All‑Covalent Organic Framework Nanofilms Assembled Lithium‑Ion Capacitor to Solve the Imbalanced Charge Storage Kinetics

Free-standing covalent organic framework(COFs)nanofilms exhibit a remarkable ability to rapidly intercalate/de-intercalate Li^(+) in lithium-ion batteries,while simultaneously exposing affluent active sites in supercapacitors.The development of these nanofilms offers a promising solution to address the persistent challenge of imbalanced charge storage kinetics between battery-type anode and capacitor-type cathode in lithium-ion capacitors(LICs).Herein,for the first time,custom-made COFBTMB-TP and COFTAPB-BPY nanofilms are synthesized as the anode and cathode,respectively,for an all-COF nanofilm-structured LIC.The COFBTMB-TP nanofilm with strong electronegative–CF3 groups enables tuning the partial electron cloud density for Li^(+) migration to ensure the rapid anode kinetic process.The thickness-regulated cathodic COFTAPB-BPY nanofilm can fit the anodic COF nanofilm in the capacity.Due to the aligned 1D channel,2D aromatic skeleton and accessible active sites of COF nanofilms,the whole COFTAPB-BPY//COFBTMB-TP LIC demonstrates a high energy density of 318 mWh cm^(−3) at a high-power density of 6 W cm^(−3),excellent rate capability,good cycle stability with the capacity retention rate of 77%after 5000-cycle.The COFTAPB-BPY//COFBTMB-TP LIC represents a new benchmark for currently reported film-type LICs and even film-type supercapacitors.After being comprehensively explored via ex situ XPS,7Li solid-state NMR analyses,and DFT calculation,it is found that the COFBTMB-TP nanofilm facilitates the reversible conversion of semi-ionic to ionic C–F bonds during lithium storage.COFBTMB-TP exhibits a strong interaction with Li^(+) due to the C–F,C=O,and C–N bonds,facilitating Li^(+) desolation and absorption from the electrolyte.This work addresses the challenge of imbalanced charge storage kinetics and capacity between the anode and cathode and also pave the way for future miniaturized and wearable LIC devices.

Accelerating Oxygen Electrocatalysis Kinetics on Metal-Organic Frameworks via Bond Length Optimization

Metal-organic frameworks(MOFs)have been developed as an ideal platform for exploration of the relationship between intrinsic structure and catalytic activity,but the limited catalytic activity and stability has hampered their practical use in water splitting.Herein,we develop a bond length adjustment strategy for optimizing naphthalene-based MOFs that synthesized by acid etching Co-naphthalenedicarboxylic acid-based MOFs(donated as AE-CoNDA)to serve as efficient catalyst for water splitting.AE-CoNDA exhibits a low overpotential of 260 mV to reach 10 mA cm^(−2)and a small Tafel slope of 62 mV dec^(−1)with excellent stability over 100 h.After integrated AE-CoNDA onto BiVO_(4),photocurrent density of 4.3 mA cm^(−2)is achieved at 1.23 V.Experimental investigations demonstrate that the stretched Co-O bond length was found to optimize the orbitals hybridization of Co 3d and O 2p,which accounts for the fast kinetics and high activity.Theoretical calculations reveal that the stretched Co-O bond length strengthens the adsorption of oxygen-contained intermediates at the Co active sites for highly efficient water splitting.

U-Net Models for Representing Wind Stress Anomalies over the Tropical Pacific and Their Integrations with an Intermediate Coupled Model for ENSO Studies

El Niño-Southern Oscillation(ENSO)is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific,and numerous dynamical and statistical models have been developed to simulate and predict it.In some simplified coupled ocean-atmosphere models,the relationship between sea surface temperature(SST)anomalies and wind stress(τ)anomalies can be constructed by statistical methods,such as singular value decomposition(SVD).In recent years,the applications of artificial intelligence(AI)to climate modeling have shown promising prospects,and the integrations of AI-based models with dynamical models are active areas of research.This study constructs U-Net models for representing the relationship between SSTAs andτanomalies in the tropical Pacific;the UNet-derivedτmodel,denoted asτUNet,is then used to replace the original SVD-basedτmodel of an intermediate coupled model(ICM),forming a newly AI-integrated ICM,referred to as ICM-UNet.The simulation results obtained from ICM-UNet demonstrate their ability to represent the spatiotemporal variability of oceanic and atmospheric anomaly fields in the equatorial Pacific.In the ocean-only case study,theτUNet-derived wind stress anomaly fields are used to force the ocean component of the ICM,the results of which also indicate reasonable simulations of typical ENSO events.These results demonstrate the feasibility of integrating an AI-derived model with a physics-based dynamical model for ENSO modeling studies.Furthermore,the successful integration of the dynamical ocean models with the AI-based atmospheric wind model provides a novel approach to ocean-atmosphere interaction modeling studies.

