Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditiona...Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditional CSI feedback approaches face challenges such as performance degradation due to feedback delay and channel aging caused by user mobility.To address these issues,we propose a novel spatio-temporal predictive network(STPNet)that jointly integrates CSI feedback and prediction modules.STPNet employs stacked Inception modules to learn the spatial correlation and temporal evolution of CSI,which captures both the local and the global spatiotemporal features.In addition,the signal-to-noise ratio(SNR)adaptive module is designed to adapt flexibly to diverse feedback channel conditions.Simulation results demonstrate that STPNet outperforms existing channel prediction methods under various channel conditions.展开更多
In this article,we comment on the paper by Kakinuma et al published recently.We focus specifically on the diagnosis of uterine pseudoaneurysm,but we also review other uterine vascular anomalies that may be the cause o...In this article,we comment on the paper by Kakinuma et al published recently.We focus specifically on the diagnosis of uterine pseudoaneurysm,but we also review other uterine vascular anomalies that may be the cause of life-threating hemorrhage and the different causes of uterine pseudoaneurysms.Uterine artery pseudoaneurysm is a complication of both surgical gynecological and nontraumatic procedures.Massive hemorrhage is the consequence of the rupture of the pseudoaneurysm.Uterine artery pseudoaneurysm can develop after obstetric or gynecological procedures,being the most frequent after cesarean or vaginal deliveries,curettage and even during pregnancy.However,there are several cases described unrelated to pregnancy,such as after conization,hysteroscopic surgery or laparoscopic myomectomy.Hemorrhage is the clinical manifestation and it can be life-threatening so suspicion of this vascular lesion is essential for early diagnosis and treatment.However,there are other uterine vascular anomalies that may be the cause of severe hemorrhage,which must be taken into account in the differential diagnosis.Computed tomography angiography and embolization is supposed to be the first therapeutic option in most of them.展开更多
BACKGROUND Post-operative massive hemorrhage is a critical concern in oral cancer surgery,associated with severe complications and heightened morbidity and mortality rates.CASE SUMMARY A 46-year-old male with advanced...BACKGROUND Post-operative massive hemorrhage is a critical concern in oral cancer surgery,associated with severe complications and heightened morbidity and mortality rates.CASE SUMMARY A 46-year-old male with advanced poorly differentiated squamous cell carcinoma(ypT4aN3bN0M0)of the oral floor underwent extensive surgery,including total glossectomy,partial mandibulectomy,and free flap reconstruction.Postoperatively,he developed life-threatening hemorrhage on day 3 due to wound dehiscence.Rapid nursing interventions-prompt suture removal,pressure hemostasis,and multidisciplinary collaboration-controlled bleeding.Postoperative care emphasized hemodynamic monitoring,infection prevention,and rehabilitation.Despite comorbidities(hypertension,diabetes,prior stroke),the patient achieved functional recovery:Oral flap epithelialization,restored swallowing(water swallow test:Grade 1),70% tongue mobility,and 80% preoperative chewing efficiency at 6-month follow-up.This case underscores the critical role of structured nursing protocols in managing postoperative hemorrhage and optimizing outcomes in high-risk oral cancer surgery.CONCLUSION This case report highlights the pivotal role of structured nursing interventions in managing life-threatening postoperative hemorrhage following complex oral cancer surgery.By integrating meticulous preoperative risk stratification,intraoperative hemostatic collaboration,and vigilant postoperative monitoring(e.g.,timely suture management,pressure hemostasis,blood product administration),the interdisciplinary team achieved rapid hemorrhage control.Comprehensive psychological care and rehabilitation protocols further facilitated functional recovery,enabling the patient to regain swallowing,speech,and mobility despite advanced disease and comorbidities.The findings underscore that standardized nursing workflows,balancing procedural rigor with holistic patient support,are essential for mitigating complications and enhancing outcomes in high-risk head and neck surgical populations.展开更多
The growing demand for wireless connectivity has made massive multiple-input multiple-output(MIMO)a cornerstone of modern communication systems.To optimize network performance and resource allocation,an efficient and ...The growing demand for wireless connectivity has made massive multiple-input multiple-output(MIMO)a cornerstone of modern communication systems.To optimize network performance and resource allocation,an efficient and robust approach is joint device activity detection and channel estimation.In this paper,we present an approach utilizing score-based generative models to address the underdetermined nature of channel estimation,which is data-driven and well-suited for the complex and dynamic environment of massive MIMO systems.Our experimental results,based on a comprehensive dataset generated through Monte-Carlo sampling,demonstrate the high precision of our channel estimation approach,with errors reduced to as low as-45 d B,and exceptional accuracy in detecting active devices.展开更多
In this paper,the N-soliton solutions for the massive Thirring model(MTM)in laboratory coordinates are analyzed via the Riemann-Hilbert(RH)approach.The direct scattering including the analyticity,symmetries,and asympt...In this paper,the N-soliton solutions for the massive Thirring model(MTM)in laboratory coordinates are analyzed via the Riemann-Hilbert(RH)approach.The direct scattering including the analyticity,symmetries,and asymptotic behaviors of the Jost solutions as|λ|→∞andλ→0 are given.Considering that the scattering coefficients have simple zeros,the matrix RH problem,reconstruction formulas and corresponding trace formulas are also derived.Further,the N-soliton solutions in the reflectionless case are obtained explicitly in the form of determinants.The propagation characteristics of one-soliton solutions and interaction properties of two-soliton solutions are discussed.In particular,the asymptotic expressions of two-soliton solutions as|t|→∞are obtained,which show that the velocities and amplitudes of the asymptotic solitons do not change before and after interaction except the position shifts.In addition,three types of bounded states for two-soliton solutions are presented with certain parametric conditions.展开更多
