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Abstract For the transport networks only equipped with sparse or isolated detectors, short‐term traffic flow forecasting faces the following problems: (1) there are only temporal information and no spatial information; (2) the noises in the traffic flow significantly affect the forecasting performance. In this paper, a lag‐related noise shrinkage stacked long short‐term memory (LSTM) network is proposed for the traffic flow forecasting task only related to temporal information. To extract effective temporal features, the optimal time lags are selected in the traffic flow and converted into lag‐related multi‐dimensional data. Then, a discrete wavelet threshold denoising shrinkage algorithm is designed to filter the noises to construct a more reliable training set. A multi‐level stacked LSTM network is employed to learn the features of the training set to map the past traffic flow to the future flow. Four benchmark datasets are to evaluate the forecasting performance by extensive experiments. The comparison with the state‐of‐the‐art models demonstrates an average improvement of 7.28% in MAPE and 6.02% in RMSE. In addition, the proposed method has been applied in the Guilin Travel Network Bus Intelligent Dispatching System. It improves the utilization of the vehicles and reduces operating costs.

Cerebral ischemia/reperfusion causes high disability, recurrence, and mortality. Ischemic stroke is a powerful stimulus that triggers significant microglia activation. Ginsenoside Rb1 (GS-Rb1) has been demonstrated to have neuroprotective effects in the central nervous system. In this study, the effects of GS-Rb1 against cerebral ischemia/reperfusion were explored. A mouse model of middle cerebral artery occlusion (MCAO) was used to mimic the cerebral ischemia/reperfusion. Mice in MCAO + GS-Rb1 groups received 5, 10, or 20 mg/kg GS-Rb1 through intraperitoneal injection. Modified neurological severity scoring (mNSS) showed neurological function, while the open field test tested the anxiety-like behaviors. Cognitive impairment was evaluated by Morris water maze. Protein levels were evaluated by ELISA and Western blot and mRNA levels were analyzed by qRT-PCR. When compared to the MCAO mice, mice in the MCAO + GS-Rb1 group had significantly lower mNSS scores and less brain water content. GS-Rb1 alleviated both cognitive impairment and anxiety and inhibited microglial activation in the cerebral ischemia/reperfusion model. GS-Rb1 enhanced M2-type microglia polarization while inhibiting M1-type microglia polarization. In summary, we observed that GS-Rb1 had neuro-protective effects in a cerebral ischemia/reperfusion mouse model through regulating the microglia polarization.

Multitarget tracking technology is the core topic in the field of intelligent driving. Multi-target complex manoeuvring, measurement outliers and unknown environmental prior parameters strongly affect the tracking accuracy of the target state. To address the accurate tracking of multitarget under the above complex working conditions, we propose a new multitarget tracking algorithm, named the Multiconstrained Generalized Probabilistic Data Association Filtering (MCGPDAF) algorithm. In this algorithm, we use the target position and heading information to construct constraint parameters to calculate the association probability between each effective measurement combination and the target track. This algorithm can effectively suppress the measurement association anomalies and aprior information errors, as well as enable the robust association of single-sensor multitarget measurements and accurate tracking of target states under complex working conditions. On this basis, a multitarget tracking method based on composite perception fusion is further constructed, and the correlation sequential track association algorithm and covariance cross fusion algorithm are used to enable the track association and the estimation and fusion of target states among multiple sensors, which further enhances the tracking accuracy of the multitarget state. The simulation and real vehicle experiment results reveal that, compared to current advanced algorithms, the RMSE and MAPE of the MCGPDAF algorithm for multitarget tracking are enhanced by an average of 23.97% and 24.35%, respectively. Additionally, the MOTA and MOTP of the MCGPDAF algorithm improve by an average of 14.68% and 15.71%. Moreover, compared to single-sensor multitarget tracking, the RMSE and MAPE of composite perception fusion results based on the MCGPDAF algorithm are further enhanced by 26.43% and 27.15% on average, which reflects the practicality of the tracking method showcased in this research.

Vehicle-to-Vehicle (V2V) energy trading has emerged as a promising scheme to relieve the load imposed on the grid without intermediaries. The blockchain has always been regarded as the most potential solution for addressing the security and privacy issues of the Internet of Electric Vehicles (IoEV). The consensus mechanism is the core of the blockchain, it determines the security, efficiency, and scalability of the system. However, consensus mechanisms currently employed in V2V energy trading are traditional algorithms, which are unsuitable for the IoEV due to their high computational power and communication overhead. Therefore, we are motivated to propose a Block Alliance Consensus (BAC) mechanism based on Hashgraph. BAC can maintain the high throughput of the Hashgraph, and it solves the problem that the Hashgraph cannot support the dynamic addition and deletion of nodes if it is directly applied to V2V energy trading. We utilize the sharding technique, and each shard generates a consortium blockchain in accordance with the characteristic of the IoEV. The centralized component is retained to ensure macroeconomic control by state and local governments. We design a cryptography-based leader election combined with a reputation incentive mechanism to fairly elect centralized leader committee and motivate honest electric vehicles (EVs). We implement the BAC consensus and V2V energy trading blockchain (ETB) on the Hyperledger Fabric. The performance and practicality of BAC and the V2V ETB are verified through experiments.

The well-known insulin-like growth factor 1 (IGF1)/IGF-1 receptor (IGF-1R) signaling pathway is overexpressed in many tumors, and is thus an attractive target for cancer treatment. However, results have often been disappointing due to crosstalk with other signals. Here, we report that IGF-1R signaling stimulates the growth of hepatocellular carcinoma (HCC) cells through the translocation of IGF-1R into the ER to enhance sarco-endoplasmic reticulum calcium ATPase 2 (SERCA2) activity. In response to ligand binding, IGF-1Rβ is translocated into the ER by β-arrestin2 (β-arr2). Mass spectrometry analysis identified SERCA2 as a target of ER IGF-1Rβ. SERCA2 activity is heavily dependent on the increase in ER IGF-1Rβ levels. ER IGF-1Rβ phosphorylates SERCA2 on Tyr990 to enhance its activity. Mutation of SERCA2-Tyr990 disrupted the interaction of ER IGF-1Rβ with SERCA2, and therefore ER IGF-1Rβ failed to promote SERCA2 activity. The enhancement of SERCA2 activity triggered Ca2+ER perturbation, leading to an increase in autophagy. Thapsigargin blocked the interaction between SERCA2 and ER IGF-1Rβ and therefore SERCA2 activity, resulting in inhibition of HCC growth. In conclusion, the translocation of IGF-1R into the ER triggers Ca2+ER perturbation by enhancing SERCA2 activity through phosphorylating Tyr990 in HCC.