Geospatial Technology Integration in Smart City Frameworks for Achieving Climate Neutrality by 2050: A Case Study of Limassol Municipality, Cyprus

This study investigated the integration of geospatial technologies within smart city frameworks to achieve the European Union’s climate neutrality goals by 2050. Focusing on rapid urbanization and escalating climate challenges, the research analyzed how smart city frameworks, aligned with climate neutrality objectives, leverage geospatial technologies for urban planning and climate action. The study included case studies from three leading European cities, extracting lessons and best practices in implementing Climate City Contracts across sectors like energy, transport, and waste management. These insights highlighted the essential role of EU and national authorities in providing technical, regulatory, and financial support. Additionally, the paper presented the application of a WEBGIS platform in Limassol Municipality, Cyprus, demonstrating citizen engagement and acceptance of the proposed geospatial framework. Concluding with recommendations for future research, the study contributed significant insights into the advancement of urban sustainability and the effectiveness of geospatial technologies in smart city initiatives for combating climate change.

Hollow Metal-Organic Framework/MXene/Nanocellulose Composite Films for Giga/Terahertz Electromagnetic Shielding and Photothermal Conversion

With the continuous advancement of communication technology,the escalating demand for electromagnetic shielding interference(EMI)materials with multifunctional and wideband EMI performance has become urgent.Controlling the electrical and magnetic components and designing the EMI material structure have attracted extensive interest,but remain a huge challenge.Herein,we reported the alternating electromagnetic structure composite films composed of hollow metal-organic frameworks/layered MXene/nanocellulose(HMN)by alternating vacuum-assisted filtration process.The HMN composite films exhibit excellent EMI shielding effectiveness performance in the GHz frequency(66.8 dB at Kaband)and THz frequency(114.6 dB at 0.1-4.0 THz).Besides,the HMN composite films also exhibit a high reflection loss of 39.7 dB at 0.7 THz with an effective absorption bandwidth up to 2.1 THz.Moreover,HMN composite films show remarkable photothermal conversion performance,which can reach 104.6℃under 2.0 Sun and 235.4℃under 0.8 W cm^(−2),respectively.The unique micro-and macrostructural design structures will absorb more incident electromagnetic waves via interfacial polarization/multiple scattering and produce more heat energy via the local surface plasmon resonance effect.These features make the HMN composite film a promising candidate for advanced EMI devices for future 6G communication and the protection of electronic equipment in cold environments.

Expediting^(*)OH accumulation kinetics on metal-organic frameworks-derived CoOOH with CeO_(2) “accelerator” for electrocatalytic 5-hydroxymethylfurfural oxidation valorization

In this work,nickel foam supported CeO_(2)-modified CoBDC(BDC stands for terephthalic acid linker)metal-organic frameworks(NF/CoBDC@CeO_(2)) are prepared by hydrothermal and subsequent impregnation methods,which can be further transformed to NF/CoOOH@CeO_(2) by reconstruction during the electrocatalytic test.The obtained NF/CoOOH@CeO_(2) exhibits excellent performance in electrocatalytic oxidation of 5-hydroxymethylfurfural(HMF) because the introduction of CeO_(2) can optimize the electronic structure of the heterointerface and accelerate the accumulation of ^(*)OH.It requires only a potential of 1.290 V_(RHE) to provide a current density of 50 mA cm^(-2) in 1.0 M KOH+50 mM HMF,which is 222 mV lower than that required in 1,0 M KOH(1.512 V_(RHE)).In addition,density-functional theory calculation results demonstrate that CeO_(2) biases the electrons to the CoOOH side at the heterointerface and promotes the adsorption of ^(*)OH and ^(*)HMF on the catalyst surface,which lower the reaction energy barrier and facilitate the electrocata lytic oxidation process.