To improve the accuracy and efficiency of time-varying channels estimation algorithms for millimeter Wave(mmWave)massive Multiple-Input Multiple-Output(MIMO)systems in Internet of Vehicles(IoV)scenarios,the paper prop...To improve the accuracy and efficiency of time-varying channels estimation algorithms for millimeter Wave(mmWave)massive Multiple-Input Multiple-Output(MIMO)systems in Internet of Vehicles(IoV)scenarios,the paper proposes a deep learning(DL)algorithm,Squeeze-and-Excitation Attention Residual Network(SEARNet),which integrates Squeeze-and-Excitation Attention(SEAttention)mechanism and residual module.Specifically,SEARNet considers the channel information as an image matrix,and embeds a SEAttention module in residual module to construct the SEAttention-Residual block.Through a data-driven approach,SEARNet can effectively extract key information from the channel matrix using the SEAttention mechanism,thereby reducing noise interference and estimating the channel in an accurate and efficient manner.The simulation results show that compared to two traditional and two DL channel estimation algorithms,the proposed SEARNet can achieve a maximum reduction in normalized mean square error(NMSE)of 97.66%and 84.49%at SNR of-10 dB,78.18%at SNR of 5 dB,and 43.51%at SNR of 10 dB,respectively.展开更多
The continuous growth behavior of austenite grain in 20Cr peritectic steel was analyzed by experiment and theoretical modeling.The peculiar casting experiment with different cooling rates was achieved by multigradient...The continuous growth behavior of austenite grain in 20Cr peritectic steel was analyzed by experiment and theoretical modeling.The peculiar casting experiment with different cooling rates was achieved by multigradient operation scheme,and different morphologies in austenite grain were observed at the target location.The increase in austenite grain size with increasing cooling rate was firstly revealed in steels.The anomalous grain growth theoretically results from the mechanism of peritectic transformation transiting from the diffusional to massive type,and the additional energy storage stimulates the grain boundary migration.A new kinetic model to predict the growth behavior of austenite grain during continuous cooling process was developed,and the energy storage induced by massive type peritectic transformation was novelly taken into account.The parameters in the model were fitted by multiphase field modeling and experimental results.The kinetic model was finally verified by austenite grain size in laboratory test as well as the trial data at different locations in continuously cast bloom.The coarsening behavior of austenite grain during continuous casting was predicted based on the simulated temperature history.It is found that the grain coarsening occurs generally in the mold zone at high temperature for 20Cr steel and then almost levels off in the following process.The austenite finish transformation temperature Tγand primary cooling intensity show great influence on the grain coarsening.As Tγdecreases by 1℃,the austenite grain size decreases by 4μm linearly.However,the variation of Tγagainst heat flux is in a nonlinear relationship,suggesting that low cooling rate is much more harmful for austenite grain coarsening in continuous casting.展开更多
To the Editor:Chylothorax is a serious disease characterized by rupture of the thoracic tube and milky exudation from the pleural cavity,which can lead to a variety of pathological symptoms and is life threatening[1]....To the Editor:Chylothorax is a serious disease characterized by rupture of the thoracic tube and milky exudation from the pleural cavity,which can lead to a variety of pathological symptoms and is life threatening[1].Chylothorax is common after thoracic surgery or trauma.The non-traumatic chylothorax is rare in the clinical practice,and the etiology is complex and often associated with the primary disease[2].Chylothorax is a rare complication of patients with advanced cirrhosis[3],most of which are manifested as dyspnea,cough,chest pain,and the medical treatment effect is relatively poor.We performed orthotopic liver transplantation(OLT)in a patient with advanced cirrhosis combined with massive chylothorax and chyloperitoneum.The 2-year follow-up showed that the patient’s liver function was stable and no recurrence of chylothorax.展开更多
Background:Hemorrhage remains a formidable complication of severe acute pancreatitis(SAP),with a high mortality rate.However,there is currently no effective method for identifying SAP patients who are at high risk for...Background:Hemorrhage remains a formidable complication of severe acute pancreatitis(SAP),with a high mortality rate.However,there is currently no effective method for identifying SAP patients who are at high risk for massive bleeding.The present study aimed to explore risk factors for predicting massive bleeding in SAP patients and to develop a predictive nomogram,which could facilitate early prediction,and timely appropriate interventions.Methods:We conducted a multivariate logistic regression analysis to examine the relationship between massive bleeding and variables including patient demographics,disease severity,laboratory indexes and local pancreatic complications.A novel nomogram was constructed based on these factors,and was vali-dated both internally and externally assessing its discrimination,calibration,and clinical applicability.Results:The study involved 351 patients in the training cohort,104 patients in the internal validation cohort,and 123 patients in the external validation cohort.Logistic regression analysis identified several independent risk factors for massive bleeding,including computed tomography severity index score above 8 points,Acute Physiology and Chronic Health Evaluation II score greater than 16 points,abdominal com-partment syndrome,pancreatic fistula,and sepsis.The nomogram constructed from these factors yielded an area under the receiver operating characteristic curve(AUC)of 0.896 and a coefficient of determination(R²)of 0.093.The Hosmer-Lemeshow test indicated good model fitness(P=0.654).Furthermore,the nomogram demonstrated reliable performance in both validation cohorts.Conclusions:The nomogram showed strong predictive capability for massive bleeding and could be a valuable tool for clinicians in identifying SAP patients at high risk for this complication at an early stage.展开更多
