Search

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

Multiobjective multitasking optimization (MTO) is an emerging research topic in the field of evolutionary computation. In contrast to multiobjective optimization, MTO solves multiple optimization tasks simultaneously. MTO aims to improve the overall performance of multiple tasks through knowledge transfer among tasks. Recently, MTO has attracted the attention of many researchers, and several algorithms have been proposed in the literature. However, one of the crucial issues, finding useful knowledge, has been rarely studied. Keeping this in mind, this article proposes an MTO algorithm based on incremental learning (EMTIL). Specifically, the transferred solutions (the form of knowledge) will be selected by incremental classifiers, which are capable of finding valuable solutions for knowledge transfer. The training data are generated by the knowledge transfer at each generation. Furthermore, the search space of the tasks will be explored by the proposed mapping (among tasks) approach, which helps these tasks to escape from their local Pareto Fronts. Empirical studies have been conducted on 15 MTO problems to assess the effectiveness of EMTIL. The experimental results demonstrate that EMTIL works more effectively for MTO compared to the existing algorithms.

Hai-Lin Liu,1,* Rui-Hao Zhou,2,* Li-Li Luo,1 Xue Yuan,1 Ling Ye,2 He-Guo Luo1 1Department of Anesthesiology, The Third Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330008, People’s Republic of China; 2Department of Pain Management, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, People’s Republic of China*These authors contributed equally to this workCorrespondence: He-Guo LuoDepartment of Anesthesiology, The Third Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330008, People’s Republic of ChinaEmail 542534631@qq.comBackground: Although ultrasound-guided transversus abdominis plane block (TAPB) is widely used in multimodal analgesia after cesarean delivery (CD), the complications of TAPB during analgesia after CD have rarely been reported.Methods: A total of 84 cases of CD were randomly assigned to either a ropivacaine group (R group) or ropivacaine + dexamethasone group (RD group) in this double-blind trial. The pain site and pain degree at rest and during activity at 2 h, 6 h, 10 h, 12 h, 14 h, 16 h, 20 h, and 24 h after maternal surgery were recorded. The consumption of opioids at 24h, postoperative nausea, vomiting, exhaustion, and other adverse reactions were recorded.Results: A total of 80 patients were included in the analysis of results. A total of 19 patients developed ISP, 14 in the R group and 5 in the RD group. The incidence of ISP in the R and RD groups was 35% and 12.5%, respectively. The results described above showed that combining dexamethasone with ropivacaine reduced the incidence of ISP, and the difference was statistically significant (P< 0.05). Two groups of women with positive ISP had higher values of opioid consumption than women with negative ISP, but the difference was not significant.Conclusion: Dexamethasone as an adjuvant for ropivacaine can effectively relieve the ISP of ultrasound-guided TAPB after CD, and can enhance the analgesic effect of ropivacaine.Keywords: dexamethasone, ropivacaine, cesarean delivery, transversus abdominis plane block, postoperative analgesia, injection site pain

PURPOSE To evaluate the performance of polyetheretherketone (PEEK) versus titanium computer-aided designed and manufactured (CAD-CAM) framework for implant-supported fixed complete dentures (ISFCDs) with a follow-up for a duration of up to 5 years. METHODS Consecutively edentulous patients who underwent ISFCDs with a PEEK framework or titanium framework at one dental specialist center were included in this retrospective study. Implant/prosthesis survival rates, mechanical/biological complications, and bone and soft tissue parameters were analyzed. Overall survival was analyzed using Kaplan-Meier survival curves and the log-rank test. RESULTS Sixty ISFCDs (29 PEEK, 31 titanium) performed on 43 edentulous patients (331 implants) were included. An implant survival rate of 100% was obtained. There was no significant difference in the cumulative prosthesis survival rate between the PEEK (93.1%) and titanium groups (93.5%). The most common mechanical complications were fracture of the artificial veneer in both the PEEK (13.8%) and titanium (16.7%) groups. Bruxers had a higher prevalence of mechanical complications than non-bruxers (p