Tectonic framework of Qilian orogen: reveal from an aeromagnetic anomaly feature

The Qilian Orogenic belt is one of the typical orogenic belts globally and a natural laboratory for studying plate tectonics.Many researchers have studied the ophiolite and high pressure and ultra-high pressure metamorphic rocks in the Qilian orogen and obtained valuable achievements.However,a hot debate exists on the basement property,the distribution of ophiolite,and the boundaries of tectonic units.Large-scale high-precision aeromagnetic surveys have recently been conducted in the Qilian Orogenic belt and adjacent areas.In this study,we are trying to analysis the tectonic framework of the Qilian Orogen using 1:500,000 aeromagnetic data.The results provide geophysical perspectives for studying the structural framework and deformation of this area.According to the aeromagnetic∆T anomaly map,the central and Southern Qilian have the same magnetic anomaly feature that noticeably differs from the North Qilian Orogenic belt and the Qaidam Block.This result indicates that the central and Southern Qilian have a unified magnetic basement and differ from the North Qilian orogenic belt and Qaidam Block.The map shows the distribution of ophiolite in the North Qilian orogenic belt.Linear magnetic anomalies represent the ophiolites because the mafic–ultramafic rocks usually have high magnetic susceptibility.The ophiolite belts are continuously distributed in the western part of North Qilian orogenic belt and have a large scale.However,the scale of the ophiolite belt and the outcropping of mafic–ultramafic rocks reduces when they pass through Qilian County to the east.The results indicate differences in the evolution process between the eastern and western parts of North Qilian,with Qilian County as the transition zone.This study also systematically defines the geophysical boundaries of the Qaidam Block,Qilian Block,North Qilian Orogenic belt,and Alxa block.It is proposed that the sinistral displacement of the Altun Fault is adjusted and absorbed by the series of NE-trending faults in the Qilian orogen and merge into the Longshoushan–Gushi Fault.The extension of the North Qilian Orogenic belt is strengthened by the neotectonics movement along the shearing direction,which separated the North Qilian Orogenic belt into several segments and formed a series of northeast-trending faults.

Research on the Legal Framework of Content Regulations for Network Platforms

In the era of the Internet,various network platforms have evolved into new hubs for information dissemination.Currently,China has established a platform-centered content regulation framework,wherein platforms proactively enforce content regulations in accordance with legal censorship obligations.Additionally,platform policies and user agreements augment their authority in content regulation.The platforms can achieve cost-effective and highly efficient content regulation by leveraging their strategic advantages enabled by their own technical capabilities and extensive coverage.The platform self-regulation model,however,still faces challenges.First,accurately evaluating content remains a formidable task;second,ensuring effective platform publicity through self-regulation poses difficulties;third,users may potentially face disadvantages due to the platform’s right of self-regulation;and fourth,digital copyright owners face challenges when defending digital copyright disputes under the safe harbor rule.Therefore,it is imperative to establish,review,and revise the legal framework for content regulation of network platforms in order to enhance the efficiency of their governance systems.The formulation of the legal framework for content regulation of network platforms may encompass the following aspects:rationalizing obligations pertaining to platform content regulations,enhancing supervision over platform self-regulation,and establishing a dual-track responsibility system for digital copyright content regulation.This will ensure a harmonious balance among public interests,users’personal rights and interests,and commercial benefits through regulating the content on network platforms.

The Impact of Industry-Education Integration on College Students’ Motivation to Learn English under the TPACK Framework

This paper explores the impact of industry-education integration on students’motivation in college English courses under the TPACK(Technological Pedagogical Content Knowledge)framework using a comprehensive approach combining quantitative and qualitative methods.Quantitative data analysis indicates a significant positive correlation between the perception of industry-education integration and the level of student learning motivation.There is also a clear association between the perception scores of TPACK framework integration and learning motivation.Qualitative data analysis reveals students’positive experiences and recognition of the TPACK framework integration in practical application projects.The study concludes that industry-education integration and the TPACK framework play a crucial role in enhancing students’learning motivation.It suggests optimizing teaching practices through faculty training,designing practical application projects,and promoting student interaction.This comprehensive analysis provides substantial guidance for the future development of English courses.

Comprehensive integration of single-cell transcriptomic data illuminates the regulatory network architecture of plant cell fate specification

Plant morphogenesis relies on precise gene expression programs at the proper time and position which is orchestrated by transcription factors(TFs)in intricate regulatory networks in a cell-type specific manner.Here we introduced a comprehensive single-cell transcriptomic atlas of Arabidopsis seedlings.This atlas is the result of meticulous integration of 63 previously published scRNA-seq datasets,addressing batch effects and conserving biological variance.This integration spans a broad spectrum of tissues,including both below-and above-ground parts.Utilizing a rigorous approach for cell type annotation,we identified 47 distinct cell types or states,largely expanding our current view of plant cell compositions.We systematically constructed cell-type specific gene regulatory networks and uncovered key regulators that act in a coordinated manner to control cell-type specific gene expression.Taken together,our study not only offers extensive plant cell atlas exploration that serves as a valuable resource,but also provides molecular insights into gene-regulatory programs that varies from different cell types.

