An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique

Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance.

Advanced Functional Electromagnetic Shielding Materials:A Review Based on Micro‑Nano Structure Interface Control of Biomass Cell Walls

Research efforts on electromagnetic interference(EMI)shielding materials have begun to converge on green and sustainable biomass materials.These materials offer numerous advantages such as being lightweight,porous,and hierarchical.Due to their porous nature,interfacial compatibility,and electrical conductivity,biomass materials hold significant potential as EMI shielding materials.Despite concerted efforts on the EMI shielding of biomass materials have been reported,this research area is still relatively new compared to traditional EMI shielding materials.In particular,a more comprehensive study and summary of the factors influencing biomass EMI shielding materials including the pore structure adjustment,preparation process,and micro-control would be valuable.The preparation methods and characteristics of wood,bamboo,cellulose and lignin in EMI shielding field are critically discussed in this paper,and similar biomass EMI materials are summarized and analyzed.The composite methods and fillers of various biomass materials were reviewed.this paper also highlights the mechanism of EMI shielding as well as existing prospects and challenges for development trends in this field.

Association of the glycemic background patterns and the diabetes management efficacy in poorly controlled type 2 diabetes

BACKGROUND Inadequate glycemic control in patients with type 2 diabetes(T2DM)is a major public health problem and a significant risk factor for the progression of diabetic complications.AIM To evaluate the effects of intensive and supportive glycemic management strategies over a 12-month period in individuals with T2DM with glycated hemoglobin(HbA1c)≥10%and varying backgrounds of glycemic control.METHODS This prospective observational study investigated glycemic control in patients with poorly controlled T2DM over 12 months.Participants were categorized into four groups based on prior glycemic history:Newly diagnosed,previously well controlled with recent worsening,previously off-target but now worsening,and HbA1c consistently above 10%.HbA1c levels were monitored quarterly,and patients received medical,educational,and dietary support as needed.The analysis focused on the success rates of good glycemic control and the associated factors within each group.RESULTS The study showed significant improvements in HbA1c levels in all participants.The most significant improvement was observed in individuals newly diagnosed with diabetes:65%achieved an HbA1c target of≤7%.The results varied between participants with different glycemic control histories,followed by decreasing success rates:39%in participants with previously good glycemic control,21%in participants whose glycemic control had deteriorated compared to before,and only 10%in participants with persistently poor control,with mean HbA1c levels of 6.3%,7.7%,8.2%,and 9.7%,respectively.After one year,65.2%of the“newly diagnosed patients”,39.3%in the“previously controlled group”,21.9%in the“previously off-target but now worsened'”group and 10%in the“poorly controlled from the start”group had achieved HbA1c levels of 7 and below.CONCLUSION In poorly controlled diabetes,the rate at which treatment goals are achieved is associated with the glycemic background characteristics,emphasizing the need for tailored strategies.Therefore,different and comprehensive treatment approaches are needed for patients with persistent uncontrolled diabetes.

Doubly-Fed Pumped Storage Units Participation in Frequency Regulation Control Strategy for New Energy Power Systems Based on Model Predictive Control

Large-scale new energy grid connection leads to the weakening of the system frequency regulation capability,and the system frequency stability is facing unprecedented challenges.In order to solve rapid frequency fluctuation caused by new energy units,this paper proposes a new energy power system frequency regulation strategy with multiple units including the doubly-fed pumped storage unit(DFPSU).Firstly,based on the model predictive control(MPC)theory,the state space equations are established by considering the operating characteristics of the units and the dynamic behavior of the system;secondly,the proportional-differential control link is introduced to minimize the frequency deviation to further optimize the frequency modulation(FM)output of the DFPSU and inhibit the rapid fluctuation of the frequency;lastly,it is verified on theMatlab/Simulink simulation platform,and the results show that the model predictive control with proportional-differential control link can further release the FM potential of the DFPSU,increase the depth of its FM,effectively reduce the frequency deviation of the system and its rate of change,realize the optimization of the active output of the DFPSU and that of other units,and improve the frequency response capability of the system.

