Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling technique(SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.METHODS In this retrospective cohort study,we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022.The incidence of postoperative delirium was recorded for 7 d post-surgery.Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not.A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium.The SMOTE technique was applied to enhance the model by oversampling the delirium cases.The model’s predictive accuracy was then validated.RESULTS In our study involving 611 elderly patients with abdominal malignant tumors,multivariate logistic regression analysis identified significant risk factors for postoperative delirium.These included the Charlson comorbidity index,American Society of Anesthesiologists classification,history of cerebrovascular disease,surgical duration,perioperative blood transfusion,and postoperative pain score.The incidence rate of postoperative delirium in our study was 22.91%.The original predictive model(P1)exhibited an area under the receiver operating characteristic curve of 0.862.In comparison,the SMOTE-based logistic early warning model(P2),which utilized the SMOTE oversampling algorithm,showed a slightly lower but comparable area under the curve of 0.856,suggesting no significant difference in performance between the two predictive approaches.CONCLUSION This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods,effectively addressing data imbalance.

Enhancing rock fragmentation prediction in mining operations:A hybrid GWO-RF model with SHAP interpretability

In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.

Recent advances in cobalt phosphide-based materials for electrocatalytic water splitting:From catalytic mechanism and synthesis method to optimization design

Electrochemical water splitting has long been considered an effective energy conversion technology for trans-ferring intermittent renewable electricity into hydrogen fuel,and the exploration of cost-effective and high-performance electrocatalysts is crucial in making electrolyzed water technology commercially viable.Cobalt phosphide(Co-P)has emerged as a catalyst of high potential owing to its high catalytic activity and durability in water splitting.This paper systematically reviews the latest advances in the development of Co-P-based materials for use in water splitting.The essential effects of P in enhancing the catalytic performance of the hydrogen evolution reaction and oxygen evolution reaction are first outlined.Then,versatile synthesis techniques for Co-P electrocatalysts are summarized,followed by advanced strategies to enhance the electrocatalytic performance of Co-P materials,including heteroatom doping,composite construction,integration with well-conductive sub-strates,and structure control from the viewpoint of experiment.Along with these optimization strategies,the understanding of the inherent mechanism of enhanced catalytic performance is also discussed.Finally,some existing challenges in the development of highly active and stable Co-P-based materials are clarified,and pro-spective directions for prompting the wide commercialization of water electrolysis technology are proposed.

Modern approaches for detection of volatile organic compounds in metabolic studies focusing on pathogenic bacteria:Current state of the art

Pathogenic microorganisms produce numerous metabolites,including volatile organic compounds(VOCs).Monitoring these metabolites in biological matrices(e.g.,urine,blood,or breath)can reveal the presence of specific microorganisms,enabling the early diagnosis of infections and the timely implementation of tar-geted therapy.However,complex matrices only contain trace levels of VOCs,and their constituent com-ponents can hinder determination of these compounds.Therefore,modern analytical techniques enabling the non-invasive identification and precise quantification of microbial VOCs are needed.In this paper,we discuss bacterial VOC analysis under in vitro conditions,in animal models and disease diagnosis in humans,including techniques for offline and online analysis in clinical settings.We also consider the advantages and limitations of novel microextraction techniques used to prepare biological samples for VOC analysis,in addition to reviewing current clinical studies on bacterial volatilomes that address inter-species in-teractions,the kinetics of VOC metabolism,and species-and drug-resistance specificity.

Clinical diagnostic advances in intestinal anastomotic techniques:Hand suturing,stapling,and compression devices

The development of intestinal anastomosis techniques,including hand suturing,stapling,and compression anastomoses,has been a significant advancement in surgical practice.These methods aim to prevent leakage and minimize tissue fibrosis,which can lead to stricture formation.The healing process involves various phases:hemostasis and inflammation,proliferation,and remodeling.Mechanical staplers and sutures can cause inflammation and fibrosis due to the release of profibrotic chemokines.Compression anastomosis devices,including those made of nickel-titanium alloy,offer a minimally invasive option for various surgical challenges and have shown safety and efficacy.However,despite advancements,anastomotic techniques are evaluated based on leakage risk,with complications being a primary concern.Newer devices like Magnamosis use magnetic rings for compression anastomosis,demonstrating greater strength and patency compared to stapling.Magnetic technology is also being explored for other medical treatments.While there are promising results,particularly in animal models,the realworld application in humans is limited,and further research is needed to assess their safety and practicality.

