Segmentation of brain tissue from MR images provides detailed quantitative brain analysis for accurate diagnosis, detection, and classification of brain diseases, and plays an important role in neuroimaging research and clinical environments. Recently, a plethora of deep learning-based approaches have been employed to achieve brain tissue segmentation in fetuses, infants, and adults with impressive

With the rapid development and deep application of artificial intelligence, modern air combat is incrementally evolving towards intelligent combat. Although deep reinforcement learning algorithms have contributed to dramatic advances in in air combat, they still face challenges such as poor interpretability and weak transferability of adversarial strategies. In this regard, this paper proposes a tactical

In recent years, deep learning has significantly advanced the development of image depth estimation algorithms. The depth estimation network with single-view input can only extract features from a single 2D image, often neglecting the information contained in neighboring views, resulting in learned features that lack real geometrical information in the 3D world and stricter constraints on the 3D structure

The concept of green scheduling, which deals with the environmental impact of the scheduling process, is becoming increasingly important due to growing environmental concerns. Most green scheduling problem variants focus on modelling the energy consumption during the execution of the schedule. However, the dynamic unrelated machines environment is rarely considered, mainly because it is difficult to

Drug repositioning, the discovery of new therapeutic uses for existing drugs, is increasingly gaining attention as a cost-effective and high-yield drug discovery strategy. Existing methods integrate diverse biological information into heterogeneous networks, providing a comprehensive framework for understanding complex drug–disease associations, which also will introduces noise into the data and affect

Traditional education systems obscure the diverse interconnections inherent within subject knowledge, thus failing to meet the current demand for personalized and adaptive learning experiences. Recent advances have explored various relation extraction techniques to construct educational knowledge graphs that integrate dispersed subject knowledge into a unified framework. However, educational conceptual

Deep learning-based object detectors excel on mobile devices but often struggle with blurry images that are common in real-world scenarios, like unmanned aerial vehicle (UAV)-assisted images. Current models are designed for sharp images, leading to potential detection failures in blurry images. Using image deblurring before object detection is an option, but it demands significant computing power and

Identifying progressive mild cognitive impairment (pMCI) and stable mild cognitive impairment (sMCI) play a significant role in the early diagnosis of Alzheimer’s disease (AD) and can be helpful in early treatment to reduce the risk of conversion to AD. We proposed a classification method of sMCIs and pMCIs based on multi-modality data fusion of single-nucleotide polymorphisms (SNP), ratio of gray

In order to address challenges such as small target sizes, low contrast, significant intra-class variations, and indistinct inter-class differences in surface defect detection, this paper proposes the Enhanced Context-aware Parallel Fusion Network (EC-PFN). The network employs a Featur Weave Network architecture to enhance contextal awareess and parallel fusion capabilities. It utilizes a Feature Fusion

Face super-resolution technology can significantly enhance the resolution and quality of face images, which is crucial for applications such as surveillance, forensics, and face recognition. However, existing methods often fail to fully utilize multi-scale information and facial priors, resulting in poor recovery of facial structures in complex images. To address this issue, we propose a face super-resolution

Weakly supervised semantic segmentation (WSSS) utilizes weak labels to learn semantic segmentation models, significantly reducing reliance on pixel-level annotations. WSSS typically employs a multi-label classification network to extract image features for constructing localization maps. The quality of the localization map critically influences the performance of WSSS. However, non-target semantic

Emotion recognition using electroencephalogram (EEG) signals had attracted significant research attention. This paper introduced a new approach, Multi-scale-res BiLSTM (MRBiL), to enhance EEG emotion recognition. Firstly, differential entropy features were extracted from EEG data during both resting and active states. The disparity between these two states was then calculated to generate a feature

The advent of 5th Generation (5G) networks has introduced the strategy of network slicing as a paradigm shift, enabling the provision of services with distinct Quality of Service (QoS) requirements. The 5th Generation New Radio (5G NR) standard complies with the use cases Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC)

Composite materials allow the production of structures with desired and improved properties (such as high strength), while minimizing the undesirable outcomes (e.g., increased weight). This ability to tune the properties of materials and structures has put composite materials under the spotlight in many fields, ranging from medical, automotive, aerospace, marine, and civil engineering applications

Previous multiscale concurrent topology optimization methods for thermoelastic structures were primarily based on static loading and steady-state heat transfer conditions, which do not account for transient effects associated with time-dependent loads. To address this limitation, this paper establishes a novel generic multiscale concurrent topology optimization method that incorporates transient thermoelastic

The structural ultimate load-bearing capacity plays an influential role in engineering applications. Melan’s static shakedown theorem offers a valuable approach for predicting the lower bound of shakedown loading factors and providing a safer shakedown domain when the structures are subjected to cyclic variable loads. However, the associated nonlinear mathematical programming is plagued by substantial

Financial management prediction, often known as financial forecasting, is the act of estimating future financial outcomes using past data and present trends. It is an essential component of financial analysis and planning that aids businesses in making well-informed decisions and preparing for potential future events. In the healthcare domain, financial management prediction is a crucial task that