AimTo develop and validate a CpG-based classifier for preoperative discrimination of early and advanced-late stage colorectal cancer (CRC).MethodsWe identified an epigenetic signature based on methylation status of multiple CpG sites (CpGs) from 372 subjects in The Cancer Genome Atlas (TCGA) CRC cohort, and an external cohort (GSE48684) with 64 subjects by LASSO regression algorithm. A classifier derived from the methylation signature was used to establish a multivariable logistic regression model to predict the advanced-late stage of CRC. A nomogram was further developed by incorporating the classifier and some independent clinical risk factors, and its performance was evaluated by discrimination and calibration analysis. The prognostic value of the classifier was determined by survival analysis. Furthermore, the diagnostic performance of several CpGs in the methylation signature was evaluated.ResultsThe eight-CpG-based methylation signature discriminated early stage from advanced-late stage CRC, with a satisfactory AUC of more than 0.700 in both the training and validation sets. This methylation classifier was identified as an independent predictor for CRC staging. The nomogram showed favorable predictive power for preoperative staging, and the C-index reached 0.817 (95% CI: 0.753–0.881) and 0.817 (95% CI: 0.721–0.913) in another training set and validation set respectively, with good calibration. The patients stratified in the high-risk group by the methylation classifier had significantly worse survival outcome than those in the low-risk group. Combination diagnosis utilizing only four of the eight specific CpGs performed well, even in CRC patients with low CEA level or at early stage.ConclusionsOur classifier is a valuable predictive indicator that can supplement established methods for more accurate preoperative staging and also provides prognostic information for CRC patients. Besides, the combination of multiple CpGs has a high value in the diagnosis of CRC.

Objective To observe the effects of glutaminase (GLS) inhibitors on autophagy and proliferation of pancreatic stellate cells, and to explore their functions in pancreatic cancer. Methods Pancreatic cancer cells were divided into two groups. Group A was the control untreated group, and group B cells were treated with GLS inhibitors. Western blotting was used to detect the expression of Atg5, Bcl-2, Bax, and Bid proteins. The bromodeoxyuridine assay and scratch test were employed to investigate cell proliferation and invasion, respectively. The expression of E-cadherin, vimentin, cell adhesion molecule 2 (CADM2), and Snail protein was investigated by immunofluorescence. Results The expression of Atg5, Bax, and Bid was higher in group A than in group B, while Bcl-2 expression was lower in group A than in group B. Group A cells demonstrated greater proliferation and invasion than group B cells. The expression of E-cadherin was lower in group A cells than group B cells, while vimentin, CADM2, and Snail were expressed at higher levels in group A than group B cells. Conclusion The inhibition of glutamine isozymes reduces autophagy and apoptosis in astrocytes, and inhibits pancreatic cancer cell proliferation and metastasis, while reducing their invasiveness.

Noncoding RNAs, including long non-coding RNAs (lncRNAs) and microRNAs, are involved in the development of neuropathic pain. Currently, we investigated that lncRNA X inactive-specific transcript (XIST) and toll-like receptor 5 (TLR5) were greatly upregulated in chronic constriction injury rat models, whereas miR-154-5p (microRNA-154-5p) was significantly downregulated. Bioinformatics analysis was used to predict miR-154-5p as a target gene of XIST, and dual-luciferase reporter tests proved the correlation between them. We observed that miR-154-5p was negatively modulated by XIST in vitro. XIST overexpression markedly induced neuropathic pain development in rats with chronic constriction injury, whereas the upregulation of miR-154-5p could reverse this phenomenon. Furthermore, TLR5 was demonstrated to be a target gene of miR-154-5p by using bioinformatics predictions. miR-154-5p negatively regulated TLR5 expression in vitro, and TLR5 was able to promote neuropathic pain development. In addition, overexpressing miR-154-5p can reverse the role of TLR5 neuropathic pain in vivo. Taken these together, we indicated that XIST could increase TLR5 expression by acting as a sponge of miR-154-5p in neuropathic pain development. This study revealed that XIST can contribute to neuropathic pain progression in rats through decreasing miR-154-5p and increasing TLR5. The XIST/miR-154-5p/ TLR5 axis can be provided as a novel therapeutic target in treating neuropathic pain.

We investigated in this study the expression of ENO1 in tissues and plasma of PDAC patients to evaluate its clinicopathological and diagnostic significance. ENO1 protein expression was detected in tissue microarray of human PDAC and adjacent noncancer tissues. Electrochemiluminescence immunoassay and amplified luminescent proximity homogeneous assay (AlphaLISA) were performed to measure CA19-9 and ENO1 concentration in plasma from PDAC patients and healthy controls. We demonstrated that ENO1 overexpression is positively correlated with clinical stage, lymph node metastasis, and poor prognosis of PDAC; ENO1 may function as a hopeful candidate diagnostic marker in combination with CA19-9 in PDAC diagnosis.

