Single-phase,non-isolated microinverters used in photovoltaic(PV)systems commonly encounter two persistent challenges:High-frequency leakage current and fluctuating power delivery.This paper presents a novel single-ph...Single-phase,non-isolated microinverters used in photovoltaic(PV)systems commonly encounter two persistent challenges:High-frequency leakage current and fluctuating power delivery.This paper presents a novel single-phase,non-isolated,multi-input microinverter topology with a common-ground structure that effectively eliminates ground leakage current without requiring additional active components.The proposed microinverter architecture integrates a dual-boost configuration and uses only four active switches.This is especially advantageous in terms of the component count,which is beneficial to enhance reliability,reduce cost,and simplify the overall system design.With one,two,or four PV inputs,it can operate without interruption under unbalanced voltage or partial shading and even if some inputs drop to zero.A tailored modulation scheme minimizes conduction losses while maintaining a stable direct-current(DC)-link voltage,and a decoupling capacitor efficiently absorbs the single-phase pulsating power,thus overcoming one major limitation in existing microinverter designs.By validating with a 1-kW GaN-based prototype,both the simulated and experimental results demonstrate its high efficiency,robustness,and practical suitability for cost-effective PV applications,with a peak efficiency value of 94.8%.展开更多
This paper proposes a novel multivalued recurrent neural network model driven by external inputs,along with two innovative learning algorithms.By incorporating a multivalued activation function,the proposed model can ...This paper proposes a novel multivalued recurrent neural network model driven by external inputs,along with two innovative learning algorithms.By incorporating a multivalued activation function,the proposed model can achieve multivalued many-to-one associative memory,and the newly developed algorithms enable effective storage of many-to-one patterns in the coefficient matrix while maintaining the indispensability of inputs in many-to-one associative memory.The proposed learning algorithm addresses a critical limitation of existing models which fail to ensure completely erroneous outputs when facing partial input missing in many-to-one associative memory tasks.The methodology is rigorously derived through theoretical analysis,incorporating comprehensive verification of both the existence and global exponential stability of equilibrium points.Demonstrative examples are provided in the paper to show the effectiveness of the proposed theory.展开更多
Dear Editor,Aiming at the consensus tracking problem of a class of unknown heterogeneous nonlinear multiagent systems(MASs)with input constraints,a novel data-driven iterative learning consensus control(ILCC)protocol ...Dear Editor,Aiming at the consensus tracking problem of a class of unknown heterogeneous nonlinear multiagent systems(MASs)with input constraints,a novel data-driven iterative learning consensus control(ILCC)protocol based on zeroing neural networks(ZNNs)is proposed.First,a dynamic linearization data model(DLDM)is acquired via dynamic linearization technology(DLT).展开更多
In the version of the article originally published in the volume 68,issue 12,2025 of Sci China Mater(pages 4413-4422,https://doi.org/10.1007/s40843-025-3667-7),the Chinese name of the co-first author(肖天孝)was incorr...In the version of the article originally published in the volume 68,issue 12,2025 of Sci China Mater(pages 4413-4422,https://doi.org/10.1007/s40843-025-3667-7),the Chinese name of the co-first author(肖天孝)was incorrect.The corrected Chinese name is:肖天笑.展开更多
Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential f...Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential for executing sophisticated collective tasks.This paper presents a Distributed Formation Control and Obstacle Avoidance(DFCOA)framework for multi-unmanned ground vehicles(UGV).DFCOA integrates a virtual leader structure for global guidance,an improved A^(*)path planning algorithm with an advanced cost function for efficient path planning,and a repulsive-force-based improved vector field histogram star(VFH^(*))technique for collision avoidance.The virtual leader generates a reference trajectory while enabling distributed execution;the improved A^(*)algorithm reduces planning time and number of nodes to determine the shortest path from the starting position to the goal;and the improved VFH^(*)uses 2D LiDAR data with inter-agent repulsive force to simultaneously avoid collision with obstacles and maintain safe inter-vehicle distances.The formation stability of the proposed DFCOA reaches 95.8%and 94.6%in two scenarios,with root mean square(RMS)centroid errors of 0.9516 and 1.0008 m,respectively.Velocity tracking is precise(velocity centroid error RMS of 0.2699 and 0.1700 m/s),and linear velocities closely match the desired 0.3 m/s.Safety metrics showed average collision risks of 0.7773 and 0.5143,with minimum inter-vehicle distances of 0.4702 and 0.8763 m,confirming collision-free navigation of four UGVs.DFCOA outperforms conventional methods in formation stability,path efficiency,and scalability,proving its suitability for decentralized multi-UGV applications.展开更多