Using the existing positioning technology can easily obtain high-precision positioning information,which can save resources and reduce complexity when used in the communication field.In this paper,we propose a locatio...Using the existing positioning technology can easily obtain high-precision positioning information,which can save resources and reduce complexity when used in the communication field.In this paper,we propose a location-based user scheduling and beamforming scheme for the downlink of a massive multi-user input-output system.Specifically,we combine an analog outer beamformer with a digital inner beamformer.An outer beamformer can be selected from a codebook formed by antenna steering vectors,and then a reduced-complexity inner beamformer based on iterative orthogonal matrices and right triangular matrices(QR)decomposition is applied to cancel interuser interference.Then,we propose a low-complexity user selection algorithm using location information in this paper.We first derive the geometric angle between channel matrices,which represent the correlation between users.Furthermore,we derive the asymptotic signal to interference-plus-noise ratio(SINR)of the system in the context of two-stage beamforming using random matrix theory(RMT),taking into account inter-channel correlations and energies.Simulation results show that the algorithm can achieve higher system and speed while reducing computational complexity.展开更多
BACKGROUND Diagnosing posterior inferior cerebellar artery dissection(PICAD)using radio-logical images is challenging.Massive cerebellar infarctions resulting from spon-taneous,isolated PICAD are rare,and the associat...BACKGROUND Diagnosing posterior inferior cerebellar artery dissection(PICAD)using radio-logical images is challenging.Massive cerebellar infarctions resulting from spon-taneous,isolated PICAD are rare,and the associated clinical,imaging,and treat-ment options remain unclear.CASE SUMMARY A 39-year-old man was admitted for dizziness and unstable gait for two days.Ph-ysical examination revealed decreased right-limb muscle strength and right-sided ataxia.Brain magnetic resonance imaging(MRI)showed a massive acute right cerebellar infarction,but other modalities,including head and neck computed tomographic angiography(CTA)and magnetic resonance angiography(MRA),showed no obvious abnormalities.High-resolution vessel wall MRI(HR-VW-MRI)revealed right PICAD.The patient was diagnosed with massive cerebellar infarction caused by PICAD and active conservative treatment was initiated.The initial PICAD lesion disappeared 1.5 months after discharge,after which the patient experienced only slight weakness in his right limb for three months.CONCLUSION Since MRA and CTA may fail to identify PICAD,HR-VW-MRI is key in diagnosis and follow-up evaluation.Aggressive medication may be effective and safe for treating PICAD.展开更多
The best way to check the validity of our theories(models)is by direct comparison with the experiment(observations).However,this process suffers from numerical inaccuracies,which are not frequently studied and often r...The best way to check the validity of our theories(models)is by direct comparison with the experiment(observations).However,this process suffers from numerical inaccuracies,which are not frequently studied and often remain mostly unknown.In this study,we focus on addressing the numerical inaccuracies intrinsic to the process of comparing theory and observations.To achieve this goal,we built four-dimensional(4D)spectral grids for Wolf–Rayet stars(WC and WN spectral classes)and blue supergiants characterized by low metallicity similar to that of the Small Magellanic Cloud.In contrast to lighter(three-dimensional)grids,which rely on a priori assumptions about certain stellar parameters(e.g.,wind velocity)and thus have limited applicability,our 4D grids vary four independent parameters,enabling more flexible and broadly applicable spectral fitting.Utilizing these 4D grids,we developed and validated a fitting approach facilitating direct fits to observed spectra.Through rigorous testing on designated“test”models,we demonstrated that the numerical precision of derived essential stellar parameters,including effective temperature,mass-loss rate,luminosity,and wind velocity,is better than 0.05 dex.Furthermore,we explored the influence of unaccounted factors,including variations in the metal abundances,wind acceleration laws,and clumping,on the precision of the derived parameters.The results indicate that the first two factors have the strongest influence on the numerical accuracy of the derived stellar parameters.Variations in abundances predominantly influenced the mass-loss rate for weak-wind scenarios,while effective temperature and luminosity remained robust.We found that the wind acceleration law influences the numerical uncertainty of the derived wind parameters mostly for models with weak winds.Interestingly,different degrees of clumping demonstrated good precision for spectra with strong winds,contrasting with a decrease in the precision for weak-wind cases.We found also that the accuracy of our approach depends on spectral range and the inclusion of ultraviolet spectral range improves the precision of derived parameters,especially for an object with weak winds.展开更多
