The human retina,a complex and highly specialized structure,includes multiple cell types that work synergistically to generate and transmit visual signals.However,genetic predisposition or age-related degeneration can...The human retina,a complex and highly specialized structure,includes multiple cell types that work synergistically to generate and transmit visual signals.However,genetic predisposition or age-related degeneration can lead to retinal damage that severely impairs vision or causes blindness.Treatment options for retinal diseases are limited,and there is an urgent need for innovative therapeutic strategies.Cell and gene therapies are promising because of the efficacy of delivery systems that transport therapeutic genes to targeted retinal cells.Gene delivery systems hold great promise for treating retinal diseases by enabling the targeted delivery of therapeutic genes to affected cells or by converting endogenous cells into functional ones to facilitate nerve regeneration,potentially restoring vision.This review focuses on two principal categories of gene delivery vectors used in the treatment of retinal diseases:viral and non-viral systems.Viral vectors,including lentiviruses and adeno-associated viruses,exploit the innate ability of viruses to infiltrate cells,which is followed by the introduction of therapeutic genetic material into target cells for gene correction.Lentiviruses can accommodate exogenous genes up to 8 kb in length,but their mechanism of integration into the host genome presents insertion mutation risks.Conversely,adeno-associated viruses are safer,as they exist as episomes in the nucleus,yet their limited packaging capacity constrains their application to a narrower spectrum of diseases,which necessitates the exploration of alternative delivery methods.In parallel,progress has also occurred in the development of novel non-viral delivery systems,particularly those based on liposomal technology.Manipulation of the ratios of hydrophilic and hydrophobic molecules within liposomes and the development of new lipid formulations have led to the creation of advanced non-viral vectors.These innovative systems include solid lipid nanoparticles,polymer nanoparticles,dendrimers,polymeric micelles,and polymeric nanoparticles.Compared with their viral counterparts,non-viral delivery systems offer markedly enhanced loading capacities that enable the direct delivery of nucleic acids,mRNA,or protein molecules into cells.This bypasses the need for DNA transcription and processing,which significantly enhances therapeutic efficiency.Nevertheless,the immunogenic potential and accumulation toxicity associated with non-viral particulate systems necessitates continued optimization to reduce adverse effects in vivo.This review explores the various delivery systems for retinal therapies and retinal nerve regeneration,and details the characteristics,advantages,limitations,and clinical applications of each vector type.By systematically outlining these factors,our goal is to guide the selection of the optimal delivery tool for a specific retinal disease,which will enhance treatment efficacy and improve patient outcomes while paving the way for more effective and targeted therapeutic interventions.展开更多
Nanocrystals have emerged as cutting-edge functional materials benefiting from the increased surface and enhanced coupling of electronic states.High-resolution imaging in transmission electron microscope can provide i...Nanocrystals have emerged as cutting-edge functional materials benefiting from the increased surface and enhanced coupling of electronic states.High-resolution imaging in transmission electron microscope can provide invaluable structural information of crystalline materials,albeit it remains greatly challenging to nanocrystals due to the arduousness of accurate zone axis adjustment.Herein,a homemade software package,called SmartAxis,is developed for rapid yet accurate zone axis alignment of nanocrystals.Incident electron beam tilt is employed as an eccentric goniometer to measure the angular deviation of a crystal to a zone axis,and then serves as a linkage to calculate theαandβtilts of goniometer based on an accurate quantitative relationship.In this way,high-resolution imaging of one identical small Au nanocrystal,as well as electron beam-sensitive MIL-101 metal-organic framework crystals,along multiple zone axes,was performed successfully by using this accurate,time-and electron dose-saving zone axis alignment software package.展开更多
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
In this paper,we investigate the distributed Nash equilibrium(NE)seeking problem for aggregative games with multiple uncertain Euler–Lagrange(EL)systems over jointly connected and weight-balanced switching networks.T...In this paper,we investigate the distributed Nash equilibrium(NE)seeking problem for aggregative games with multiple uncertain Euler–Lagrange(EL)systems over jointly connected and weight-balanced switching networks.The designed distributed controller consists of two parts:a dynamic average consensus part that asymptotically reproduces the unknown NE,and an adaptive reference-tracking module responsible for steering EL systems’positions to track a desired trajectory.The generalized Barbalat’s Lemma is used to overcome the discontinuity of the closed-loop system caused by the switching networks.The proposed algorithm is illustrated by a sensor network deployment problem.展开更多
Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now en...Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now encompasses large-scale electronic medical records(EMR)and experimental molecular data.Artificial intelligence(AI)has demonstrated its utility in medicine through the development of various expert systems(e.g.,MYCIN)since the 1970s.With the emergence of deep learning and large language models(LLMs),AI’s potential in medicine shows considerable promise.Consequently,the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction.This survey provides an insightful overview of TCM AI research,summarizing related research tasks from three perspectives:systems-level biological mechanism elucidation,real-world clinical evidence inference,and personalized clinical decision support.The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice.To critically assess the current state of the field,this work identifies major challenges and opportunities that constrain the development of robust research capabilities—particularly in the mechanistic understanding of TCM syndromes and herbal formulations,novel drug discovery,and the delivery of high-quality,patient-centered clinical care.The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality,large-scale data repositories;the construction of comprehensive and domain-specific knowledge graphs(KGs);deeper insights into the biological mechanisms underpinning clinical efficacy;rigorous causal inference frameworks;and intelligent,personalized decision support systems.展开更多
A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source di...A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.展开更多