The present study investigates the quest for a fully distributed Nash equilibrium(NE) in networked non-cooperative games, with particular emphasis on actuator limitations. Existing distributed NE seeking approaches of...The present study investigates the quest for a fully distributed Nash equilibrium(NE) in networked non-cooperative games, with particular emphasis on actuator limitations. Existing distributed NE seeking approaches often overlook practical input constraints or rely on centralized information. To address these issues, a novel edge-based double-layer adaptive control framework is proposed. Specifically, adaptive scaling parameters are embedded into the edge weights of the communication graph, enabling a fully distributed scheme that avoids dependence on centralized or global knowledge. Every participant modifies its strategy by exclusively utilizing local information and communicating with its neighbors to iteratively approach the NE. By incorporating damping terms into the design of the adaptive parameters, the proposed approach effectively suppresses unbounded parameter growth and consequently guarantees the boundedness of the adaptive gains. In addition, to account for actuator saturation, the proposed distributed NE seeking approach incorporates a saturation function, which ensures that control inputs do not exceed allowable ranges. A rigorous Lyapunov-based analysis guarantees the convergence and boundedness of all system variables. Finally, the presentation of simulation results aims to validate the efficacy and theoretical soundness of the proposed approach.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
Soil greenhouse gas(GHG)emissions contribute profoundly to global warming;however,how plant detritus input alters GHG emissions is poorly understood.Here,we used detritus input and removal treatments(i.e.,DIRT:control...Soil greenhouse gas(GHG)emissions contribute profoundly to global warming;however,how plant detritus input alters GHG emissions is poorly understood.Here,we used detritus input and removal treatments(i.e.,DIRT:control,CK;double litter,DL;no roots with double litter,NRDL;no litter,NL;no roots,NR;no roots and no litter,NRNL)to assess the effects of litter and root inputs on soil CO_(2),CH_(4),and N_(2)O fluxes in soils in a coniferous(Pinus yunnanensis)and a broad-leaf forest(Quercus pannosa)in a subalpine region in southwestern China.Litter addition increased CO_(2) emissions on average 22.22%,but did not significantly alter CH_(4) uptake and N_(2)O emission compared to the CK.Litter removal(NL and NRNL)significantly reduced CO_(2) emissions on average 30.22%and N_(2)O emissions on average 31.16%from both forest soils,but did not significantly affect soil CH_(4) uptake.Root removal(NR and NRNL)generally decreased these three soil GHG fluxes.Changes inβ-1,4-glucosidase(BG)involved in C and phospholipid fatty acid(PLFAs)biomass were projected to influence CO_(2) emissions,while soil microclimates(temperature and moisture)combined with BG activity mainly regulated CH_(4) uptake.Alterations in dissolved organic nitrogen,microbial biomass nitrogen and BG were mainly responsible for changes in N_(2)O emissions.Interestingly,coniferous forest soil seemed to promote CH_(4) uptake more than the broad-leaf forest soil,but CO_(2) and N_(2)O fluxes were not significantly affected by the forest types.As expected,litter addition significantly increased the warming potential,while litter removal relatively lowered it.These findings revealed the divergent roles of plant detritus input and forest type in shaping soil GHG fluxes,thereby providing insights into forest management and predicting contributions of subalpine forests to global warming.展开更多
