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Surface engineering of highly ordered Bi_(2)S_(3) film with open channels toward high-performance broadband photodetection

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InfoMat 2024年第11期6卷 108-119页

作者： Ping Rong Shiyong Gao Lin Li Wen He Mingyi Zhang Shuai Ren Yajie Han Shujie Jiao Qing Chen Jinzhong Wang School of Materials Science and Engineering Harbin Institute of TechnologyHarbinthe People's Republic of China National Key Laboratory of Precision Hot Processing of Metals Harbin Institute of TechnologyHarbinthe People's Republic of China Key Laboratory for Photonic and Electronic Bandgap Materials Ministry of EducationSchool of Physics and Electronic EngineeringHarbin Normal UniversityHarbinthe People's Republic of China Sauvage Laboratory for Smart Materials School of Materials Science and EngineeringHarbin Institute of Technology(Shenzhen)Shenzhenthe People's Republic of China

The highly ordered film assembled by regularly 1D nanostructures has potentialprospects in electronic,photoelectronic and other fields because of its excellentlight-trapping effect and electronic transport ***,the controlledgrowth of highly ordered film remains a great ***,large-area andhighly ordered Bi_(2)S_(3) film is synthesized on fluorophlogopite mica substrate bychemical vapor deposition *** Bi2S3 film features hollowed-outcrosslinked network structure,assembled by 1D nanobelts that regularly distributein three orientations,which agrees well with the first principles *** on the as-grown Bi_(2)S_(3) film,the broadband photodetector with a responserange from 365 to 940 nm is fabricated,exhibiting a maximum responsivity upto 98.51 mA W^(–1),specific detectivity of 2.03×10^(10)Jones and fast response timeof 35.19 *** stable instantaneous on/off behavior for 500 cycles and reliablephotoresponse characteristics of the Bi_(2)S_(3) photodetector after storage in air for6 months confirm its excellent long-term stability and air ***,as sensing pixel and signal receiving terminal,the device successfully achieveshigh-resolution imaging of characters of“H”,“I”and“T”,and secure transmissionof confidential *** work shows a great potential of the largeareaand highly ordered Bi_(2)S_(3) film toward the development of future multiplefunctional photoelectronic applications.

关键词： broadband photodetection encrypted communication highly ordered Bi_(2)S_(3) film imaging open channel

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QoS-Aware Application Assignment and Resource Utilisation Maximization Using AHP in Edge Computing

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IEEE Internet of Things Journal 2025年第11期12卷 17717-17728页

作者： Koganti, Yasasvitha Sridhar, Vidhyuth Yadav, Ram Narayan Pratap, Ajay Iitram Department of Electrical and Computer Science Engineering Ahmedabad380026 India Indiana University Department of Computer Science and Engineering Bloomington United States Banaras Hindu University Indian Institute of Technology Department of Computer Science and Engineering Varanasi India

Edge computing has emerged as a promising technology to satisfy the demand for data computational resources in Internet of Things (IoT) networks. With edge computing, processing of the massive data-intensive tasks can be done in the proximity of IoT users. Thus, the required constraints related to tasks such as latency requirements, and the Quality Of Service (QoS) can be guaranteed. However, the question that how to determine the task offloading strategy under various constraints of resources, distance, and cost is still an open issue. In this paper, we study the task offloading problem from a matching perspective and propose an Edge-User Assignment Algorithm (EUAA) that aims to maximize the resource utilization of edge servers and maximize the number of assigned IoT users. The main concern, in any matching algorithm, is how to generate the preference order of either side. To generate preference orders for edge servers, we use the concept of the Analytical Hierarchy Process (AHP). We have considered the following criteria: distance from users to the server, latency, available resources, and pricing. This generates the priority of the users for matching to edge servers. From IoT users' perspective, we use cost, and QoS parameters to improve their satisfaction. We have used state-of-the-art schemes to compare the performance of the proposed algorithm under different simulation scenarios. We compare the performance using the number of assigned users, servers' profit, number of satisfied users, edge server resources used, and execution time. The simulation results confirm that significant profit and satisfied user can be achieved by the proposed algorithm. © 2014 IEEE.

