Preoperative prediction of lymphovascular and perineural invasion in gastric cancer using spectral computed tomography imaging and machine learning

BACKGROUND Lymphovascular invasion(LVI)and perineural invasion(PNI)are important prognostic factors for gastric cancer(GC)that indicate an increased risk of metastasis and poor outcomes.Accurate preoperative prediction of LVI/PNI status could help clinicians identify high-risk patients and guide treatment deci-sions.However,prior models using conventional computed tomography(CT)images to predict LVI or PNI separately have had limited accuracy.Spectral CT provides quantitative enhancement parameters that may better capture tumor invasion.We hypothesized that a predictive model combining clinical and spectral CT parameters would accurately preoperatively predict LVI/PNI status in GC patients.AIM To develop and test a machine learning model that fuses spectral CT parameters and clinical indicators to predict LVI/PNI status accurately.METHODS This study used a retrospective dataset involving 257 GC patients(training cohort,n=172;validation cohort,n=85).First,several clinical indicators,including serum tumor markers,CT-TN stages and CT-detected extramural vein invasion(CT-EMVI),were extracted,as were quantitative spectral CT parameters from the delineated tumor regions.Next,a two-step feature selection approach using correlation-based methods and information gain ranking inside a 10-fold cross-validation loop was utilized to select informative clinical and spectral CT parameters.A logistic regression(LR)-based nomogram model was subsequently constructed to predict LVI/PNI status,and its performance was evaluated using the area under the receiver operating characteristic curve(AUC).RESULTS In both the training and validation cohorts,CT T3-4 stage,CT-N positive status,and CT-EMVI positive status are more prevalent in the LVI/PNI-positive group and these differences are statistically significant(P<0.05).LR analysis of the training group showed preoperative CT-T stage,CT-EMVI,single-energy CT values of 70 keV of venous phase(VP-70 keV),and the ratio of standardized iodine concentration of equilibrium phase(EP-NIC)were independent influencing factors.The AUCs of VP-70 keV and EP-NIC were 0.888 and 0.824,respectively,which were slightly greater than those of CT-T and CT-EMVI(AUC=0.793,0.762).The nomogram combining CT-T stage,CT-EMVI,VP-70 keV and EP-NIC yielded AUCs of 0.918(0.866-0.954)and 0.874(0.784-0.936)in the training and validation cohorts,which are significantly higher than using each of single independent factors(P<0.05).CONCLUSION The study found that using portal venous and EP spectral CT parameters allows effective preoperative detection of LVI/PNI in GC,with accuracy boosted by integrating clinical markers.

In situ and operando infrared spectroscopy of battery systems:Progress and opportunities

In situ and operando infrared spectroscopies are powerful techniques to support the design of novel materials for batteries and the development of new battery systems.These techniques can support the study of batteries by identifying the formation of new species and monitoring electrochemical energy stability.However,few works have employed these techniques,which can be used to investigate various materials,including systems beyond lithium-ion technology,in the research of batteries.Therefore,this review presents a comprehensive overview focusing on the main contributions of in situ and operando infrared spectroscopy for lithium-ion batteries(LIBs)and other battery systems.These techniques can successfully identify the formation of species during the electrolyte reduction,electrode degradation,and the formation of the solid-electrolyte interphase(SEI)layer.From these outcomes,it is possible to conclude that this characterization approach should be employed as a protocol to overcome remaining issues in batteries,consequently supporting battery research.This review aims to be a guide on how infrared spectroscopy can contribute to monitoring battery systems and to lead researchers interested in applying this technique.

Fusion of color and hallucinated depth features for enhanced multimodal deep learning-based damage segmentation

Recent advances in computer vision and deep learning have shown that the fusion of depth information can significantly enhance the performance of RGB-based damage detection and segmentation models.However,alongside the advantages,depth-sensing also presents many practical challenges.For instance,the depth sensors impose an additional payload burden on the robotic inspection platforms limiting the operation time and increasing the inspection cost.Additionally,some lidar-based depth sensors have poor outdoor performance due to sunlight contamination during the daytime.In this context,this study investigates the feasibility of abolishing depth-sensing at test time without compromising the segmentation performance.An autonomous damage segmentation framework is developed,based on recent advancements in vision-based multi-modal sensing such as modality hallucination(MH)and monocular depth estimation(MDE),which require depth data only during the model training.At the time of deployment,depth data becomes expendable as it can be simulated from the corresponding RGB frames.This makes it possible to reap the benefits of depth fusion without any depth perception per se.This study explored two different depth encoding techniques and three different fusion strategies in addition to a baseline RGB-based model.The proposed approach is validated on computer-generated RGB-D data of reinforced concrete buildings subjected to seismic damage.It was observed that the surrogate techniques can increase the segmentation IoU by up to 20.1%with a negligible increase in the computation cost.Overall,this study is believed to make a positive contribution to enhancing the resilience of critical civil infrastructure.