In the research of cyber threat intelligence knowledge graphs, the current challenge is that there are errors, inconsistencies, or missing knowledge graph triples, which makes it difficult to cope with the complexity and diversified application requirements. Currently, the predominant approach in quality assessment research for knowledge graphs involves employing word embeddings. This method evaluates

Two-photon microscopy is indispensable in cell and molecular biology for its high-resolution visualization of cellular and molecular dynamics. However, the inevitable low signal-to-noise conditions significantly degrade image quality, obscuring essential details and complicating morphological analysis. While existing denoising methods such as CNNs, Noise2Noise, and DeepCAD serve broad applications

To better balance the spectral efficiency (SE) and energy efficiency (EE) in the massive multiple-input multiple output system with a large number of users (MaMIMO-LU), the SE-EE tradeoff is originally constructed as a constrained large-scale multi-objective problem (CLSMOP) for the power allocation of users. To solve this CLSMOP, a constrained large-scale multi-objective evolutionary algorithm (CLSMOEA)

Containers have reshaped application deployment and life-cycle management in recent cloud platforms. The paradigm shift from large monolithic applications to complex graphs of loosely-coupled microservices aims to increase deployment flexibility and operational efficiency. However, efficient allocation and scaling of microservice applications is challenging due to their intricate inter-dependencies

Performance envelopes provide a novel methodology that quantifies the load bearing capacity of a structure in a reduced dimension load space. The envelopes relate the complex loads acting on a structure to the corresponding structural failure constraints and may find many applications within the aircraft structural design process. Constructing envelopes for industrial problems is of particular interest

Data-driven methods for vibration-based Structural Health Monitoring (SHM) have gained significant popularity for their straightforward modeling process and real-time tracking capabilities. However, developing complex models such as deep neural networks can pose challenges, including limited interpretability and substantial computational demands, due to the large number of parameters and deep layer

The current study proposes a computational-based method to employ the non-classical micropolar continuum for modelling plates with in-plane functionally graded porosities. Initially, a homogenisation method is developed to derive the micropolar parameters of porous heterogenous plates based on strain energy equivalence in various designed deformations simulated via finite element analysis. The modelling

Energy efficiency of Convolutional Neural Networks (CNNs) has become an important area of research, with various strategies being developed to minimize the power consumption of these models. Previous efforts, including techniques like model pruning, quantization, and hardware optimization, have made significant strides in this direction. However, there remains a need for more effective on device AI

Federated learning is a distributed machine learning paradigm that trains a shared model using data from various clients, it faces a core challenge in data heterogeneity arising from diverse client settings and environments. Existing methods typically focus on weight divergence mitigation and aggregation strategy enhancements, they overlook the mixed skew in label and feature distributions prevalent

Authenticated Key Exchange (AKE) has been playing a significant role in ensuring communication security. However, in some Multi-access Edge Computing (MEC) scenarios where a moving end-node switchedly connects to a sequence of edge-nodes, it is costly in terms of time and computing resources to repeatedly run AKE protocols between the end-node and each edge-node. Moreover, the cloud needs to be involved

In recent years, due to the existence of intricate interactions between multiple entities in complex networks, ranging from biology to social or economic networks, community detection has helped us to better understand these networks. In fact, research in community detection aims at extracting several almost separate sub-networks called communities from the complex structure of a network in order to

Competitive swarm optimizer (CSO) based on multidirectional search plays a crucial role in addressing large-scale multiobjective optimization problems (LSMOPs). However, relying solely on uniform or cluster partitioning of the objective space for sampling, along with two search directions constructed with upper and lower boundaries of global variables, sometimes lacks consideration of regional information

Serverless edge computing provides a lightweight and easily scalable new paradigm for edge computing, which is widespread in many fields. However, its characteristics of fine-grained tasks, short startup times, and fast execution speed bring new challenges in task offloading and latency reduction. In this paper, we consider the task offloading problem of serverless functions in a multi-edge-to-cloud

Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. However, much less attention has been paid to the fairness-aware multi-objective optimization, which is indeed commonly seen in real life, such as fair resource allocation problems and data-driven multi-objective optimization

Data sharing is key to enabling data analysis and research advancement, and that is especially true in healthcare. Due to the inherited sensitivity of health data, institutions are still wary of sharing their data, especially with the increasing number of breaches in recent years and the strict privacy legislation involved (GDPR, HIPAA, etc.). Privacy and security concerns exist when making data available

An extended formulation of the macro-element (ME) based models, representing for both adherends and adhesive along the entire overlap in only one four-node element, is presented. Compared to earlier modelling, continuum ME (CME) and discrete ME (DME) based models, the adherend parts are also modelled as plane continuum media, for which high order displacement fields are freely supposed. Both extended

Corrosion-induced material deterioration poses a pervasive threat to structural integrity, necessitating an in-depth understanding of its intricate behaviors. Pitting corrosion, a critical concern in this context, accelerates the degradation of materials. The limitations of conventional models arise from their neglect of the subsurface electrode boundary layer dynamics during the dissolution process