An End-To-End Hyperbolic Deep Graph Convolutional Neural Network Framework

Graph Convolutional Neural Networks(GCNs)have been widely used in various fields due to their powerful capabilities in processing graph-structured data.However,GCNs encounter significant challenges when applied to scale-free graphs with power-law distributions,resulting in substantial distortions.Moreover,most of the existing GCN models are shallow structures,which restricts their ability to capture dependencies among distant nodes and more refined high-order node features in scale-free graphs with hierarchical structures.To more broadly and precisely apply GCNs to real-world graphs exhibiting scale-free or hierarchical structures and utilize multi-level aggregation of GCNs for capturing high-level information in local representations,we propose the Hyperbolic Deep Graph Convolutional Neural Network(HDGCNN),an end-to-end deep graph representation learning framework that can map scale-free graphs from Euclidean space to hyperbolic space.In HDGCNN,we define the fundamental operations of deep graph convolutional neural networks in hyperbolic space.Additionally,we introduce a hyperbolic feature transformation method based on identity mapping and a dense connection scheme based on a novel non-local message passing framework.In addition,we present a neighborhood aggregation method that combines initial structural featureswith hyperbolic attention coefficients.Through the above methods,HDGCNN effectively leverages both the structural features and node features of graph data,enabling enhanced exploration of non-local structural features and more refined node features in scale-free or hierarchical graphs.Experimental results demonstrate that HDGCNN achieves remarkable performance improvements over state-ofthe-art GCNs in node classification and link prediction tasks,even when utilizing low-dimensional embedding representations.Furthermore,when compared to shallow hyperbolic graph convolutional neural network models,HDGCNN exhibits notable advantages and performance enhancements.

A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce overtreatment.However,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency.This paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present state-of-the-artmodels.Our study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction models.In the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the dataset.The original dataset is used in trainingmachine learning models,and further used in generating SHAP values fromthesemodels.In the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based analysis.This new integrated dataset is used in re-training the machine learning models.The new SHAP values generated from these models help in validating the contributions of feature sets in predicting malignancy.The conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making systems.In this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the predictions.The study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of explainability.The proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area under the receiver operating characteristic(AUROC)are also higher than the baseline models.The results of the proposed model help us identify the dominant feature sets that impact thyroid cancer classification and prediction.The features{calcification}and{shape}consistently emerged as the top-ranked features associated with thyroid malignancy,in both association-rule based interestingnessmetric values and SHAPmethods.The paper highlights the potential of the rule-based integrated models with SHAP in bridging the gap between the machine learning predictions and the interpretability of this prediction which is required for real-world medical applications.

A Study on Internet Buzzwords in the Last Four Years Based on Conceptual Integration Theory

Internet buzzwords,as distinct forms of language,consist of short sentences yet carry rich meanings and spread rapidly.To better study this intriguing and unique linguistic phenomenon,this paper employed conceptual integration theory for its powerful explanatory capabilities to analyze the origins,composition,and emergence of meanings of internet buzzwords over the past four years.These 40 internet buzzwords can be categorized into five types:semantic derivation,generalization,abbreviation,metonymy,and compounding.Furthermore,this paper conducted cognitive analysis from different conceptual integration network perspectives,elucidating specific cognitive operations involved in the construction of meanings and revealing the emergence process of layered meanings in recent internet buzzwords over the past four years.

Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

Research on the Impact and Strategies of Rail Transit Network Integration in the Context of Metropolitan Area Development:A Case Study of the Wuhan Metropolitan Area

The metropolitan area is one of the key focal points in the construction and development of China’s new urbanization.Urban integration is an emerging trend in metropolitan areas.This paper explores the traffic demand characteristics and economic aspects of rail transit within metropolitan regions and argues that the construction of an integrated urban rail transit network is an effective approach to support their development.Rail transit in metropolitan areas offers both technical and economic advantages,improving the efficiency of time and space resource utilization,fostering economic cooperation,and ultimately contributing to an integrated development model.However,the integration of rail transit networks faces several challenges,including road network planning,technical standards,and operational organization.Using the Wuhan metropolitan area as a case study,this paper analyzes the challenges of rail transit network integration and proposes strategic solutions for development.