Measuring tree stem diameters and straightness with depth-image computer vision

Current techniques of forest inventory rely on manual measurements and are slow and labor intensive.Recent developments in computer vision and depth sensing can produce accurate measurement data at significantly reduced time and labor costs.We developed the ForSense system to measure the diameters of trees at various points along the stem as well as stem straightness.Time use,mean absolute error(MAE),and root mean squared error(RMSE)metrics were used to compare the system against manual methods,and to compare the system against itself(reproducibility).Depth-derived diameter measurements of the stems at the heights of 0.3,1.4,and 2.7 m achieved RMSE of 1.7,1.5,and 2.7 cm,respectively.The ForSense system produced straightness measurement data that was highly correlated with straightness ratings by trained foresters.The ForSense system was also consistent,achieving sub-centimeter diameter difference with subsequent measures and less than 4%difference in straightness value between runs.This method of forest inventory,which is based on depth-image computer vision,is time efficient compared to manual methods and less computationally and technologically intensive compared to Structure-from-Motion(SFM)photogrammetry and ground-based LiDAR or terrestrial laser scanning(TLS).

Progress of Brain Network Studies on Anesthesia and Consciousness: Framework and Clinical Applications

Although the relationship between anesthesia and consciousness has been investigated for decades, our understanding of the underlying neural mechanisms of anesthesia and consciousness remains rudimentary, which limits the development of systems for anesthesia monitoring and consciousness evaluation. Moreover, the current practices for anesthesia monitoring are mainly based on methods that do not provide adequate information and may present obstacles to the precise application of anesthesia. Most recently, there has been a growing trend to utilize brain network analysis to reveal the mechanisms of anesthesia, with the aim of providing novel insights to promote practical application. This review summarizes recent research on brain network studies of anesthesia, and compares the underlying neural mechanisms of consciousness and anesthesia along with the neural signs and measures of the distinct aspects of neural activity. Using the theory of cortical fragmentation as a starting point, we introduce important methods and research involving connectivity and network analysis. We demonstrate that whole-brain multimodal network data can provide important supplementary clinical information. More importantly, this review posits that brain network methods, if simplified, will likely play an important role in improving the current clinical anesthesia monitoring systems.

DPAL-BERT:A Faster and Lighter Question Answering Model

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.

Learning-based user association and dynamic resource allocation in multi-connectivity enabled unmanned aerial vehicle networks

Unmanned Aerial Vehicles(UAvs)as aerial base stations to provide communication services for ground users is a flexible and cost-effective paradigm in B5G.Besides,dynamic resource allocation and multi-connectivity can be adopted to further harness the potentials of UAVs in improving communication capacity,in such situations such that the interference among users becomes a pivotal disincentive requiring effective solutions.To this end,we investigate the Joint UAV-User Association,Channel Allocation,and transmission Power Control(J-UACAPC)problem in a multi-connectivity-enabled UAV network with constrained backhaul links,where each UAV can determine the reusable channels and transmission power to serve the selected ground users.The goal was to mitigate co-channel interference while maximizing long-term system utility.The problem was modeled as a cooperative stochastic game with hybrid discrete-continuous action space.A Multi-Agent Hybrid Deep Reinforcement Learning(MAHDRL)algorithm was proposed to address this problem.Extensive simulation results demonstrated the effectiveness of the proposed algorithm and showed that it has a higher system utility than the baseline methods.

Winding Function Model-based Performance Evaluation of a PM Transverse Flux Generator for Applications in Direct-drive Systems

The magnetic flux in a permanent magnet transverse flux generator(PMTFG) is three-dimensional(3D), therefore, its efficacy is evaluated using 3D magnetic field analysis. Although the 3D finite-element method(FEM) is highly accurate and reliable for machine simulation, it requires a long computation time, which is crucial when it is to be used in an iterative optimization process. Therefore, an alternative to 3DFEM is required as a rapid and accurate analytical technique. This paper presents an analytical model for PMTFG analysis using winding function method. To obtain the air gap MMF distribution, the excitation magneto-motive force(MMF) and the turn function are determined based on certain assumptions. The magnetizing inductance, flux density, and back-electro-magnetomotive force of the winding are then determined. To assess the accuracy of the proposed method, the analytically calculated parameters of the generator are compared to those obtained by a 3D-FEM. The presented method requires significantly shorter computation time than the 3D-FEM with comparable accuracy.