关键词： Computation offloading

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ResdenseNet: a lightweight dense ResNet enhanced with depthwise separable convolutions and its applications for early plant disease classification

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Neural Computing and Applications 2025年第8期37卷 6305-6326页

作者： Nagpal, Jyoti Goel, Lavika Department of Computer Science & Engineering The NorthCap University Gurugram122017 India Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India

In recent years, artificial intelligence has undergone robust development, leading to the emergence of numerous autonomous AI applications. However, a crucial challenge lies in optimizing computational efficiency and reducing training time while maintaining high accuracy with limited hardware resources. This paper introduces ResdenseNet, a model built upon the MobileNet, DenseNet, and ResNet architectures. ResdenseNet combines dense blocks and residual blocks from the DenseNet and ResNet architectures. In these dense blocks, the standard convolutional units are replaced by depthwise separable convolutional units, a significant part of the MobileNet architecture. The experimental outcomes are contrasted with established models and their iterations, including ResNet-50, ResNet-101, MobileNet-V1, MobileNet-V2, DenseNet-121, and DenseNet-169. The proposed model is tested on benchmark and proposed datasets, showcasing its efficiency in reducing computations and accelerating the training process. Emphasizing hyperparameter importance, ResdenseNet, optimized with a growth rate of 64, 6 layers, and ReLU activation, achieves an accuracy of (98.73%) and a F1-score of (98.20%) on the wheat and barley dataset. The results indicate that ResdenseNet significantly decreases the number of parameters to 0.72M and efficiently shortens training time to 5983.54 s. Particularly noteworthy is ResdenseNet’s superiority over other models in terms of having the fewest parameters, the shortest training time, and the highest accuracy, especially when dealing with wheat, barley, and maize datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Plant diseases

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Question classification task based on deep learning models with self-attention mechanism

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Multimedia Tools and Applications 2025年第10期84卷 7777-7806页

作者： Mondal, Subhash Barman, Manas Nag, Amitava Computer Science and Engineering Central Institute of Technology Kokrajhar Assam Kokrajhar India Computer Science and Engineering [AI & ML Dayananda Sagar University Karnataka Bengaluru India

Question classification (QC) is a process that involves classifying questions based on their type to enable systems to provide accurate responses by matching the question type with relevant information. To understand and respond to natural language questions posed by humans, machine learning models or systems must comprehend the type of information requested, which can often be inferred from the structure and wording of the question. The high dimensionality and sparse nature of text data lead to challenges for text classification. These tasks can be improved using deep learning (DL) approaches to process complex patterns and features within input data. By training on large amounts of labeled data, deep learning algorithms can automatically extract relevant features and representations from text, resulting in more accurate and robust classification. This study utilizes a dataset comprising 5452 instances of questions and six output labels and uses two different word embedding techniques, like GloVe and Word2Vec, tested on the dataset using three deep learning models, LSTM, BiLSTM, and GRU, followed by a convolution layer. Additionally, a self-attention layer is included, which helps the model to focus on more relevant information when making predictions. Finally, an analytical discussion of the proposed models and their performance results provide insight into how GloVe and Word2Vec perform on the above-mentioned models. The GloVe embedding outperforms by achieving 97.68% accuracy and a moderate loss of 16.98 with the GRU model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Embeddings

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Critical Relation Path Aggregation-Based Industrial Control Component Exploitable Vulnerability Reasoning

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computers, Materials & Continua 2023年第5期75卷 2957-2979页

作者： Zibo Wang Chaobin Huo Yaofang Zhang Shengtao Cheng Yilu Chen Xiaojie Wei Chao Li Bailing Wang School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China National Computer System Engineering Research Institute of China Beijing100083China School of Cyber Science and Technology Harbin Institute of TechnologyHarbin150001China Weihai Cyberguard Technologies Co.Ltd. Weihai264209China Faculty of Science Vrije Universiteit AmsterdamAmsterdam1081HVNetherlands

With the growing discovery of exposed vulnerabilities in the Industrial Control Components(ICCs),identification of the exploitable ones is urgent for Industrial Control System(ICS)administrators to proactively forecast potential ***,it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis *** address these challenges,we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph(KG)in which relation paths contain abundant potential evidence to support the *** reasoning task in this work refers to determining whether a specific relation is valid between an attacker entity and a possible exploitable vulnerability entity with the help of a collective of the critical *** proposed method consists of three primary building blocks:KG construction,relation path representation,and query relation reasoning.A security-oriented ontology combines exploit modeling,which provides a guideline for the integration of the scattered knowledge while constructing the *** emphasize the role of the aggregation of the attention mechanism in representation learning and ultimate *** order to acquire a high-quality representation,the entity and relation embeddings take advantage of their local structure and related *** critical paths are assigned corresponding attentive weights and then they are aggregated for the determination of the query relation *** particular,similarity calculation is introduced into a critical path selection algorithm,which improves search and reasoning ***,the proposed algorithm avoids redundant paths between the given pairs of *** results show that the proposed method outperforms the state-of-the-art ones in the aspects of embedding quality and query relation reasoning accuracy.