With a one-dimensional stellar evolution model,we find that massive main sequence stars can accrete mass at very high mass accretion rates without expanding much if they lose a significant fraction of this mass from t...With a one-dimensional stellar evolution model,we find that massive main sequence stars can accrete mass at very high mass accretion rates without expanding much if they lose a significant fraction of this mass from their outer layers simultaneously with mass accretion.We assume the accretion process is via an accretion disk that launches powerful jets from its inner zones.These jets remove the outer high-entropy layers of the mass-accreting star.This process operates in a negative feedback cycle,as the jets remove more envelope mass when the star expands.With the one-dimensional model,we mimic the mass removal by jets by alternating mass addition and mass removal phases.For the simulated models of 30M☉and 60M☉,the star does not expand much if we remove more than about half of the added mass in not-too-short episodes.This holds even if we deposit the energy the jets do not carry into the envelope.As the star does not expand much,its gravitational potential well stays deep,and the jets are energetic.These results are relevant to bright transient events of binary systems powered by accretion and the launching of jets,e.g.,intermediate luminosity optical transients,including some luminous red novae,the grazing envelope evolution,and the 1837–1856 Great Eruption of Eta Carinae.展开更多
The fifth-generation (5G) communication requires a highly accurate estimation of the channel state information (CSI)to take advantage of the massive multiple-input multiple-output(MIMO) system. However, traditional ch...The fifth-generation (5G) communication requires a highly accurate estimation of the channel state information (CSI)to take advantage of the massive multiple-input multiple-output(MIMO) system. However, traditional channel estimation methods do not always yield reliable estimates. The methodology of this paper consists of deep residual shrinkage network (DRSN)neural network-based method that is used to solve this problem.Thus, the channel estimation approach, based on DRSN with its learning ability of noise-containing data, is first introduced. Then,the DRSN is used to train the noise reduction process based on the results of the least square (LS) channel estimation while applying the pilot frequency subcarriers, where the initially estimated subcarrier channel matrix is considered as a three-dimensional tensor of the DRSN input. Afterward, a mixed signal to noise ratio (SNR) training data strategy is proposed based on the learning ability of DRSN under different SNRs. Moreover, a joint mixed scenario training strategy is carried out to test the multi scenarios robustness of DRSN. As for the findings, the numerical results indicate that the DRSN method outperforms the spatial-frequency-temporal convolutional neural networks (SF-CNN)with similar computational complexity and achieves better advantages in the full SNR range than the minimum mean squared error (MMSE) estimator with a limited dataset. Moreover, the DRSN approach shows robustness in different propagation environments.展开更多
Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development...Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.展开更多
The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,th...The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.展开更多
Deep coal seams show low permeability,low elastic modulus,high Poisson’s ratio,strong plasticity,high fracture initiation pressure,difficulty in fracture extension,and difficulty in proppants addition.We proposed the...Deep coal seams show low permeability,low elastic modulus,high Poisson’s ratio,strong plasticity,high fracture initiation pressure,difficulty in fracture extension,and difficulty in proppants addition.We proposed the concept of large-scale stimulation by fracture network,balanced propagation and effective support of fracture network in fracturing design and developed the extreme massive hydraulic fracturing technique for deep coalbed methane(CBM)horizontal wells.This technique involves massive injection with high pumping rate+high-intensity proppant injection+perforation with equal apertures and limited flow+temporary plugging and diverting fractures+slick water with integrated variable viscosity+graded proppants with multiple sizes.The technique was applied in the pioneering test of a multi-stage fracturing horizontal well in deep CBM of Linxing Block,eastern margin of the Ordos Basin.The injection flow rate is 18 m^(3)/min,proppant intensity is 2.1 m^(3)/m,and fracturing fluid intensity is 16.5 m^(3)/m.After fracturing,a complex fracture network was formed,with an average fracture length of 205 m.The stimulated reservoir volume was 1987×10^(4)m^(3),and the peak gas production rate reached 6.0×10^(4)m^(3)/d,which achieved efficient development of deep CBM.展开更多
In this paper,ambient IoT is used as a typical use case of massive connections for the sixth generation(6G)mobile communications where we derive the performance requirements to facilitate the evaluation of technical s...In this paper,ambient IoT is used as a typical use case of massive connections for the sixth generation(6G)mobile communications where we derive the performance requirements to facilitate the evaluation of technical solutions.A rather complete design of unsourced multiple access is proposed in which two key parts:a compressed sensing module for active user detection,and a sparse interleaver-division multiple access(SIDMA)module are simulated side by side on a same platform at balanced signal to noise ratio(SNR)operating points.With a proper combination of compressed sensing matrix,a convolutional encoder,receiver algorithms,the simulated performance results appear superior to the state-of-the-art benchmark,yet with relatively less complicated processing.展开更多
基金supported in part by the Natural Science Foundation of China under Grant Nos.U2468201 and 62221001ZTE Industry-University-Institute Cooperation Funds under Grant No.IA20240420002。
文摘Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditional CSI feedback approaches face challenges such as performance degradation due to feedback delay and channel aging caused by user mobility.To address these issues,we propose a novel spatio-temporal predictive network(STPNet)that jointly integrates CSI feedback and prediction modules.STPNet employs stacked Inception modules to learn the spatial correlation and temporal evolution of CSI,which captures both the local and the global spatiotemporal features.In addition,the signal-to-noise ratio(SNR)adaptive module is designed to adapt flexibly to diverse feedback channel conditions.Simulation results demonstrate that STPNet outperforms existing channel prediction methods under various channel conditions.
