Dynamic constraint and objective generation approach for real-time train rescheduling model under human-computer interaction

Real-time train rescheduling plays a vital role in railway transportation as it is crucial for maintaining punctuality and reliability in rail operations.In this paper,we propose a rescheduling model that incorporates constraints and objectives generated through human-computer interaction.This approach ensures that the model is aligned with practical requirements and daily operational tasks while facilitating iterative train rescheduling.The dispatcher’s empirical knowledge is integrated into the train rescheduling process using a human-computer interaction framework.We introduce six interfaces to dynamically construct constraints and objectives that capture human intentions.By summarizing rescheduling rules,we devise a rule-based conflict detection-resolution heuristic algorithm to effectively solve the formulated model.A series of numerical experiments are presented,demonstrating strong performance across the entire system.Furthermore,theflexibility of rescheduling is enhanced through secondary analysis-driven solutions derived from the outcomes of humancomputer interactions in the previous step.This proposed interaction method complements existing literature on rescheduling methods involving human-computer interactions.It serves as a tool to aid dispatchers in identifying more feasible solutions in accordance with their empirical rescheduling strategies.

Enhancing Human-Machine Interaction:Real-Time Emotion Recognition through Speech Analysis

Humans,as intricate beings driven by a multitude of emotions,possess a remarkable ability to decipher and respond to socio-affective cues.However,many individuals and machines struggle to interpret such nuanced signals,including variations in tone of voice.This paper explores the potential of intelligent technologies to bridge this gap and improve the quality of conversations.In particular,the authors propose a real-time processing method that captures and evaluates emotions in speech,utilizing a terminal device like the Raspberry Pi computer.Furthermore,the authors provide an overview of the current research landscape surrounding speech emotional recognition and delve into our methodology,which involves analyzing audio files from renowned emotional speech databases.To aid incomprehension,the authors present visualizations of these audio files in situ,employing dB-scaled Mel spectrograms generated through TensorFlow and Matplotlib.The authors use a support vector machine kernel and a Convolutional Neural Network with transfer learning to classify emotions.Notably,the classification accuracies achieved are 70% and 77%,respectively,demonstrating the efficacy of our approach when executed on an edge device rather than relying on a server.The system can evaluate pure emotion in speech and provide corresponding visualizations to depict the speaker’s emotional state in less than one second on a Raspberry Pi.These findings pave the way for more effective and emotionally intelligent human-machine interactions in various domains.

基于Real-time PCR法检测乳粉中牛源性成分定量研究

基于Real-timePCR建立了乳粉中牛源性成分相对定量检测方法,并对牛的特异性引物与探针进行了特异性、灵敏度和稳定性测试。通过模拟不同浓度牛乳粉与马乳粉混合样本,根据其△Ct值的函数关系进行线性拟合进而绘制标准曲线,建立乳粉中牛源性成分的相对定量检测。结果显示,该方法的最低检测限为0.00001 mg/mL,回收率为91.11%~119.2%,组间变异系数≤0.58%、组内变异系数≤1.44%。说明该方法在特异性与稳定性上适用于乳粉中牛源性成分及含量的掺假检测。

Machine learning with active pharmaceutical ingredient/polymer interaction mechanism:Prediction for complex phase behaviors of pharmaceuticals and formulations

The high throughput prediction of the thermodynamic phase behavior of active pharmaceutical ingredients(APIs)with pharmaceutically relevant excipients remains a major scientific challenge in the screening of pharmaceutical formulations.In this work,a developed machine-learning model efficiently predicts the solubility of APIs in polymers by learning the phase equilibrium principle and using a few molecular descriptors.Under the few-shot learning framework,thermodynamic theory(perturbed-chain statistical associating fluid theory)was used for data augmentation,and computational chemistry was applied for molecular descriptors'screening.The results showed that the developed machine-learning model can predict the API-polymer phase diagram accurately,broaden the solubility data of APIs in polymers,and reproduce the relationship between API solubility and the interaction mechanisms between API and polymer successfully,which provided efficient guidance for the development of pharmaceutical formulations.