Operando FTIR study on water additive in lithium-sulfur batteries to mitigate shuttle effect

Additives in the electrolytes of Li-S batteries aim to increase overall capacity,improve Li ion conductivity,enhance cyclability,and mitigate the shuttle effect,which is one of the major issues of this system.Here,the use of water as an additive in the commonly used electrolyte,1.0 M LiTFSI/1.0%(w/w) LiNO_(3) and a 1:1 mixture of 1,3-dioxolane(DOL) and 1,2-dimethoxyethane(DME) was investigated.We used Co_(2)Mn_(0.5)Al_(0.5)O_(4)(CMA) as an electrocatalyst anchored on an activated carbon(AC) electrode with added sulfur via a melt-diffusion process.The structural analysis of CMA via Rietveld refinement showed interatomic spaces that can promote ionic conductivity,facilitating Li^(+) ion migration.Electrochemical tests determined 1600 ppm as the optimal water concentration,significantly reducing the shuttle effect.Post-mortem XPS analysis focused on the lithium metal anode revealed the formation of Li_(2)O layers in dry samples and LiOH in wet samples.Better capacity was observed in wet samples,which can be attributed to the superior ionic conductivity of LiOH at the electrode/electrolyte interface,surpassing that of Li_(2)O by 12 times.Finally,Operando FTIR experiments provided real-time insights into electrolyte degradation and SEI formation,elucidating the activity mechanisms of water and Li_(2)CO_(3) over the cycles.This work presents results that could aid future advancements in Li-S battery technology,offering possibilities to mitigate its challenges with inexpensive and scalable additives.

Security and Privacy Challenges in SDN-Enabled IoT Systems: Causes, Proposed Solutions,and Future Directions

Software-Defined Networking(SDN)represents a significant paradigm shift in network architecture,separating network logic from the underlying forwarding devices to enhance flexibility and centralize deployment.Concur-rently,the Internet of Things(IoT)connects numerous devices to the Internet,enabling autonomous interactions with minimal human intervention.However,implementing and managing an SDN-IoT system is inherently complex,particularly for those with limited resources,as the dynamic and distributed nature of IoT infrastructures creates security and privacy challenges during SDN integration.The findings of this study underscore the primary security and privacy challenges across application,control,and data planes.A comprehensive review evaluates the root causes of these challenges and the defense techniques employed in prior works to establish sufficient secrecy and privacy protection.Recent investigations have explored cutting-edge methods,such as leveraging blockchain for transaction recording to enhance security and privacy,along with applying machine learning and deep learning approaches to identify and mitigate the impacts of Denial of Service(DoS)and Distributed DoS(DDoS)attacks.Moreover,the analysis indicates that encryption and hashing techniques are prevalent in the data plane,whereas access control and certificate authorization are prominently considered in the control plane,and authentication is commonly employed within the application plane.Additionally,this paper outlines future directions,offering insights into potential strategies and technological advancements aimed at fostering a more secure and privacy-conscious SDN-based IoT ecosystem.

Study of the Electric Filed Around a Metal Oxide Surge Arrester： Measurement and Simulation

The study of the electric field around a surge arrester is useful for design procedures and diagnostic tests. The current work com- putes the electric field around a medium voltage gapless surge arrester using 2D and 3D representation of the arrester. The 2D simulation design, which is described in IEC 60099-4 Standard, cannot include the non symmetrical parts of the arrester geometry and the test arrange- ment. 3D simulation procedures have the advantage that takes into account these asymmetries, giving more accurate results for each measurement position. In order to confirm the suitability of the created models, the simulation results of the electric field, using the 2D and 3D edition of PC Opera, are compared with recorded measurements, which are obtained in laboratory using appropriate calibrated field meters.

Terahertz spectral properties of melamine and its deuterated isotope,melamine-d_6

The far-infrared optical properties of melamine and its deuterated isotope,melamine-d_6 were experimentally and theoretically investigated in the frequency range from 0.2 to 3.0 THz.Under the room temperature and dry air nitrogen conditions,three absorption bands were observed at 2.0,2.3 and 2.6 THz in the melamine sample by use of terahertz time-domain spectroscopy.Whereas,in the melamine-d_6 sample,the observed absorption bands shift towards lower frequencies and the relative intensity of the absorption bands reduces.Numerical simulation results based on the Parameterized Model number 3(PM3) were compared with the experimental data and the observed vibration spectra were assigned according to the PM3 calculations.The absorption bands of the measured melamine samples at terahertz frequencies are highly correlated with the intermolecular hydrogen bond stretching and π-π stacking vibration.Also,the red shift of the absorption bands is due to hydrogen/deuterium substitution.