Discussion on the Integration of“Post,Course,Competition,Certificate”:Taking the Internet of Things Application Technology Courses in Higher Vocational Colleges as an Example

This article explores the development of the Internet of Things(IoT)application technology course in higher vocational colleges under the background of“post,course,competition,certificate.”It first emphasizes the importance of IoT talent training and course construction in higher vocational colleges and deeply analyzes the core concept of“post,course,competition,certificate”integration.In view of the problems faced in the course construction of IoT application technology in higher vocational colleges and the practical experience of Tianjin Vocational College of Mechanics and Electricity,the implementation strategy of the course construction of IoT application technology in higher vocational colleges is elaborated in detail based on the integrated concept of“post,course,competition,certificate.”

基于.NET Framework技术的测试用例系统设计

软件测试环节直接影响着产品的成败,本文所介绍的测试用例系统设计是使用基于.NET的面向对象的可视化编程工具与SQL Server数据库相结合,采用应用服务器技术的C/S架构,按项目测试流程进行设计,把测试用例融入项目设计中,贯穿整个开发过程,大大减少重复工作量以及充分发挥每个人的效率,提高测试效率和速度。所设计的系统包括用户管理,项目管理,测试用例库管理,缺陷管理等功能模块,集成安全性与可维护性,实现集中管理,使整个软件系统开发的业务周期缩短、开发成本降低,也同步提高项目的核心竞争力。

SOSNet:一种非对称编码器-解码器结构的非小细胞肺癌CT图像分割模型

非小细胞肺癌严重损害人类健康,早期非小细胞肺癌CT(Computed Tomography)图像中的肿瘤结节体积小,不易发现,极易造成漏诊和误诊.为了精确分割非小细胞肺癌CT图像中的小体积肿瘤结节,本文提出SOSNet(Small Object Segmentation Networks)自动分割模型,利用ResNet(Residual Network)基础层和空洞卷积构造非对称编码器-解码器结构作为分割主网络,利用轴向取反注意力模块ARA(Axial Reverse Attention)逐步擦除背景中对分割有影响的结构,再使用结构细化模块SR(Structure Refinement)对主网络输出的粗略特征图进行结构细化,从而实现非小细胞肺癌肿瘤结节分割.在非小细胞肺癌公开数据集的实验测试表明,本文提出的小目标自动分割模型SOSNet可以有效分割出非小细胞肺癌CT图像中的小体积肿瘤结节,其mDice(mean-Dice)、mIoU(mean Intersection over Union)、Sensitivity、F1、Specificity、平均绝对误差MAE(Mean Absolute Error)均优于当前最先进的小目标分割模型CaraNet(Context Axial Reverse Attention Network).

Tasks-Oriented Joint Resource Allocation Scheme for the Internet of Vehicles with Sensing, Communication and Computing Integration

With the development of artificial intelligence(AI)and 5G technology,the integration of sensing,communication and computing in the Internet of Vehicles(Io V)is becoming a trend.However,the large amount of data transmission and the computing requirements of intelligent tasks lead to the complex resource management problems.In view of the above challenges,this paper proposes a tasks-oriented joint resource allocation scheme(TOJRAS)in the scenario of Io V.First,this paper proposes a system model with sensing,communication,and computing integration for multiple intelligent tasks with different requirements in the Io V.Secondly,joint resource allocation problems for real-time tasks and delay-tolerant tasks in the Io V are constructed respectively,including communication,computing and caching resources.Thirdly,a distributed deep Q-network(DDQN)based algorithm is proposed to solve the optimization problems,and the convergence and complexity of the algorithm are discussed.Finally,the experimental results based on real data sets verify the performance advantages of the proposed resource allocation scheme,compared to the existing ones.The exploration efficiency of our proposed DDQN-based algorithm is improved by at least about 5%,and our proposed resource allocation scheme improves the m AP performance by about 0.15 under resource constraints.

Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control

In recent years,the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks.Such challenges can be potentially overcome by integrating communication,computing,caching,and control(i4C)technologies.In this survey,we first give a snapshot of different aspects of the i4C,comprising background,motivation,leading technological enablers,potential applications,and use cases.Next,we describe different models of communication,computing,caching,and control(4C)to lay the foundation of the integration approach.We review current stateof-the-art research efforts related to the i4C,focusing on recent trends of both conventional and artificial intelligence(AI)-based integration approaches.We also highlight the need for intelligence in resources integration.Then,we discuss the integration of sensing and communication(ISAC)and classify the integration approaches into various classes.Finally,we propose open challenges and present future research directions for beyond 5G networks,such as 6G.