Energy efficiency of the wireless networks has drawn more and more attentions due to the requirement of the green communication. The base station sleeping strategy and resource allocation can effectively improve the energy efficiency for the wireless networks. Meanwhile, renewable energy is important to decrease the carbon emission. In this paper, the authors propose a joint BS sleeping strategy, resource allocation and renewable energy procurement scheme to maximize the profit of the network operators and minimize the carbon emission. Then, a joint optimization problem is formulated, which is a mixed integer programming problem. To solve it, they adopt the bi-velocity discrete particle swarm optimization (BVDPSO) algorithm to optimize the BS sleeping strategy. When the BS sleeping strategy is fixed, the authors propose an optimal algorithm based on Lagrange dual domain method to optimize the power allocation, subcarrier assignment and energy procurement. Numerical results illustrate the effectiveness of their proposed scheme and algorithm.

Neuropathic pain is defined as a chronic pain. It could be resulted from a lesion of nervous systems. MicroRNAs have been reported to modulate multiple genes and pathways in neuropathic pain and neuroinflammation. Therefore, identifying the potential expression patterns of microRNAs under neuropathic pain conditions is significant. miR-128-3p has been identified in many cancers. But, its biological role in neuropathic pain progression remains poorly known. Currently, we aimed to explore the possible functions of miR-128-3p in the modulation of neuropathic pain. We displayed in the CCI rats, miR-128-3p was greatly down-regulated in the spinal cord tissues and the isolated microglias. Subsequently, LV-miR-128-3p could attenuate CCI-triggered mechanical allodynia and thermal hyperalgesia. Meanwhile, some common pro-inflammatory cytokines were significantly reduced while anti-inflammatory cytokine IL-10 was increased by miR-128-3p. Here, in the current investigation, by utilizing dual-luciferase reporter assays, ZEB1 was proved as a direct target of miR-128-3p. LV-miR-128-3p significantly repressed ZEB1 expression in microglia of CCI rats. Moreover, ZEB1 was reported to be increased in CCI rats. miR-128-3p rescued the effects of ZEB1 on neuropathic pain progression via inhibiting neuroinflammation. Taken these together, we implied miR-128-3p alleviated the progression of neuropathic pain via modulating ZEB1.

Many-objective optimization problems (MaOPs) bring great hardship to most of the existing classical evolutionary multi-objective optimization algorithms, respecting selection operators, computational cost, visualization of the high-dimensional Pareto front, and so on. Objective reduction can help mitigate such hardship by removing the redundant objectives in the original objective set to some extent. Objective reduction problem is inherently a multiobjective optimization problem because we aim to preserve the dominance structure by using as little objectives as possible. In this paper, we propose an objective reduction algorithm based on constrained evolutionary multiobjective algorithm. A novel local search is presented for finding the Pareto-front by searching the neighbor. We compared the proposed algorithm with three algorithms on eleven test instances. The experimental results show the proposed algorithm can obtain a stable performance without any parameter need to fine tune.

MicroRNAs (miRNA) are reported to be a vital regulator of neuropathic pain. Even so, the molecular mechanisms of miRNA function on neuropathic pain development are known little. Our research was designed to investigate the role of miRNA in neuropathic pain development in rat modle set up by chronic sciatic nerve injury (CCI). Increasing miR-150 expression could significantly alleviate neuropathic pain in CCI rats. For farther researching the regulation mechanism of miR-150 on neuropathic pain, we screened AKT3 as a possible target of miR-150 by bioinformatic mechods and predicted a possible target of miR-150 in 3′-untranslated region (UTR) of AKT3 who serves as an oncogene. In rat model, the expression both of AKT3 mRNA and protein were significantly upregulated. The overexpressed miR-150 importantly repressed the level of AKT3 and simultaneously alleviate mechanical and thermal hyperalgesia in rat model. These suppressant impacts of miR-150 on neuropathic pain process can be reversed by the overexpression of AKT3. Considering all above results, our research declared that miR-150 can restrain neuropathic pain process though targeting AKT3 in vivo, suggesting that miR-150 could be the therapeutic target for neuropathic pain therapy by regulating AKT3.

