Search

Health is one of the most important considerations for everyone. However, environmental pollution damages people's health seriously, especially those who live in industrial cities. Gas waste, water waste, and waste residue seriously endanger the health of people. Therefore, pollution regulation is one of the keys to improving people's health. In this article, we propose an intelligent pollution monitoring system (PMS) based on the Industrial Internet of Things to prevent direct emission of harmful substances and to build healthier cities. The PMS has three main parts: 1. Real-time pollutant monitoring equipment, which can monitor harmful substances in the gas and water in real time, and further cannot be damaged easily 2. A real-time alarm system, which can send alerts immediately after detecting harmful substances 3. An intelligent and fast response system, which can act to respond to an alarm The experimental results show that the proposed PMS can improve the efficiency of responses to pollution and increase people's health. [ABSTRACT FROM AUTHOR]

Single-instruction-set architecture (Single-ISA) heterogeneous multi-core processors (HMP) are superior to Symmetric Multi-core processors in performance per watt. They are popular in many aspects of the Internet of Things, including mobile multimedia cloud computing platforms. One Single-ISA HMP integrates both fast out-of-order cores and slow simpler cores, while all cores are sharing the same ISA. The quality of service (QoS) is most important for virtual machine (VM) resource management in multimedia mobile computing, particularly in Single-ISA heterogeneous multi-core cloud computing platforms. Therefore, in this paper, we propose a dynamic cloud resource management (DCRM) policy to improve the QoS in multimedia mobile computing. DCRM dynamically and optimally partitions shared resources according to service or application requirements. Moreover, DCRM combines resource-aware VM allocation to maximize the effectiveness of the heterogeneous multi-core cloud platform. The basic idea for this performance improvement is to balance the shared resource allocations with these resources requirements. The experimental results show that DCRM behaves better in both response time and QoS, thus proving that DCRM is good at shared resource management in mobile media cloud computing. [ABSTRACT FROM AUTHOR]

Main memory dynamic voltage and frequency scaling (DVFS) has been proposed recently for improving energy efficiency further. However, recent work overlook the operating systems (OS) problems incurred by it, such as unpredictable performance decreasing, unfair performance sharing and priority inversion, which may render performance analysis, optimization and isolation extremely difficult. In this paper, we analyze the OS problems incurred by memory DVFS in detail firstly, and propose dynamic time-slice scaling (DTS) to address these problems, where allocating each thread a time-slice according to threads' memory accessing behavior and memory frequency. Our paper has three main contributions: 1) we analyze the OS problems incurred by the newly approach of memory active low-power modes, the first work paying attention to the effect of up-to-date DVFS memory architecture; 2) performance decrease is more predictable and share is more fairness through adjusting time-slice; 3) priority inversion is solved with starvation forbidden. Simulation results show that the proposed methods can substantially reduce unpredictable performance degradation, improve fairness of performance sharing and solve the priority inversion. [ABSTRACT FROM AUTHOR]

Schizophrenia is a debilitating psychiatric disorder that can significantly affect a patient's quality of life and lead to permanent brain damage. Although medical research has identified certain genetic risk factors, the specific pathogenesis of the disorder remains unclear. Despite the prevalence of research employing magnetic resonance imaging, few studies have focused on the gene level and gene expression profile involving a large number of screened genes. However, the high dimensionality of genetic data presents a great challenge to accurately modeling the data. To tackle the current challenges, this study presents a novel feature selection strategy that utilizes heuristic feature fusion and a multi-objective optimization genetic algorithm. The goal is to improve classification performance and identify the key gene subset for schizophrenia diagnostics. Traditional gene screening techniques are inadequate for accurately determining the precise number of key genes associated with schizophrenia. Our innovative approach integrates a filter-based feature selection method to reduce data dimensionality and a multi-objective optimization genetic algorithm for improved classification tasks. By combining the filtering and wrapper methods, our strategy leverages their respective strengths in a deliberate manner, leading to superior classification accuracy and a more efficient selection of relevant genes. This approach has demonstrated significant improvements in classification results across 11 out of 14 relevant datasets. The performance on the remaining three datasets is comparable to the existing methods. Furthermore, visual and enrichment analyses have confirmed the practicality of our proposed method as a promising tool for the early detection of schizophrenia. [ABSTRACT FROM AUTHOR]