In the rapidly evolving domain of mobile edge–cloud computing (MECC), the proliferation of Internet of Things (IoT) devices and mobile applications poses significant challenges, particularly in dynamically managing computational demands and user mobility. Current research has partially addressed aspects of service migration and resource allocation, yet it often falls short in thoroughly examining the nuanced interdependencies between migration strategies and resource allocation, the consequential impacts of migration delays, and the intricacies of handling incomplete tasks during migration. This study advances the discourse by introducing a sophisticated framework optimized through a deep reinforcement learning (DRL) strategy, underpinned by a Markov decision process (MDP) that dynamically adapts service migration and resource allocation strategies. This refined approach facilitates continuous system monitoring, adept decision making, and iterative policy refinement, significantly enhancing operational efficiency and reducing response times in MECC environments. By meticulously addressing these previously overlooked complexities, our research not only fills critical gaps in the literature but also enhances the practical deployment of edge computing technologies, contributing profoundly to both theoretical insights and practical implementations in contemporary digital ecosystems.

Connected devices in IoT systems usually have low computing and storage capacity and lack uniform standards and protocols, making them easy targets for cyberattacks. Implementing security measures like cryptographic authentication, access control, and firewalls for IoT devices is insufficient to fully address the inherent vulnerabilities and potential cyberattacks within the IoT environment. To improve the defensive capabilities of IoT systems, some research has focused on using deep learning techniques to provide new solutions for intrusion detection systems. However, some existing deep learning-based intrusion detection methods suffer from inadequate feature extraction and insufficient model generalization capability. To address the shortcomings of existing detection methods, we propose an intrusion detection model based on temporal convolutional residual modules. An attention mechanism is introduced to assess feature scores and enhance the model’s ability to concentrate on critical features, thereby boosting its detection performance. We conducted extensive experiments on the ToN_IoT dataset and the UNSW-NB15 dataset, and the proposed model achieves accuracies of 99.55% and 89.23% on the ToN_IoT and UNSW-NB15 datasets, respectively, with improvements of 0.14% and 15.3% compared with the current state-of-the-art models. These results demonstrate the superior detection performance of the proposed model.

Deep neural networks (DNNs) have shown remarkable performance across a wide range of fields, including image recognition, natural language processing, and speech processing. However, recent studies indicate that DNNs are highly vulnerable to well-crafted adversarial samples, which can cause incorrect classifications and predictions. These samples are so similar to the original ones that they are nearly undetectable by human vision, posing a significant security risk to DNNs in the real world due to the impact of adversarial attacks. Currently, the most common adversarial attack methods explicitly add adversarial perturbations to image samples, often resulting in adversarial samples that are easier to distinguish by humans. To address this issue, we are motivated to develop more effective methods for generating adversarial samples that remain undetectable to human vision. This paper proposes a pixel-level adversarial attack method based on attention mechanism and high-frequency information separation, named DIPA. Specifically, our approach involves constructing an attention suppression loss function and utilizing gradient information to identify and perturb sensitive pixels. By suppressing the model’s attention to the correct classes, the neural network is misled to focus on irrelevant classes, leading to incorrect judgments. Unlike previous studies, DIPA enhances the attack of adversarial samples by separating the imperceptible details in image samples to more effectively hide the adversarial perturbation while ensuring a higher attack success rate. Our experimental results demonstrate that under the extreme single-pixel attack scenario, DIPA achieves higher attack success rates for neural network models with various architectures. Furthermore, the visualization results and quantitative metrics illustrate that the DIPA can generate more imperceptible adversarial perturbation.

Insulin-like growth factor-1 receptor (IGF-1R) has been made an attractive anticancer target due to its overexpression in cancers. However, targeting it has often produced the disappointing results as the role played by cross talk with numerous downstream signalings. Here, we report a disobliging IGF-1R signaling which promotes growth of cancer through triggering the E3 ubiquitin ligase MEX3A-mediated degradation of RIG-I. The active β-arrestin-2 scaffolds this disobliging signaling to talk with MEX3A. In response to ligands, IGF-1Rβ activated the basal βarr2 into its active state by phosphorylating the interdomain domain on Tyr64 and Tyr250, opening the middle loop (Leu130‒Cys141) to the RING domain of MEX3A through the conformational changes of βarr2. The models of βarr2/IGF-1Rβ and βarr2/MEX3A could interpret the mechanism of the activated-IGF-1R in triggering degradation of RIG-I. The assay of the mutants βarr2Y64A and βarr2Y250A further confirmed the role of these two Tyr residues of the interlobe in mediating the talk between IGF-1Rβ and the RING domain of MEX3A. The truncated-βarr2 and the peptide ATQAIRIF, which mimicked the RING domain of MEX3A could prevent the formation of βarr2/IGF-1Rβ and βarr2/MEX3A complexes, thus blocking the IGF-1R-triggered RIG-I degradation. Degradation of RIG-I resulted in the suppression of the IFN-I-associated immune cells in the TME due to the blockade of the RIG-I-MAVS-IFN-I pathway. Poly(I:C) could reverse anti-PD-L1 insensitivity by recovery of RIG-I. In summary, we revealed a disobliging IGF-1R signaling by which IGF-1Rβ promoted cancer growth through triggering the MEX3A-mediated degradation of RIG-I.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by continuous and selective degeneration or death of dopamine neurons in the midbrain, leading to dysfunction of the nigrostriatal neural circuits. Current clinical treatments for PD include drug treatment and surgery, which provide short-term relief of symptoms but are associated with many side effects and cannot reverse the progression of PD. Pluripotent/multipotent stem cells possess a self-renewal capacity and the potential to differentiate into dopaminergic neurons. Transplantation of pluripotent/multipotent stem cells or dopaminergic neurons derived from these cells is a promising strategy for the complete repair of damaged neural circuits in PD. This article reviews and summarizes the current preclinical/clinical treatments for PD, their efficacies, and the advantages/disadvantages of various stem cells, including pluripotent and multipotent stem cells, to provide a detailed overview of how these cells can be applied in the treatment of PD, as well as the challenges and bottlenecks that need to be overcome in future translational studies.

Science is entering a new era—the fifth paradigm—that is being heralded as the main character of knowledge integrating into different fields to intelligence-driven work in the computational community based on the omnipresence of machine learning systems. Here, we vividly illuminate the nature of the fifth paradigm by a typical platform case specifically designed for catalytic materials constructed on the Tianhe-1 supercomputer system, aiming to promote the cultivation of the fifth paradigm in other fields. This fifth paradigm platform mainly encompasses automatic model construction (raw data extraction), automatic fingerprint construction (neural network feature selection), and repeated iterations concatenated by the interdisciplinary knowledge (“volcano plot”). Along with the dissection is the performance evaluation of the architecture implemented in iterations. Through the discussion, the intelligence-driven platform of the fifth paradigm can greatly simplify and improve the extremely cumbersome and challenging work in the research, and realize the mutual feedback between numerical calculations and machine learning by compensating for the lack of samples in machine learning and replacing some numerical calculations caused by insufficient computing resources to accelerate the exploration process. It remains a challenging of the synergy of interdisciplinary experts and the dramatic rise in demand for on-the-fly data in data-driven disciplines. We believe that a glimpse of the fifth paradigm platform can pave the way for its application in other fields.