Understanding the factors triggering slope failure is essential to ensure the safety of buildings and transportation infrastructure on slopes. Specifically,the failure of stabilizing piles due to groundwater migration...Understanding the factors triggering slope failure is essential to ensure the safety of buildings and transportation infrastructure on slopes. Specifically,the failure of stabilizing piles due to groundwater migration and freeze–thaw(FT) cycles is a significant factor causing slope failure. This study aims to investigate the transmedia seepage characteristics at slope–concrete stabilizing pile interface systems by using silty clay and concrete with varying microstructure characteristics under FT cycles. To this end, a self-developed indoor test device for transmedia water migration, combined with a macro-meso-micro multiscale testing approach, was used to analyze the laws and mechanisms of transmedia seepage at the interface systems. The effect of the medium's microstructure characteristics on the transmedia seepage behavior at the interface systems under FT cycles was also assessed. Results indicated that the transmedia water migration exhibited particularity due to the migration of soil particles and the low permeability characteristics of concrete. The water content in the media increased significantly within the range of 1/3–2/3 of the height from the interface for soil and within 5 mm from the interface for concrete.FT cycles promoted the increase and penetration of cracks within the medium, enhancing the permeability of the slope-concrete stabilizing pile interface systems.With the increase in FT cycles, the porosity inside the medium first decreased and then increased, and the porosity reached the minimum after 25 FT cycles and the maximum after 75 FT cycles, and the water content of the medium after water migration was positively correlated with the porosity. FT cycles also significantly influenced the temporal variation characteristics of soil moisture and the migration path of water in concrete. The study results could serve as a reference for related research on slope stability assessment.展开更多
The 7 ka old Qixiangzhan lava flow(QXZ,Tianchi volcano)represents the last eruptive event before the 946 CE,caldera-forming‘Millennium’eruption(ME).Petrographic,whole rock,mineral composition,Sr-Nd isotopic data on ...The 7 ka old Qixiangzhan lava flow(QXZ,Tianchi volcano)represents the last eruptive event before the 946 CE,caldera-forming‘Millennium’eruption(ME).Petrographic,whole rock,mineral composition,Sr-Nd isotopic data on QXZ show that:(a)the lava consists of two components,constituted by comenditic obsidian fragments immersed in a continuous,aphanitic component;(b)both components have the same geochemical and isotopic variations of the ME magma.The QXZ and ME comendites result from fractional crystallization and crustal assimilation processes.The temperature of the QXZ magma was about 790℃ and the depth of the magma reservoir around 7 km,the same values as estimated for ME.QXZ had a viscosity of 10^(5.5)-10^(9) Pa s and a velocity of 3-10 km/yr.The emplacement time was 0.5-1.6yr and the flow rate 0.48-1.50 m^(3)/s.These values lie within the range estimated for other rhyolitic flows worldwide.The QXZ lava originated through a mixed explosive-effusive activity with the obsidian resulting from the ascent of undercooling,degassing and the fragmentation of magma along the conduit walls,whereas the aphanitic component testifies to the less undercooled and segregated flow at the center of the conduit.The QXZ lava demonstrates the extensive history of the ME magma chamber.展开更多
Renewable Energy Systems(RES)provide a sustainable solution to climate warming and environmental pollution by enhancing stability and reliability through status acquisition and analysis on cloud platforms and intellig...Renewable Energy Systems(RES)provide a sustainable solution to climate warming and environmental pollution by enhancing stability and reliability through status acquisition and analysis on cloud platforms and intelligent processing on edge servers(ES).However,securely distributing encrypted data stored in the cloud to terminals that meet decryption requirements has become a prominent research topic.Additionally,managing attributes,including addition,deletion,and modification,is a crucial issue in the access control scheme for RES.To address these security concerns,a trust-based ciphertext-policy attribute-based encryption(CP-ABE)device access control scheme is proposed for RES(TB-CP-ABE).This scheme effectivelymanages the distribution and control of encrypted data on the cloud through robust attribute key management.By introducing trust management mechanisms and outsourced decryption technology,the ES system can effectively assess and manage the trust worthiness of terminal devices,ensuring that only trusted devices can participate in data exchange and access sensitive information.Besides,the ES system dynamically evaluates trust scores to set decryption trust thresholds,thereby regulating device data access permissions and enhancing the system’s security.To validate the security of the proposed TB-CP-ABE against chosen plaintext attacks,a comprehensive formal security analysis is conducted using the widely accepted random oraclemodel under the decisional q-Bilinear Diffie-Hellman Exponent(q-BDHE)assumption.Finally,comparative analysis with other schemes demonstrates that the TB-CP-ABE scheme cuts energy/communication costs by 43%,and scaleswell with rising terminals,maintaining average latency below 50ms,ensuring real-time service feasibility.The proposed scheme not only provides newinsights for the secure management of RES but also lays a foundation for future secure energy solutions.展开更多
Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emp...Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.展开更多
A high pattern resolution is critical for fabricating roll-to-roll printed electronics(R2RPE)products.For enhanced overlay alignment accuracy,position errors between the printer and the substrate web must be eliminate...A high pattern resolution is critical for fabricating roll-to-roll printed electronics(R2RPE)products.For enhanced overlay alignment accuracy,position errors between the printer and the substrate web must be eliminated,particularly in inkjet printing applications.This paper proposes a novel five-degree-of-freedom(5-DOF)flexure-based alignment stage to adjust the posture of an inkjet printer head.The stage effectively compensates for positioning errors between the actuation mechanism and manipulated objects through a series-parallel combination of compliant substructures.Voice coil motors(VCMs)and linear motors serve as actuators to achieve the required motion.Theoretical models were established using a pseudo-rigid-body model(PRBM)methodology and were validated through finite element analysis(FEA).Finally,an alignment stage prototype was fabricated for an experiment.The prototype test results showed that the developed positioning platform attains 5-DOF motion capabilities with 335.1μm×418.9μm×408.1μm×3.4 mrad×3.29 mrad,with cross-axis coupling errors below 0.11%along y-and z-axes.This paper pro-poses a novel 5-DOF flexure-based alignment stage that can be used for error compensation in R2RPE and effectively improves the interlayer alignment accuracy of multi-layer printing.展开更多