Recently, evolutionary multi-tasking (EMT) has been proposed as a new evolutionary search paradigm that op-timizes multiple problems simultaneously. Due to the knowledge transfer across optimization tasks occurs along the evolutionary search process, EMT has been demonstrated to outperform the traditional single-task evolutionary search algorithms on many complex optimization problems, such as multimodal continuous optimization problems, NP-hard combinatorial optimization problems, and constrained optimization problems. Today, EMT has attracted lots of attentions, and many EMT algorithms have been proposed in the literature. The explicit EMT algorithm (EEMTA) is a recent proposed new EMT algorithm. In contrast to most of existing EMT algorithms, which employ a single population using unified space and common search operators for solving multiple problems, the EEMTA uses multiple populations which possess problem-specific solution representations and search mechanisms for different problems in evolutionary multi-tasking, which thus could lead to enhanced optimization performance. However, the original EEMTA was proposed for solving only two tasks. As knowledge transfer from inappropriate tasks may lead to negative effect on the evolutionary optimization process, additional designs of identifying task pairs for knowledge transfer is necessary in EEMTA for evolutionary multi-tasking with tasks more than two. To the best of our knowledge, there is no research effort has been conducted on this issue. Keeping this in mind, in this paper, we present a preliminary study on the task selection in EEMTA for many-task optimization. As task similarity may lose to capture the usefulness between tasks in evolutionary search, instead of using similarity measures for task selection, here we propose a credit assignment approach for selecting proper task to conduct knowledge transfer in explicit evolutionary many-tasking. The proposed approach is based on the feedbacks from the transferred solutions across tasks, which is adaptively updated along the evolutionary search. To confirm the efficacy of the proposed method, empirical studies on the many-task optimization problem, which consists of 7 commonly used optimization benchmarks, have been presented and discussed.

For solving constrained multi-objective optimization problems (CMOPs), an effective constraint-handling technique (CHT) is of great importance. Recently, many CHTs have been proposed for solving CMOPs. However, no single CHT can outperform all kinds of CMOPs. This paper proposes an algorithm, namely, ACHT-M2M, which adaptively allocates the existing CHTs in an M2M framework for solving CMOPs. To be more specific, a CMOP is first decomposed into several constrained multi-objective optimization subproblems by ACHT-M2M. Each subproblem has a subpopulation in a subregion. CHT for each subregion is adaptively allocated according to a proposed composite performance measure. Population for the next generation is selected from subregions by selection operators with different CHTs and the obtained nondominated feasible solutions in each generation are used to update a predefined archive. ACHT-M2M assembles the advantages of different CHTs and makes them cooperate with each other. The proposed ACHT-M2M is finally compared with the other 12 representative algorithms on benchmark CMOPs and the experimental results further confirm the effectiveness of ACHT-M2M for solving CMOPs.

Wireless powered sensor network has captured a lot of attentions due to its higher lifetime. In this paper, we investigate the resource allocation in the non-orthogonal multiple access (NOMA) multiple-input multiple-output (MIMO)-based wireless powered sensor networks in terms of the wireless energy transfer time allocated and power allocation, and the joint resource allocation is formulated as a nonconvex optimisation problem. To reduce the complexity, the original optimisation problem is decomposed into the time allocation subproblem and power allocation subproblem. Specifically, the particle swarm optimisation (PSO) is adopted to solve the time allocation subproblem. TheDC(difference of two convex functions) programming method is adopted to solve the power allocation subproblem, and the objective function is approximated as a convex function by the first order Taylor expansion. Simulation results illustrate that the proposed joint resource allocation scheme can significantly improve the system throughput.

For many-objective optimization problems (MaOPs), in which the number of objectives is greater than three, the performance of most existing evolutionary multi-objective optimization algorithms generally deteriorates over the number of objectives. As some MaOPs may have redundant or correlated objectives, it is desirable to reduce the number of the objectives in such circumstances. However, the Pareto solution of the reduced MaOP obtained by most of the existing objective reduction methods, based on objective selection, may not be the Pareto solution of the original MaOP. In this paper, we propose an objective extraction method (OEM) for MaOPs. It formulates the reduced objective as a linear combination of the original objectives to maximize the conflict between the reduced objectives. Subsequently, the Pareto solution of the reduced MaOP obtained by the proposed algorithm is that of the original MaOP, and the proposed algorithm can thus preserve the dominance structure as much as possible. Moreover, we propose a novel framework that features both simple and complicated Pareto set shapes for many-objective test problems with an arbitrary number of essential objectives. Within this framework, we can control the importance of essential objectives. As there is no direct performance metric for the objective reduction algorithms on the benchmarks, we present a new metric that features simplicity and usability for the objective reduction algorithms. We compare the proposed OEM with three objective reduction methods, i.e., REDGA, L-PCA, and NL-MVU-PCA, on the proposed test problems and benchmark DTLZ5 with different numbers of objectives and essential objectives. Our numerical studies show the effectiveness and robustness of the proposed approach.