文摘In this article,we comment on the paper by Kakinuma et al published recently.We focus specifically on the diagnosis of uterine pseudoaneurysm,but we also review other uterine vascular anomalies that may be the cause of life-threating hemorrhage and the different causes of uterine pseudoaneurysms.Uterine artery pseudoaneurysm is a complication of both surgical gynecological and nontraumatic procedures.Massive hemorrhage is the consequence of the rupture of the pseudoaneurysm.Uterine artery pseudoaneurysm can develop after obstetric or gynecological procedures,being the most frequent after cesarean or vaginal deliveries,curettage and even during pregnancy.However,there are several cases described unrelated to pregnancy,such as after conization,hysteroscopic surgery or laparoscopic myomectomy.Hemorrhage is the clinical manifestation and it can be life-threatening so suspicion of this vascular lesion is essential for early diagnosis and treatment.However,there are other uterine vascular anomalies that may be the cause of severe hemorrhage,which must be taken into account in the differential diagnosis.Computed tomography angiography and embolization is supposed to be the first therapeutic option in most of them.
基金Supported by the First Batch of 2024 Social Welfare and Basic Research Projects in Zhongshan City(General Projects in the Field of Healthcare),No.2024B1100Guangdong Provincial Administration of Traditional Chinese Medicine,No.20241357.
文摘BACKGROUND Post-operative massive hemorrhage is a critical concern in oral cancer surgery,associated with severe complications and heightened morbidity and mortality rates.CASE SUMMARY A 46-year-old male with advanced poorly differentiated squamous cell carcinoma(ypT4aN3bN0M0)of the oral floor underwent extensive surgery,including total glossectomy,partial mandibulectomy,and free flap reconstruction.Postoperatively,he developed life-threatening hemorrhage on day 3 due to wound dehiscence.Rapid nursing interventions-prompt suture removal,pressure hemostasis,and multidisciplinary collaboration-controlled bleeding.Postoperative care emphasized hemodynamic monitoring,infection prevention,and rehabilitation.Despite comorbidities(hypertension,diabetes,prior stroke),the patient achieved functional recovery:Oral flap epithelialization,restored swallowing(water swallow test:Grade 1),70% tongue mobility,and 80% preoperative chewing efficiency at 6-month follow-up.This case underscores the critical role of structured nursing protocols in managing postoperative hemorrhage and optimizing outcomes in high-risk oral cancer surgery.CONCLUSION This case report highlights the pivotal role of structured nursing interventions in managing life-threatening postoperative hemorrhage following complex oral cancer surgery.By integrating meticulous preoperative risk stratification,intraoperative hemostatic collaboration,and vigilant postoperative monitoring(e.g.,timely suture management,pressure hemostasis,blood product administration),the interdisciplinary team achieved rapid hemorrhage control.Comprehensive psychological care and rehabilitation protocols further facilitated functional recovery,enabling the patient to regain swallowing,speech,and mobility despite advanced disease and comorbidities.The findings underscore that standardized nursing workflows,balancing procedural rigor with holistic patient support,are essential for mitigating complications and enhancing outcomes in high-risk head and neck surgical populations.
文摘The growing demand for wireless connectivity has made massive multiple-input multiple-output(MIMO)a cornerstone of modern communication systems.To optimize network performance and resource allocation,an efficient and robust approach is joint device activity detection and channel estimation.In this paper,we present an approach utilizing score-based generative models to address the underdetermined nature of channel estimation,which is data-driven and well-suited for the complex and dynamic environment of massive MIMO systems.Our experimental results,based on a comprehensive dataset generated through Monte-Carlo sampling,demonstrate the high precision of our channel estimation approach,with errors reduced to as low as-45 d B,and exceptional accuracy in detecting active devices.
