Design Principles and Mechanistic Understandings of Non-Noble-Metal Bifunctional Electrocatalysts for Zinc-Air Batteries

Zinc-air batteries(ZABs)are promising energy storage systems because of high theoretical energy density,safety,low cost,and abundance of zinc.However,the slow multi-step reaction of oxygen and heavy reliance on noble-metal catalysts hinder the practical applications of ZABs.Therefore,feasible and advanced non-noble-metal elec-trocatalysts for air cathodes need to be identified to promote the oxygen catalytic reaction.In this review,we initially introduced the advancement of ZABs in the past two decades and provided an overview of key developments in this field.Then,we discussed the work-ing mechanism and the design of bifunctional electrocatalysts from the perspective of morphology design,crystal structure tuning,interface strategy,and atomic engineering.We also included theoretical studies,machine learning,and advanced characterization technologies to provide a comprehensive understanding of the structure-performance relationship of electrocatalysts and the reaction pathways of the oxygen redox reactions.Finally,we discussed the challenges and prospects related to designing advanced non-noble-metal bifunctional electrocatalysts for ZABs.

Multimodal Sentiment Analysis Based on a Cross-Modal Multihead Attention Mechanism

Multimodal sentiment analysis aims to understand people’s emotions and opinions from diverse data.Concate-nating or multiplying various modalities is a traditional multi-modal sentiment analysis fusion method.This fusion method does not utilize the correlation information between modalities.To solve this problem,this paper proposes amodel based on amulti-head attention mechanism.First,after preprocessing the original data.Then,the feature representation is converted into a sequence of word vectors and positional encoding is introduced to better understand the semantic and sequential information in the input sequence.Next,the input coding sequence is fed into the transformer model for further processing and learning.At the transformer layer,a cross-modal attention consisting of a pair of multi-head attention modules is employed to reflect the correlation between modalities.Finally,the processed results are input into the feedforward neural network to obtain the emotional output through the classification layer.Through the above processing flow,the model can capture semantic information and contextual relationships and achieve good results in various natural language processing tasks.Our model was tested on the CMU Multimodal Opinion Sentiment and Emotion Intensity(CMU-MOSEI)and Multimodal EmotionLines Dataset(MELD),achieving an accuracy of 82.04% and F1 parameters reached 80.59% on the former dataset.

Cross-Modal Consistency with Aesthetic Similarity for Multimodal False Information Detection

With the explosive growth of false information on social media platforms, the automatic detection of multimodalfalse information has received increasing attention. Recent research has significantly contributed to multimodalinformation exchange and fusion, with many methods attempting to integrate unimodal features to generatemultimodal news representations. However, they still need to fully explore the hierarchical and complex semanticcorrelations between different modal contents, severely limiting their performance detecting multimodal falseinformation. This work proposes a two-stage detection framework for multimodal false information detection,called ASMFD, which is based on image aesthetic similarity to segment and explores the consistency andinconsistency features of images and texts. Specifically, we first use the Contrastive Language-Image Pre-training(CLIP) model to learn the relationship between text and images through label awareness and train an imageaesthetic attribute scorer using an aesthetic attribute dataset. Then, we calculate the aesthetic similarity betweenthe image and related images and use this similarity as a threshold to divide the multimodal correlation matrixinto consistency and inconsistencymatrices. Finally, the fusionmodule is designed to identify essential features fordetectingmultimodal false information. In extensive experiments on four datasets, the performance of the ASMFDis superior to state-of-the-art baseline methods.

The Four Dimensional Connotation Definition of Scientific Understanding of the Ocean and Human Practical Achievements

By analyzing the sustainable development process of strengthening marine environmental protection and global reporting and assessment of marine environmental conditions since the Human Environment Conference, this paper summarizes the scientific connotation of “scientific understanding of the ocean” reflected in the United Nations Global Marine Environmental Assessment Report, proposes the etymological definition and specific coverage, representative global and regional practical experience of scientific understanding of the ocean, and further analyzes and defines the human activities and cognitive evolution process of “scientific understanding of the ocean”. It marks the leap in human cognition in four dimensions: observation and evaluation, intervention and regulation, disciplinary knowledge system, and supporting guarantee system. It condenses the connotation definitions and human practical achievements of each dimension, and puts forward countermeasures and suggestions to strengthen marine environmental protection and sustainable development.