Abstract Dynamic complexity in brain functional connectivity has hindered the effective use of signal processing or machine learning methods to diagnose neurological disorders such as epilepsy. This paper proposed a new graph-generative neural network (GGN) model for the dynamic discovery of brain functional connectivity via deep analysis of scalp electroencephalogram (EEG) signals recorded from various regions of a patient’s scalp. Brain functional connectivity graphs are generated for the extraction of spatial–temporal resolution of various onset epilepsy seizure patterns. Our supervised GGN model was substantiated by seizure detection and classification experiments. We train the GGN model using a clinically proven dataset of over 3047 epileptic seizure cases. The GGN model achieved a 91% accuracy in classifying seven types of epileptic seizure attacks, which outperformed the 65%, 74%, and 82% accuracy in using the convolutional neural network (CNN), graph neural networks (GNN), and transformer models, respectively. We present the GGN model architecture and operational steps to assist neuroscientists or brain specialists in using dynamic functional connectivity information to detect neurological disorders. Furthermore, we suggest to merge our spatial–temporal graph generator design in upgrading the conventional CNN and GNN models with dynamic convolutional kernels for accuracy enhancement.

The primary objective of multi-objective optimization techniques is to identify optimal solutions within the context of conflicting objective functions. While the multi-objective gray wolf optimization (MOGWO) algorithm has been widely adopted for its superior performance in solving multi-objective optimization problems, it tends to encounter challenges such as local optima and slow convergence in the later stages of optimization. To address these issues, we propose a Modified Boltzmann-Based MOGWO, referred to as MBB-MOGWO. The performance of the proposed algorithm is evaluated on multiple multi-objective test functions. Experimental results demonstrate that MBB-MOGWO exhibits rapid convergence and a reduced likelihood of being trapped in local optima. Furthermore, in the context of the Internet of Things (IoT), the quality of web service composition significantly impacts complexities related to sensor resource scheduling. To showcase the optimization capabilities of MBB-MOGWO in real-world scenarios, the algorithm is applied to address a Multi-Objective Problem (MOP) within the domain of web service composition, utilizing real data records from the QWS dataset. Comparative analyses with four representative algorithms reveal distinct advantages of our MBB-MOGWO-based method, particularly in terms of solution precision for web service composition. The solutions obtained through our method demonstrate higher fitness and improved service quality.

Massive amounts of data drive the performance of deep learning models, but in practice, data resources are often highly dispersed and bound by data privacy and security concerns, making it difficult for multiple data sources to share their local data directly. Data resources are difficult to aggregate effectively, resulting in a lack of support for model training. How to collaborate between data sources in order to aggregate the value of data resources is therefore an important research question. However, existing distributed-collaborative-learning architectures still face serious challenges in collaborating between nodes that lack mutual trust, with security and trust issues seriously affecting the confidence and willingness of data sources to participate in collaboration. Blockchain technology provides trusted distributed storage and computing, and combining it with collaboration between data sources to build trusted distributed-collaborative-learning architectures is an extremely valuable research direction for application. We propose a trusted distributed-collaborative-learning mechanism based on blockchain smart contracts. Firstly, the mechanism uses blockchain smart contracts to define and encapsulate collaborative behaviours, relationships and norms between distributed collaborative nodes. Secondly, we propose a model-fusion method based on feature fusion, which replaces the direct sharing of local data resources with distributed-model collaborative training and organises distributed data resources for distributed collaboration to improve model performance. Finally, in order to verify the trustworthiness and usability of the proposed mechanism, on the one hand, we implement formal modelling and verification of the smart contract by using Coloured Petri Net and prove that the mechanism satisfies the expected trustworthiness properties by verifying the formal model of the smart contract associated with the mechanism. On the other hand, the model-fusion method based on feature fusion is evaluated in different datasets and collaboration scenarios, while a typical collaborative-learning case is implemented for a comprehensive analysis and validation of the mechanism. The experimental results show that the proposed mechanism can provide a trusted and fair collaboration infrastructure for distributed-collaboration nodes that lack mutual trust and organise decentralised data resources for collaborative model training to develop effective global models.

The method of building and deploying applications through the combination of container virtualization technology and a microservices framework has been widely used in Internet-of-Things clouds. However, there are gaps and a lack of coordination mechanisms between the Internet-of-Things and cloud computing. This study constructs a resource management platform, which is based on application container virtualization technology and combined with the microservices framework. The platform provide a support environment for the construction and deployment of Internet-of-Things cloud applications. However, there is no unified specification for the microservices templates. Therefore, a new service model called tool service was designed. The invocation relationship between services is studied, and developers can combine services through the invocation relationship between services to form a service function chain. However, container-based service deployment remains an unresolved issue. The deployment method of a container involves the quality of service of end users and the profit of cloud providers. To balance the profits of both parties, it is necessary to minimize the service response time and improve the resource utilization of the cloud data center. To address this problem, an accelerated particle swarm optimization strategy is proposed to realize service deployment. Through the invocation relationship between services, the execution containers are aggregated, so as to reduce the service transmission overhead and improve resource utilization. Compared with the experimental results of existing deployment strategies, the proposed optimization strategy has significantly improved performance parameters such as service transmission overhead, container aggregation, and resource utilization.

Container virtualization methods based on application deployment levels have been widely adopted in cloud-computing environments to implement application construction, deployment, and migration. However, most application containers focus on the interface between the applications and hosts and lack collaboration between application containers. This study proposes a new application container model that contains users, application services, documents, and messages, called Band-area Application Container. A salient feature of the Band-area is that it can express a variety of things in reality, such as organizations or individuals. End users can build a complex and changeable application system through cooperation between the Band-areas. However, the resource allocation of non Internet-of-Thing and Internet-of-Thing tasks from the application container is an open issue. The resource allocation method of tasks should not only improve the quality of the user experience, but also reduce energy consumption by improving the resource utilization of the server. To solve this problem, an artificial fish swarm algorithm is proposed to optimize container-based task scheduling. The algorithm considers not only the reliability, processing time overhead, and energy consumption of the task, but also the resource utilization of the servers. Experimental evaluation shows that, compared with the existing three algorithms, the algorithm obtains a better improvement rate in task processing time overhead, energy consumption, reliability, and cluster load balancing.