基金supported by the National Natural Science Foundation of China(Grant Nos.12475003 and11705284)by the Natural Science Foundation of Beijing Municipality(Grant Nos.1232022 and 1212007)。
文摘In this paper,the N-soliton solutions for the massive Thirring model(MTM)in laboratory coordinates are analyzed via the Riemann-Hilbert(RH)approach.The direct scattering including the analyticity,symmetries,and asymptotic behaviors of the Jost solutions as|λ|→∞andλ→0 are given.Considering that the scattering coefficients have simple zeros,the matrix RH problem,reconstruction formulas and corresponding trace formulas are also derived.Further,the N-soliton solutions in the reflectionless case are obtained explicitly in the form of determinants.The propagation characteristics of one-soliton solutions and interaction properties of two-soliton solutions are discussed.In particular,the asymptotic expressions of two-soliton solutions as|t|→∞are obtained,which show that the velocities and amplitudes of the asymptotic solitons do not change before and after interaction except the position shifts.In addition,three types of bounded states for two-soliton solutions are presented with certain parametric conditions.
基金supported in part by the National Natural Science Foundation of China under Grants U2001213 and 62261024in part by National Key Research and Development Project under Grant 2020YFB1807204in part by Key Laboratory of Universal Wireless Communications(BUPT),Ministry of Education under Grant KFKT2022101.
文摘To improve the accuracy and efficiency of time-varying channels estimation algorithms for millimeter Wave(mmWave)massive Multiple-Input Multiple-Output(MIMO)systems in Internet of Vehicles(IoV)scenarios,the paper proposes a deep learning(DL)algorithm,Squeeze-and-Excitation Attention Residual Network(SEARNet),which integrates Squeeze-and-Excitation Attention(SEAttention)mechanism and residual module.Specifically,SEARNet considers the channel information as an image matrix,and embeds a SEAttention module in residual module to construct the SEAttention-Residual block.Through a data-driven approach,SEARNet can effectively extract key information from the channel matrix using the SEAttention mechanism,thereby reducing noise interference and estimating the channel in an accurate and efficient manner.The simulation results show that compared to two traditional and two DL channel estimation algorithms,the proposed SEARNet can achieve a maximum reduction in normalized mean square error(NMSE)of 97.66%and 84.49%at SNR of-10 dB,78.18%at SNR of 5 dB,and 43.51%at SNR of 10 dB,respectively.
基金supported by the Fundamental Research Funds for the Central Universities(No.FRF-TP-19-017A3)National Natural Science Foundation of China(No.51874026).
文摘The continuous growth behavior of austenite grain in 20Cr peritectic steel was analyzed by experiment and theoretical modeling.The peculiar casting experiment with different cooling rates was achieved by multigradient operation scheme,and different morphologies in austenite grain were observed at the target location.The increase in austenite grain size with increasing cooling rate was firstly revealed in steels.The anomalous grain growth theoretically results from the mechanism of peritectic transformation transiting from the diffusional to massive type,and the additional energy storage stimulates the grain boundary migration.A new kinetic model to predict the growth behavior of austenite grain during continuous cooling process was developed,and the energy storage induced by massive type peritectic transformation was novelly taken into account.The parameters in the model were fitted by multiphase field modeling and experimental results.The kinetic model was finally verified by austenite grain size in laboratory test as well as the trial data at different locations in continuously cast bloom.The coarsening behavior of austenite grain during continuous casting was predicted based on the simulated temperature history.It is found that the grain coarsening occurs generally in the mold zone at high temperature for 20Cr steel and then almost levels off in the following process.The austenite finish transformation temperature Tγand primary cooling intensity show great influence on the grain coarsening.As Tγdecreases by 1℃,the austenite grain size decreases by 4μm linearly.However,the variation of Tγagainst heat flux is in a nonlinear relationship,suggesting that low cooling rate is much more harmful for austenite grain coarsening in continuous casting.
基金supported by grants from the Clinical Research and Development Program of Zhongnan Hospital of Wuhan University(LCYF 202205)the National Natural Science Foundation of China(82370671)。
文摘To the Editor:Chylothorax is a serious disease characterized by rupture of the thoracic tube and milky exudation from the pleural cavity,which can lead to a variety of pathological symptoms and is life threatening[1].Chylothorax is common after thoracic surgery or trauma.The non-traumatic chylothorax is rare in the clinical practice,and the etiology is complex and often associated with the primary disease[2].Chylothorax is a rare complication of patients with advanced cirrhosis[3],most of which are manifested as dyspnea,cough,chest pain,and the medical treatment effect is relatively poor.We performed orthotopic liver transplantation(OLT)in a patient with advanced cirrhosis combined with massive chylothorax and chyloperitoneum.The 2-year follow-up showed that the patient’s liver function was stable and no recurrence of chylothorax.
基金supported by grants from the National Nature Science Foundation of China(82370651 and 82070657).