Accurate medical image segmentation can effectively assist disease diagnosis and treatment. While neural networks were often applied to solve the segmentation problem in recent computer-aided diagnosis, the metadata of patients was usually neglected. Motivated by this, we propose a medical image segmentation and diagnosis framework that takes advantage of both the image and the patient’s metadata, such as gender and age. We present MMY-NET: a new multi-modal network for simultaneous tumor segmentation and diagnosis exploiting patient metadata. Our architecture consists of three parts: a visual encoder, a text encoder, and a decoder with a self-attention block. Specifically, we design a text preprocessing block to embed metadata effectively, and the image features and text embedding features are then fused on several layers between the two encoders. Moreover, Interlaced Sparse Self-Attention is added to the decoder to further boost the performance. We apply our algorithm on 1 private dataset (ZJU2), and 1 private dataset (LISHUI) for zero-shot validation. Results show that our algorithm combined with metadata outperforms its counterpart without metadata by a large margin for basal cell carcinoma segmentation (14.3 % improvement of IoU and 8.5 % of Dice on the ZJU2 dataset, and 7.1 % IoU on the LIZHUI validation dataset). Additionally, we applied MMY-Net to 1 public segmentation dataset to demonstrate its general segmentation capability. MMY-Net outperforms the state-of-the-art methods on the GlaS dataset. [ABSTRACT FROM AUTHOR]

With the rapid advancement of 5G mobile networks and Internet of Things technology, an increasing number of data-intensive applications are generating massive amounts of information, such as face recognition, video stream analysis, and augmented reality. These applications not only demand significant computational resources but also require high real-time performance, posing challenges to the existing cloud-computing model. Deploying these data-intensive applications in a mobile edge computing environment can reduce response time for processing user tasks and meet low-latency requirements. However, conventional CPU scheduling strategies fail to effectively enhance the performance of data-intensive applications on edge nodes with limited computing resources. In this study, we focus on real-time application feature awareness and propose a strategy considering data arrival rate and cost fair CPU scheduling (RCFS) with two components: CPU resource allocation based on process weights and process scheduling based on distributed weighted round-robin. In compared to the default scheduling strategy in Linux, named Completely Fair Scheduler, experiments on edge nodes show that the RCFS strategy can effectively enhance CPU utilization, reduce running time of data-intensive applications, and improve the system's data throughput. In the best-case scenario, the RCFS strategy achieved a remarkable increase in CPU utilization of data-intensive applications by up to 98.5%, reduced running time by up to 45.5%, and improved data throughput by up to 83.6%. [ABSTRACT FROM AUTHOR]

Protein succinylation is a novel type of post-translational modification in recent decade years. It played an important role in biological structure and functions verified by experiments. However, it is time consuming and laborious for the wet experimental identification of succinylation sites. Traditional technology cannot adapt to the rapid growth of the biological sequence data sets. In this study, a new computational method named SuccSPred2.0 was proposed to identify succinylation sites in the protein sequences based on multifeature fusion and maximal information coefficient (MIC) method. SuccSPred2.0 was implemented based on a two-step strategy. At first, high-dimension features were reduced by linear discriminant analysis to prevent overfitting. Subsequently, MIC method was employed to select the important features binding classifiers to predict succinylation sites. From the compared experiments on 10-fold cross-validation and independent test data sets, SuccSPred2.0 obtained promising improvements. Comparative experiments showed that SuccSPred2.0 was superior to previous tools in identifying succinylation sites in the given proteins. [ABSTRACT FROM AUTHOR]

With the increasing complexity of autonomous driving tasks, the computational demands on single vehicular computing units have escalated, more and more tasks need to be offloaded to the edge. These tasks vary in latency sensitivity: real-time tasks, critical for passenger safety, require strict deadline adherence, whereas the latency of standard tasks mainly affects the user experience and has more flexible constraints. Addressing the challenge of selecting suitable edge computing nodes to enhance the offloading success rate of real-time tasks amidst a vast and heterogeneous cluster becomes crucial. This paper introduces the adaptive priority-based hierarchical task offloading (APHTO) algorithm, which optimizes task offloading strategies by accounting for the diverse latency constraints of different task types. Experiments demonstrate that under optimal performance conditions, APHTO significantly outperforms existing algorithms such as Min–Min, Max–Min, CUS, and FMS in reducing task latency by 20.31%, increasing offloading success rates by 35.83%, and improving resource utilization by 30.21%, marking a substantial advancement in task offloading strategies for autonomous driving integrated with MEC. [ABSTRACT FROM AUTHOR]