An analysis of the radar backscatter from oil-covered sea surfaces using moment method and Monte-Carlo simulation: preliminary results

An analysis of the radar backscattering from the ocean surface covered by oil spill is presented using a mi- crowave scattering model and Monte-Carlo simulation. In the analysis, a one-dimensional rough sea sur- face is numerically generated with an ocean waveheight spectrum for a given wind velocity. A two-layered medium is then generated by adding a thin oil layer on the simulated rough sea surface. The electric fields backscattered from the sea surface with two-layered medium are computed with the method of moments （MoM）, and the backscattering coefficients are statistically obtained with N independent samples for each oil-spilled surface using the Monte-Carlo technique for various conditions of surface roughness, oil-layer thickness, frequency, polarization and incidence angle. The numerical simulation results are compared with theoretical models for clean sea surfaces and SAR images of an off-spilled sea surface caused by the Hebei （Hebei province, China） Spirit oil tanker in 2007. Further, conditions for better oil spill extraction are sought by the numerical simulation on the effects of wind speed and oil-layer thickness at different inci- dence angles on the backscattering coefficients.

Three-Dimensional Simulation of Plasma Deformation During Contact Opening in a Circuit Breaker,Including the Analysis of Kink Instability and Sausage Instability

A three-dimensional （3-D） transient model has been developed to investigate plasma deformation driven by a magnetic field and its influence on arc stability in a circuit breaker. The 3-D distribution of electric current density is obtained from a current continuity equation along with the generalized Ohm＇s law; while the magnetic field induced by the current flowing through the arc column is calculated by the magnetic vector potential equation. When gas interacts with an arc column, fundamental factors, such as Ampere＇s law, Ohm＇s law, the turbulence model, transport equations of mass, momentum and energy of plasma flow, have to be coupled for aria- lyzing the phenomenon. The coupled interactions between arc and plasma flow are described in the fl＇amework of time-dependent magnetohydrodynamic （MHD） equations in conjunction with a K-~ turbulence model. Simulations have been focused on sausage and kink instabilities in plasma （these phenomena are related tO pinch effects and electromagnetic fields）. The 3-D sjm- ulation reveals the relation between plasma deformation and instability phenomena, which affect arc stability during circuit breaker operation. Plasma deformation is the consequence of coupled interactions between the electromagnetic force and plasma flow described in simulations.

Dynamic analysis, simulation, and control of a 6-DOF IRB-120 robot manipulator using sliding mode control and boundary layer method

Because of its ease of implementation,a linear PID controller is generally used to control robotic manipulators.Linear controllers cannot effectively cope with uncertainties and variations in the parameters;therefore,nonlinear controllers with robust performance which can cope with these are recommended.The sliding mode control(SMC)is a robust state feedback control method for nonlinear systems that,in addition having a simple design,efficiently overcomes uncertainties and disturbances in the system.It also has a very fast transient response that is desirable when controlling robotic manipulators.The most critical drawback to SMC is chattering in the control input signal.To solve this problem,in this study,SMC is used with a boundary layer(SMCBL)to eliminate the chattering and improve the performance of the system.The proposed SMCBL was compared with inverse dynamic control(IDC),a conventional nonlinear control method.The kinematic and dynamic equations of the IRB-120 robot manipulator were initially extracted completely and accurately,and then the control of the robot manipulator using SMC was evaluated.For validation,the proposed control method was implemented on a 6-DOF IRB-120 robot manipulator in the presence of uncertainties.The results were simulated,tested,and compared in the MATLAB/Simulink environment.To further validate our work,the results were tested and confirmed experimentally on an actual IRB-120 robot manipulator.

3D point cloud data to quantitatively characterize size and shape of shrub crops

Size and shape are important properties of shrub crops such as blueberries,and they can be particularly useful for evaluating bush architecture suited to mechanical harvesting.The overall goal of this study was to develop a 3D imaging approach to measure size-related traits and bush shape that are relevant to mechanical harvesting.3D point clouds were acquired for 367 bushes from five genotype groups.Point cloud data were preprocessed to obtain clean bush points for characterizing bush architecture,including bush morphology(height,width,and volume),crown size,and shape descriptors(path curveλand five shape indices).One-dimensional traits(height,width,and crown size)had high correlations(R^(2)=0.88–0.95)between proposed method and manual measurements,whereas bush volume showed relatively lower correlations(R^(2)=0.78–0.85).These correlations suggested that the present approach was accurate in measuring one-dimensional size traits and acceptable in estimating three-dimensional bush volume.Statistical results demonstrated that the five genotype groups were statistically different in crown size and bush shape.The differences matched with human evaluation regarding optimal bush architecture for mechanical harvesting.In particular,a visualization tool could be generated using crown size and path curveλ,which showed great potential of determining bush architecture suitable for mechanical harvesting quickly.Therefore,the processing pipeline of 3D point cloud data presented in this study is an effective tool for blueberry breeding programs(in particular for mechanical harvesting)and farm management.