A Brief Discussion on the Development of the Silk-Weaving Industry Along the “Southern Silk Road” in Yunnan

Ancient Yunnan was one of the most significant regions along China’s ancient“Southern Silk Road.”During the Nanzhao period(738–902)of the late Tang Dynasty,Yunnan’s silk-weaving industry underwent a qualitative leap as skilled silk craftsmen from the Bashu area migrated to Yunnan and introduced mulberry planting,silkworm breeding,and advanced silk-weaving techniques from Sichuan to the region.Consequently,people in Yunnan gradually acquired expertise in brocade weaving and embroidery.Many even mastered complex silk-weaving techniques.The development and progress of the silk-weaving industry in the ancient Yunnan region were intricately linked to the economic function and value of silk as both a commodity and currency along the“Southern Silk Road.”The local government in ancient Yunnan was greatly motivated by the economic interests brought by the development of silk-related industries and recognized the significance of developing the local silk industry.They even initiated a campaign to capture skilled silk craftsmen from Sichuan,aiming to foster the growth of the silk-weaving industry in Yunnan.After years of dedicated efforts from the local government in ancient Yunnan,the region emerged as a significant hub for silk production along China’s ancient“Southern Silk Road.”Despite the devastation caused by the wars in other parts of the country,Yunnan’s silk industry continued to thrive and provide ample silk products to sustain trade along this renowned route.In the contemporary era,amidst the decline of the silk-weaving industry in eastern China,Yunnan has proposed an industrial development strategy known as“relocating the silk-weaving industry from east to west.”This involves introducing advanced silk production techniques from the eastern regions into Yunnan to enhance and enrich its local silk industry,thereby establishing it as a traditional national sector and securing a competitive position within the global silk market.The historical experience of Yunnan’s silk industry demonstrated that economic development opportunities can only be seized through proactive endeavors rather than passive anticipation.The modern Yunnan silk industry,which upholds its historical traditions,continues to actively engage in international high-end technical cooperation,thus ensuring the enduring vitality of the ancient“Southern Silk Road.”

Epileptic brain network mechanisms and neuroimaging techniques for the brain network

Epilepsy can be defined as a dysfunction of the brain network,and each type of epilepsy involves different brain-network changes that are implicated diffe rently in the control and propagation of interictal or ictal discharges.Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice.An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tra ctography,diffusion kurtosis imaging-based fiber tractography,fiber ball imagingbased tra ctography,electroencephalography,functional magnetic resonance imaging,magnetoencephalography,positron emission tomography,molecular imaging,and functional ultrasound imaging have been extensively used to delineate epileptic networks.In this review,we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy,and extensively analyze the imaging mechanisms,advantages,limitations,and clinical application ranges of each technique.A greater focus on emerging advanced technologies,new data analysis software,a combination of multiple techniques,and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

Application of Regularization Methods in the Sky Map Reconstruction of the Tianlai Cylinder Pathfinder Array

The Tianlai cylinder pathfinder is a radio interferometer array to test 21 cm intensity mapping techniques in the post-reionization era.It works in passive drift scan mode to survey the sky visible in the northern hemisphere.To deal with the large instantaneous field of view and the spherical sky,we decompose the drift scan data into m-modes,which are linearly related to the sky intensity.The sky map is reconstructed by solving the linear interferometer equations.Due to incomplete uv coverage of the interferometer baselines,this inverse problem is usually ill-posed,and regularization method is needed for its solution.In this paper,we use simulation to investigate two frequently used regularization methods,the Truncated Singular Value Decomposition(TSVD),and the Tikhonov regularization techniques.Choosing the regularization parameter is very important for its application.We employ the generalized cross validation method and the L-curve method to determine the optimal value.We compare the resulting maps obtained with the different regularization methods,and for the different parameters derived using the different criteria.While both methods can yield good maps for a range of regularization parameters,in the Tikhonov method the suppression of noisy modes are more gradually applied,produce more smooth maps which avoids some visual artefacts in the maps generated with the TSVD method.