Large language models(LLMs)represent significant advancements in artificial intelligence.However,their increasing capabilities come with a serious challenge:misalignment,which refers to the deviation of model behavior...Large language models(LLMs)represent significant advancements in artificial intelligence.However,their increasing capabilities come with a serious challenge:misalignment,which refers to the deviation of model behavior from the designers’intentions and human values.This review aims to synthesize the current understanding of the LLM misalignment issue and provide researchers and practitioners with a comprehensive overview.We define the concept of misalignment and elaborate on its various manifestations,including generating harmful content,factual errors(hallucinations),propagating biases,failing to follow instructions,emerging deceptive behaviors,and emergent misalignment.We explore the multifaceted causes of misalignment,systematically analyzing factors from surface-level technical issues(e.g.,training data,objective function design,model scaling)to deeper fundamental challenges(e.g.,difficulties formalizing values,discrepancies between training signals and real intentions).This review covers existing and emerging techniques for detecting and evaluating the degree of misalignment,such as benchmark tests,red-teaming,and formal safety assessments.Subsequently,we examine strategies to mitigate misalignment,focusing on mainstream alignment techniques such as RLHF,Constitutional AI(CAI),instruction fine-tuning,and novel approaches that address scalability and robustness.In particular,we analyze recent advances in misalignment attack research,including system prompt modifications,supervised fine-tuning,self-supervised representation attacks,and model editing,which challenge the robustness of model alignment.We categorize and analyze the surveyed literature,highlighting major findings,persistent limitations,and current contentious points.Finally,we identify key open questions and propose several promising future research directions,including constructing high-quality alignment datasets,exploring novel alignment methods,coordinating diverse values,and delving into the deep philosophical aspects of alignment.This work underscores the complexity and multidimensionality of LLM misalignment issues,calling for interdisciplinary approaches to reliably align LLMs with human values.展开更多
To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities...To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.展开更多
Electric Vehicle Charging Systems(EVCS)are increasingly vulnerable to cybersecurity threats as they integrate deeply into smart grids and Internet ofThings(IoT)environments,raising significant security challenges.Most...Electric Vehicle Charging Systems(EVCS)are increasingly vulnerable to cybersecurity threats as they integrate deeply into smart grids and Internet ofThings(IoT)environments,raising significant security challenges.Most existing research primarily emphasizes network-level anomaly detection,leaving critical vulnerabilities at the host level underexplored.This study introduces a novel forensic analysis framework leveraging host-level data,including system logs,kernel events,and Hardware Performance Counters(HPC),to detect and analyze sophisticated cyberattacks such as cryptojacking,Denial-of-Service(DoS),and reconnaissance activities targeting EVCS.Using comprehensive forensic analysis and machine learning models,the proposed framework significantly outperforms existing methods,achieving an accuracy of 98.81%.The findings offer insights into distinct behavioral signatures associated with specific cyber threats,enabling improved cybersecurity strategies and actionable recommendations for robust EVCS infrastructure protection.展开更多
A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,...A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,social network)in the corresponding social-environmental systems(SES).To address these challenges,we need to understand decisions made and actions taken by agents,the outcomes of their actions,including the feedbacks on the corresponding agents and environment.The science of complex adaptive systems-complex adaptive sys tems(CAS)science-has a significant potential to handle such challenges.We address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science,the generic features of CAS,and the key advances and challenges in modeling CAS.Artificial intelligence and data science combined with agent-based modeling promise to improve understanding of agents’behaviors,detect SES struc tures,and formulate SES mechanisms.展开更多
This paper introduces an optimized planning approach for integrating photovoltaic as distributed generation (PV-DG) into the radial distribution power systems, utilizing exhaustive load flow (ELF), loss sensitivity fa...This paper introduces an optimized planning approach for integrating photovoltaic as distributed generation (PV-DG) into the radial distribution power systems, utilizing exhaustive load flow (ELF), loss sensitivity factor (LSF), genetic algorithms (GA) methods, and numerical method based on LSF. The methodology aims to determine the optimal allocation and sizing of multiple PV-DG to minimize power loss through time series power flow analysis. An approach utilizing continuous sensitivity analysis is developed and inherently leverages power flow and loss equations to compute LSF of all buses in the system towards employing a dynamic PV-DG model for more accurate results. The algorithm uses a numerical grid search method to optimize PV-DG placement in a power distribution system, focusing on minimizing system losses. It combines iterative analysis, sensitivity assessment, and comprehensive visualization to identify and present the optimal PV-DG configurations. The present-ed algorithms are verified through co-simulation framework combining MATLAB and OpenDSS to carry out analysis for 12-bus radial distribution test system. The proposed numerical method is compared with other algorithms, such as ELF, LSF methods, and Genetic Algorithms (GA). Results show that the proposed numerical method performs well in comparison with LSF and ELF solutions.展开更多
The study was carried out in the Tahoua region at the market gardening sites of the Taddis 1 and 2 valley. Small-scale pumping irrigation is one of the most interesting uses of solar energy. The objective of this stud...The study was carried out in the Tahoua region at the market gardening sites of the Taddis 1 and 2 valley. Small-scale pumping irrigation is one of the most interesting uses of solar energy. The objective of this study is to carry out a comparative analysis of two dewatering pumping systems (Solar Kit and GMP) for water mobilization on a certain number of criteria such as sustainable use, economic aspect and performance. To achieve this, the adapted methodology consisted first of all in the development of a data collection tool in the field. Then flow measurements, estimation of fuel consumption, pressure height, etc., were carried out. Thus, the data collection involved a sample of 120 irrigators who had to use the two (2) types of pumping systems. The collected data were analyzed and processed with appropriate software. The results of the study show that the two pumping systems studied have strengths and constraints. Thus, the solar pumping system has a significant investment cost, very low maintenance and a low operating cost. On the other hand, the system with a generator has a relatively low investment cost (25 to 30 times less than solar), but a relatively high operating, upkeep and maintenance cost. He adds that these assets and constraints must be taken into consideration when an investment is made. This study shows that 74% of producers use GMP compared to 26% who use the Solar Kit. But in practice, the Solar Kit is more reliable for producers from the point of view of planted area, environmental management and investment costs, supply of fuel and lubricant. These results indicate better performance of the solar pumping system compared to GMP at the study sites.展开更多
Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective metho...Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective method to mitigate the problem,which is able to learn an adaptive segmentation model by transferring knowledge from a rich-labeled source domain.In this paper,we propose a multi-level distribution alignment-based unsupervised domain adaptation network(MDA-Net)for segmentation of 3D neuronal soma images.Distribution alignment is performed in both feature space and output space.In the feature space,features from different scales are adaptively fused to enhance the feature extraction capability for small target somata and con-strained to be domain invariant by adversarial adaptation strategy.In the output space,local discrepancy maps that can reveal the spatial structures of somata are constructed on the predicted segmentation results.Then thedistribution alignment is performed on the local discrepancies maps across domains to obtain a superior discrepancy map in the target domain,achieving refined segmentation performance of neuronal somata.Additionally,after a period of distribution align-ment procedure,a portion of target samples with high confident pseudo-labels are selected as training data,which assist in learning a more adaptive segmentation network.We verified the superiority of the proposed algorithm by comparing several domain adaptation networks on two 3D mouse brain neuronal somata datasets and one macaque brain neuronal soma dataset.展开更多
The phenomenon of pyroptosis has gained increasing prominence in recent decades as a significant contributor to cellular mortality.The process of pyroptosis plays a crucial role in the regulation of various types of c...The phenomenon of pyroptosis has gained increasing prominence in recent decades as a significant contributor to cellular mortality.The process of pyroptosis plays a crucial role in the regulation of various types of cancers.The induction of pyroptosis can be achieved through various mechanisms,including the activation of small molecule pyrogen inducers.The use of.small molecule pyrogen inducer alone,however,has limitations.On one hand,we benefit from the utilization of nano delivery systems(NDS).On the other hand,there is an enhanced comprehension of the underlying mechanism governing pyroptosis.A novel therapeutic strategy,resulting from a clever amalgamation of the two approaches,has demonstrated significant efficacy in experimental treatment of certain diseases.A variety of nanocarriers,including liposomes,hydrogels,polymer micelles,exosomes,metal-organic frameworks protein nanoparticles,cell membrane biomimetic nanocarriers,carbon nanotubes,dendrimers,polymer conjugates and polymer nanoparticles are utilized for the delivery of drugs that induce pyroptosis in cells.By integrating the aforementioned approaches,a diverse range of pyroptosis strategies have been developed utilizing NDS,encompassing stem cell targeting,disruption of ion homeostasis,augmentation of reactive oxygen species generation,induction of epigenetic modifications,and transportation of gaseous protein gasdermins family proteins.However,the clinical application of these strategies still encounters numerous challenges that need to be addressed,including limited comprehension of NDS,incomplete understanding of the interaction mechanisms between nanomaterials and biological systems,and insufficient knowledge regarding nanocarrier materials.In this study,we aim to advance the field of pyroptosis in cancer treatment.The induction of pyroptotic cell death is believed to hold great promise as an ideal therapeutic approach for the management,regulation,and treatment of numerous types of cancers.展开更多
In order to promote the development of the mineral industry in their countries,Tanzania and Uganda have revised their mining acts in recent years,and the reform of the mineral rights licensing system is one of the key...In order to promote the development of the mineral industry in their countries,Tanzania and Uganda have revised their mining acts in recent years,and the reform of the mineral rights licensing system is one of the key points.This paper is intended to make a comparative analysis of the two countries’mineral rights licensing systems in terms of the main body of approval,approval time,approval information,approval conditions and application fees.Through comparison,it can be seen that the two countries focus on the role of the government in the mineral rights licensing,jurisdiction is more centralized,access system is fairer,the review is more stringent,and the provisions are clearer and more concise.On the whole,Tanzania’s mineral rights licensing system is more detailed and standardized than Uganda’s,and is more operational in practice.In addition to exploring the advantages of the two countries’mineral rights licensing system,this paper also summarizes and analyzes the shortcomings of the two countries’mineral rights licensing system,how to verify the review of information in the two countries’mineral rights licensing system,how to effectively supervise the activities after licensing,and how to continue to deepen the reform of the two countries’ministries of mines and minerals,which are responsible for the important task of strengthening the administrative capacity and improving the efficiency of the administration,is still an important issue that deserves continuous and in-depth study for the two countries.For both countries,this is still a topic that deserves continuous and in-depth research.Through the comparative analysis of the Tanzanian and Ugandan mineral rights licensing systems,this paper clearly demonstrates the similarities and differences between the two systems as well as their advantages and disadvantages,which can provide decision-making references for relevant mining investments and help investors more comprehensively assess the legal environment,policy risks and operating costs of mining development in the two countries,so as to optimize their investment strategies and reduce compliance risks.展开更多
基金Hongguang Wu,Both authors contributed equally to this work and share first authorshipLing Dong,Both authors contributed equally to this work and share first authorship。