Neuropathic pain, resulting from somatosensory nervous system dysfunction, remains a serious public health problem worldwide. microRNAs are involved in the physiological processes of neuropathic pain. However, the biological roles of miR-98 in neuropathic pain development have not been investigated. Therefore, in our current study, we focused on the effects of miR-98 in neuropathic pain. It was shown that miR-98 was significantly downregulated in chronic sciatic nerve injury (CCI) rat models. In addition, high mobility group A2 (HMGA2) was obviously upregulated in CCI rats. Overexpression of miR-98 inhibited neuropathic pain progression, including mechanical and thermal hyperalgesia. By a bioinformatics analysis, HMGA2 was predicted as a direct target of miR-98. The negative correlation between miR-98 and HMGA2 was validated in our present study. Furthermore, overexpression of miR-98 dramatically repressed HMGA2 protein and messenger RNA (mRNA) expression. Neuroinflammation participates in neural-immune interactions, which can contribute to the neuropathic pain development. Meanwhile, we found that inflammatory cytokine (interleukin [IL]-6, IL-1β, and COX-2) protein expression in rats infected with LV-miR-98 was greatly suppressed. Taking these results together, we concluded that miR-98 might depress neuropathic pain development through modulating HMGA2.

Emerging evidence has suggested that microRNAs play a critical role in neuropathic pain development. However, the biological role of miRNAs in regulating neuropathic pain remains barely known. In our present study, we found that miR-124-3p was significantly downregulated in rats after chronic sciatic nerve injury (CCI). In addition, it was showed that overexpression of miR-124-3p obviously repressed mechanical allodynia and heat hyperalgesia. Meanwhile, it has been reported that neuroinflammation can contribute a lot to neuropathic pain progression. Here, we found that inflammatory cytokine (IL-6, IL-1β, and TNF-⍺) protein expression in rats after CCI greatly increased and miR-124-3p mimics depressed inflammation cytokine levels. Consistently, miR-124-3p alleviated inflammation production in lipopolysaccharide-incubated spinal microglial cells. Bioinformatics analysis revealed that EZH2 acted as a direct target of miR-124-3p, which participated in the miR-124-3p-modulated effects on neuropathic pain development and neuroinflammation. We observed that miR-124-3p was able to promote neuroinflammation and neuropathic pain through targeting EZH2. The direct correlation between them was validated in our current study using dual-luciferase reporter assays. Subsequently, it was manifested that EZH2 abrogated the inhibitory role of miR-124-3p on neuropathic pain progression in CCI rats. Taken these together, our findings highlighted a novel contribution of miR-124-3p to neuropathic pain and indicated the possibilities for developing novel therapeutic options for neuropathic pain.

MicroRNA are emerging as significant regulators of neuropathic pain progression. In addition, neuroinflammation contributes a lot to neuropathic pain. miR-26a-5p has been identified as an inflammation-associated miRNA in multiple pathological processes. However, little is known about the biological role of miR-26a-5p in neuroinflammation and neuropathic pain development. Therefore, we aimed to investigate the function of miR-26a-5p in neuropathic pain by establishing a rat model using chronic sciatic nerve injury (CCI). A significant decrease of miR-26a-5p expression was observed in the spinal cord tissues form the CCI rats compared to the control group. Moreover, overexpression of miR-26a-5p significantly repressed neuropathic pain and neuroinflammation in CCI rats. MAPK6 was identified as a direct downstream target gene of miR-26a-5p and confirmed by dual-luciferase reporter assays. As displayed, overexpression of miR-26a-5p greatly reduced MAPK6 levels in vitro and in vivo. Meanwhile, MAPK6 expression and miR-26a-5p were oppositely correlated in CCI rats. Furthermore, up-regulation of MAPK6 obviously reversed the suppressive effect of miR-26a-5p on neuroinflammation and neuropathic pain progression. Taken these together, our results implied that miR-26a-5p could act as a negative regulator of neuropathic pain development through targeting MAPK6, which indicated that miR-26a-5p might serve as a potential therapeutic target for neuropathic pain.

In this paper, we suggest a new multi-objective artificial bee colony (ABC) algorithm by introducing an elitism strategy. The algorithm uses a fixed-size archive that is maintained based on crowding-distance to store non-dominated solutions found during the search process. In the proposed algorithm, an improved artificial bee colony algorithm with an elitism strategy is adopted for the purpose of avoiding premature convergence. Specifically, the elites in the archive are selected and used to generate new food sources in both employed and onlooker bee phases in each cycle. To keep diversity, a member located at the most crowded region will be removed when the archive overflows. The algorithm is very easy to be implemented and it employs only a few control parameters. The proposed algorithm is tested on a wide range of multi-objective problems, and compared with other state-of-the-art algorithms in terms of often-used quality indicators with the help of a nonparametric test. It is revealed by the test procedure that the algorithm produces better or comparable results when compared with other well-known algorithms, and it can be used as a promising alternative tool to solve multi-objective problems with the advantage of being simple and effective.