关键词： Path-based reasoning representation learning attention mechanism vulnerability knowledge graph industrial control component

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Toward stable and highly reversible zinc anodes for aqueous batteries via electrolyte engineering

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Journal of Energy Chemistry 2023年第8期83卷 209-228,I0007页

作者： Ang Li Jiayi Li Yurong He Maochun Wu Department of Mechanical Engineering The Hong Kong Polytechnic UniversityHong Kong 999077China Schoolof Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001HeilongjiangChina

Featuring low cost, high abundance, low electrochemical potential, and large specific capacity, zinc(Zn)metal holds great potential as an anode material for next-generation rechargeable aqueous ***, the poor reversibility resulting from dendrite formation and side reactions poses a major obstacle for its practical application. Electrolyte, which is regarded as the “blood” of batteries, has a direct impact on reaction kinetics, mass transport, and side reactions and thus plays a key role in determining the electrochemical performance of Zn electrodes. Therefore, considerable efforts have been devoted to modulating the electrolytes to improve the performance of Zn electrodes. Although significant progress has been made, achieving stable and highly reversible Zn electrodes remains a critical challenge. This review aims to provide a systematic summary and discussion on electrolyte strategies for highperformance aqueous Zn batteries. The(electro)-chemical behavior and fundamental challenges of Zn electrodes in aqueous electrolytes are first discussed. Electrolyte modulation strategies developed to address these issues are then classified and elaborated according to the underlying ***, remaining challenges and promising future research directions on aqueous electrolyte engineering are highlighted. This review offers insights into the design of highly efficient electrolytes for new generation of rechargeable Zn batteries.

关键词： Rechargeable aqueous zinc batteries Zinc anode Dendrite growth Side reactions Electrolyte engineering

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Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases

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computer Modeling in engineering & sciences 2024年第6期139卷 3085-3099页

作者： Jinbo Yang Hai Huang Lailai Yin Jiaxing Qu Wanjuan Xie School of Computer Science and Technology Harbin University of Science and TechnologyHarbin150080China Shanghai Futures Information Technology Co. Ltd.Shanghai201201China Heilongjiang Province Cyberspace Research Center Harbin150001China Information Network Engineering and Research Center South China University of TechnologyGuangzhou510641China

Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources,including clinical symptoms,physical signs,biochemical test results,imaging findings,pathological examination data,and even genetic *** applying machine learning modeling to predict and diagnose multi-stage diseases,several challenges need to be ***,the model needs to handle multimodal data,as the data used by doctors for diagnosis includes image data,natural language data,and structured ***,privacy of patients’data needs to be protected,as these data contain the most sensitive and private ***,considering the practicality of the model,the computational requirements should not be too *** address these challenges,this paper proposes a privacy-preserving federated deep learning diagnostic method for multi-stage *** method improves the forward and backward propagation processes of deep neural network modeling algorithms and introduces a homomorphic encryption step to design a federated modeling algorithm without the need for an *** also utilizes dedicated integrated circuits to implement the hardware Paillier algorithm,providing accelerated support for homomorphic encryption in ***,this paper designs and conducts experiments to evaluate the proposed *** experimental results show that in privacy-preserving federated deep learning diagnostic modeling,the method in this paper achieves the same modeling performance as ordinary modeling without privacy protection,and has higher modeling speed compared to similar algorithms.