一种基于real-time PCR技术的TTV检测方法的建立及应用

目的:本研究旨在开发一种具有更高灵敏度和特异性的TTV检测技术,为揭示TTV在多种疾病过程中的作用提供重要的技术支持。方法:为了更精确、灵敏的检测TTV,本研究分析了目前公布的所有亚型的TTV基因序列,在此基础上建立了一种基于UTR区域的real-time PCR检测方法,并与文献报道应用较为广泛的PCR检测方法进行了对比。结果:本研究建立的方法在1×10^(7)~1×10^(1) copies/μL标准品浓度范围内具有良好的线性关系,相关系数为1.000,斜率为-3.446,检测下限为1×10^(1) copies/μL。重复性试验结果显示,组内变异系数为7.22%,表明本方法重复性、稳定性较强。针对30份临床样本,使用本研究建立的real-time PCR检测方法及目前被多个研究所使用的4套引物进行对比。结果表明,本研究所建立的方法灵敏度显著高于文献中报道的4种方法(P<0.01);Sanger测序结果表明,本方法检测出的30份阳性样本均为TTV,检测特异性为100%。结论:本研究采用基于TaqMan探针的real-time PCR检测方法,检测灵敏性高、覆盖基因型范围广,尤其对于TTV病毒载量较低的情况下能够进行定量检测,对于TTV病毒的致病性及作为免疫标志物的应用提供重要的技术支持。

Electrostatic Interaction-directed Construction of Hierarchical Nanostructured Carbon Composite with Dual Electrical Conductive Networks for Zinc-ion Hybrid Capacitors with Ultrastability

Metal-organic framework(MOF)-derived carbon composites have been considered as the promising materials for energy storage.However,the construction of MOF-based composites with highly controllable mode via the liquid-liquid synthesis method has a great challenge because of the simultaneous heterogeneous nucleation on substrates and the self-nucleation of individual MOF nanocrystals in the liquid phase.Herein,we report a bidirectional electrostatic generated self-assembly strategy to achieve the precisely controlled coatings of single-layer nanoscale MOFs on a range of substrates,including carbon nanotubes(CNTs),graphene oxide(GO),MXene,layered double hydroxides(LDHs),MOFs,and SiO_(2).The obtained MOF-based nanostructured carbon composite exhibits the hierarchical porosity(V_(meso)/V_(micro)∶2.4),ultrahigh N content of 12.4 at.%and"dual electrical conductive networks."The assembled aqueous zinc-ion hybrid capacitor(ZIC)with the prepared nanocarbon composite as a cathode shows a high specific capacitance of 236 F g^(-1)at 0.5 A g^(-1),great rate performance of 98 F g^(-1)at 100 A g^(-1),and especially,an ultralong cycling stability up to 230000 cycles with the capacitance retention of 90.1%.This work develops a repeatable and general method for the controlled construction of MOF coatings on various functional substrates and further fabricates carbon composites for ZICs with ultrastability.

Biological Interaction and Imaging of Ultrasmall Gold Nanoparticles

Ultrasmall gold nanoparticles(AuNPs)typically includes atomically precise gold nanoclusters(AuNCs)and AuNPs with a core size below 3 nm.Serving as a bridge between small molecules and traditional inorganic nanoparticles,the ultrasmall AuNPs show the unique advantages of both small molecules(e.g.,rapid distribution,renal clearance,low non-specific organ accumulation)and nanoparticles(e.g.,long blood circulation and enhanced permeability and retention effect).The emergence of ultrasmall AuNPs creates significant opportunities to address many challenges in the health field including disease diagnosis,monitoring and treatment.Since the nano–bio interaction dictates the overall biological applications of the ultrasmall AuNPs,this review elucidates the recent advances in the biological interactions and imaging of ultrasmall AuNPs.We begin with the introduction of the factors that influence the cellular interactions of ultrasmall AuNPs.We then discuss the organ interactions,especially focus on the interactions of the liver and kidneys.We further present the recent advances in the tumor interactions of ultrasmall AuNPs.In addition,the imaging performance of the ultrasmall AuNPs is summarized and discussed.Finally,we summarize this review and provide some perspective on the future research direction of the ultrasmall AuNPs,aiming to accelerate their clinical translation.