Analysis of Security Vulnerabilities and Countermeasures of Ethernet Passive Optical Network(EPON)

Ethernet-based Passive Optical Network(EPON) is considered a very promising solution for the first mile problem of the next generation networks.Due to its particular characteristic of shared media structure,EPON suffers many security vulnerabilities. Communication security must be guaranteed when EPON is applied in practice.This paper gives a general introduction to the EPON system,analyzes the potential threats and attacks pertaining to the EPON system,and presents effective countermea-sures against these threats and attacks with emphasis on the authentication protocols and key distribution.

Design and Comparison of H∞/H2 Controllers for Frigate Rudder Roll Stabilization

Roll motion of ships can be distinguished in two parts:an unavoidable part due to their natural movement while turning and an unwanted and avoidable part that is due to encounter with waves and rough seas in general.For the attenuation of the unwanted part of roll motion,ways have been developed such as addition of controllable fins and changes in shape.This paper investigates the effectiveness of augmenting the rudder used for rejecting part of the unwanted roll,while maintaining steering and course changing ability.For this purpose,a controller is designed,which acts through intentional superposition of fast,compared with course change,movements of rudder,in order to attenuate the high-frequency roll effects from encountering rough seas.The results obtained by simulation to exogenous disturbance support the conclusion that the roll stabilization for displacement can be effective at least when displacement hull vessels are considered.Moreover,robust stability and performance is verified for the proposed control scheme over the entire operating range of interest.

Comparison of the Performance of Two Direct Wave Energy Conversion Systems: Archimedes Wave Swing and Power Buoy

Many wave energy conversion devices have not been well received. The main reasons are that they are too complicated and not economical. However, in the last two decades direct conversion systems have drawn the attention of researchers to their widely distributed energy source due to their simple structure and low cost. The most well-known direct conversion systems presently in use include the Archimedes Wave Swing (AWS) and Power Buoy (PB). In this paper, these two systems were simulated in the same conditions and their behaviors were studied in different wave conditions. In order to verify the simulations, results of the generator of the finite element computations were followed. An attempt was made to determine the merits and drawbacks of each method under different wave conditions by comparing the performance of the two systems. The wave conditions suitable for each system were specified.

Comparison of clinical outcomes between culprit vessel only and multivessel percutaneous coronary intervention for ST-segment elevation myocardial infarction patients with multivessel coronary diseases

Background The clinical significance of complete revascularization for ST segment elevation myocardial infarction （STEMI） pa- tients during admission is still debatable. Methods A total of 1406 STEMI patients from the Korean Myocardial Infarction Registry with multivessel diseases without cardiogenic shock who underwent primary percutaneous coronary intervention （PPCI） were analyzed. We used propensity score matching （PSM） to control differences of baseline characteristics between culprit only intervention （CP） and multivessel percutaneous coronary interventions （MP）, and between double vessel disease （DVD） and triple vessel disease （TVD）. The major adverse cardiac event （MACE） was analyzed for one year after discharge. Results TVD patients showed higher incidence of MACE （14.2% vs. 8.6%, P = 0.01）, any cause of revascularization （10.6% vs. 5.9%, P - 0.01）, and repeated PCI （9.5% vs. 5.7%, P = 0.02）, as compared to DVD patients during one year after discharge. MP reduced MACE effectively （7.3% vs. 13.8%, P = 0.03）, as compared to CP for one year, but all cause of death （1.6% vs. 3.2%, P= 0.38）, Ml （0.4% vs. 0.8%, P = 1.00）, and any cause ofrevascularization （5.3% vs. 9.7%, P = 0.09） were comparable in the two treatment groups. Conclusions STEMI patients with TVD showed higher rate of MACE, as compared to DVD MP performed during PPCI or ad hoc during admission for STEMI patients without cardiogenic shock showed lower rate of MACE in this large scaled database. Therefore, MP could be considered as an effective treatment option for STEMI patients without cardiogenic shock.