The automatic generation of Chinese enterprise abbreviations is a task that extracts enterprise abbreviations to represent the enterprise full name. Traditional methods do not divide abbreviations in detail, which leads to a poor generation effect of irregular Chinese enterprise abbreviations generation, and the best selection method of candidate abbreviations among traditional methods is still coarse-grained relationship modeling. To solve the problem of irregular abbreviation generation and abbreviation screening, this paper proposes a two-channel Chinese enterprise automatic abbreviation generation method. First, in the two-channel method, the enterprise component channel outputs regular candidate abbreviations, and the single-character channel outputs irregular candidate abbreviations to improve the processing effect of the method on irregular abbreviations. Then we design a Bayesian filtering model based on the position relationship of abbreviations in enterprise components to improve the final effect of the automatic generation of Chinese enterprise abbreviations. The results show that our effect is the best in the data performance of Chinese enterprises.

Abstract Derived from the most abundant natural polymer, cellulose nanocrystal materials have attracted attention in recent decades due to their chemical and mechanical properties. However, still unclear is the influence of different exposed facets of the cellulose nanocrystals on the physicochemical properties. Herein, we first designed cellulose II nanocrystals with different exposed facets, the hydroxymethyl conformations distribution, hydrogen bond (HB) analysis, as well as the relative structural stability of these models (including crystal facets {A, B, O} and Type-A models vary in size) are theoretically investigated. The results reveal that the HB network of terminal anhydroglucose depends on the adjacent chain’s contact sites in nanocrystals exposed with different facets. Compared to nanocrystals exposed with inclined facet, these exposed with flat facet tend to be the most stable. Therefore, the strategy of tuning exposed crystal facets will guide the design of novel cellulose nanocrystals with various physicochemical properties.

BackgroundPeritoneal dissemination (PD) is the most common mode of metastasis for advanced gastric cancer (GC) with poor prognosis. It is of great significance to accurately predict preoperative PD and develop optimal treatment strategies for GC patients. Our study assessed the diagnostic potential of serum tumor markers and clinicopathologic features, to improve the accuracy of predicting the presence of PD in GC patients.MethodsIn our study, 1264 patients with GC at Fudan University Shanghai Cancer Center and Wenzhou people’s hospital from 2018 to 2020 were retrospectively analyzed, including 316 cases of PD and 948 cases without PD. All patients underwent enhanced CT scan or magnetic resonance imaging (MRI) before surgery and treatment. Clinicopathological features, including tumor diameter and tumor stage (depth of tumor invasion, nearby lymph node metastasis and distant metastasis), were obtained by imaging examination. The independent risk factors for PD were screened through univariate and multivariate logistic regression analyses, and the results were expressed with 95% confidence intervals (CIs). A model of PD diagnosis and prediction was established by using Cox proportional hazards regression model of training set. Furthermore, the accuracy of the prediction model was verified by ROC curve and calibration plots.ResultsUnivariate analysis showed that PD in GC was significantly related to tumor diameter (odds ratio (OR)=12.06, p

Optical motion capture (MOCAP) is a commonly used technology to record the motion of non-rigid objects with high accuracy in 3D space. However, the MOCAP data has to be processed further before it can be used. The scattered reconstructed motion data must constitute a human configuration by labelling process according to the predefined template, and the missing markers have to be reconstructed to produce a stable motion trajectory. In this work, we propose a novel labelling method for motion sequences. First, a novel graph matching method is employed to determine the connection relationship of the scattered motion data for a single frame. Then, Kalman filtering is used for tracking in the motion sequence. As for the challenge coming from missing markers, we propose a new motion data preprocessing method considering the bone length constraint, which represents the information of variation in the relative position of adjacent markers. The processed motion data is input into a Long-Short Term Memory (LSTM) model to recover the missing markers and de-noise the motion data. The experiment conducted on our own dataset proves that our labelling method achieves a similar effect to Cortex, which is a commonly used commercial motion data analysis software. The experiment on CMU dataset demonstrates that our missing marker reconstruction method can achieve an art-of-state result. The labelling code will be pulished on https://github.com/Lijianfang6930/Graph-Matching-for-Marker-Labelling.

Testing has been a major factor that limits our response to the COVID-19 pandemic. The method of sample pooling and group test has recently been introduced and adopted. However, it is still not clearly known how to determine the appropriate group size. In this paper, we treat asymptomatic COVID-19 screening acceleration as an optimization problem, and solve the problem using an analytical approach and an algorithmic procedure. We develop a two-level hierarchical pooling strategy for accelerating asymptomatic COVID-19 screening. In the first level, a population is divided into groups, which results in inter-group acceleration. In the second level, a group is divided into subgroups, which results in intra-group and inter-subgroup acceleration. By using our analytical methods and numerical algorithms, we determine the optimal group size and the optimal subgroup size, which minimize the total number of tests, maximize the speedup of the hierarchical pooling strategy, and minimize both time and cost of testing. It is discovered that the optimal group size and the optimal subgroup size are determined by the fraction of infected people. Furthermore, the optimal group size, the optimal subgroup size, and the achieved speedup grow sublinearly with the reciprocal of the fraction of infected people. Our research has important social implications and financial impacts. For example, if the fraction of infected people is 0.01, by using group size of 25 and subgroup size of 5, we can achieve speedup of at least 11, which means that months of testing time can be reduced to days, and over 91% of the testing cost can be saved. Such results have not been available in the known literature. The paper makes significant progress and great advance in pooling strategy optimization for accelerating asymptomatic COVID-19 screening, and represents the contribution of computer science to the global pandemic.

Glioblastoma is the most common malignant primary brain tumor with poor prognosis and low long-term survival rate. Here we described the production of an iPSC line generated from peripheral blood mononuclear cells (PBMCs) of a 24-year-old female gliocytoma patient. The PBMCs were reprogrammed by the non-integrating Sendai Virus with human OKSM (OCT3/4, SOX2, KLF4, and c-MYC) transcription factors. The generated iPSCs were positive for pluripotent stem cell markers demonstrated by immunocytochemistry. Besides, they displayed a normal karyotype and had the potential to differentiate spontaneously three germ layers in vitro. Our model might be useful in studying the pathogenesis of gliocytoma patients.

The intelligent electrical power system is a comprehensive symmetrical system that controls the power supply and power consumption. As a basis for intelligent power supply control, load demand forecasting in power system operation management has attracted considerable research attention in energy management. In this study, we proposed a novel unsupervised multi-dimensional feature learning forecasting model, named MultiDBN-T, based on a deep belief network and transformer encoder to accurately forecast short-term power load demand and implement power generation planning and scheduling. In the model, the first layer (pre-DBN), based on a deep belief network, was designed to perform unsupervised multi-feature extraction feature learning on the data, and strongly coupled features between multiple independent observable variables were obtained. Next, the encoder layer (D-TEncoder), based on multi-head self-attention, was used to learn the coupled features between various locations, times, or time periods in historical data. The feature embedding of the original multivariate data was performed after the hidden variable relationship was determined. Finally, short-term power load forecasting was conducted. Experimental comparison and analysis of various sequence learning algorithms revealed that the forecasting results of MultiDBN-T were the best, and its mean absolute percentage error and root mean square error were improved by more than 40% on average compared with other algorithms. The effectiveness and accuracy of the model were experimentally verified.