Phytomelatonin,an emerging plant hormone,plays vital roles in plant growth,development,and stress adaptation(Arnao et al.,2022;Ullah et al.,2024).It acts both as a direct antioxidant and a signaling molecule,engaging ...Phytomelatonin,an emerging plant hormone,plays vital roles in plant growth,development,and stress adaptation(Arnao et al.,2022;Ullah et al.,2024).It acts both as a direct antioxidant and a signaling molecule,engaging complex networks and interacting with other phytohormones(Liu et al.,2022;Khan et al.,2023).Although phytomelatonin receptors(PMTRs)have been identified in many plants(Wei et al.,2018;Wang et al.,2022;Liu et al.,2025),the downstream signaling mechanisms,particularly receptor-mediated protein modifications and transcriptional regulation,remain poorly characterized.展开更多
High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging ...High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).展开更多
Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technol...Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technology overcomes the limitations of traditional single-organ models,providing a novel platform for investigating complex disease mechanisms and evaluating drug efficacy and toxicity.Although it demonstrates broad application prospects,its development still faces critical bottlenecks,including inadequate physiological coupling between organs,short functional maintenance durations,and limited real-time monitoring capabilities.Contemporary research is advancing along three key directions,including functional coupling,sensor integration,and full-process automation systems,to propel the technology toward enhanced levels of physiological relevance and predictive accuracy.展开更多
The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measu...The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.展开更多
A 1×8 multimode interference power splitter with multimode input/output waveguides in SOI material is designed by the beam propagation method and fabricated by the inductive coupled plasma etching technology for...A 1×8 multimode interference power splitter with multimode input/output waveguides in SOI material is designed by the beam propagation method and fabricated by the inductive coupled plasma etching technology for use in fiber optics communication systems.The fabricated device exhibits low loss and good coupling uniformity.The excess loss is lower than 0 8dB,and the uniformity is 0 45dB at the wavelength of 1550nm.Moreover,the polarization dependent loss is lower than 0 7dB at 1550nm.The device size is only 2mm×10mm.展开更多
基金supported by Libyan Cultural Affair/London,Libya under Grant No.13840.
文摘Single-phase,non-isolated microinverters used in photovoltaic(PV)systems commonly encounter two persistent challenges:High-frequency leakage current and fluctuating power delivery.This paper presents a novel single-phase,non-isolated,multi-input microinverter topology with a common-ground structure that effectively eliminates ground leakage current without requiring additional active components.The proposed microinverter architecture integrates a dual-boost configuration and uses only four active switches.This is especially advantageous in terms of the component count,which is beneficial to enhance reliability,reduce cost,and simplify the overall system design.With one,two,or four PV inputs,it can operate without interruption under unbalanced voltage or partial shading and even if some inputs drop to zero.A tailored modulation scheme minimizes conduction losses while maintaining a stable direct-current(DC)-link voltage,and a decoupling capacitor efficiently absorbs the single-phase pulsating power,thus overcoming one major limitation in existing microinverter designs.By validating with a 1-kW GaN-based prototype,both the simulated and experimental results demonstrate its high efficiency,robustness,and practical suitability for cost-effective PV applications,with a peak efficiency value of 94.8%.
基金supported by the National Natural Science Foundation of China(Grant Nos.62376105,12101208,and 61906072)the Fundamental Research Funds for the Central Universities(Grant No.2662022XXQD001).
文摘This paper proposes a novel multivalued recurrent neural network model driven by external inputs,along with two innovative learning algorithms.By incorporating a multivalued activation function,the proposed model can achieve multivalued many-to-one associative memory,and the newly developed algorithms enable effective storage of many-to-one patterns in the coefficient matrix while maintaining the indispensability of inputs in many-to-one associative memory.The proposed learning algorithm addresses a critical limitation of existing models which fail to ensure completely erroneous outputs when facing partial input missing in many-to-one associative memory tasks.The methodology is rigorously derived through theoretical analysis,incorporating comprehensive verification of both the existence and global exponential stability of equilibrium points.Demonstrative examples are provided in the paper to show the effectiveness of the proposed theory.