What-If XAI Framework (WiXAI): From Counterfactuals towards Causal Understanding

People learn causal relations since childhood using counterfactual reasoning. Counterfactual reasoning uses counterfactual examples which take the form of “what if this has happened differently”. Counterfactual examples are also the basis of counterfactual explanation in explainable artificial intelligence (XAI). However, a framework that relies solely on optimization algorithms to find and present counterfactual samples cannot help users gain a deeper understanding of the system. Without a way to verify their understanding, the users can even be misled by such explanations. Such limitations can be overcome through an interactive and iterative framework that allows the users to explore their desired “what-if” scenarios. The purpose of our research is to develop such a framework. In this paper, we present our “what-if” XAI framework (WiXAI), which visualizes the artificial intelligence (AI) classification model from the perspective of the user’s sample and guides their “what-if” exploration. We also formulated how to use the WiXAI framework to generate counterfactuals and understand the feature-feature and feature-output relations in-depth for a local sample. These relations help move the users toward causal understanding.

The Role of Music in Multicultural Education:A Tool for Promoting Cultural Understanding and Inclusion

In this essay,it will be examined how music can be a powerful tool in multicultural education in promoting humanity and cultural sensitivity.As classroom diversity increases,the challenge lies with educators to ensure that an equal and appropriate learning environment for all students with cultural sensitivity is maintained.To address this problem,therefore,the study explores how music may be effectively incorporated into intercultural education approaches.This paper has evidence that music is a language that has transcended cultures and promotes tolerance,appreciation,and acceptance of diversity through a synthesis of literature and examples.Several implications posit that music can make people feel they belong,enhance the relations of people from diverse cultures,and provide a practical way of learning about other cultures.The potential of music as a tool to engage social justice issues and liberate oppressed groups is also discussed in this research.It exists,but with caution to cultural appropriation and stereotyping of students,thus requiring educators to be sensitive and socially-apperceived on the best practice ways on how to integrate music into classrooms.The end explains how music helps in nurturing a generation of embracing the world,more sensitive and more inclined to the happenings in society to create a society that will be more sensitive and tolerant with the growing society which is diversifying.Therefore,the current article recommends further exploration and development of music-facilitated/inclined pedagogy of ME in hopefully enhancing cultural consciousness/sensitivity and fostering more social integration.

A Study on High School Mathematics Teaching Design Based on Teaching for Robust Understanding: Taking the Cosine Theorem as an Example

Teachers’teaching behavior plays a crucial role in students’development,and there are problems in the current teaching behavior of mathematics teachers such as ignoring students’cognitive needs,lack of equal opportunities for students’classroom performance as well as lack of formative evaluation of students.In order to solve the phenomenon,this paper analyzes and explains how to promote teaching based on the Teaching for Robust Understanding(TRU)evaluation framework with the goal of focusing on the development of all students,taking the teaching design of The Cosine Theorem as an example,and provides ideas and methods for first-line high school mathematics teachers.

A Multi-Level Circulant Cross-Modal Transformer for Multimodal Speech Emotion Recognition