Passive activity enhances residual control ability in patients with complete spinal cord injury

Patients with complete spinal cord injury retain the potential for volitional muscle activity in muscles located below the spinal injury level.However,because of prolonged inactivity,initial attempts to activate these muscles may not effectively engage any of the remaining neurons in the descending pathway.A previous study unexpectedly found that a brief clinical round of passive activity significantly increased volitional muscle activation,as measured by surface electromyography.In this study,we further explored the effect of passive activity on surface electromyographic signals during volitional control tasks among individuals with complete spinal cord injury.Eleven patients with chronic complete thoracic spinal cord injury were recruited.Surface electromyography data from eight major leg muscles were acquired and compared before and after the passive activity protocol.The results indicated that the passive activity led to an increased number of activated volitional muscles and an increased frequency of activation.Although the cumulative root mean square of surface electromyography amplitude for volitional control of movement showed a slight increase after passive activity,the difference was not statistically significant.These findings suggest that brief passive activity may enhance the ability to initiate volitional muscle activity during surface electromyography tasks and underscore the potential of passive activity for improving residual motor control among patients with motor complete spinal cord injury.

A matrix metalloproteinase-responsive hydrogel system controls angiogenic peptide release for repair of cerebral ischemia/reperfusion injury

Vascular endothelial growth factor and its mimic peptide KLTWQELYQLKYKGI(QK)are widely used as the most potent angiogenic factors for the treatment of multiple ischemic diseases.However,conventional topical drug delivery often results in a burst release of the drug,leading to transient retention(inefficacy)and undesirable diffusion(toxicity)in vivo.Therefore,a drug delivery system that responds to changes in the microenvironment of tissue regeneration and controls vascular endothelial growth factor release is crucial to improve the treatment of ischemic stroke.Matrix metalloproteinase-2(MMP-2)is gradually upregulated after cerebral ischemia.Herein,vascular endothelial growth factor mimic peptide QK was self-assembled with MMP-2-cleaved peptide PLGLAG(TIMP)and customizable peptide amphiphilic(PA)molecules to construct nanofiber hydrogel PA-TIMP-QK.PA-TIMP-QK was found to control the delivery of QK by MMP-2 upregulation after cerebral ischemia/reperfusion and had a similar biological activity with vascular endothelial growth factor in vitro.The results indicated that PA-TIMP-QK promoted neuronal survival,restored local blood circulation,reduced blood-brain barrier permeability,and restored motor function.These findings suggest that the self-assembling nanofiber hydrogel PA-TIMP-QK may provide an intelligent drug delivery system that responds to the microenvironment and promotes regeneration and repair after cerebral ischemia/reperfusion injury.

Application of Fructo-oligosaccharides Quality Control Chart in Milk Powder

The quality control chart of fructo-oligosaccharides in milk powder was established to determine whether the detection process and results are in control state.The content of fructo-oligosaccharides in milk powder control samples was determined by ion chromatography,and the quality control chart of fructo-oligosaccharides was established to analyze the controlled state.The results indicate that the median of the quality control chart is 1613.14 mg/100 g,and the standard deviation is 85.57 mg/100 g.The new quality control points were evaluated and analyzed,and the precision changed,but the mean value did not change.Further F test was conducted to determine that the precision did not change significantly,indicating that the test was in a statistical control state,and the detection process,method and results were controlled.

Design of Voltage Equalization Circuit and Control Method for Lithium-ion Battery Packs

The active equalization of lithium-ion batteries involves transferring energy from high-voltage cells to low-voltage cells,ensuring consistent voltage levels across the battery pack and maintaining safety.This paper presents a voltage balancing circuit and control method.First,a single capacitor method is used to design the circuit topology for energy transfer.Next,real-time voltage detection and control are employed to balance energy between cells.Finally,simulation and experimental results demonstrate the effectiveness of the proposed method,achieving balanced voltages of 3.97 V from initial voltages of 4.10,3.97,and 3.90 V.The proposed circuit is simple,reliable,and effectively prevents overcharge and overdischarge.

