischemic state-space metrics were positively correlated with the pre-occlusion baseline appearance.Stroke patients exhibited impaired temporal variability in the affected ischemic hemisphere.The state-space analysis revealed damaged CBF temporal variability during cerebral ischemia and predicted baseline-ischemia connections.
文摘Accurate precipitation estimation in semiarid,topographically complicated areas is critical for water resource management and climate risk monitoring.This work provides a detailed,multi-scale evaluation of four major satellite precipitation products(CHIRPS,PERSIANN-CDR,IMERG-F v07,and GSMaP)over Isfahan province,Iran,over a 9-year period(2015-2023).The performance of these products was benchmarked against a dense network of 98 rain gauges using a suite of continuous and categorical statistical metrics,following a two-stage quality control protocol to remove outliers and false alarms.The results revealed that the performance of all products improves with temporal aggregation.At the daily level,GSMaP performed marginally better,although all products were linked with considerable uncertainty.At the monthly and annual levels,the GPM-era products(IMERG and GSMaP)clearly beat the other two,establishing themselves as dependable tools for long-term hydro-climatological studies.Error analysis revealed that topography is the dominant regulating factor,creating a systematic elevationdependent bias,largely characterized by underestimation from most products in high-elevation areas,though the PERSIANN-CDR product exhibited a contrasting overestimation tendency.Finally,the findings highlight the importance of implementing local,elevation-dependent calibration before deploying these products in hydrological modeling.
基金supported by the National Natural Science Foundation of China under Grant No.72293575.
文摘Temporal knowledge graph completion(TKGC),which merges temporal information into traditional static knowledge graph completion(SKGC),has garnered increasing attention recently.Among numerous emerging approaches,translation-based embedding models constitute a prominent approach in TKGC research.However,existing translation-based methods typically incorporate timestamps into entities or relations,rather than utilizing them independently.This practice fails to fully exploit the rich semantics inherent in temporal information,thereby weakening the expressive capability of models.To address this limitation,we propose embedding timestamps,like entities and relations,in one or more dedicated semantic spaces.After projecting all embeddings into a shared space,we use the relation-timestamp pair instead of the conventional relation embedding as the translation vector between head and tail entities.Our method elevates timestamps to the same representational significance as entities and relations.Based on this strategy,we introduce two novel translation-based embedding models:TE-TransR and TE-TransT.With the independent representation of timestamps,our method not only enhances capabilities in link prediction but also facilitates a relatively underexplored task,namely time prediction.To further bolster the precision and reliability of time prediction,we introduce a granular,time unit-based timestamp setting and a relation-specific evaluation protocol.Extensive experiments demonstrate that our models achieve strong performance on link prediction benchmarks,with TE-TransR outperforming existing baselines in the time prediction task.
文摘Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.
基金funded by the National Natural Science Foundation of China(NSFC)(No.62066024)funded by Basic Scientific Research Projects of Higher Education Institutions in Liaoning Province(LJ212411632063)the National Undergraduate Training Program for Innovation and Entrepreneurship(S202511632045).
文摘The accuracy of photovoltaic(PV)power prediction is significantly influenced by meteorological and environmental factors.To enhance ultra-short-term forecasting precision,this paper proposes an interpretable feedback prediction method based on a parallel dual-stream Temporal Convolutional Network-Bidirectional Long Short-Term Memory(TCN-BiLSTM)architecture incorporating a spatiotemporal attention mechanism.Firstly,during data preprocessing,the optimal historical time window is determined through autocorrelation analysis while highly correlated features are selected as model inputs using Pearson correlation coefficients.Subsequently,a parallel dual-stream TCN-BiLSTM model is constructed where the TCN branch extracts localized transient features and the BiLSTM branch captures long-term periodic patterns,with spatiotemporal attention dynamically weighting spatiotemporal dependencies.Finally,Shapley Additive explanations(SHAP)additive analysis quantifies feature contribution rates and provides optimization feedback to the model.Validation using operational data from a PV power station in Northeast China demonstrates that compared to conventional deep learning models,the proposed method achieves a 17.6%reduction in root mean square error(RMSE),a 5.4%decrease in training time consumption,and a 4.78%improvement in continuous ranked probability score(CRPS),exhibiting significant advantages in both prediction accuracy and generalization capability.This approach enhances the application effectiveness of ultra-short-term PV power forecasting while simultaneously improving prediction accuracy and computational efficiency.