文摘The human retina,a complex and highly specialized structure,includes multiple cell types that work synergistically to generate and transmit visual signals.However,genetic predisposition or age-related degeneration can lead to retinal damage that severely impairs vision or causes blindness.Treatment options for retinal diseases are limited,and there is an urgent need for innovative therapeutic strategies.Cell and gene therapies are promising because of the efficacy of delivery systems that transport therapeutic genes to targeted retinal cells.Gene delivery systems hold great promise for treating retinal diseases by enabling the targeted delivery of therapeutic genes to affected cells or by converting endogenous cells into functional ones to facilitate nerve regeneration,potentially restoring vision.This review focuses on two principal categories of gene delivery vectors used in the treatment of retinal diseases:viral and non-viral systems.Viral vectors,including lentiviruses and adeno-associated viruses,exploit the innate ability of viruses to infiltrate cells,which is followed by the introduction of therapeutic genetic material into target cells for gene correction.Lentiviruses can accommodate exogenous genes up to 8 kb in length,but their mechanism of integration into the host genome presents insertion mutation risks.Conversely,adeno-associated viruses are safer,as they exist as episomes in the nucleus,yet their limited packaging capacity constrains their application to a narrower spectrum of diseases,which necessitates the exploration of alternative delivery methods.In parallel,progress has also occurred in the development of novel non-viral delivery systems,particularly those based on liposomal technology.Manipulation of the ratios of hydrophilic and hydrophobic molecules within liposomes and the development of new lipid formulations have led to the creation of advanced non-viral vectors.These innovative systems include solid lipid nanoparticles,polymer nanoparticles,dendrimers,polymeric micelles,and polymeric nanoparticles.Compared with their viral counterparts,non-viral delivery systems offer markedly enhanced loading capacities that enable the direct delivery of nucleic acids,mRNA,or protein molecules into cells.This bypasses the need for DNA transcription and processing,which significantly enhances therapeutic efficiency.Nevertheless,the immunogenic potential and accumulation toxicity associated with non-viral particulate systems necessitates continued optimization to reduce adverse effects in vivo.This review explores the various delivery systems for retinal therapies and retinal nerve regeneration,and details the characteristics,advantages,limitations,and clinical applications of each vector type.By systematically outlining these factors,our goal is to guide the selection of the optimal delivery tool for a specific retinal disease,which will enhance treatment efficacy and improve patient outcomes while paving the way for more effective and targeted therapeutic interventions.
基金supported by the National Key R&D Program of China(No.2021YFA1501002)Thousand Talents Program for Distinguished Young Scholars.X.Li thanks the National Natural Science Foundation of China(No.22309021).
文摘Nanocrystals have emerged as cutting-edge functional materials benefiting from the increased surface and enhanced coupling of electronic states.High-resolution imaging in transmission electron microscope can provide invaluable structural information of crystalline materials,albeit it remains greatly challenging to nanocrystals due to the arduousness of accurate zone axis adjustment.Herein,a homemade software package,called SmartAxis,is developed for rapid yet accurate zone axis alignment of nanocrystals.Incident electron beam tilt is employed as an eccentric goniometer to measure the angular deviation of a crystal to a zone axis,and then serves as a linkage to calculate theαandβtilts of goniometer based on an accurate quantitative relationship.In this way,high-resolution imaging of one identical small Au nanocrystal,as well as electron beam-sensitive MIL-101 metal-organic framework crystals,along multiple zone axes,was performed successfully by using this accurate,time-and electron dose-saving zone axis alignment software package.
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
基金supported by the Research Grants Council of the Hong Kong Special Administration Region under the Grant No.14201621。
文摘In this paper,we investigate the distributed Nash equilibrium(NE)seeking problem for aggregative games with multiple uncertain Euler–Lagrange(EL)systems over jointly connected and weight-balanced switching networks.The designed distributed controller consists of two parts:a dynamic average consensus part that asymptotically reproduces the unknown NE,and an adaptive reference-tracking module responsible for steering EL systems’positions to track a desired trajectory.The generalized Barbalat’s Lemma is used to overcome the discontinuity of the closed-loop system caused by the switching networks.The proposed algorithm is illustrated by a sensor network deployment problem.
基金supported by the National Key Research and Development Program (No.2023YFC3502604)the National Natural Science Foundation of China (Nos.U23B2062, 82274352,82174533, 82374302, 82204941)+3 种基金the Noncommunicable Chronic Diseases-National Science and Technology Major Project (No.2023ZD0505700)the Beijing-Tianjin-Hebei Basic Research Cooperation Project (No.22JCZXJC00070)the State Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture (No.SKL2024Z0102)Key R&D project of Ningxia Autonomous Region (No.2022BEG02036).
文摘Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now encompasses large-scale electronic medical records(EMR)and experimental molecular data.Artificial intelligence(AI)has demonstrated its utility in medicine through the development of various expert systems(e.g.,MYCIN)since the 1970s.With the emergence of deep learning and large language models(LLMs),AI’s potential in medicine shows considerable promise.Consequently,the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction.This survey provides an insightful overview of TCM AI research,summarizing related research tasks from three perspectives:systems-level biological mechanism elucidation,real-world clinical evidence inference,and personalized clinical decision support.The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice.To critically assess the current state of the field,this work identifies major challenges and opportunities that constrain the development of robust research capabilities—particularly in the mechanistic understanding of TCM syndromes and herbal formulations,novel drug discovery,and the delivery of high-quality,patient-centered clinical care.The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality,large-scale data repositories;the construction of comprehensive and domain-specific knowledge graphs(KGs);deeper insights into the biological mechanisms underpinning clinical efficacy;rigorous causal inference frameworks;and intelligent,personalized decision support systems.
基金co-supported by the National Key R&D Program of China(No.2023YFB4704400)the Zhejiang Provincial Natural Science Foundation of China(No.LQ24F030012)the National Natural Science Foundation of China General Project(No.62373033)。
文摘A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.