文摘Background:Hemorrhage remains a formidable complication of severe acute pancreatitis(SAP),with a high mortality rate.However,there is currently no effective method for identifying SAP patients who are at high risk for massive bleeding.The present study aimed to explore risk factors for predicting massive bleeding in SAP patients and to develop a predictive nomogram,which could facilitate early prediction,and timely appropriate interventions.Methods:We conducted a multivariate logistic regression analysis to examine the relationship between massive bleeding and variables including patient demographics,disease severity,laboratory indexes and local pancreatic complications.A novel nomogram was constructed based on these factors,and was vali-dated both internally and externally assessing its discrimination,calibration,and clinical applicability.Results:The study involved 351 patients in the training cohort,104 patients in the internal validation cohort,and 123 patients in the external validation cohort.Logistic regression analysis identified several independent risk factors for massive bleeding,including computed tomography severity index score above 8 points,Acute Physiology and Chronic Health Evaluation II score greater than 16 points,abdominal com-partment syndrome,pancreatic fistula,and sepsis.The nomogram constructed from these factors yielded an area under the receiver operating characteristic curve(AUC)of 0.896 and a coefficient of determination(R²)of 0.093.The Hosmer-Lemeshow test indicated good model fitness(P=0.654).Furthermore,the nomogram demonstrated reliable performance in both validation cohorts.Conclusions:The nomogram showed strong predictive capability for massive bleeding and could be a valuable tool for clinicians in identifying SAP patients at high risk for this complication at an early stage.
基金supported by the National Natural Science Foundation of China(61901341).
文摘Using the existing positioning technology can easily obtain high-precision positioning information,which can save resources and reduce complexity when used in the communication field.In this paper,we propose a location-based user scheduling and beamforming scheme for the downlink of a massive multi-user input-output system.Specifically,we combine an analog outer beamformer with a digital inner beamformer.An outer beamformer can be selected from a codebook formed by antenna steering vectors,and then a reduced-complexity inner beamformer based on iterative orthogonal matrices and right triangular matrices(QR)decomposition is applied to cancel interuser interference.Then,we propose a low-complexity user selection algorithm using location information in this paper.We first derive the geometric angle between channel matrices,which represent the correlation between users.Furthermore,we derive the asymptotic signal to interference-plus-noise ratio(SINR)of the system in the context of two-stage beamforming using random matrix theory(RMT),taking into account inter-channel correlations and energies.Simulation results show that the algorithm can achieve higher system and speed while reducing computational complexity.
基金Supported by Shenzhen Second People’s Hospital Clinical Research Fund of Shenzhen High-level Hospital Construction Project,No.20243357001.
文摘BACKGROUND Diagnosing posterior inferior cerebellar artery dissection(PICAD)using radio-logical images is challenging.Massive cerebellar infarctions resulting from spon-taneous,isolated PICAD are rare,and the associated clinical,imaging,and treat-ment options remain unclear.CASE SUMMARY A 39-year-old man was admitted for dizziness and unstable gait for two days.Ph-ysical examination revealed decreased right-limb muscle strength and right-sided ataxia.Brain magnetic resonance imaging(MRI)showed a massive acute right cerebellar infarction,but other modalities,including head and neck computed tomographic angiography(CTA)and magnetic resonance angiography(MRA),showed no obvious abnormalities.High-resolution vessel wall MRI(HR-VW-MRI)revealed right PICAD.The patient was diagnosed with massive cerebellar infarction caused by PICAD and active conservative treatment was initiated.The initial PICAD lesion disappeared 1.5 months after discharge,after which the patient experienced only slight weakness in his right limb for three months.CONCLUSION Since MRA and CTA may fail to identify PICAD,HR-VW-MRI is key in diagnosis and follow-up evaluation.Aggressive medication may be effective and safe for treating PICAD.
文摘The best way to check the validity of our theories(models)is by direct comparison with the experiment(observations).However,this process suffers from numerical inaccuracies,which are not frequently studied and often remain mostly unknown.In this study,we focus on addressing the numerical inaccuracies intrinsic to the process of comparing theory and observations.To achieve this goal,we built four-dimensional(4D)spectral grids for Wolf–Rayet stars(WC and WN spectral classes)and blue supergiants characterized by low metallicity similar to that of the Small Magellanic Cloud.In contrast to lighter(three-dimensional)grids,which rely on a priori assumptions about certain stellar parameters(e.g.,wind velocity)and thus have limited applicability,our 4D grids vary four independent parameters,enabling more flexible and broadly applicable spectral fitting.Utilizing these 4D grids,we developed and validated a fitting approach facilitating direct fits to observed spectra.Through rigorous testing on designated“test”models,we demonstrated that the numerical precision of derived essential stellar parameters,including effective temperature,mass-loss rate,luminosity,and wind velocity,is better than 0.05 dex.Furthermore,we explored the influence of unaccounted factors,including variations in the metal abundances,wind acceleration laws,and clumping,on the precision of the derived parameters.The results indicate that the first two factors have the strongest influence on the numerical accuracy of the derived stellar parameters.Variations in abundances predominantly influenced the mass-loss rate for weak-wind scenarios,while effective temperature and luminosity remained robust.We found that the wind acceleration law influences the numerical uncertainty of the derived wind parameters mostly for models with weak winds.Interestingly,different degrees of clumping demonstrated good precision for spectra with strong winds,contrasting with a decrease in the precision for weak-wind cases.We found also that the accuracy of our approach depends on spectral range and the inclusion of ultraviolet spectral range improves the precision of derived parameters,especially for an object with weak winds.