Capicua (Cic), a transcriptional repressor frequently mutated in brain cancer oligodendroglioma, is highly expressed in adult neurons. However, its function in the dendritic growth of neurons in the hippocampus remains poorly understood. Here, we confirmed that Cic was expressed in hippocampal neurons during the main period of dendritogenesis, suggesting that Cic has a function in dendrite growth. Loss-of-function and gain-of function assays indicated that Cic plays a central role in the inhibition of dendritic morphogenesis and dendritic spines in vitro. Further studies showed that overexpression of Cic reduced the expression of Ets in HT22 cells, while in vitro knockdown of Cic in hippocampal neurons significantly elevated the expression of Ets. These results suggest that Cic may negatively control dendrite growth through Ets, which was confirmed by ShRNA knockdown of either Etv4 or Etv5 abolishing the phenotype of Cic knockdown in cultured neurons. Taken together, our results suggest that Cic inhibits dendritic morphogenesis and the growth of dendritic spines through Ets.

Abstract Background Tandem mass spectrometry (MS/MS)-based database searching is a widely acknowledged and widely used method for peptide identification in shotgun proteomics. However, due to the rapid growth of spectra data produced by advanced mass spectrometry and the greatly increased number of modified and digested peptides identified in recent years, the current methods for peptide database searching cannot rapidly and thoroughly process large MS/MS spectra datasets. A breakthrough in efficient database search algorithms is crucial for peptide identification in computational proteomics. Results This paper presents MCtandem, an efficient tool for large-scale peptide identification on Intel Many Integrated Core (MIC) architecture. To support big data processing capability, a novel parallel match scoring algorithm, named MIC-SDP (spectrum dot product), and its two-level parallelization are presented in MCtandem’s design. In addition, a series of optimization strategies on both the host CPU side and the MIC side, which includes pre-fetching, optimized communication overlapping scheme, multithreading and hyper-threading, are exploited to improve the execution performance. Conclusions For fair comparisons, we first set up experiments and verified the 28 fold times speedup on a single MIC against the original CPU-based implementation. We then execute the MCtandem for a very large dataset on an MIC cluster (a component of the Tianhe-2 supercomputer) and achieved much higher scalability than in a benchmark MapReduce-based programs, MR-Tandem. MCtandem is an open-source software tool implemented in C++. The source code and the parameter settings are available at https://github.com/LogicZY/MCtandem.

With the large-scale deployment of cloud datacenters, high energy consumption and serious service level agreement (SLA) violations in datacenters have become an increasingly urgent problem to be addressed. Implementing an effective virtual machine (VM) consolidation methods is of great significance to reduce energy consumption and SLA violations. The VM consolidation problem is a well-known NP-hard problem. Meanwhile, efficient VM consolidation should consider multiple factors synthetically, including quality of service, energy consumption, and migration overhead, which is a multi-objective optimization problem. To solve the problem above, we propose a new multi-objective VM consolidation approach based on double thresholds and ant colony system (ACS). The proposed approach leverages double thresholds of CPU utilization to identify the host load status, VM consolidation is triggered when the host is overloaded or underloaded. During consolidation, the approach selects migration VMs and destination hosts simultaneously based on ACS, utilizing diverse selection policies according to the host load status. The extensive experiment is conducted to compare our proposed approach with the state-of-art VM consolidation approaches. The experimental results demonstrate that the proposed approach remarkably reduces energy consumption and optimizes SLA violation rates thus achieving better comprehensive performance.

Reconstructing precise dynamic points with multiple camera systems (MCSs) is a pivotal work in many computer vision applications, such as motion capture. However, the deviation of 2-D position leads to frequent mismatch when searching for correspondence from multi-view. This paper puts forward a two-stage framework based on passive optical motion capture system to reconstruct precise dynamic points with MCSs. Our proposed method improves the performance of calibration and matching simultaneously. In the calibration stage, the extrinsic parameters of numerous cameras are calibrated synchronously via an L-shaped frame, where the position of four reference points is optimized with multiple geometric constraints. Bundle adjustment occurs after calibration. In the reconstruction stage, we propose a novel sparse multi-view matching method called cyclical voting, which includes multiple pairs of global voting and in-group voting. Point residual method is proposed to exclude outliers in matching groups further. The experiments show that our proposed method can decrease mismatching significantly and achieve commendable reconstruction results compared with Cortex (one of the most successful commercial motion analysis software).

The booms of big data and graphic processing unit technologies have allowed us to explore more appropriate data structures and algorithms with smaller time complexity. However, the application of machine learning as a potential alternative for the traditional data structures, especially using deep learning, is a relatively new and largely uncharted territory. In this paper, we propose a novel recurrent neural network-based learned inverted index, called Pavo, to efficiently and flexibly organize inverted data. The basic hash function in the traditional inverted index is replaced by a hierarchical neural network, which makes Pavo be able to well adapt for various data distributions while showing lower collision rate as well as higher space utilization rate. A particular feature of our approach is that a novel unsupervised learning strategy to construct the hash function is proposed. To the best of our knowledge, there are no similar results, in which the unsupervised learning strategy is employed to design hash functions, in the existing literature. Extensive experimental results show that the unsupervised model owns some advantages than the supervised one. Our approaches not only demonstrate the feasibility of deep learning-based data structures for index purpose but also provide benefits for developers to make more accurate decisions on both the design and the configuration of data organization, operation, and parameters tuning of neural network so as to improve the performance of information searching.

Searchable encryption provides an effective mechanism that achieves secure search over encrypted data. A popular application model of searchable encryption is that a data owner stores encrypted data to a server and the server can effectively perform keyword-based search over encrypted data according to a query trapdoor submitted by a data user, where the owner’s data and the user’s queries are kept secret in the server. Recently, many searchable encryptions have been proposed to achieve better security and performance, provide secure data updatable feature (dynamics), and search results verifiable capability (verifiability). However, most of the existing works endow the data user an unlimited search capacities and do not consider a data user’s search permissions. In practical application, granting search privileges for data users is a very important measure to enforce data access control. In this paper, we propose an attribute-based searchable encryption scheme by leveraging the ciphertext-policy attribute-based encryption technique. Our scheme allows the data owner to conduct a fine-grained search authorization for a data user. The main idea is that a data owner encrypts an index keyword under a specified access policy, if and only if, a data user’s attributes satisfy the access policy, the data user can perform search over the encrypted index keyword. We provide the detailed correctness analyses, performance analyses, and security proofs for our scheme. The extensive experiments demonstrate that our proposed scheme outperforms the similar work CP-ABKS proposed by Zheng on many aspects.