基金financially supported by Jilin Provincial Natural Science Foundation (No.20220101164JC)。
文摘Understanding the factors triggering slope failure is essential to ensure the safety of buildings and transportation infrastructure on slopes. Specifically,the failure of stabilizing piles due to groundwater migration and freeze–thaw(FT) cycles is a significant factor causing slope failure. This study aims to investigate the transmedia seepage characteristics at slope–concrete stabilizing pile interface systems by using silty clay and concrete with varying microstructure characteristics under FT cycles. To this end, a self-developed indoor test device for transmedia water migration, combined with a macro-meso-micro multiscale testing approach, was used to analyze the laws and mechanisms of transmedia seepage at the interface systems. The effect of the medium's microstructure characteristics on the transmedia seepage behavior at the interface systems under FT cycles was also assessed. Results indicated that the transmedia water migration exhibited particularity due to the migration of soil particles and the low permeability characteristics of concrete. The water content in the media increased significantly within the range of 1/3–2/3 of the height from the interface for soil and within 5 mm from the interface for concrete.FT cycles promoted the increase and penetration of cracks within the medium, enhancing the permeability of the slope-concrete stabilizing pile interface systems.With the increase in FT cycles, the porosity inside the medium first decreased and then increased, and the porosity reached the minimum after 25 FT cycles and the maximum after 75 FT cycles, and the water content of the medium after water migration was positively correlated with the porosity. FT cycles also significantly influenced the temporal variation characteristics of soil moisture and the migration path of water in concrete. The study results could serve as a reference for related research on slope stability assessment.
基金funded by the National Natural Science Foundation of China(Grant Nos.41972313 and 41790453)the Engineering Research Center of Geothermal Resources Development Technology and Equipment,Ministry of Education,Jilin University。
文摘The 7 ka old Qixiangzhan lava flow(QXZ,Tianchi volcano)represents the last eruptive event before the 946 CE,caldera-forming‘Millennium’eruption(ME).Petrographic,whole rock,mineral composition,Sr-Nd isotopic data on QXZ show that:(a)the lava consists of two components,constituted by comenditic obsidian fragments immersed in a continuous,aphanitic component;(b)both components have the same geochemical and isotopic variations of the ME magma.The QXZ and ME comendites result from fractional crystallization and crustal assimilation processes.The temperature of the QXZ magma was about 790℃ and the depth of the magma reservoir around 7 km,the same values as estimated for ME.QXZ had a viscosity of 10^(5.5)-10^(9) Pa s and a velocity of 3-10 km/yr.The emplacement time was 0.5-1.6yr and the flow rate 0.48-1.50 m^(3)/s.These values lie within the range estimated for other rhyolitic flows worldwide.The QXZ lava originated through a mixed explosive-effusive activity with the obsidian resulting from the ascent of undercooling,degassing and the fragmentation of magma along the conduit walls,whereas the aphanitic component testifies to the less undercooled and segregated flow at the center of the conduit.The QXZ lava demonstrates the extensive history of the ME magma chamber.
基金supported by the Science and Technology Project of the State Grid Corporation of China,Grant number 5700-202223189A-1-1-ZN.
文摘Renewable Energy Systems(RES)provide a sustainable solution to climate warming and environmental pollution by enhancing stability and reliability through status acquisition and analysis on cloud platforms and intelligent processing on edge servers(ES).However,securely distributing encrypted data stored in the cloud to terminals that meet decryption requirements has become a prominent research topic.Additionally,managing attributes,including addition,deletion,and modification,is a crucial issue in the access control scheme for RES.To address these security concerns,a trust-based ciphertext-policy attribute-based encryption(CP-ABE)device access control scheme is proposed for RES(TB-CP-ABE).This scheme effectivelymanages the distribution and control of encrypted data on the cloud through robust attribute key management.By introducing trust management mechanisms and outsourced decryption technology,the ES system can effectively assess and manage the trust worthiness of terminal devices,ensuring that only trusted devices can participate in data exchange and access sensitive information.Besides,the ES system dynamically evaluates trust scores to set decryption trust thresholds,thereby regulating device data access permissions and enhancing the system’s security.To validate the security of the proposed TB-CP-ABE against chosen plaintext attacks,a comprehensive formal security analysis is conducted using the widely accepted random oraclemodel under the decisional q-Bilinear Diffie-Hellman Exponent(q-BDHE)assumption.Finally,comparative analysis with other schemes demonstrates that the TB-CP-ABE scheme cuts energy/communication costs by 43%,and scaleswell with rising terminals,maintaining average latency below 50ms,ensuring real-time service feasibility.The proposed scheme not only provides newinsights for the secure management of RES but also lays a foundation for future secure energy solutions.
文摘Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.
基金Supported by Natural Science Research Project of Anhui Educational Committee(Grant No.2024AH040010).
文摘A high pattern resolution is critical for fabricating roll-to-roll printed electronics(R2RPE)products.For enhanced overlay alignment accuracy,position errors between the printer and the substrate web must be eliminated,particularly in inkjet printing applications.This paper proposes a novel five-degree-of-freedom(5-DOF)flexure-based alignment stage to adjust the posture of an inkjet printer head.The stage effectively compensates for positioning errors between the actuation mechanism and manipulated objects through a series-parallel combination of compliant substructures.Voice coil motors(VCMs)and linear motors serve as actuators to achieve the required motion.Theoretical models were established using a pseudo-rigid-body model(PRBM)methodology and were validated through finite element analysis(FEA).Finally,an alignment stage prototype was fabricated for an experiment.The prototype test results showed that the developed positioning platform attains 5-DOF motion capabilities with 335.1μm×418.9μm×408.1μm×3.4 mrad×3.29 mrad,with cross-axis coupling errors below 0.11%along y-and z-axes.This paper pro-poses a novel 5-DOF flexure-based alignment stage that can be used for error compensation in R2RPE and effectively improves the interlayer alignment accuracy of multi-layer printing.