SEIR Mathematical Model for Influenza-Corona Co-Infection with Treatment and Hospitalization Compartments and Optimal Control Strategies

The co-infection of corona and influenza viruses has emerged as a significant threat to global public health due to their shared modes of transmission and overlapping clinical symptoms.This article presents a novel mathematical model that addresses the dynamics of this co-infection by extending the SEIR(Susceptible-Exposed-Infectious-Recovered)framework to incorporate treatment and hospitalization compartments.The population is divided into eight compartments,with infectious individuals further categorized into influenza infectious,corona infectious,and co-infection cases.The proposed mathematical model is constrained to adhere to fundamental epidemiological properties,such as non-negativity and boundedness within a feasible region.Additionally,the model is demonstrated to be well-posed with a unique solution.Equilibrium points,including the disease-free and endemic equilibria,are identified,and various properties related to these equilibrium points,such as the basic reproduction number,are determined.Local and global sensitivity analyses are performed to identify the parameters that highly influence disease dynamics and the reproduction number.Knowing the most influential parameters is crucial for understanding their impact on the co-infection’s spread and severity.Furthermore,an optimal control problem is defined to minimize disease transmission and to control strategy costs.The purpose of our study is to identify the most effective(optimal)control strategies for mitigating the spread of the co-infection with minimum cost of the controls.The results illustrate the effectiveness of the implemented control strategies in managing the co-infection’s impact on the population’s health.This mathematical modeling and control strategy framework provides valuable tools for understanding and combating the dual threat of corona and influenza co-infection,helping public health authorities and policymakers make informed decisions in the face of these intertwined epidemics.

The chemical composition, quality control and pharmacological effects of Gualou-Xiebai-Banxia Decoction: a review

Gualou-Xiebai-Banxia Decoction(GXBD)is a traditional Chinese herbal formula including four traditional Chinese medicines:Gualou(Trichosanthis Fructus,TF),Xiebai(Allii Macrostemonis Bulbus,AMB),Banxia(Pinelliae Rhizoma,PR)and yellow wine.It is a classical therapy for chest stuffiness and pain syndrome and is widely used in the clinical treatment of coronary heart disease.It also shows significant therapeutic effects on pulmonary heart disease,hyperlipidemia,and arrhythmia.This study conducted a literature review and collected information on GXBD from databases such as PubMed,Web of Science,China National Knowledge Infrastructure,and ScienceDirect.The result indicated that the main active ingredients of GXBD are steroids,flavonoids,terpenoids,alkaloids,amino acids,and organic acids.Trigonelline,macrostemonoside and cucurbitacin B can provide reference for its quality control.GXBD may exert therapeutic effects on coronary heart disease through AMPK,PI3K-AKT,oxLDL,VEGF,and NF-κB signal pathways.This review provides a comprehensive analysis and summary of the chemical composition and in vivo metabolism of three traditional Chinese medicines(TF,AMB,and PR),along with an evaluation of the chemical composition,quality control,pharmacological effects,and clinical application of GXBD.Based on these,areas requiring further research on GXBD have been proposed to provide a reference for its further development and new drug research.

Synchronization Characterization of DC Microgrid Converter Output Voltage and Improved Adaptive Synchronization Control Methods

This paper deeply introduces a brand-new research method for the synchronous characteristics of DC microgrid bus voltage and an improved synchronous control strategy.This method mainly targets the problem of bus voltage oscillation caused by the bifurcation behavior of DC microgrid converters.Firstly,the article elaborately establishes a mathematical model of a single distributed power source with hierarchical control.On this basis,a smallworld network model that can better adapt to the topology structure of DC microgrids is further constructed.Then,a voltage synchronization analysis method based on the main stability function is proposed,and the synchronous characteristics of DC bus voltage are deeply studied by analyzing the size of the minimum non-zero eigenvalue.In view of the situation that the line coupling strength between distributed power sources is insufficient to achieve bus voltage synchronization,this paper innovatively proposes a new improved adaptive controller to effectively control voltage synchronization.And the convergence of the designed controller is strictly proved by using Lyapunov’s stability theorem.Finally,the effectiveness and feasibility of the designed controller in this paper are fully verified through detailed simulation experiments.After comparative analysis with the traditional adaptive controller,it is found that the newly designed controller can make the bus voltages of each distributed power source achieve synchronization more quickly,and is significantly superior to the traditional adaptive controller in terms of anti-interference performance.