Ecological network analysis reveals complex responses of tree species life stage interactions to stand variables

Tree interactions are essential for the structure,dynamics,and function of forest ecosystems,but variations in the architecture of life-stage interaction networks(LSINs)across forests is unclear.Here,we constructed 16 LSINs in the mountainous forests of northwest Hebei,China based on crown overlap from four mixed forests with two dominant tree species.Our results show that LSINs decrease the complexity of stand densities and basal areas due to the interaction cluster differentiation.In addition,we found that mature trees and saplings play different roles,the first acting as“hub”life stages with high connectivity and the second,as“bridges”controlling information flow with high centrality.Across the forests,life stages with higher importance showed better parameter stability within LSINs.These results reveal that the structure of tree interactions among life stages is highly related to stand variables.Our efforts contribute to the understanding of LSIN complexity and provide a basis for further research on tree interactions in complex forest communities.

Real-time data processing method for CO_(2) dispersion interferometer on EAST

A real-time data processing system is designed for the carbon dioxide dispersion interferometer(CO_(2)-DI)on EAST.The system utilizes the parallel and pipelining capabilities of an fieldprogrammable gate array(FPGA)to digitize and process the intensity of signals from the detector.Finally,the real-time electron density signals are exported through a digital-to-analog converter(DAC)module in the form of analog signals.The system has been successfully applied in the CO_(2)-DI system to provide low-latency electron density input to the plasma control system on EAST.Experimental results of the latest campaign with long-pulse discharges on EAST(2022–2023)demonstrate that the system can respond effectively in the case of rapid density changes,proving its reliability and accuracy for future electron density calculation.

A real-time performance improvement method for composite time scale

The composite time scale(CTS)provides a stable,accurate,and reliable time scale for modern society.The improvement of CTS’s real-time performance will improve its stability,which strengths related applications’performance.Aiming at this goal,a method achieved by determining the optimal calculation interval and accelerating adjustment stage is proposed in this paper.The determinants of the CTS’s calculation interval(characteristics of the clock ensemble,the measurement noise,the time and frequency synchronization system’s noise and the auxiliary output generator noise floor)are studied and the optimal calculation interval is obtained.We also investigate the effect of ensemble algorithm’s initial parameters on the CTS’s adjustment stage.A strategy to get the reasonable initial parameters of ensemble algorithm is designed.The results show that the adjustment stage can be finished rapidly or even can be shorten to zero with reasonable initial parameters.On this basis,we experimentally generate a distributed CTS with a calculation interval of 500 s and its stability outperforms those of the member clocks when the averaging time is longer than1700 s.The experimental result proves that the CTS’s real-time performance is significantly improved.

Real-Time Intelligent Diagnosis of Co-frequency Vibration Faults in Rotating Machinery Based on Lightweight-Convolutional Neural Networks

The co-frequency vibration fault is one of the common faults in the operation of rotating equipment,and realizing the real-time diagnosis of the co-frequency vibration fault is of great significance for monitoring the health state and carrying out vibration suppression of the equipment.In engineering scenarios,co-frequency vibration faults are highlighted by rotational frequency and are difficult to identify,and existing intelligent methods require more hardware conditions and are exclusively time-consuming.Therefore,Lightweight-convolutional neural networks(LW-CNN)algorithm is proposed in this paper to achieve real-time fault diagnosis.The critical parameters are discussed and verified by simulated and experimental signals for the sliding window data augmentation method.Based on LW-CNN and data augmentation,the real-time intelligent diagnosis of co-frequency is realized.Moreover,a real-time detection method of fault diagnosis algorithm is proposed for data acquisition to fault diagnosis.It is verified by experiments that the LW-CNN and sliding window methods are used with high accuracy and real-time performance.

Study of the Relationship Between New Ionic Interaction Parameters and Salt Solubility in Electrolyte Solutions Based on Molecular Dynamics Simulation

Studying the relationship between ionic interactions and salt solubility in seawater has implications for seawater desalination and mineral extraction.In this paper,a new method of expressing ion-to-ion interaction is proposed by using molecular dynamics simulation,and the relationship between ion-to-ion interaction and salt solubility in a simulated seawater water-salt system is investigated.By analyzing the variation of distance and contact time between ions in an electrolyte solution,from both spatial and temporal perspectives,new parameters were proposed to describe the interaction between ions:interaction distance(ID),and interaction time ratio(ITR).The best correlation between characteristic time ratio and solubility was found for a molar ratio of salt-to-water of 10:100 with a correlation coefficient of 0.96.For the same salt,a positive correlation was found between CTR and the molar ratio of salt and water.For type 1-1,type 2-1,type 1-2,and type 2-2 salts,the correlation coefficients between CTR and solubility were 0.93,0.96,0.92,and 0.98 for a salt-to-water molar ratio of 10:100,respectively.The solubility of multiple salts was predicted by simulations and compared with experimental values,yielding an average relative deviation of 12.4%.The new ion-interaction parameters offer significant advantages in describing strongly correlated and strongly hydrated electrolyte solutions.