关键词： Vertical federation homomorphic encryption deep neural network intelligent diagnosis machine learning and big data

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SysML modeling tools for improved storage efficiency 4

SysML modeling tools for improved storage efficiency

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4th International Conference on Signal Processing and computer science, SPCS 2023

作者： Jin, Qinchao Yang, Peihao Wang, Tiantian Wu, Xianghu Wang, Kechao School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Information Engineering Harbin University Harbin China

ISBN: (纸本)9781510672505

With the development and widespread application of Model-Based Systems engineering (MBSE) in the aerospace domain continue to grow, System Modeling Language (SysML) has become increasingly important as the most popular modeling language in MBSE. It is crucial to provide developers with an efficient and secure SysML editing tool to enhance their productivity. However, our research has revealed issues with the existing SysML editing tools, particularly in terms of data storage, resulting in a decline in efficiency as project data grows. Therefore, this paper proposes a GEF-based SysML development tool that supports developers in creating, editing, and exporting graphics. This tool addresses the data storage issues, improving development efficiency. Additionally, we present a model validation approach and extend the existing validation rules to ensure the correctness, reliability, and security of graphical elements and diagrams. © 2023 SPIE.

关键词： Modeling languages

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Galerkin-based quasi-smooth manifold element(QSME)method for anisotropic heat conduction problems in composites with complex geometry

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Applied Mathematics and Mechanics(English Edition) 2024年第1期45卷 137-154页

作者： Pan WANG Xiangcheng HAN Weibin WEN Baolin WANG Jun LIANG School of Civil Engineering Central South UniversityChangsha 410083China Institute of Advanced Structure Technology Beijing Institute of TechnologyBeijing 100081China School of Science Harbin Institute of TechnologyShenzhen 518055Guangdong ProvinceChina

The accurate and efficient analysis of anisotropic heat conduction problems in complex composites is crucial for structural design and performance evaluation. Traditional numerical methods, such as the finite element method(FEM), often face a trade-off between calculation accuracy and efficiency. In this paper, we propose a quasi-smooth manifold element(QSME) method to address this challenge, and provide the accurate and efficient analysis of two-dimensional(2D) anisotropic heat conduction problems in composites with complex geometry. The QSME approach achieves high calculation precision by a high-order local approximation that ensures the first-order derivative *** results demonstrate that the QSME method is robust and stable, offering both high accuracy and efficiency in the heat conduction analysis. With the same degrees of freedom(DOFs), the QSME method can achieve at least an order of magnitude higher calculation accuracy than the traditional FEM. Additionally, under the same level of calculation error, the QSME method requires 10 times fewer DOFs than the traditional FEM. The versatility of the proposed QSME method extends beyond anisotropic heat conduction problems in complex composites. The proposed QSME method can also be applied to other problems, including fluid flows, mechanical analyses, and other multi-field coupled problems, providing accurate and efficient numerical simulations.

关键词： anisotropic heat conduction quasi-smooth manifold element(QSME) composite with complex geometry numerical simulation finite element method(FEM)

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High mass-loading and binder-free flexible vanadium-based oxide cathode for zinc-ion battery via a bridge of MXene

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Journal of Materials science & technology 2023年第12期143卷 84-92页

作者： Kan Fang Fei Li Gui-Gen Wang Yi-Lin Liu Man-Lin Tan Da-Qiang Zhao Hua-Yu Zhang Jie-Cai Han Shenzhen Key Laboratory for Advanced Materials School of Materials Science and EngineeringHarbin Institute of TechnologyShenzhen 518055China National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin Institute of TechnologyHarbin 150080China Research Institute of Tsinghua University in Shenzhen Shenzhen 518055China

Flexible zinc-ion batteries(ZIBs)are promising power sources for portable devices due to their high safety and low production ***,the low mass-loading and limited areal capacity of cathode materials are the major problems blocking the practicability of ***,a high mass-loading and binder-free flexible vanadium oxide(MCV@CC)cathode with a large areal capacity was fabricated via the bridge effect of *** functional MXene bridge induces the growth of the vanadium oxide active layer on the carbon cloth(CC)flexible *** binder-free cathode can reduce the electrochemically inactive weight of the whole electrode,which enhances the energy density of ***,the MCV@CC cathode(mass-loading of∼7 mg cm^(−2))delivers a desirable areal capacity(2.36 mAh cm^(−2))and good cycling stability(capacity retention of 86.1%after 1200 cycles at 10 mA cm^(−2)).Moreover,several ex-situ characterization results indicate that the reaction mechanism upon battery cycling is based on the reversible Zn^(2+)/H^(+)(de)intercalation in the vanadium oxide ***,the assembled quasi-solid-state MCV@CC//Zn flexible battery exhibits decent performance at different bending *** a bridge effect strategy sheds light on the construction of high mass-loading flexible electrodes for ZIBs applications.

关键词： Zinc-ion batteries Vanadium oxide MXene bridge High mass-loading Flexible electrode

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