Device to device (D2D) is a promising technology for the next generation mobile communication technology, and attracts a lot of attentions. Since D2D technology allows devices to connect directly without the assistance of the base station (BS), D2D can effectively improve system throughput and decrease the energy consumption. In this paper, we propose a novel spectrum sharing scheme between D2D pair and cellular users to improve the system sum rate. In the proposed scheme, cellular users can share spectrum resources with different D2D pairs which can effectively improve the spectrum efficiency. Meanwhile, the transmit power of both the D2D pairs and BS should be careful optimized since the spectrum sharing scheme will also lead to co-channel interference. Then, both the resource block (RB) assignment and power allocation are considered at the same time, and we formulate the resource allocation problem as a mixed integer programming problem which is difficult to tackle. To solve the problem, in this paper, we adopt the Nr-PSO to solve the proposed problem. The Nr-PSO is a current evolutionary algorithm which can improve the local search ability. Simulation results show that the effectiveness of the algorithm.

Background Emergence agitation (EA) is a common phenomenon in preschool children during emergence from general anesthesia. This study evaluated the safety and efficacy of dezocine for emergence agitation in preschool children anesthetized with sevoflurane-remifentanil. Methods A total of 100 preschool children, scheduled for elective laparoscopic repair of an inguinal hernia by high ligation of the hernia sac under sevoflurane-remifentanil anesthesia were randomized into two groups: Group C (n = 50) received Ringer’s lactate 10 mL and Group D received Ringer’s lactate 10 mL containing dezocine 0.1 mg/kg, postoperatively. Results Incidence of EA, defined as a score ≥ 3 on Aono’s four point scale or Pediatric Anesthesia Emergence Delirium (PAED) score ≥ 10 in the PACU (10% vs. 76%) and the percentage of patients with severe EA (PAED score ≥ 13) (12% vs. 76%) were significantly lower in Group D compared to Group C (P

Green communication has become a hot topic in the field of wireless communication. This paper aims to improve the Quality of Service (QoS) of the system and minimizes the energy consumption by the spectrum-energy cooperation between adjacent base stations. We formulate the proposed spectrum-energy cooperation model as a hybrid constrained many-objective optimization problem (MaOP). To improve the efficiency of optimization algorithm, an alternate optimization algorithm is presented to address the proposed complex MaOP. The evolutionary multiobjective algorithm is employed for spectrum cooperation optimization which is discrete optimization problem, meanwhile classical optimization method is employed for energy consumption optimization and energy cooperation optimization that are continuous optimization problems. Simulation results show the effectiveness of the algorithm.

Aimed at the applications of technology of camera calibration to 3D reconstruction, the ideal camera model is discussed, especially on the influences and solving methods of lens radial distortion andtangential distortion, and an arithmetic of camera calibration based on OpenCV (open source computer vision library) in Visual C++ environment is given. This arithmetic makes use of the functions of the library effectively, so it has high calibration precision and good robust. It can meet the needs of august reality and othercomputer vision systems.

This paper presents a new multi-objective artificial bee colony algorithm called dMOABC by dividing the whole searching space S into two independent parts S 1 and S 2. In this algorithm, two "basic" colonies are assigned to search potential solutions in regions S 1 and S 2, while the so-called "synthetic" colony explores in S. This multi-colony model could enable the good diversity of the population, and three colonies share information in a special way. A fixed-size external archive is used to store the non-dominated solutions found so far. The diversity over the archived solutions is controlled by utilizing a self-adaptive grid. For basic colonies, neighbor information is used to generate new food sources. For the synthetic colony, besides neighbor information, the global best food source gbest selected from the archive, is also adopted to guide the flying trajectory of both employed and onlooker bees. The scout bees are used to get rid of food sources with poor qualities. The proposed algorithm is evaluated on a set of unconstrained multi-objective test problems taken from CEC09, and is compared with 11 other state-of-the-art multi-objective algorithms by applying Friedman test in terms of four indicators: HV, SPREAD, EPSILON and IGD. It is shown by the test results that our algorithm significantly surpasses its competitors.

How to improve the success rate of the industry-university-research cooperation and form a benign circulation, is the primary topic of current research studies, reasonable investment in research and development is an important guarantee for sharing research results between enterprises and scientific research institutes. It applies the dynamic information game to analysize the input relation between the enterprise and the scientific research institute in the process of the industry-university-research cooperation, and demonstrates the effect of the collaborative pattern on the profit reliezation of the both sides.