Integrated strategy for real-time wind power fluctuation mitigation and energy storage system control

To address the impact of wind-power fluctuations on the stability of power systems,we propose a comprehensive approach that integrates multiple strategies and methods to enhance the efficiency and reliability of a system.First,we employ a strategy that restricts long-and short-term power output deviations to smoothen wind power fluctuations in real time.Second,we adopt the sliding window instantaneous complete ensemble empirical mode decomposition with adaptive noise(SW-ICEEMDAN)strategy to achieve real-time decomposition of the energy storage power,facilitating internal power distribution within the hybrid energy storage system.Finally,we introduce a rule-based multi-fuzzy control strategy for the secondary adjustment of the initial power allocation commands for different energy storage components.Through simulation validation,we demonstrate that the proposed comprehensive control strategy can smoothen wind power fluctuations in real time and decompose energy storage power.Compared with traditional empirical mode decomposition(EMD),ensemble empirical mode decomposition(EEMD),and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)decomposition strategies,the configuration of the energy storage system under the SW-ICEEMDAN control strategy is more optimal.Additionally,the state-of-charge of energy storage components fluctuates within a reasonable range,enhancing the stability of the power system and ensuring the secure operation of the energy storage system.

Interatomic Interaction Models for Magnetic Materials:Recent Advances

Atomistic modeling is a widely employed theoretical method of computational materials science.It has found particular utility in the study of magnetic materials.Initially,magnetic empirical interatomic potentials or spinpolarized density functional theory(DFT)served as the primary models for describing interatomic interactions in atomistic simulations of magnetic systems.Furthermore,in recent years,a new class of interatomic potentials known as magnetic machine-learning interatomic potentials(magnetic MLIPs)has emerged.These MLIPs combine the computational efficiency,in terms of CPU time,of empirical potentials with the accuracy of DFT calculations.In this review,our focus lies on providing a comprehensive summary of the interatomic interaction models developed specifically for investigating magnetic materials.We also delve into the various problem classes to which these models can be applied.Finally,we offer insights into the future prospects of interatomic interaction model development for the exploration of magnetic materials.

Nanomaterial-assisted wearable glucose biosensors for noninvasive real-time monitoring:Pioneering point-of-care and beyond

This review explores glucose monitoring and management strategies,emphasizing the need for reliable and userfriendly wearable sensors that are the next generation of sensors for continuous glucose detection.In addition,examines key strategies for designing glucose sensors that are multi-functional,reliable,and cost-effective in a variety of contexts.The unique features of effective diabetes management technology are highlighted,with a focus on using nano/biosensor devices that can quickly and accurately detect glucose levels in the blood,improving patient treatment and control of potential diabetes-related infections.The potential of next-generation wearable and touch-sensitive nano biomedical sensor engineering designs for providing full control in assessing implantable,continuous glucose monitoring is also explored.The challenges of standardizing drug or insulin delivery doses,low-cost,real-time detection of increased blood sugar levels in diabetics,and early digital health awareness controls for the adverse effects of injectable medication are identified as unmet needs.Also,the market for biosensors is expected to expand significantly due to the rising need for portable diagnostic equipment and an ever-increasing diabetic population.The paper concludes by emphasizing the need for further research and development of glucose biosensors to meet the stringent requirements for sensitivity and specificity imposed by clinical diagnostics while being cost-effective,stable,and durable.

Resistive field generation in intense proton beam interaction with solid targets

The Brown-Preston-Singleton(BPS)stopping power model is added to our previously developed hybrid code to model ion beam-plasma interaction.Hybrid simulations show that both resistive field and ion scattering effects are important for proton beam transport in a solid target,in which they compete with each other.When the target is not completely ionized,the self-generated resistive field effect dominates over the ion scattering effect.However,when the target is completely ionized,this situation is reversed.Moreover,it is found that Ohmic heating is important for higher current densities and materials with high resistivity.The energy fraction deposited as Ohmic heating can be as high as 20%-30%.Typical ion divergences with half-angles of about 5°-10°will modify the proton energy deposition substantially and should be taken into account.