On the Identification of N-rich Metal-poor Field Stars with Future Chinese Space Station Telescope

During the long term evolution of globular clusters(GCs), some member stars are lost to the field. The recently found nitrogen-rich(N-rich) metal-poor field stars are promising candidates of these GC escapees, since N enhancement is the fingerprint of chemically enhanced populations in GCs. In this work, we discuss the possibility of identifying N-rich metal-poor field stars with the upcoming Chinese Space Station Telescope(CSST). We focus on the main survey camera with NUV, u, g, r, i, z, y filters and slitless spectrograph with a resolution about 200.The combination of UV sensitive equipment and prominent N-related molecular lines in the UV band bodes well for the identification: the color–color diagram of(u-g) versus(g-r) is capable of separating N-rich field stars from normal halo stars, if metallicity can be estimated without using the information on u-band photometry.Besides, the synthetic spectra show that a signal-to-noise ratio of 10 is sufficient to identify N-rich field stars. In the near future, a large sample of N-rich field stars found by CSST, combined with state-of-the-art N-body simulations will be crucial to deciphering GC-Galaxy co-evolution.

A Galaxy Image Augmentation Method Based on Few-shot Learning and Generative Adversarial Networks

Galaxy morphology classifications based on machine learning are a typical technique to handle enormous amounts of astronomical observation data,but the key challenge is how to provide enough training data for the machine learning models.Therefore this article proposes an image data augmentation method that combines few-shot learning and generative adversarial networks.The Galaxy10 DECaLs data set is selected for the experiments with consistency,variance,and augmentation effects being evaluated.Three popular networks,including AlexNet,VGG,and ResNet,are used as examples to study the effectiveness of different augmentation methods on galaxy morphology classifications.Experiment results show that the proposed method can generate galaxy images and can be used for expanding the classification model’s training set.According to comparative studies,the best enhancement effect on model performance is obtained by generating a data set that is 0.5–1 time larger than the original data set.Meanwhile,different augmentation strategies have considerably varied effects on different types of galaxies.FSL-GAN achieved the best classification performance on the ResNet network for In-between Round Smooth Galaxies and Unbarred Loose Spiral Galaxies,with F1 Scores of 89.54%and 63.18%,respectively.Experimental comparison reveals that various data augmentation techniques have varied effects on different categories of galaxy morphology and machine learning models.Finally,the best augmentation strategies for each galaxy category are suggested.

Analysis of the Impact of the Blazhko Effect Both on the Van Hoof Effect and Radial Velocity Amplitude in the Star RR Lyr

The Van Hoof effect is a phase shift existing between the radial velocity curves of hydrogen and metallic lines within the atmosphere of pulsating stars.In this article,we present a study of this phenomenon through the spectra of the brightest pulsating star RR Lyr of RR Lyrae stars recorded for 22 yr.We based ourselves,on the one hand,on 1268 spectra(41 nights of observation)recorded between the years 1994 and 1997 at the Observatory of Haute Provence(OHP,France)previously observed by Chadid and Gillet,and on the other hand on 1569 spectra(46nights of observation)recorded at our Oukaimeden Observatory(Morocco)between 2015 and 2016.Through this study,we have detected information on atmospheric dynamics that had not previously been detected.Indeed,the Van Hoof effect which results in a clear correlation between the radial velocities of hydrogen and those of the metallic lines has been observed and analyzed at different Blazhko phases.A correlation between the radial velocities of different metallic lines located in the lower atmosphere has been observed as well.For the first time,we were able to show that the amplitude of the radial velocity curves deduced from the lines of hydrogen and that of FeⅡ(λ4923.921?)increases toward the minimum of the Blazhko cycle and decreases toward the maximum of the same Blazhko cycle.Furthermore,we found that the Van Hoof effect is also modulated by the Blazhko effect.Thus,toward the minimum of the Blazhko cycle the Van Hoof effect is more visible and at the maximum of the Blazhko cycle,this effect is minimal.We also observed the temporal evolution of the amplitudes of the radial velocities of the lower and upper atmosphere.When observed over a long time,we can interpret it as a function of the Blazhko phases.