Speech emotion recognition,as an important component of humancomputer interaction technology,has received increasing attention.Recent studies have treated emotion recognition of speech signals as a multimodal task,due to its inclusion of the semantic features of two different modalities,i.e.,audio and text.However,existing methods often fail in effectively represent features and capture correlations.This paper presents a multi-level circulant cross-modal Transformer(MLCCT)formultimodal speech emotion recognition.The proposed model can be divided into three steps,feature extraction,interaction and fusion.Self-supervised embedding models are introduced for feature extraction,which give a more powerful representation of the original data than those using spectrograms or audio features such as Mel-frequency cepstral coefficients(MFCCs)and low-level descriptors(LLDs).In particular,MLCCT contains two types of feature interaction processes,where a bidirectional Long Short-term Memory(Bi-LSTM)with circulant interaction mechanism is proposed for low-level features,while a two-stream residual cross-modal Transformer block is appliedwhen high-level features are involved.Finally,we choose self-attention blocks for fusion and a fully connected layer to make predictions.To evaluate the performance of our proposed model,comprehensive experiments are conducted on three widely used benchmark datasets including IEMOCAP,MELD and CMU-MOSEI.The competitive results verify the effectiveness of our approach.

Electrochemical Carbon Dioxide Reduction to Ethylene:From Mechanistic Understanding to Catalyst Surface Engineering

Electrochemical carbon dioxide reduction reaction(CO_(2)RR)provides a promising way to convert CO_(2)to chemicals.The multicarbon(C_(2+))products,especially ethylene,are of great interest due to their versatile industrial applications.However,selectively reducing CO_(2)to ethylene is still challenging as the additional energy required for the C–C coupling step results in large overpotential and many competing products.Nonetheless,mechanistic understanding of the key steps and preferred reaction pathways/conditions,as well as rational design of novel catalysts for ethylene production have been regarded as promising approaches to achieving the highly efficient and selective CO_(2)RR.In this review,we first illustrate the key steps for CO_(2)RR to ethylene(e.g.,CO_(2)adsorption/activation,formation of~*CO intermediate,C–C coupling step),offering mechanistic understanding of CO_(2)RR conversion to ethylene.Then the alternative reaction pathways and conditions for the formation of ethylene and competitive products(C_1 and other C_(2+)products)are investigated,guiding the further design and development of preferred conditions for ethylene generation.Engineering strategies of Cu-based catalysts for CO_(2)RR-ethylene are further summarized,and the correlations of reaction mechanism/pathways,engineering strategies and selectivity are elaborated.Finally,major challenges and perspectives in the research area of CO_(2)RR are proposed for future development and practical applications.

TECMH:Transformer-Based Cross-Modal Hashing For Fine-Grained Image-Text Retrieval

In recent years,cross-modal hash retrieval has become a popular research field because of its advantages of high efficiency and low storage.Cross-modal retrieval technology can be applied to search engines,crossmodalmedical processing,etc.The existing main method is to use amulti-label matching paradigm to finish the retrieval tasks.However,such methods do not use fine-grained information in the multi-modal data,which may lead to suboptimal results.To avoid cross-modal matching turning into label matching,this paper proposes an end-to-end fine-grained cross-modal hash retrieval method,which can focus more on the fine-grained semantic information of multi-modal data.First,the method refines the image features and no longer uses multiple labels to represent text features but uses BERT for processing.Second,this method uses the inference capabilities of the transformer encoder to generate global fine-grained features.Finally,in order to better judge the effect of the fine-grained model,this paper uses the datasets in the image text matching field instead of the traditional label-matching datasets.This article experiment on Microsoft COCO(MS-COCO)and Flickr30K datasets and compare it with the previous classicalmethods.The experimental results show that this method can obtain more advanced results in the cross-modal hash retrieval field.

ViT2CMH:Vision Transformer Cross-Modal Hashing for Fine-Grained Vision-Text Retrieval

In recent years,the development of deep learning has further improved hash retrieval technology.Most of the existing hashing methods currently use Convolutional Neural Networks(CNNs)and Recurrent Neural Networks(RNNs)to process image and text information,respectively.This makes images or texts subject to local constraints,and inherent label matching cannot capture finegrained information,often leading to suboptimal results.Driven by the development of the transformer model,we propose a framework called ViT2CMH mainly based on the Vision Transformer to handle deep Cross-modal Hashing tasks rather than CNNs or RNNs.Specifically,we use a BERT network to extract text features and use the vision transformer as the image network of the model.Finally,the features are transformed into hash codes for efficient and fast retrieval.We conduct extensive experiments on Microsoft COCO(MS-COCO)and Flickr30K,comparing with baselines of some hashing methods and image-text matching methods,showing that our method has better performance.