The thickness of the choroid is considered to be an important indicator of clinical diagnosis. Therefore, accurate choroid segmentation in retinal OCT images is crucial for monitoring various ophthalmic diseases. However, this is still challenging due to the blurry boundaries and interference from other lesions. To address these issues, we propose a novel prior-guided and knowledge diffusive network (PGKD-Net) to fully utilize retinal structural information to highlight choroidal region features and boost segmentation performance. Specifically, it is composed of two parts: a Prior-mask Guided Network (PG-Net) for coarse segmentation and a Knowledge Diffusive Network (KD-Net) for fine segmentation. In addition, we design two novel feature enhancement modules, Multi-Scale Context Aggregation (MSCA) and Multi-Level Feature Fusion (MLFF). The MSCA module captures the long-distance dependencies between features from different receptive fields and improves the model's ability to learn global context. The MLFF module integrates the cascaded context knowledge learned from PG-Net to benefit fine-level segmentation. Comprehensive experiments are conducted to evaluate the performance of the proposed PGKD-Net. Experimental results show that our proposed method achieves superior segmentation accuracy over other state-of-the-art methods. Our code is made up publicly available at: https://github.com/yzh-hdu/choroid-segmentation. • Designed a multi-scale context aggregation module to capture non-local information. • Propose a multi-level feature fusion module to boost segmentation performance. • Conduct sufficient experiments and the state-of-the-art performance is achieved. [ABSTRACT FROM AUTHOR]

Edge computing perfectly integrates cloud computing centers and edge-end devices together, but there are not many related researches on how the edge-end node devices work to form an edge network and what the protocols used to implement the communication among nodes in the edge network. Aiming at the problem of coordinated communication among edge nodes in the current edge computing network architecture, this paper proposes an edge network routing and forwarding protocol based on target tracking scenarios. This protocol can meet the dynamic changes of node locations, and the elastic expansion of node scale. Individual node failures will not affect the overall network, and the network ensures efficient real-time with less communication overhead. The experimental results display that the protocol can effectively reduce the communications volume of the edge network, improve the overall efficiency of the network, and set the optimal sampling period, so as to ensure that the network delay is minimized. [ABSTRACT FROM AUTHOR]

Accurate measurements of ophthalmic parameters and postoperative appearance prediction are essential for the diagnosis and treatment of many ophthalmic diseases. Nevertheless, it remains challenging due to (1) inconsistent ophthalmic image sampling standards, including ocular-camera distance, facial angle, and patient number, (2) complicated ocular morphology, such as subconjunctival hemorrhage, ocular movements, lighting effects, and morphological aging. It is difficult for a model to measure parameters and make predictions in variable sampling methods and morphology conditions. Therefore, the Global attention-based Ophthalmic Image Measurement and Postoperative Appearance Prediction System (GOMPS) is proposed, which quantifies ophthalmic image parameters to diagnose disease and simultaneously predict postoperative appearance of blepharoptosis. By perceiving the global structure of the ophthalmic image, GOMPS makes logical inference predictions of the sclera and cornea morphology, to overcome the above difficulties. Concretely, a global attention unit (GAU) and a novel global attention structure-aware network (GASA-Net) are designed to enhance GOMPS's global structure awareness ability to perform logical reasoning. Extensive experimental results on our collected ophthalmic dataset for diagnosis & prediction (OD2P) demonstrate that GOMPS surpasses the state-of-the-art methods in segmentation accuracy and achieves the current optimal performance in measurement and postoperative prediction under many clinical scenes. • Showcased GOMPS, the initial ophthalmic measurement & post-op look prediction system. • Presented the largest Ophthalmic Dataset for Diagnosis and Prediction OD2P. • Presented GASA-Net for accurate, robust segmentation in difficult ophthalmic images. • Investigated medial canthi & upper eyelid invariance in blepharoptosis treatment. • Realized in-depth ophthalmic surgery simulation using structural information. [ABSTRACT FROM AUTHOR]

For location-based services (LBS), the path with the shortest travel time is much more meaningful than the one with the shortest network distance, as it considers the live traffics in road networks. Usually, there are two ways for an LBS provider to provide the shortest travel-time paths for its queries, i.e., computing by itself or retrieving from external Web mapping services. However, both of these two ways are expensive, like costly computation and long latency. To accelerate shortest path query processing, we design an effective cache of shortest travel-time paths for LBS providers in this paper. Unlike the conventional path caching techniques, where the caches are only suitable for distance-based paths, our proposed cache is dynamic and can support travel-time-based paths. More importantly, a new cache operation, namely path join, is devised to estimate the path information for a query based on the cached paths, when the cache can not answer the query directly. Experimental results on a real Web mapping service and datasets confirm the effectiveness of the proposed techniques. [ABSTRACT FROM AUTHOR]