Real-Time Detection and Instance Segmentation of Strawberry in Unstructured Environment

The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-ronment is a challenging task.Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy.To this end,the present study proposes an Efficient YOLACT(E-YOLACT)algorithm for strawberry detection and segmentation based on the YOLACT framework.The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism,pyramid squeeze shuffle attention(PSSA),for efficient feature extraction.Additionally,an attention-guided context-feature pyramid network(AC-FPN)is employed instead of FPN to optimize the architecture’s performance.Furthermore,a feature-enhanced model(FEM)is introduced to enhance the prediction head’s capabilities,while efficient fast non-maximum suppression(EF-NMS)is devised to improve non-maximum suppression.The experimental results demonstrate that the E-YOLACT achieves a Box-mAP and Mask-mAP of 77.9 and 76.6,respectively,on the custom dataset.Moreover,it exhibits an impressive category accuracy of 93.5%.Notably,the E-YOLACT also demonstrates a remarkable real-time detection capability with a speed of 34.8 FPS.The method proposed in this article presents an efficient approach for the vision system of a strawberry-picking robot.

Isotope Tracking of Surface Water Groundwater Interaction in the Beninese Part of the Iullemeden Aquifer System

The Kandi basin is located in northeast Benin (West Africa). This study is focused on the estimation of water fluxes exchanged between the river Niger (and its tributaries) and the transboundary Iullemeden Aquifer System. In that framework, an innovative approach based on the application of the Bayesian Mixing Model (MixSIAR) analysis on water isotopes (oxygen-18, deuterium and tritium) was performed. Moreover, to assess the relevance of the model outputs, Pearson’s correlation and Principal Component Analysis (PCA) have been done. A complex relationship between surface water and groundwater has been found. Sixty percent (60%) of groundwater samples are made of more than 70% river water and rainwater;while 31.25% of surface water samples are made of about 84% groundwater. To safeguard sustainable water resources for the well-being of the local communities, surface water and groundwater must be managed as a unique component in the Kandi basin.

Models to Simulate Effective Coverage of Fire Station Based on Real-Time Travel Times

In recent years,frequent fire disasters have led to enormous damage in China.Effective firefighting rescues can minimize the losses caused by fires.During the rescue processes,the travel time of fire trucks can be severely affected by traffic conditions,changing the effective coverage of fire stations.However,it is still challenging to determine the effective coverage of fire stations considering dynamic traffic conditions.This paper addresses this issue by combining the traveling time calculationmodelwith the effective coverage simulationmodel.In addition,it proposes a new index of total effective coverage area(TECA)based on the time-weighted average of the effective coverage area(ECA)to evaluate the urban fire services.It also selects China as the case study to validate the feasibility of the models,a fire station(FS-JX)in Changsha.FS-JX station and its surrounding 9,117 fire risk points are selected as the fire service supply and demand points,respectively.A total of 196 simulation scenarios throughout a consecutiveweek are analyzed.Eventually,1,933,815 sets of valid sample data are obtained.The results showed that the TECA of FS-JX is 3.27 km^(2),which is far below the standard requirement of 7.00 km^(2) due to the traffic conditions.The visualization results showed that three rivers around FS-JX interrupt the continuity of its effective coverage.The proposed method can provide data support to optimize the locations of fire stations by accurately and dynamically determining the effective coverage of fire stations.

FPGA implementation of 500-MHz high-count-rate high-time-resolution real-time digital neutron-gamma discrimination for fast liquid detectors

Fast neutron flux measurements with high count rates and high time resolution have important applications in equipment such as tokamaks.In this study,real-time neutron and gamma discrimination was implemented on a self-developed 500-Msps,12-bit digitizer,and the neutron and gamma spectra were calculated directly on an FPGA.A fast neutron flux measurement system with BC-501A and EJ-309 liquid scintillator detectors was developed and a fast neutron measurement experiment was successfully performed on the HL-2 M tokamak at the Southwestern Institute of Physics,China.The experimental results demonstrated that the system obtained the neutron and gamma spectra with a time accuracy of 1 ms.At count rates of up to 1 Mcps,the figure of merit was greater than 1.05 for energies between 50 keV and 2.8 MeV.