Adequate alignment and interaction for cross-modal retrieval

Background Cross-modal retrieval has attracted widespread attention in many cross-media similarity search applications,particularly image-text retrieval in the fields of computer vision and natural language processing.Recently,visual and semantic embedding(VSE)learning has shown promising improvements in image text retrieval tasks.Most existing VSE models employ two unrelated encoders to extract features and then use complex methods to contextualize and aggregate these features into holistic embeddings.Despite recent advances,existing approaches still suffer from two limitations:(1)without considering intermediate interactions and adequate alignment between different modalities,these models cannot guarantee the discriminative ability of representations;and(2)existing feature aggregators are susceptible to certain noisy regions,which may lead to unreasonable pooling coefficients and affect the quality of the final aggregated features.Methods To address these challenges,we propose a novel cross-modal retrieval model containing a well-designed alignment module and a novel multimodal fusion encoder that aims to learn the adequate alignment and interaction of aggregated features to effectively bridge the modality gap.Results Experiments on the Microsoft COCO and Flickr30k datasets demonstrated the superiority of our model over state-of-the-art methods.

The Effect of Latinization on Reading Time and Understanding: Greeklish in Communication and Social Media

The increasing prevalence of technology in society has an impact on young people’s language use and development. Greeklish is the writing of Greek texts using the Latin instead of the Greek alphabet, a practice known as Latinization, also employed for many non-latin alphabet languages. The primary aim of this research is to evaluate the effect of Greeklish on reading time. A sample of 732 young Greeks were asked about their habits when communicating through e-mail and social media with their friends and they then participated in an experiment in which they were asked to read and understand two short texts, one written in Greek and the other in Greeklish. The findings of the research show that nearly one third of the participants use Greeklish. The results of the experiment conducted reveal that understanding is not affected by the alphabet used but reading Greeklish is significantly more time consuming than reading Greek independently of the sex and the familiarity of the participants with Greeklish. The findings suggest that amending social and communication media with software utilities related to Latinization such as language identifiers and converters may reduce reading time and thus facilitate written communication among the users.

Solving Algebraic Problems with Geometry Diagrams Using Syntax-Semantics Diagram Understanding

Solving Algebraic Problems with Geometry Diagrams(APGDs)poses a significant challenge in artificial intelligence due to the complex and diverse geometric relations among geometric objects.Problems typically involve both textual descriptions and geometry diagrams,requiring a joint understanding of these modalities.Although considerable progress has been made in solving math word problems,research on solving APGDs still cannot discover implicit geometry knowledge for solving APGDs,which limits their ability to effectively solve problems.In this study,a systematic and modular three-phase scheme is proposed to design an algorithm for solving APGDs that involve textual and diagrammatic information.The three-phase scheme begins with the application of the statetransformer paradigm,modeling the problem-solving process and effectively representing the intermediate states and transformations during the process.Next,a generalized APGD-solving approach is introduced to effectively extract geometric knowledge from the problem’s textual descriptions and diagrams.Finally,a specific algorithm is designed focusing on diagram understanding,which utilizes the vectorized syntax-semantics model to extract basic geometric relations from the diagram.A method for generating derived relations,which are essential for solving APGDs,is also introduced.Experiments on real-world datasets,including geometry calculation problems and shaded area problems,demonstrate that the proposed diagram understanding method significantly improves problem-solving accuracy compared to methods relying solely on simple diagram parsing.