基金funded by scientific research projects under Grant JY2024B011.
文摘With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.
基金supported by the Key Program of National Science of China(Grant No.:42030509 and 42141005)。
文摘The latitudinal diversity gradient(LDG)is one of the most notable biodiversity patterns in biogeography.The metabolic theory of ecology(MTE)explains ecological patterns,including the LDG.However,little is known about whether the LDG remains stable over time as climate warming progresses and whether MTE remains applicable to clarify this pattern.In this study,forest data spanning temperate,subtropical,and tropical zones across China were used to analyze long-term changes in the LDG of tree species over 2005-2020.Based on the MTE framework,spatial scales were considered to assess temperature dependence of typical forest trees species.Our results show that species richness decreased with increasing latitude,and that temperature was the primary driver of this change.Although temperature in China has significantly increased over the past two decades,the LDG of tree species has remained stable.However,there was a decrease in species richness in tropical regions over time.With predictions of the MTE,the logarithm of typical forest tree species richness exhibited negative linear relationships with the inverse of ambient temperature,indicating temperature dependence of species richness.However,the relationship remained stable and was strongly influenced by spatial scale,intensifying as spatial scale increased.The findings emphasize the important role of temperature in shaping the LDG.The effects of spatial scale,in particular,should be considered when biodiversity management plans are developed for future climate change.
基金funded by the Deanship of Scientific Research and Libraries at Princess Nourah bint Abdulrahman University,through the“Nafea”Program,Grant No.(NP-45-082).
文摘Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.
基金supported in part by the National Natural Science Foundation of China (No. U23B2011)。
文摘Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.
基金funded by the National Natural Science Foundation of China(U23A2063)the Gansu Province Top-notch Leading Talents Project(E339040101)the National Natural Science Foundation of China(41771290,42377043,41773086).
文摘Tenebrionid beetles represent a crucial arthropod taxon in the Gobi desert ecosystems owing to their species richness and high biomass,both of which are essential for maintaining ecosystem health and stability.However,the spatiotemporal variations of tenebrionid beetle assemblages in the Gobi desert remain poorly understood.In this study,the monthly dynamics of tenebrionid beetles in the central part of the Hexi Corridor,Northwest China,a representative area of the Gobi desert ecosystems,were monitored using pitfall trapping during 2015-2020.The following results were showed:(1)monthly activity of tenebrionid beetles was observed from March to October,with monthly activity peaking in spring and summer,and monthly activity periods and peak of tenebrionid beetle species exhibited interspecific differences that varied from year to year;(2)spatial distribution of tenebrionid beetle community was influenced by structural factors.Specifically,at a spatial scale of 24.00 m,tenebrionid beetle community was strongly and positively correlated with the dominant species,with distinct spatial distribution patterns observed for Blaps gobiensis and Microdera kraatzi alashanica;(3)abundance of tenebrionid beetles was positively correlated with monthly mean precipitation and monthly mean temperature,whereas monthly abundance of B.gobiensis and M.kraatzi alashanica was positively correlated with monthly mean precipitation;and(4)the cover of Reaumuria soongarica(Pall.)Maxim.and Nitraria sphaerocarpa Maxim.had a positive influence on the number of tenebrionid beetles captured.In conclusion,monthly variation in precipitation significantly influences the community dynamic of tenebrionid beetles,with precipitation and shrub cover jointly determining the spatial distribution pattern of these beetles in the Gobi desert ecosystems.
基金supported by the National R&D Pro-gram through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(Nos.RS-2024-00356939 and RS-2024-00405691).