Low-power and short-latency memory access is critical to the performance of chip multiprocessor (CMP) system devices, especially to bridge the performance gap between memory and CPU. Together with increased demand for low-energy consumption and high-speed memory, scratch-pad memory (SPM) has been widely adopted in multiprocessor systems. In this paper, we employ a hybrid SPM, composed of a static random-access memory and a nonvolatile memory (NVM), to replace the cache in CMP. However, there are several challenges related to the CMP that need to be addressed, including how to dynamically assign processors to application tasks and dynamically allocate data to memories. To solve these problems based on this architecture, we propose a novel dynamic data allocation and task scheduling algorithm, i.e., dynamic greedy data allocation and task scheduling (DGDATS). Experiments on DSP benchmarks demonstrate the effectiveness and efficiency of our proposed algorithms; namely, our proposed algorithm can generate a highly efficient dynamic data allocation and task scheduling approach to minimize the total execution cost and produce the least amount of write operations on NVMs. Our extensive simulation study demonstrates that our proposed algorithm exhibits an excellent performance compared with the heuristic allocation (HA) and adaptive genetic algorithm for data allocation (AGADA) algorithms. Based on the CMP systems with hybrid SPMs, DGDATS reduces the total execution cost by 22.18% and 51.37% compared with those of the HA and AGADA algorithms, respectively. Additionally, the average number of write operations on NVM is 19.82% lower than that of HA.

We investigate optimal power and performance management for heterogeneous and arbitrary cloud servers in a data center. In particular, we study the problems of power-constrained performance optimization and performance-constrained power optimization in a data center with multiple heterogeneous and arbitrary servers. These problems are essential to find optimal server speeds, such that: 1) the average task response time is minimized, and that the total power consumption does not exceed certain power constraint or 2) the total power consumption is minimized, and that the average task response time does not exceed certain performance constraint. Each server is treated as a G/G/1 queuing system, whose task interarrival times and task execution requirements can have arbitrary probability distributions. Furthermore, these servers are entirely heterogeneous in terms of task interarrival time, task execution requirement, and power consumption models. The main contributions of this paper are summarized as follows: 1) we formulate the average task response time as well as the total power consumption in a data center with multiple heterogeneous and arbitrary servers as the functions of server speeds; 2) we define our optimization problems by finding optimal server speeds, since the server speeds determine both the average task response time and total power consumption; 3) we develop algorithms to find the optimal solutions and demonstrate numerical data; and 4) we also develop several closed-form heuristic solutions and compare their quality with that of the optimal solution. Our approach provides an analytical way of studying the power-performance tradeoff at the data center level.

Over these years, object tracking algorithms combined with correlation filters and convolutional features have achieved excellent performance in accuracy and real-time speed. However, tracking failures in some challenging sequences are caused by the insensitivity of deeper convolutional features to target appearance changes and the unreasonable updating of correlation filters. In this paper, we propose dual model learning combined with multiple feature selection for accurate visual tracking. First, we fuse the handcrafted features with the multi-layer features extracted from the convolutional neural network to construct a correlation filter learning model, which can precisely localize the target. Second, we propose an index named hierarchical peak to sidelobe ratio (HPSR). The fluctuation of HPSR determines the activation of an online classifier learning model to redetect the target. Finally, the target locations predicted by the dual learning models mentioned above are combined to obtain the final target position. With the help of dual learning models, the accuracy and performance of tracking have been greatly improved. The results on the OTB-2013 and OTB-2015 datasets show that the proposed algorithm achieves the highest success rate and precision compared with the 12 state-of-the-art tracking algorithms. The proposed method is better adaptive to various challenges in visual object tracking.

This research revisits the fundamental problem of processing privacy-preserving Boolean queries over outsourced databases on untrusted public clouds. Much current searchable encryption (SE) schemes try to seek an appropriate trade-off between security and efficiency, yet most of them suffer from an unacceptable query leakage due to their conjunctive/disjunctive terms that are processed individually. We show, however, this trade-off still can be deeply optimized for more security. We consider a Boolean formula as a set of deterministic finite automatons (DFAs) and propose a novel approach to running an encrypted DFA, which can be effectively and efficiently processed by the cloud. We give three constructions for conjunctive, disjunctive, and Boolean queries, respectively. Their notable advantages are single-round, highly-efficient, adaptively-secure, and leakage-minimized. A lot of experiments are made to evaluate overall efficiency. Testing results show that the schemes achieve enhanced security almost without sacrificing anything of search efficiency.

Virtualization technology, as a key technology in cloud computing, makes the virtual machine placement (VMP) play an important role in improving the energy efficiency of data centers. In this paper, an energy-aware algorithm named GATA is proposed for the VMP problem. It combines the genetic algorithm with the tabu search algorithm. The goal is to obtain an optimal VMP scheme to achieve energy efficiency while maximizing load balance among various resources. The algorithm is compared with two meta-heuristic algorithms and a newly proposed algorithm that is based on ant colony algorithm. The execution time of these algorithms is also discussed. The results show that the proposed algorithm is superior to those methods mentioned above.

The proliferation of cloud computing has resulted in the establishment of large-scale data centers containing thousands of computing nodes and consuming enormous amounts of electrical energy. However, the low-cost and high-efficiency slogans are getting louder and louder, and the IT industry is also striving for this pursuit. Therefore, it is vital to minimizing the energy consumption for cloud providers while ensuring the quality of service for cloud users. In this paper, we propose several heuristic strategies to optimize these two metrics based on a two-level management model under a heterogeneous cloud environment. First, to detect whether a physical node is continuously overloaded, we propose an empirical forecast algorithm, which predicts the future state of the host by statistically analyzing the historical utilization data of the host. Second, we propose a weighted priority virtual machine (VM) selection algorithm. For each VM on the overloaded host, we weight several utilization factors and calculate its migration priority. Then, we simulate the proposed approach and compare it with the existing overloaded hosts detection algorithms with different VM selection policies under different workloads.

With the enhancement of people's health awareness, more and more users are willing to wear portable micro-health monitoring equipment and communicate with remote medicine center for real-time diagnosis. Although, under normal circumstances, users' health status can be detected at any time, in extreme circumstances, such as earthquakes, how to make the medical center monitor user data for a long time for rescue will be of great significance. In this paper, we will study the networking of portable wearable devices based on wireless sensor networks. We mainly use minimal connected dominating sets (MCDSs) to organize nodes in extreme environments effectively, form virtual backbone networks, send data to the rescue or medical personnel, and maximize network lifetime. Specifically, we propose an adverse dominator selection procedure (ADSP), where the dominators are selected by their children-independent nodes. The ADSP has two versions, which are Independent node degree-based Adverse Dominator Selection Procedure (IADSP) and residual Energy-based Adverse Dominator Selection Procedure (EADSP). Based on IADSP and EADSP, two approximation MCDS construction algorithms named Independent node degree based MCDS (IMCDS) and Energy-efficient Independent neighbor-based MCDS (EIMCDS) are proposed, respectively. Both of them have the message complexity as O($N\Delta $ ). The performance ratio of IMCDS has an upper bound as O($\sqrt {N}$ ). The simulation results show that IMCDS and EIMCDS perform well in terms of CDS size, and the routing algorithm based on EIMCDS has better energy efficiency performance than that of IMCDS and classical routing protocol.