Review of Visible-Infrared Cross-Modality Person Re-Identification

Person re-identification(ReID)is a sub-problem under image retrieval.It is a technology that uses computer vision to identify a specific pedestrian in a collection of pictures or videos.The pedestrian image under cross-device is taken from a monitored pedestrian image.At present,most ReID methods deal with the matching between visible and visible images,but with the continuous improvement of security monitoring system,more and more infrared cameras are used to monitor at night or in dim light.Due to the image differences between infrared camera and RGB camera,there is a huge visual difference between cross-modality images,so the traditional ReID method is difficult to apply in this scene.In view of this situation,studying the pedestrian matching between visible and infrared modalities is particularly crucial.Visible-infrared person re-identification(VI-ReID)was first proposed in 2017,and then attracted more and more attention,and many advanced methods emerged.

The Chinese Association for International Understanding Holds Lecture II of the Civilisation Lecture Series

On December 6th,2023,the Chinese Association for International Understanding(CAFIU)held Lecture II of the Civilisation Lecture Series in Paramaribo,the capital of Suriname.Ai Ping,Vice-President of CAFIU,delivered a keynote speech.Han Jing,the Chinese Ambassador to Suriname,delivered a speech.

Tiêu Equation Experimentation of Understanding by Energy Transfer Quantum Mechanics

Background: The Tiêu equation has a ground roots approach to the process of Quantum Biology and goes deeper through the incorporation of Quantum Mechanics. The process can be measured in plant, animal, and human usage through a variety of experimental or testing forms. Animal studies were conducted for which, in the first day of the study all the animals consistently gained dramatic weight, even as a toxic substance was introduced as described in the introduction of the paper to harm animal subjects which induced weight loss through toxicity. Tests can be made by incorporating blood report results. Human patients were also observed to show improvement to their health as administration of the substance was introduced to the biological mechanism and plants were initially exposed to the substance to observe results. This is consistent with the Tiêu equation which provides that wave function is created as the introduction of the substance to the biological mechanism which supports Quantum Mechanics. The Tiêu equation demonstrates that Quantum Mechanics moves a particle by temperature producing energy thru the blood-brain barrier for example. Methods: The methods for the Tiêu equation incorporate animal studies to include the substance administered through laboratory standards using Good Laboratory Practices under Title 40 C.F.R. § 158. Human patients were treated with the substance by medical professionals who are experts in their field and have knowledge to the response of patients. Plant applications were acquired for observation and guidance of ongoing experiments of animals’ representative for the biologics mechanism. Results: The animal studies along with patient blood testing results have been an impressive line that has followed the Tiêu equation to consistently show improvement in the introduction of the innovation to biologic mechanisms. The mechanism responds to the substance by producing energy to the mechanism with efficient effect. For plant observations, plant organisms responded, and were seen as showing improvement thru visual observation.

Current Situation and Implementation Paths of International Understanding Education in Local Application-oriented Universities

It is urgent and necessary to implement international understanding education in local application-oriented colleges and universities.Improving the international competitiveness of talents and cultivating global citizens is one of the goals of higher education.This paper discussed the problems of international understanding education in local application-oriented colleges and universities,including weak policy orientation,insufficient practical exploration,aphasia of national culture,etc.It is recommended to implement the international understanding education from two ways:subject penetration and project-based professional courses.In addition,the introduction of Chinese culture cannot be ignored to prevent the absence of national culture in cross-cultural communication.

Research on the Interrelationship among Understanding, Memory and Oral Expression in Listening Teaching of non-English in Higher Vocational College

This is a research report on the interrelationship among understanding, memory and oral expression in listening comprehension training. The research investigated 150 non-English majors through a questionnaire in a higher vocational college. The result indicates that the interrelationship among understanding, memory and oral expression in listening classes which are determined by the listening materials and teaching method directly influences students' listening proficiency and speaking ability. It is suggested that some strategies to be used to foster students' abilities in these three areas so as to improve their listening ability.

Failure of Aspired Understanding Among the Human Race——On the Theme of A Passage to India

The author attempts to interpret the theme of A Passsage to India by analyzing the major events and characterization. It is indicated in the thesis that E.M.Forster highlights the failure of aspired understanding between different races, different people and even within a person. In other words, in many cases people have aspirations for understanding, but in reality the real understanding is very difficult to fulfill.