基金supported by National Natural Science Foundation of China(62462019,62172350)Guangdong Basic and Applied Basic Research Foundation(2023A1515012846)+6 种基金Guangxi Science and Technology Major Program(AA24263010)The Key Research and Development Program of Guangxi(AB24010085)Key Laboratory of Equipment Data Security and Guarantee Technology,Ministry of Education(GDZB2024060500)2024 Higher Education Scientific Research Planning Project(No.24NL0419)Nantong Science and Technology Project(No.JC2023070)the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province(GrantNo.SKLACSS-202407)sponsored by the Cultivation of Young andMiddle-aged Academic Leaders in the“Qing Lan Project”of Jiangsu Province and the 2025 Outstanding Teaching Team in the“Qing Lan Project”of Jiangsu Province.
文摘Large language models(LLMs)represent significant advancements in artificial intelligence.However,their increasing capabilities come with a serious challenge:misalignment,which refers to the deviation of model behavior from the designers’intentions and human values.This review aims to synthesize the current understanding of the LLM misalignment issue and provide researchers and practitioners with a comprehensive overview.We define the concept of misalignment and elaborate on its various manifestations,including generating harmful content,factual errors(hallucinations),propagating biases,failing to follow instructions,emerging deceptive behaviors,and emergent misalignment.We explore the multifaceted causes of misalignment,systematically analyzing factors from surface-level technical issues(e.g.,training data,objective function design,model scaling)to deeper fundamental challenges(e.g.,difficulties formalizing values,discrepancies between training signals and real intentions).This review covers existing and emerging techniques for detecting and evaluating the degree of misalignment,such as benchmark tests,red-teaming,and formal safety assessments.Subsequently,we examine strategies to mitigate misalignment,focusing on mainstream alignment techniques such as RLHF,Constitutional AI(CAI),instruction fine-tuning,and novel approaches that address scalability and robustness.In particular,we analyze recent advances in misalignment attack research,including system prompt modifications,supervised fine-tuning,self-supervised representation attacks,and model editing,which challenge the robustness of model alignment.We categorize and analyze the surveyed literature,highlighting major findings,persistent limitations,and current contentious points.Finally,we identify key open questions and propose several promising future research directions,including constructing high-quality alignment datasets,exploring novel alignment methods,coordinating diverse values,and delving into the deep philosophical aspects of alignment.This work underscores the complexity and multidimensionality of LLM misalignment issues,calling for interdisciplinary approaches to reliably align LLMs with human values.
基金partially supported by the National Natural Science Foundation of China under Grants 62471493 and 62402257(for conceptualization and investigation)partially supported by the Natural Science Foundation of Shandong Province,China under Grants ZR2023LZH017,ZR2024MF066,and 2023QF025(for formal analysis and validation)+1 种基金partially supported by the Open Foundation of Key Laboratory of Computing Power Network and Information Security,Ministry of Education,Qilu University of Technology(Shandong Academy of Sciences)under Grant 2023ZD010(for methodology and model design)partially supported by the Russian Science Foundation(RSF)Project under Grant 22-71-10095-P(for validation and results verification).
文摘To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.
文摘Electric Vehicle Charging Systems(EVCS)are increasingly vulnerable to cybersecurity threats as they integrate deeply into smart grids and Internet ofThings(IoT)environments,raising significant security challenges.Most existing research primarily emphasizes network-level anomaly detection,leaving critical vulnerabilities at the host level underexplored.This study introduces a novel forensic analysis framework leveraging host-level data,including system logs,kernel events,and Hardware Performance Counters(HPC),to detect and analyze sophisticated cyberattacks such as cryptojacking,Denial-of-Service(DoS),and reconnaissance activities targeting EVCS.Using comprehensive forensic analysis and machine learning models,the proposed framework significantly outperforms existing methods,achieving an accuracy of 98.81%.The findings offer insights into distinct behavioral signatures associated with specific cyber threats,enabling improved cybersecurity strategies and actionable recommendations for robust EVCS infrastructure protection.
基金The National Science Foundation funded this research under the Dy-namics of Coupled Natural and Human Systems program(Grants No.DEB-1212183 and BCS-1826839)support from San Diego State University and Auburn University.
文摘A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,social network)in the corresponding social-environmental systems(SES).To address these challenges,we need to understand decisions made and actions taken by agents,the outcomes of their actions,including the feedbacks on the corresponding agents and environment.The science of complex adaptive systems-complex adaptive sys tems(CAS)science-has a significant potential to handle such challenges.We address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science,the generic features of CAS,and the key advances and challenges in modeling CAS.Artificial intelligence and data science combined with agent-based modeling promise to improve understanding of agents’behaviors,detect SES struc tures,and formulate SES mechanisms.
文摘This paper introduces an optimized planning approach for integrating photovoltaic as distributed generation (PV-DG) into the radial distribution power systems, utilizing exhaustive load flow (ELF), loss sensitivity factor (LSF), genetic algorithms (GA) methods, and numerical method based on LSF. The methodology aims to determine the optimal allocation and sizing of multiple PV-DG to minimize power loss through time series power flow analysis. An approach utilizing continuous sensitivity analysis is developed and inherently leverages power flow and loss equations to compute LSF of all buses in the system towards employing a dynamic PV-DG model for more accurate results. The algorithm uses a numerical grid search method to optimize PV-DG placement in a power distribution system, focusing on minimizing system losses. It combines iterative analysis, sensitivity assessment, and comprehensive visualization to identify and present the optimal PV-DG configurations. The present-ed algorithms are verified through co-simulation framework combining MATLAB and OpenDSS to carry out analysis for 12-bus radial distribution test system. The proposed numerical method is compared with other algorithms, such as ELF, LSF methods, and Genetic Algorithms (GA). Results show that the proposed numerical method performs well in comparison with LSF and ELF solutions.