基金supported by the National Nature Science Foundation of China(U21A20166)the Science and Technology Development Foundation of Jilin Province(20230508095RC)+2 种基金the Major Science and Technology Projects of Jilin Province and Changchun City(20220301033GX)the Development and Reform Commission Foundation of Jilin Province(2023C034-3)the Interdisciplinary Integration and Innovation Project of JLU(JLUXKJC2020202).
文摘Dear Editor,Aiming at the consensus tracking problem of a class of unknown heterogeneous nonlinear multiagent systems(MASs)with input constraints,a novel data-driven iterative learning consensus control(ILCC)protocol based on zeroing neural networks(ZNNs)is proposed.First,a dynamic linearization data model(DLDM)is acquired via dynamic linearization technology(DLT).
文摘In the version of the article originally published in the volume 68,issue 12,2025 of Sci China Mater(pages 4413-4422,https://doi.org/10.1007/s40843-025-3667-7),the Chinese name of the co-first author(肖天孝)was incorrect.The corrected Chinese name is:肖天笑.
文摘Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential for executing sophisticated collective tasks.This paper presents a Distributed Formation Control and Obstacle Avoidance(DFCOA)framework for multi-unmanned ground vehicles(UGV).DFCOA integrates a virtual leader structure for global guidance,an improved A^(*)path planning algorithm with an advanced cost function for efficient path planning,and a repulsive-force-based improved vector field histogram star(VFH^(*))technique for collision avoidance.The virtual leader generates a reference trajectory while enabling distributed execution;the improved A^(*)algorithm reduces planning time and number of nodes to determine the shortest path from the starting position to the goal;and the improved VFH^(*)uses 2D LiDAR data with inter-agent repulsive force to simultaneously avoid collision with obstacles and maintain safe inter-vehicle distances.The formation stability of the proposed DFCOA reaches 95.8%and 94.6%in two scenarios,with root mean square(RMS)centroid errors of 0.9516 and 1.0008 m,respectively.Velocity tracking is precise(velocity centroid error RMS of 0.2699 and 0.1700 m/s),and linear velocities closely match the desired 0.3 m/s.Safety metrics showed average collision risks of 0.7773 and 0.5143,with minimum inter-vehicle distances of 0.4702 and 0.8763 m,confirming collision-free navigation of four UGVs.DFCOA outperforms conventional methods in formation stability,path efficiency,and scalability,proving its suitability for decentralized multi-UGV applications.
基金supported by the National Natural Science Foundation of China (Grant No.62173009)the National Key Research and Development Program of China (Grant No.2021ZD0112302)。
文摘The present study investigates the quest for a fully distributed Nash equilibrium(NE) in networked non-cooperative games, with particular emphasis on actuator limitations. Existing distributed NE seeking approaches often overlook practical input constraints or rely on centralized information. To address these issues, a novel edge-based double-layer adaptive control framework is proposed. Specifically, adaptive scaling parameters are embedded into the edge weights of the communication graph, enabling a fully distributed scheme that avoids dependence on centralized or global knowledge. Every participant modifies its strategy by exclusively utilizing local information and communicating with its neighbors to iteratively approach the NE. By incorporating damping terms into the design of the adaptive parameters, the proposed approach effectively suppresses unbounded parameter growth and consequently guarantees the boundedness of the adaptive gains. In addition, to account for actuator saturation, the proposed distributed NE seeking approach incorporates a saturation function, which ensures that control inputs do not exceed allowable ranges. A rigorous Lyapunov-based analysis guarantees the convergence and boundedness of all system variables. Finally, the presentation of simulation results aims to validate the efficacy and theoretical soundness of the proposed approach.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
基金supported by the National Natural Science Foundation of China(32130069)the National Key Research and Development Program of China(2024YFF1306700)the Scientific Research Foundation of Education Department of Yunnan Province(2024Y004).