Software-defined networks (SDNs) are a trend of research in networks. Rule placement, a common SDN operation, becomes a challenging problem due to the capacity limitation of devices in which a large number of rules need to be deployed. Prior works mostly consider rule placement in a single device. However, the position relationships between neighbor devices also have influences on rule placement and should be considered. Our basic idea is to classify the devices position relationships into two categories: the serial relationship and the parallel relationship, and we present novel strategies for rule placement based on the two different position relationships. There are two challenges of implementing our strategies: to check whether a rule is contained by a rule set or not and to check whether a rule can be merged with other rules or not. To handle the challenges, we propose a novel data structure called OPTree to represent the rules, which is convenient to check whether a rule is covered by other rules. We design an insertion algorithm and a search algorithm for OPTree. Extensive experiments show that our approach can effectively reduce the number of rules while ensuring placed rules work. On the other hand, the experimental results also demonstrate that it is necessary to consider the position relationships between neighbor devices when placing rules.

Clustering, which explores the visualization and distribution of data, has recently been widely studied. Although current clustering algorithms such as DBSCAN, can detect the arbitrary-shape clusters and work well, the parameters involved in these methods are often difficult to determine. Clustering using a fast search of density peaks is a promising technique for solving this problem. However, the current methods suffer from the problem of uneven distribution within local clusters. To solve this problem, we propose a new density peak based clustering algorithm employing a hierarchical strategy, namely, HCFS, which consists mainly of two stages. In the first stage, the HCFS estimates the density and distance of each point. The points with higher density and distance are selected as candidate centers, and then subclusters centered on them are further obtained. In the second stage, considering that adjacent subclusters based on certain candidate centers are highly similar and connected within the same cluster, we propose a new mechanism for measuring dissimilarity and connectivity between the subclusters. Those highly similar and connected subclusters are merged to increase the dissimilarity between different clusters and to obtain the final clustering results. The experiments conducted on a large number of datasets show that our method can effectively identify unevenly distributed clusters and yield better or comparable performance for different datasets.

There are two fundamental purposes in mobile edge computing, i.e., performance enhancement and cost reduction. By offloading computation tasks to a mobile edge cloud (MEC), a user equipment (UE), also called mobile user, mobile subscriber, or mobile device, can possibly reduce its average response time, which is the main performance measure, and can possibly reduce its average power consumption. Optimizing both performance and cost may be conflicting requirements. In this paper, we optimize the cost-performance ratio (CPR), i.e., the power-time product, which combines performance (average response time) and cost (average power consumption) into one quantity. A unique feature in mobile edge computing is the competitiveness of mobile users, who are selfish in competing for resources in a mobile edge cloud. We take a game theoretic approach to the stabilization of a competitive mobile edge computing environment. The main contributions of the paper are summarized as follows. 1) We consider a mobile edge computing environment with multiple UEs and a single MEC. We establish an M/G/1 queueing model for the UEs and an M/G/m queueing model for the MEC. The UEs are entirely heterogeneous in terms of task characteristics, computation and communication speeds, and power consumption models for both computation and communication. 2) We analytically derive the average response time and the average power consumption of each UE and the MEC, so that cost-performance ratio optimization can be studied mathematically and rigorously. 3) We establish a non-cooperative game framework to systematically study the stabilization of a competitive mobile edge computing environment. Our framework includes a set of seven non-cooperative games among the UEs and the MEC, each attempts to minimize its payoff function, i.e., its cost-performance ratio. These games are different in terms of the number of variables to play and which variables to play. 4) We develop efficient algorithms for each player to find the best response in each game. All these algorithms are the poly-log time in the length of an initial search interval and the accuracy requirement. We also develop an iterative algorithm to find the Nash equilibrium of the games. 5) We demonstrate the numerical examples of our algorithms and performance data of our games for the idle-speed model and the constant-speed model respectively.

Visual Question Answering (VQA) is a challenging multi-modal learning task since it requires an understanding of both visual and textual modalities simultaneously. Therefore, the approaches used to represent the images and questions in a fine-grained manner play key roles in the performance. In order to obtain the fine-grained image and question representations, we develop a co-attention mechanism using an end-to-end deep network architecture to jointly learn both the image and the question features. Specifically, textual attention implemented by a self-attention model will reduce unrelated information and extract more discriminative features for question-level representations, which is in turn used to guide visual attention. We also note that a lot of finished works use complex models to extract feature representations but neglect to use high-level information summary such as question types in learning. Hence, we introduce the question type in our work by directly concatenating it with the multi-modal joint representation to narrow down the candidate answer space. A new network architecture combining the proposed co-attention mechanism and question type provides a unified model for VQA. The extensive experiments on two public datasets demonstrate the effectiveness of our model as compared with several state-of-the-art approaches.

To explore the underlying mechanism of dietary restriction (DR) induced lifespan extension in fruit flies at protein level, we performed proteome sequencing in Drosophila at day 7 (young) and day 42 (old) under DR and ad libitum (AL) conditions. A total of 18629 unique peptides were identified in Uniprot, corresponding to 3,662 proteins. Among them, 383 and 409 differentially expressed proteins (DEPs) were identified from comparison between DR vs AL at day 7 and 42, respectively. Bioinformatics analysis revealed that membrane-related processes, post-transcriptional processes, spliceosome and reproduction related processes, were highlighted significantly. In addition, expression of proteins involved in pathways such as spliceosomes, oxidative phosphorylation, lysosomes, ubiquitination, and riboflavin metabolism was relatively higher during DR. A relatively large number of DEPs were found to participate in longevity and age-related disease pathways. We identified 20 proteins that were consistently regulated during DR and some of which are known to be involved in ageing, such as mTORC1, antioxidant, DNA damage repair and autophagy. In the integration analysis, we found 15 genes that were stably regulated by DR at both transcriptional as well as translational levels. Our results provided a useful dataset for further investigations on the mechanism of DR and aging.