Data-Driven Adaptive Predictive Control Method With Autotuned Weighting Factor for Nonlinear Systems Using Triangular Dynamic Linearization

Dear Editor,In this letter,a novel data-driven adaptive predictive control method is proposed using the triangular dynamic linearization technique.The proposed method only contains one time-varying parameter with explicit physical meaning,which can prevent severe deviation in parameter estimation.Specifically,a triangular dynamic linearization(TDL)data model is employed to predict future system outputs,and then to correct inaccurate predictive outputs,a feedback regulator is designed.An autotuned weighing factor is introduced to alleviate the computational burden in practical applications and further improve output tracking performance.Closed-loop stability conditions are derived by rigorous analysis.Simulation results are provided to demonstrate the efficacy of the proposed method.

Large Tunneling Magnetoresistance and Perfect Spin Filtering Effect in van der Waals Cu/FeX_(2)/h-BN/FeX_(2)/Cu(X=Cl,Br,I)Magnetic Tunnel Junctions

The two-dimensional magnetic van der Waals heterojunctions have opened unprecedented opportunities to explore new physics due to their potential for spintronic applications.Here,combing density functional theory with non-equilibrium Green’s function technique.

Nonlinear Robust Stabilization of Ship Roll by Convex Optimization

Dear Editor,This letter presents a nonlinear robust controller design method for ship roll stabilization by combining the dual of Lyapunov's stability theorem with the sum of squares(SOS) technique. Varying initial metacentric height and ship speed are regarded as uncertainties, sea waves are considered as external disturbances, and then the robust nonlinear controller is designed. Taking a container ship as an example, simulations are performed to verify the effectiveness of the proposed design scheme.

The Velocity Aberration Effect of the CSST Main Survey Camera

In this study,we conducted simulations to find the geometric aberrations expected for images taken by the Main Survey Camera of the Chinese Space Station Telescope(CSST)due to its motion.As anticipated by previous work,our findings indicate that the geometric distortion of light impacts the focal plane's apparent scale,with a more pronounced influence as the size of the focal plane increases.Our models suggest that the effect consistently influences the pixel scale in both the vertical and parallel directions.The apparent scale variation follows a sinusoidal distribution throughout one orbital period.Simulations reveal that the effect is particularly pronounced in the center of the Galaxy and gradually diminishes along the direction of ecliptic latitude.At low ecliptic latitudes,the total aberration leads to about a 0.94 pixel offset(a 20 minute exposure)and a 0.26 pixel offset(a 300 s exposure)at the edge of the field of view.Appropriate processings for the geometric effect during the CSST pre-and post-observation phases are presented.

Recent Advance in Synaptic Plasticity Modulation Techniques for Neuromorphic Applications

Manipulating the expression of synaptic plasticity of neuromorphic devices provides fascinating opportunities to develop hardware platforms for artifi-cial intelligence.However,great efforts have been devoted to exploring biomimetic mechanisms of plasticity simulation in the last few years.Recent progress in various plasticity modulation techniques has pushed the research of synaptic electronics from static plasticity simulation to dynamic plasticity modulation,improving the accuracy of neuromorphic computing and providing strategies for implementing neuromorphic sensing functions.Herein,several fascinating strategies for synap-tic plasticity modulation through chemical techniques,device structure design,and physical signal sensing are reviewed.For chemical techniques,the underly-ing mechanisms for the modification of functional materials were clarified and its effect on the expression of synaptic plasticity was also highlighted.Based on device structure design,the reconfigurable operation of neuromorphic devices was well demonstrated to achieve programmable neuromorphic functions.Besides,integrating the sensory units with neuromorphic processing circuits paved a new way to achieve human-like intelligent perception under the modulation of physical signals such as light,strain,and temperature.Finally,considering that the relevant technology is still in the basic exploration stage,some prospects or development suggestions are put forward to promote the development of neuromorphic devices.

Electrospinning-hot pressing technique for the fabrication of thermal and electrical storage membranes and its applications