Due to the increasing demand for mobile radio services, 3G wireless network planning has been becoming one of the most important research f ields. 3G system, e.g. WCDMA, is based on Code Division Multiple Access [1], which is quite different from Time Division Multiple Access (TDMA) as used in 2G system.In the 3G network planning, not only are coverage, capacity and quality of the signal interrelated, but multi-rate and mixed-business also utilize the common carrier at the same time. As a result, it makes the 3G network planning become more challenging. In this paper, we will only concentrate on the WCDMA network planning. Since the WCDMA systems have self-interference and the effects of cell-breathing, it makes the coverage, capacity and interference of the base stations (BSs) restrain each other [2], [3]. That is, the area actually covered by a BS depends on the Quality of Service (QoS) and the traffic demand distribution. Therefore, the relationship between coverage, capacity and interference should be fully taken into account in the planning process.

In this paper, we study the relay node placement problem of, given a deployment of sensor nodes, finding the minimum number of relay nodes to increase the lifetime and reduce the deployment costs of wireless sensor networks (WSNs) simultaneously. In particular, we use the average remaining energy of the relay nodes to express the lifetimes of the WSNs. Due to the conflict of the average remaining time and deployment costs of relay nodes, we first formulate the relay node placement problem as a multi-objective optimisation problem, which is difficult to address using traditional methods. Based on this, a novel evolutionary multi-objective algorithm is presented to solve the problem. Furthermore, a local search method is presented to improve the convergence of the algorithm. Simulation results show that the proposed relay node placement scheme can efficiently reduce the cost and increase the lifetime.

The chambers of certain coal mine in Shandong such as central substation situate in soft rock which consists of mudstone and fine sandstone, etc. Obvious ground pressure behaviors, large deformation and failure of surrounding rock have serious effect on mine safety production, with the impact of various complicated deep large ground pressure. This paper presents a rational scheme to control the surrounding rock steadily, based on analysis of deformation and failure on large section soft rock chamber, combined with the concept of the" combined supporting technology of long and short anchors" and "the combined supporting technology of three anchors".

Energy harvesting has drawn more and more attention because of the requirement of the green communication. It is well known that energy harvesting is one of the most important technologies of the green communication. Due to the intermittency of the renewable energy and the different space of the Base stations (BSs), the renewable energy cooperation is necessary to improve the renewable energy efficiency. In this paper, we propose a renewable energy cooperation scheme among different BSs, in which one BS can collect (share) energy from (to) another BSs. It is obvious that the energy cooperation efficiency will be improved when the number of the BSs that join the cooperation scheme is large. In this paper, our objective is to minimize the networks delay of each BS. To minimize the networks delay, it is reasonable that each BS hopes to transfer its energy to another BSs as little as possible, and collects energy from another BSs as much as possible. Thus, the objective of each BS are conflicting. We formulate the joint optimization problem into a many-objective mixed integer optimization problem which is difficult to optimize. We adopt the evolutionary many-objective algorithm based on decomposition and reference distance to optimize the problem. Simulation results show that the effectiveness of the algorithm.

In recent years, many constraint-handling techniques maintaining infeasible individuals in the population have demonstrated their effectiveness for solving constrained optimization problems. Generally, the effective utilization of the potential of the infeasible individuals will improve the performance of the algorithms. This paper proposes an approach based on decomposition for constrained multi-objective optimization problems. By transforming the degree of constraint violation into one objective, a constraint-handling technique is introduced to exploit useful infeasible individuals during the evolution process. Besides, along with the evolution, it puts more emphasis on infeasible individuals with better objective values and smaller constraint violations, since they can provide more importance information for improving the convergence and diversity of the population. The performance of the proposed algorithm is evaluated by using 8 well-known test instances, i.e. CTP-series test instances. And the experimental results indicate that our algorithm is superior to the compared algorithm.

Numerical calculations for the damping effect of foam concrete cushioning layer in crossing high-speed railway tunnel were carried out, based on the fluctuation theory using the Newmark implicit integration algorithm and he Lysmer viscous boundary condition. Then the measure's damping effects were evaluated by indicators such as the displacement, acceleration and stress response of the lining structure. Results indicated that foam concrete cushioning layer had good damping effects on the lining structure and showed significant damping effects within the thickness of 5 to 10 cm. In addition, closer to the crossing point, better the damping effects were. These results could make valuable reference for the anti-vibration design in crossing high-speed railway tunnel.