文摘The study was carried out in the Tahoua region at the market gardening sites of the Taddis 1 and 2 valley. Small-scale pumping irrigation is one of the most interesting uses of solar energy. The objective of this study is to carry out a comparative analysis of two dewatering pumping systems (Solar Kit and GMP) for water mobilization on a certain number of criteria such as sustainable use, economic aspect and performance. To achieve this, the adapted methodology consisted first of all in the development of a data collection tool in the field. Then flow measurements, estimation of fuel consumption, pressure height, etc., were carried out. Thus, the data collection involved a sample of 120 irrigators who had to use the two (2) types of pumping systems. The collected data were analyzed and processed with appropriate software. The results of the study show that the two pumping systems studied have strengths and constraints. Thus, the solar pumping system has a significant investment cost, very low maintenance and a low operating cost. On the other hand, the system with a generator has a relatively low investment cost (25 to 30 times less than solar), but a relatively high operating, upkeep and maintenance cost. He adds that these assets and constraints must be taken into consideration when an investment is made. This study shows that 74% of producers use GMP compared to 26% who use the Solar Kit. But in practice, the Solar Kit is more reliable for producers from the point of view of planted area, environmental management and investment costs, supply of fuel and lubricant. These results indicate better performance of the solar pumping system compared to GMP at the study sites.
基金supported by the Fund of Key Laboratory of Biomedical Engineering of Hainan Province(No.BME20240001)the STI2030-Major Projects(No.2021ZD0200104)the National Natural Science Foundations of China under Grant 61771437.
文摘Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective method to mitigate the problem,which is able to learn an adaptive segmentation model by transferring knowledge from a rich-labeled source domain.In this paper,we propose a multi-level distribution alignment-based unsupervised domain adaptation network(MDA-Net)for segmentation of 3D neuronal soma images.Distribution alignment is performed in both feature space and output space.In the feature space,features from different scales are adaptively fused to enhance the feature extraction capability for small target somata and con-strained to be domain invariant by adversarial adaptation strategy.In the output space,local discrepancy maps that can reveal the spatial structures of somata are constructed on the predicted segmentation results.Then thedistribution alignment is performed on the local discrepancies maps across domains to obtain a superior discrepancy map in the target domain,achieving refined segmentation performance of neuronal somata.Additionally,after a period of distribution align-ment procedure,a portion of target samples with high confident pseudo-labels are selected as training data,which assist in learning a more adaptive segmentation network.We verified the superiority of the proposed algorithm by comparing several domain adaptation networks on two 3D mouse brain neuronal somata datasets and one macaque brain neuronal soma dataset.
文摘The phenomenon of pyroptosis has gained increasing prominence in recent decades as a significant contributor to cellular mortality.The process of pyroptosis plays a crucial role in the regulation of various types of cancers.The induction of pyroptosis can be achieved through various mechanisms,including the activation of small molecule pyrogen inducers.The use of.small molecule pyrogen inducer alone,however,has limitations.On one hand,we benefit from the utilization of nano delivery systems(NDS).On the other hand,there is an enhanced comprehension of the underlying mechanism governing pyroptosis.A novel therapeutic strategy,resulting from a clever amalgamation of the two approaches,has demonstrated significant efficacy in experimental treatment of certain diseases.A variety of nanocarriers,including liposomes,hydrogels,polymer micelles,exosomes,metal-organic frameworks protein nanoparticles,cell membrane biomimetic nanocarriers,carbon nanotubes,dendrimers,polymer conjugates and polymer nanoparticles are utilized for the delivery of drugs that induce pyroptosis in cells.By integrating the aforementioned approaches,a diverse range of pyroptosis strategies have been developed utilizing NDS,encompassing stem cell targeting,disruption of ion homeostasis,augmentation of reactive oxygen species generation,induction of epigenetic modifications,and transportation of gaseous protein gasdermins family proteins.However,the clinical application of these strategies still encounters numerous challenges that need to be addressed,including limited comprehension of NDS,incomplete understanding of the interaction mechanisms between nanomaterials and biological systems,and insufficient knowledge regarding nanocarrier materials.In this study,we aim to advance the field of pyroptosis in cancer treatment.The induction of pyroptotic cell death is believed to hold great promise as an ideal therapeutic approach for the management,regulation,and treatment of numerous types of cancers.
基金Shenzhen Polytechnic University’s Key Scientific Research Project,The Field Exploitation of Shenzhen Enterprises’Investment in Africa and the Prevention and Control of Legal Risks(Project No.:6024310006S)。
文摘In order to promote the development of the mineral industry in their countries,Tanzania and Uganda have revised their mining acts in recent years,and the reform of the mineral rights licensing system is one of the key points.This paper is intended to make a comparative analysis of the two countries’mineral rights licensing systems in terms of the main body of approval,approval time,approval information,approval conditions and application fees.Through comparison,it can be seen that the two countries focus on the role of the government in the mineral rights licensing,jurisdiction is more centralized,access system is fairer,the review is more stringent,and the provisions are clearer and more concise.On the whole,Tanzania’s mineral rights licensing system is more detailed and standardized than Uganda’s,and is more operational in practice.In addition to exploring the advantages of the two countries’mineral rights licensing system,this paper also summarizes and analyzes the shortcomings of the two countries’mineral rights licensing system,how to verify the review of information in the two countries’mineral rights licensing system,how to effectively supervise the activities after licensing,and how to continue to deepen the reform of the two countries’ministries of mines and minerals,which are responsible for the important task of strengthening the administrative capacity and improving the efficiency of the administration,is still an important issue that deserves continuous and in-depth study for the two countries.For both countries,this is still a topic that deserves continuous and in-depth research.Through the comparative analysis of the Tanzanian and Ugandan mineral rights licensing systems,this paper clearly demonstrates the similarities and differences between the two systems as well as their advantages and disadvantages,which can provide decision-making references for relevant mining investments and help investors more comprehensively assess the legal environment,policy risks and operating costs of mining development in the two countries,so as to optimize their investment strategies and reduce compliance risks.