基金A grant from the Pazy Foundation supported this research.
文摘With a one-dimensional stellar evolution model,we find that massive main sequence stars can accrete mass at very high mass accretion rates without expanding much if they lose a significant fraction of this mass from their outer layers simultaneously with mass accretion.We assume the accretion process is via an accretion disk that launches powerful jets from its inner zones.These jets remove the outer high-entropy layers of the mass-accreting star.This process operates in a negative feedback cycle,as the jets remove more envelope mass when the star expands.With the one-dimensional model,we mimic the mass removal by jets by alternating mass addition and mass removal phases.For the simulated models of 30M☉and 60M☉,the star does not expand much if we remove more than about half of the added mass in not-too-short episodes.This holds even if we deposit the energy the jets do not carry into the envelope.As the star does not expand much,its gravitational potential well stays deep,and the jets are energetic.These results are relevant to bright transient events of binary systems powered by accretion and the launching of jets,e.g.,intermediate luminosity optical transients,including some luminous red novae,the grazing envelope evolution,and the 1837–1856 Great Eruption of Eta Carinae.
基金supported by the National Key Scientific Instrument and Equipment Development Project(61827801).
文摘The fifth-generation (5G) communication requires a highly accurate estimation of the channel state information (CSI)to take advantage of the massive multiple-input multiple-output(MIMO) system. However, traditional channel estimation methods do not always yield reliable estimates. The methodology of this paper consists of deep residual shrinkage network (DRSN)neural network-based method that is used to solve this problem.Thus, the channel estimation approach, based on DRSN with its learning ability of noise-containing data, is first introduced. Then,the DRSN is used to train the noise reduction process based on the results of the least square (LS) channel estimation while applying the pilot frequency subcarriers, where the initially estimated subcarrier channel matrix is considered as a three-dimensional tensor of the DRSN input. Afterward, a mixed signal to noise ratio (SNR) training data strategy is proposed based on the learning ability of DRSN under different SNRs. Moreover, a joint mixed scenario training strategy is carried out to test the multi scenarios robustness of DRSN. As for the findings, the numerical results indicate that the DRSN method outperforms the spatial-frequency-temporal convolutional neural networks (SF-CNN)with similar computational complexity and achieves better advantages in the full SNR range than the minimum mean squared error (MMSE) estimator with a limited dataset. Moreover, the DRSN approach shows robustness in different propagation environments.
基金National Key Research and Development Program of China(No.2023YFB3907103).
文摘Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grants 61941104,61921004the Key Research and Development Program of Shandong Province under Grant 2020CXGC010108+1 种基金the Southeast University-China Mobile Research Institute Joint Innovation Centersupported in part by the Scientific Research Foundation of Graduate School of Southeast University under Grant YBPY2118.
文摘The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.
基金Supported by the National Natural Science Foundation of China Project(52274014)Comprehensive Scientific Research Project of China National Offshore Oil Corporation(KJZH-2023-2303)。
文摘Deep coal seams show low permeability,low elastic modulus,high Poisson’s ratio,strong plasticity,high fracture initiation pressure,difficulty in fracture extension,and difficulty in proppants addition.We proposed the concept of large-scale stimulation by fracture network,balanced propagation and effective support of fracture network in fracturing design and developed the extreme massive hydraulic fracturing technique for deep coalbed methane(CBM)horizontal wells.This technique involves massive injection with high pumping rate+high-intensity proppant injection+perforation with equal apertures and limited flow+temporary plugging and diverting fractures+slick water with integrated variable viscosity+graded proppants with multiple sizes.The technique was applied in the pioneering test of a multi-stage fracturing horizontal well in deep CBM of Linxing Block,eastern margin of the Ordos Basin.The injection flow rate is 18 m^(3)/min,proppant intensity is 2.1 m^(3)/m,and fracturing fluid intensity is 16.5 m^(3)/m.After fracturing,a complex fracture network was formed,with an average fracture length of 205 m.The stimulated reservoir volume was 1987×10^(4)m^(3),and the peak gas production rate reached 6.0×10^(4)m^(3)/d,which achieved efficient development of deep CBM.
文摘In this paper,ambient IoT is used as a typical use case of massive connections for the sixth generation(6G)mobile communications where we derive the performance requirements to facilitate the evaluation of technical solutions.A rather complete design of unsourced multiple access is proposed in which two key parts:a compressed sensing module for active user detection,and a sparse interleaver-division multiple access(SIDMA)module are simulated side by side on a same platform at balanced signal to noise ratio(SNR)operating points.With a proper combination of compressed sensing matrix,a convolutional encoder,receiver algorithms,the simulated performance results appear superior to the state-of-the-art benchmark,yet with relatively less complicated processing.