A Damage Control Model for Reinforced Concrete Pier Columns Based on Pre-Damage Tests under Cyclic Reverse Loading

To mitigate the challenges in managing the damage level of reinforced concrete(RC)pier columns subjected to cyclic reverse loading,this study conducted a series of cyclic reverse tests on RC pier columns.By analyzing the outcomes of destructive testing on various specimens and fine-tuning the results with the aid of the IMK(Ibarra Medina Krawinkler)recovery model,the energy dissipation capacity coefficient of the pier columns were able to be determined.Furthermore,utilizing the calibrated damage model parameters,the damage index for each specimen were calculated.Based on the obtained damage levels,three distinct pre-damage conditions were designed for the pier columns:minor damage,moderate damage,and severe damage.The study then predicted the variations in hysteresis curves and damage indices under cyclic loading conditions.The experimental findings reveal that the displacement at the top of the pier columns can serve as a reliable indicator for controlling the damage level of pier columns post-loading.Moreover,the calibrated damage index model exhibits proficiency in accurately predicting the damage level of RC pier columns under cyclic loading.

Wind Turbine Composite Blades:A Critical Review of Aeroelastic Modeling and Vibration Control

With the gradual increase in the size and flexibility of composite blades in large wind turbines,problems related toaeroelastic instability and blade vibration are becoming increasingly more important.Given their impact on thelifespan of wind turbines,these subjects have become important topics in turbine blade design.In this article,firstaspects related to the aeroelastic(structural and aerodynamic)modeling of large wind turbine blades are summarized.Then,two main methods for blade vibration control are outlined(passive control and active control),including the case of composite blades.Some improvement schemes are proposed accordingly,with a specialfocus on the industry’s outstanding suppression scheme for stall-induced nonlinear flutter and a new high-frequencymicro-vibration control scheme.Finally,future research directions are indicated based on existingresearch.

A Novel Control Strategy Based onπ-VSG for Inter-Face Converter in Hybrid Microgrid

The rapid development of new energy power generation technology and the transformation of power electronics in the core equipment of source-grid-load drives the power system towards the“double-high”development pattern of“high proportion of renewable energy”and“high proportion of power electronic equipment”.To enhance the transient performance of AC/DC hybrid microgrid(HMG)in the context of“double-high,”aπtype virtual synchronous generator(π-VSG)control strategy is applied to bidirectional interface converter(BIC)to address the issues of lacking inertia and poor disturbance immunity caused by the high penetration rate of power electronic equipment and new energy.Firstly,the virtual synchronous generator mechanical motion equations and virtual capacitance equations are used to introduce the virtual inertia control equations that consider the transient performance of HMG;based on the equations,theπ-type equivalent control model of the BIC is established.Next,the inertia power is actively transferred through the BIC according to the load fluctuation to compensate for the system’s inertia deficit.Secondly,theπ-VSG control utilizes small-signal analysis to investigate howthe fundamental parameters affect the overall stability of the HMG and incorporates power step response curves to reveal the relationship between the control’s virtual parameters and transient performance.Finally,the PSCAD/EMTDC simulation results show that theπ-VSG control effectively improves the immunity of AC frequency and DC voltage in the HMG system under the load fluctuation condition,increases the stability of the HMG system and satisfies the power-sharing control objective between the AC and DC subgrids.