The combination of electrospinning and hot pressing,namely the electrospinning-hot pressing technique(EHPT),is an efficient and convenient method for preparing nanofibrous composite materials with good energy storage performance.The emerging composite membrane prepared by EHPT,which exhibits the advantages of large surface area,controllable morphology,and compact structure,has attracted immense attention.In this paper,the conduction mechanism of composite membranes in thermal and electrical energy storage and the performance enhancement method based on the fabrication process of EHPT are systematically discussed.Moreover,the state-of-the-art applications of composite membranes in these two fields are introduced.In particular,in the field of thermal energy storage,EHPT-prepared membranes have longitudinal and transverse nanofibers,which generate unique thermal conductivity pathways;also,these nanofibers offer enough space for the filling of functional materials.Moreover,EHPT-prepared membranes are beneficial in thermal management systems,building energy conservation,and electrical energy storage,e.g.,improving the electrochemical properties of the separators as well as their mechanical and thermal stability.The application of electrospinning-hot pressing membranes on capacitors,lithium-ion batteries(LIBs),fuel cells,sodium-ion batteries(SIBs),and hydrogen bromine flow batteries(HBFBs)still requires examination.In the future,EHPT is expected to make the field more exciting through its own technological breakthroughs or be combined with other technologies to produce intelligent materials.

GLOBAL CONVERGENCE OF A CAUTIOUS PROJECTION BFGS ALGORITHM FOR NONCONVEX PROBLEMS WITHOUT GRADIENT LIPSCHITZ CONTINUITY

A cautious projection BFGS method is proposed for solving nonconvex unconstrained optimization problems.The global convergence of this method as well as a stronger general convergence result can be proven without a gradient Lipschitz continuity assumption,which is more in line with the actual problems than the existing modified BFGS methods and the traditional BFGS method.Under some additional conditions,the method presented has a superlinear convergence rate,which can be regarded as an extension and supplement of BFGS-type methods with the projection technique.Finally,the effectiveness and application prospects of the proposed method are verified by numerical experiments.

Purification of copper foils driven by single crystallization

High-purity copper(Cu) with excellent thermal and electrical conductivity, is crucial in modern technological applications, including heat exchangers, integrated circuits, and superconducting magnets. The current purification process is mainly based on the zone/electrolytic refining or anion exchange, however, which excessively relies on specific integrated equipment with ultra-high vacuum or chemical solution environment, and is also bothered by external contaminants and energy consumption. Here we report a simple approach to purify the Cu foils from 99.9%(3N) to 99.99%(4N) by a temperature-gradient thermal annealing technique, accompanied by the kinetic evolution of single crystallization of Cu.The success of purification mainly relies on(i) the segregation of elements with low effective distribution coefficient driven by grain-boundary movements and(ii) the high-temperature evaporation of elements with high saturated vapor pressure.The purified Cu foils display higher flexibility(elongation of 70%) and electrical conductivity(104% IACS) than that of the original commercial rolled Cu foils(elongation of 10%, electrical conductivity of ~ 100% IACS). Our results provide an effective strategy to optimize the as-produced metal medium, and therefore will facilitate the potential applications of Cu foils in precision electronic products and high-frequency printed circuit boards.

Pretreatment and analysis techniques development of TKIs in biological samples for pharmacokinetic studies and therapeutic drug monitoring

Tyrosine kinase inhibitors(TKIs)have emerged as the first-line small molecule drugs in many cancer therapies,exerting their effects by impeding aberrant cell growth and proliferation through the modulation of tyrosine kinase-mediated signaling pathways.However,there exists a substantial inter-individual variability in the concentrations of certain TKIs and their metabolites,which may render patients with compromised immune function susceptible to diverse infections despite receiving theoretically efficacious anticancer treatments,alongside other potential side effects or adverse reactions.Therefore,an urgent need exists for an up-to-date review concerning the biological matrices relevant to bioanalysis and the sampling methods,clinical pharmacokinetics,and therapeutic drug monitoring of different TKIs.This paper provides a comprehensive overview of the advancements in pretreatment methods,such as protein precipitation(PPT),liquid-liquid extraction(LLE),solid-phase extraction(SPE),micro-SPE(μ-SPE),magnetic SPE(MSPE),and vortex-assisted dispersive SPE(VA-DSPE)achieved since 2017.It also highlights the latest analysis techniques such as newly developed high performance liquid chromatography(HPLC)and high-resolution mass spectrometry(HRMS)methods,capillary electrophoresis(CE),gas chromatography(GC),supercritical fluid chromatography(SFC)procedures,surface plasmon resonance(SPR)assays as well as novel nanoprobes-based biosensing techniques.In addition,a comparison is made between the advantages and disadvantages of different approaches while presenting critical challenges and prospects in pharmacokinetic studies and therapeutic drug monitoring.