Urban hotspots refer to regions where flourishing shopping centers are located, the travel volume is very large, and there is high traffic. The formation of hotspots is strongly correlated with many features, i.e., time, space, and the distribution of points of interest; however, most studies have used qualitative analyses to describe the relevance of these features, and there is a lack of quantitative analyses. Therefore, we propose the concept and a model of the attractiveness index of a hotspot that is used to quantify the spatio-temporal distribution of the hotspots and determine the degree of attractiveness to the residents. In addition, a novel algorithm of hotspot similarity is designed and implemented to improve the efficiency of summarizing the hotspot data, which is usually performed manually. We mine the data and determine different hotspots using a one-week GPS trajectory dataset collected from 6599 taxis in Kunming. Furthermore, we calculate the attractiveness index of hotspots and visualize their characteristics. The research results provide a scientific basis and reference for public infrastructure planning, land-value evaluation, store location planning, consumption recommendations, and other applications.

The advanced productivity of the modern society has created a wide range of similar commodities. However, the descriptions of commodities are always incomplete. Therefore, it is difficult for consumers to make choices. In the face of this problem, skyline query is a useful tool. However, the existing algorithms are unable to address incomplete probabilistic databases. In addition, it is necessary to wait for query completion to obtain even partial results. Furthermore, traditional skyline algorithms are usually serial. Thus, they cannot utilize multi-core processors effectively. Therefore, a parallel progressive skyline query algorithm for incomplete databases is imperative, which provides answers gradually and much faster. To address these problems, we design a new algorithm that uses multi-level grouping, pruning strategies, and pruning tuple transferring, which significantly decreases the computational costs. Experimental results demonstrate that the skyline results can be obtained in a short time. The parallel efficiency for an Octa-core processor reaches 90% on high-dimensional, large databases.

In this paper, we address the problem of accurately modeling the cloud data center energy consumption. As minimizing energy consumption has become a crucial issue for the efficient operation and management of cloud data centers, an energy consumption model plays an important role in cloud datacenter energy management and control. Moreover, such model is essential for guiding energy-aware algorithms, such as resource provisioning policies and virtual machine migration policies. To this end, we propose a holistic cloud data center energy consumption model that is based on the principal component analysis and regression methods. Unlike the exiting approaches that focus on single system component in the datacenter, the proposed approach takes into account the energy consumption of the processing unit, memory, disk, and network interface card as well as the application characteristics. The proposed approach is validated through extensive experiments with the SPECpower benchmark. The experimental results show that the proposed energy consumption model achieves more than 95% prediction accuracy.

With the considerable growth of cybersecurity risks in modern automobiles, cybersecurity issues in the in-vehicle network environment have attracted significant attention from security researchers in recent years. Enhancing the cybersecurity ability of in-vehicle networks while considering the computing resource and cost constraints become an urgent issue. To address this problem, a novel information entropy-based method is proposed in this paper, which uses a fixed number of messages as sliding windows. By improving the sliding window strategy and optimizing the decision conditions, the detection accuracy is increased and the false positive rate is reduced. Experimental results demonstrate that the proposed method can provide real-time response to attacks with a considerably improved detection precision for intrusion detection in the in-vehicle network environment.

Cybersecurity is increasingly important for the safety and reliability of autonomous vehicles. The controller area network (CAN) is the most widely used in-vehicle network for automotive safety-critical applications. Enhancing the cybersecurity ability of CAN while considering the real-time, schedulability, and cost constraints becomes an urgent issue. To address this problem, a real-time, and schedulability analysis-guaranteed security mechanism [identification hopping CAN (IDH-CAN)] is proposed in this paper, which aims to improve the security performance of CAN under the constraints of automotive real-time applications. In order to support the operation of the IDH-CAN mechanism, an IDH-CAN controller is also designed and implemented on a field-programmable gate array, which can work as a hardware firewall in the data link layer of CAN to isolate cyberattacks from the physical layer. Meanwhile, to maximize the information entropy of the CAN message ID on the physical layer, the ID hopping table generation and optimization algorithms for IDH-CAN are also proposed. Then, information security evaluation experiments based on information entropy comparison are deployed. The simulation and practical evaluations demonstrate the effectiveness of the proposed mechanism in defending reverse engineering, targeted denial of service, and replay attacks without violating real-time and schedulability constraints.

The problem of optimal temporal partitioning of a multicore server processor for virtual machine allocation in cloud computing is addressed as multivariable optimization problems and solved algorithmically and numerically. Analytical models for virtual machines are developed, i.e., partially available multi-server systems. The problem of optimal temporal partitioning of a multicore server processor for virtual machine allocation is formulated, where the overall performance (i.e., the average task response time) of a group of virtual machines is optimized. An algorithm is developed to solve the problem numerically. The problem of optimal temporal partitioning of a multicore server processor with power consumption constraint is also formulated and solved, where the overall performance of a group of virtual machines is optimized and the total power consumption of the virtual machines does not exceed certain available power. A virtual machine is treated as a queuing system with multiple servers, i.e., an M/M/m queuing system. The system performance measures are the average task response time and the average power consumption. Two core speed and power consumption models are considered, namely, the idle-speed model and the constant-speed model. Numerical examples are presented to demonstrate our methods.

Abstract MicroRNAs (miRNAs) performs crucial roles in various human diseases, but miRNA-related pathogenic mechanisms remain incompletely understood. Revealing the potential relationship between miRNAs and diseases is a critical problem in biomedical research. Considering limitation of existing computational approaches, we develop improved low-rank matrix recovery (ILRMR) for miRNA-disease association prediction. ILRMR is a global method that can simultaneously prioritize potential association for all diseases and does not require negative samples. ILRMR can also identify promising miRNAs for investigating diseases without any known related miRNA. By integrating miRNA-miRNA similarity information, disease-disease similarity information, and miRNA family information to matrix recovery, ILRMR performs better than other methods in cross validation and case studies.

Effective patient queue management to minimize patient wait delays and patient overcrowding is one of the major challenges faced by hospitals. Unnecessary and annoying waits for long periods result in substantial human resource and time wastage and increase the frustration endured by patients. For each patient in the queue, the total treatment time of all the patients before him is the time that he must wait. It would be convenient and preferable if the patients could receive the most efficient treatment plan and know the predicted waiting time through a mobile application that updates in real time. Therefore, we propose a Patient Treatment Time Prediction (PTTP) algorithm to predict the waiting time for each treatment task for a patient. We use realistic patient data from various hospitals to obtain a patient treatment time model for each task. Based on this large-scale, realistic dataset, the treatment time for each patient in the current queue of each task is predicted. Based on the predicted waiting time, a Hospital Queuing-Recommendation (HQR) system is developed. HQR calculates and predicts an efficient and convenient treatment plan recommended for the patient. Because of the large-scale, realistic dataset and the requirement for real-time response, the PTTP algorithm and HQR system mandate efficiency and low-latency response. We use an Apache Spark-based cloud implementation at the National Supercomputing Center in Changsha to achieve the aforementioned goals. Extensive experimentation and simulation results demonstrate the effectiveness and applicability of our proposed model to recommend an effective treatment plan for patients to minimize their wait times in hospitals.