Optimizing risk management for post-amputation wound complications in diabetic patients: Focus on glycemic and immunosuppressive control

This study highlights the importance of identifying and addressing risk factors associated with wound complications following transtibial amputation in diabetic patients.These amputations,often necessitated by severe diabetic foot ulcers,carry significant risks of postoperative complications such as infection and delayed wound healing.Elevated hemoglobin A1c levels,indicative of poor glycemic control,and a history of kidney transplantation,due to required immunosuppressive therapy,are key factors influencing these outcomes.This paper emphasizes the need for enhanced glycemic management and personalized postoperative care,particularly for immunocompromised individuals,to minimize complications and improve patient prognosis.Future research should focus on prospective studies to validate targeted interventions and optimize care strategies,ultimately aiming to reduce the healthcare burden associated with diabetic foot complications.

Spatial Patterns and Controlling Factors of Soil Organic Carbon and Total Nitrogen in the Three River Headwaters Region,China

The alpine ecosystem has great potential for carbon sequestration.Soil organic carbon(SOC)and total nitrogen(TN)are highly sensitive to climate change,and their dynamics are crucial to revealing the effect of climate change on the structure,function,and services of the ecosystem.However,the spatial distribution and controlling factors of SOC and TN across various soil layers and vegetation types within this unique ecosystem remain inadequately understood.In this study,256 soil samples in 89 sites were collected from the Three River Headwaters Region(TRHR)in China to investigate SOC and TN and to explore the primary factors affecting their distribution,including soil,vegetation,climate,and geography factors.The results show that SOC and TN contents in 0-20,20-40,40-60,and 60-80 cm soil layers are 24.40,18.03,14.04,12.40 g/kg and 2.46,1.90,1.51,1.17 g/kg,respectively;with higher concentrations observed in the southeastern region compared to the northwest of the TRHR.One-way analysis of variance reveals that SOC and TN levels are elevated in the alpine meadow and the alpine shrub relative to the alpine steppe in the 0-60 cm soil layers.The structural equation model explores that soil water content is the main controlling factor affecting the variation of SOC and TN.Moreover,the geography,climate,and vegetation factors notably indirectly affect SOC and TN through soil factors.Therefore,it can effectively improve soil water and nutrient conditions through vegetation restoration,soil improvement,and grazing management,and the change of SOC and TN can be fully understood by establishing monitoring networks to better protect soil carbon and nitrogen.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

Vibration Control of A Flexible Marine Riser System Subject to Input Dead Zone and Extraneous Disturbances

An observer-based adaptive backstepping boundary control is proposed for vibration control of flexible offshore riser systems with unknown nonlinear input dead zone and uncertain environmental disturbances.The control algorithm can update the control law online through real-time data to make the controller adapt to the environment and improve the control precision.Specifically,based on the adaptive backstepping framework,virtual control laws and Lyapunov functions are designed for each subsystem.Three direction interference observers are designed to track the timevarying boundary disturbance.On this basis,the inverse of the dead zone and linear state transformation are used to compensate for the original system and eliminate the adverse effects of the dead zone.In addition,the stability of the closed-loop system is proven by Lyapunov stability theory.All the system states are bounded,and the vibration offset of the riser converges to a small area of the initial position.Finally,four examples of flexible marine risers are simulated in MATLAB to verify the effectiveness of the proposed controller.

Adaptive state-constrained/model-free iterative sliding mode control for aerial robot trajectory tracking

This paper develops a novel hierarchical control strategy for improving the trajectory tracking capability of aerial robots under parameter uncertainties.The hierarchical control strategy is composed of an adaptive sliding mode controller and a model-free iterative sliding mode controller(MFISMC).A position controller is designed based on adaptive sliding mode control(SMC)to safely drive the aerial robot and ensure fast state convergence under external disturbances.Additionally,the MFISMC acts as an attitude controller to estimate the unmodeled dynamics without detailed knowledge of aerial robots.Then,the adaption laws are derived with the Lyapunov theory to guarantee the asymptotic tracking of the system state.Finally,to demonstrate the performance and robustness of the proposed control strategy,numerical simulations are carried out,which are also compared with other conventional strategies,such as proportional-integralderivative(PID),backstepping(BS),and SMC.The simulation results indicate that the proposed hierarchical control strategy can fulfill zero steady-state error and achieve faster convergence compared with conventional strategies.