文摘As the demand for advanced computational systems capable of handling large data volumes rises,nano-electronic devices,such as memristors,are being developed for efficient data processing,especially in reservoir computing(RC).RC enables the processing of temporal information with minimal training costs,making it a promising approach for neuromorphic computing.However,current memristor devices of-ten suffer from limitations in dynamic conductance and temporal behavior,which affects their perfor-mance in these applications.In this study,we present a multilayered indium-tin-oxide(ITO)/ZnO/indium-gallium-zinc oxide(IGZO)/ZnO/ITO memristor fabricated via radiofrequency sputtering to explore its fil-amentary and nonfilamentary resistive switching(RS)characteristics.High-resolution transmission elec-tron microscopy confirmed the polycrystalline structure of the ZnO/IGZO/ZnO active layer.Dual-switching modes were demonstrated by controlling the current compliance(I_(CC)).In the filamentary mode,the memristor exhibited a large memory window(10^(3)),low-operating voltages(±2 V),excellent cycle-to-cycle stability,and multilevel switching with controlled reset-stop voltages,making it suitable for high-density memory applications.Nonfilamentary switching demonstrated stable on/off ratios above 10,en-durance up to 102 cycles,and retention suited for short-term memory.Key synaptic behaviors,such as paired-pulse facilitation(PPF),post-tetanic potentiation(PTP),and spike-rate dependent plasticity(SRDP)were successfully emulated by modulating pulse amplitude,width,and interval.Experience-dependent plasticity(EDP)was also demonstrated,further replicating biological synaptic functions.These tempo-ral properties were utilized to develop a 4-bit reservoir computing system with 16 distinct conductance states,enabling efficient information encoding.For image recognition tasks,convolutional neural net-work(CNN)simulations achieved a high accuracy of 98.45%after 25 training epochs,outperforming the accuracy achieved following artificial neural network(ANN)simulations(87.79%).These findings demon-strate that the multilayered memristor exhibits high performance in neuromorphic systems,particularly for complex pattern recognition tasks,such as digit and letter classification.
文摘Objective:Critically appraise the current state of alternate temporal bone training techniques(virtual reality(VR)simulation,3D-printed models,and mental practice(MP))compared to traditional and cadaver methods.Databases Reviewed:PubMed,Cochrane,Web of Science.Methods:Search terms utilized“temporal bone training”,“temporal bone surgical modalities”,and“training modalities temporal bone surgery”with“3D”,“rapid prototyp*”,“stereolithography”,“additive manufact*”,“plaster”,“VR”,“virtual reality”,“animal model”,“animal temporal bone”,and“synthetic”with“AND”for all literature.Exclusion criteria:non-ENT,non-English,and did not compare against alternative/traditional methods.Results:10 studies were included with 322 participants(83.9%ENT residents and 16.1%medical students).Costs include the FDM printer($300),materials($5/3D model),and<$5,000 for freeware simulator hardware.The Welling scale was used in 50%of studies.Alternate methods produced comparable or improved assessment scores to traditional and cadaver methods.Injuries were reported in three VR studies,with two reported significantly lower injury scores in the intervention groups.Time to completion was not significantly different in four VR studies,except for one finding that the time to visualize the incus was significantly lower in the intervention group.Performance after MP was not statistically different.Conclusion:More data are needed to assess whether the alternate methods are comparable to cadaveric dissection in temporal bone training.3D models and VR simulation demonstrate promising potential for novel trainees to acquire the basic skills and produce performance comparable to or significantly better than traditional methods of lectures,textbooks,CT images,and operative videos.
基金supported by the National Key Research and Development Program Project(2021YFC2400203)the Shaanxi Province Key Research and Development Program Project(2023-YBSF-120)+1 种基金the Shandong Provincial Natural Science Foundation(ZR2024QF287)the National Natural Science Foundation of China(31972907).
文摘Temporal interference(TI)is a form of stimulation that epitomizes an innovative and non-invasive approach for profound neuromodulation of the brain,a technique that has been validated in mice.Yet,the thin cranial bone structure of mice has a marginal influence on the effect of the TI technique and may not effectively showcase its effectiveness in larger animals.Based on this,we carried out TI stimulation experiments on rats.Following the TI intervention,analysis of electrophysiological data and immunofluorescence staining indicated the generation of a stimulation focus within the nucleus accumbens(depth,8.5 mm)in rats.Our findings affirm the viability of the TI methodology in the presence of thick cranial bones,furnishing efficacious parameters for profound stimulation with TI administered under such conditions.This experiment not only sheds light on the intervention effects of TI deep in the brain but also furnishes robust evidence in support of its prospective clinical utility.