文摘Soil greenhouse gas(GHG)emissions contribute profoundly to global warming;however,how plant detritus input alters GHG emissions is poorly understood.Here,we used detritus input and removal treatments(i.e.,DIRT:control,CK;double litter,DL;no roots with double litter,NRDL;no litter,NL;no roots,NR;no roots and no litter,NRNL)to assess the effects of litter and root inputs on soil CO_(2),CH_(4),and N_(2)O fluxes in soils in a coniferous(Pinus yunnanensis)and a broad-leaf forest(Quercus pannosa)in a subalpine region in southwestern China.Litter addition increased CO_(2) emissions on average 22.22%,but did not significantly alter CH_(4) uptake and N_(2)O emission compared to the CK.Litter removal(NL and NRNL)significantly reduced CO_(2) emissions on average 30.22%and N_(2)O emissions on average 31.16%from both forest soils,but did not significantly affect soil CH_(4) uptake.Root removal(NR and NRNL)generally decreased these three soil GHG fluxes.Changes inβ-1,4-glucosidase(BG)involved in C and phospholipid fatty acid(PLFAs)biomass were projected to influence CO_(2) emissions,while soil microclimates(temperature and moisture)combined with BG activity mainly regulated CH_(4) uptake.Alterations in dissolved organic nitrogen,microbial biomass nitrogen and BG were mainly responsible for changes in N_(2)O emissions.Interestingly,coniferous forest soil seemed to promote CH_(4) uptake more than the broad-leaf forest soil,but CO_(2) and N_(2)O fluxes were not significantly affected by the forest types.As expected,litter addition significantly increased the warming potential,while litter removal relatively lowered it.These findings revealed the divergent roles of plant detritus input and forest type in shaping soil GHG fluxes,thereby providing insights into forest management and predicting contributions of subalpine forests to global warming.
基金supported by the grants from the Key Research and Development Program of Xinjiang Uygur autonomous region in China(Grant No.2023B02017)the National Key Research and Development Program of China(Grant No.2024YFD2300703)+1 种基金the financial support from the Beijing Rural Revitalization Agricultural Science and Technology Project(Grant No.NY2401080000),BAIC01-2025the 2115 Talent Development Program of China Agricultural University.
文摘Phytomelatonin,an emerging plant hormone,plays vital roles in plant growth,development,and stress adaptation(Arnao et al.,2022;Ullah et al.,2024).It acts both as a direct antioxidant and a signaling molecule,engaging complex networks and interacting with other phytohormones(Liu et al.,2022;Khan et al.,2023).Although phytomelatonin receptors(PMTRs)have been identified in many plants(Wei et al.,2018;Wang et al.,2022;Liu et al.,2025),the downstream signaling mechanisms,particularly receptor-mediated protein modifications and transcriptional regulation,remain poorly characterized.
文摘High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).
基金supported by the Shenzhen Medical Research Fund(Grant No.A2303049)Guangdong Basic and Applied Basic Research(Grant No.2023A1515010647)+1 种基金National Natural Science Foundation of China(Grant No.22004135)Shenzhen Science and Technology Program(Grant No.RCBS20210706092409020,GXWD20201231165807008,20200824162253002).
文摘Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technology overcomes the limitations of traditional single-organ models,providing a novel platform for investigating complex disease mechanisms and evaluating drug efficacy and toxicity.Although it demonstrates broad application prospects,its development still faces critical bottlenecks,including inadequate physiological coupling between organs,short functional maintenance durations,and limited real-time monitoring capabilities.Contemporary research is advancing along three key directions,including functional coupling,sensor integration,and full-process automation systems,to propel the technology toward enhanced levels of physiological relevance and predictive accuracy.
文摘The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.
文摘A 1×8 multimode interference power splitter with multimode input/output waveguides in SOI material is designed by the beam propagation method and fabricated by the inductive coupled plasma etching technology for use in fiber optics communication systems.The fabricated device exhibits low loss and good coupling uniformity.The excess loss is lower than 0 8dB,and the uniformity is 0 45dB at the wavelength of 1550nm.Moreover,the polarization dependent loss is lower than 0 7dB at 1550nm.The device size is only 2mm×10mm.