A kind of porous SiO2 bulk materials with pore size distributed in nano and micron scales has been fabricated by freeze-casting of SiO2 nano-size powders in TBA (tert-butyl alcohol)/H2O suspensions. The content of TBA in the well mixed TBA/H2O binary co-solvent plays an important role in size and morphology control of the pores in the obtained SiO2 monolithic materials, since the ratio of TBA/H2O determines the competition of crystallization of TBA and ice, therefore, the microstructure of the monolithic SiO2. It was found that the content of TBA in the range from 30 to 40% was suitable for formation of more nano-pores in the SiO2 monolith. The insulation materials freeze-casted from the suspension of SiO2 in 13wt% and TBA in 40wt% has the density of 0.17g/cm3, and the specific surface area of 282m2/g. The pore size has a trimodal size distribution with three mean diameters of 15nm, 100nm and 4μm, respectively.

Within the learning framework of maximum weighted likelihood (MWL) proposed by Cheung, 2004 and 2005, this paper will develop a batch Rival Penalized Expectation-Maximization (RPEM) algorithm for density mixture clustering provided that all observations are available before the learning process. Compared to the adaptive RPEM algorithm in Cheung, 2004 and 2005, this batch RPEM need not assign the learning rate analogous to the Expectation-Maximization (EM) algorithm (Dempster et al., 1977), but still preserves the capability of automatic model selection. Further, the convergence speed of this batch RPEM is faster than the EM and the adaptive RPEM in general. The experiments show the superior performance of the proposed algorithm on the synthetic data and color image segmentation.

Background and Aim: In recent years, a great interest has been dedicated to the development of noninvasive predictive models to substitute liver biopsy for fibrosis assessment and follow-up. Our aim was to provide a simpler model consisting of routine laboratory markers for predicting liver fibrosis in patients chronically infected with hepatitis B virus (HBV) in order to optimize their clinical management. Methods: Liver fibrosis was staged in 386 chronic HBV carriers who underwent liver biopsy and routine laboratory testing. Correlations between routine laboratory markers and fibrosis stage were statistically assessed. After logistic regression analysis, a novel predictive model was constructed. This S index was validated in an independent cohort of 146 chronic HBV carriers in comparison to the SLFG model, Fibrometer, Hepascore, Hui model, Forns score and APRI using receiver operating characteristic (ROC) curves. Results: The diagnostic values of each marker panels were better than single routine laboratory markers. The S index consisting of γ-glutamyltransferase (GGT), platelets (PLT) and albumin (ALB) (S-index: 1000 × GGT/(PLT × ALB2)) had a higher diagnostic accuracy in predicting degree of fibrosis than any other mathematical model tested. The areas under the ROC curves (AUROC) were 0.812 and 0.890 for predicting significant fibrosis and cirrhosis in the validation cohort, respectively. Conclusions: The S index, a simpler mathematical model consisting of routine laboratory markers predicts significant fibrosis and cirrhosis in patients with chronic HBV infection with a high degree of accuracy, potentially decreasing the need for liver biopsy.

For the purpose of estimating the mixing matrix under the nonstrictly sparse condition, this paper presents the algorithms to approximate the mixing matrix in two different situations in which the source vectors are 1-sparse and (m - 1)-sparse. When the source signals are 1-sparse, we use the generalized spherical coordinate transformation to convert the matrix of observation signals into the new one, which makes the process of estimating column A become the process of finding the center point of these new data. For the situation that source signals are (m - 1)-sparse, we propose a new algorithm for the underdetermined mixtures blind source separation based on hyperplane clustering. The algorithm firstly finds out the linearly independent vectors from the observations, and secondly determines all the normal vectors of hyperplanes by analyzing the number of observations that are in the same hyperplane. Finally, we identify the column vectors of the mixing matrix A by calculating the vectors which are orthogonal to the clustered normal vectors. These two new algorithms for estimating the mixing matrix are more suitable for the general cases as they have lower requirement for the sparsity of the observations.

Heterogeneous networks (Het Net) is the development trend of future wireless networks, but the additive small base stations (BSs) make existing BS planning models unavailable. This paper builds a new BS planning model in which takes signal coverage, system capacity and cost as objective functions and takes interference as a very important constraint. Since these issues are conflicting, the optimization problem is a multi-objective optimization problem with constraints which is difficult to tackle. Evolutionary multi-objective optimization algorithm is considered to solve the problem in this paper. In order to improve the speed and ability of search of algorithm, this paper adds local search (LS) and decomposition strategy (DS) to the traditional genetic algorithm (GA). The simulation shows that the proposed algorithm can provide more feasible non-dominated solutions and have faster computing speed.

Recently, energy efficiency (EE) has drawn more and more attentions due to the need for green communication. It is well-known that the base station (BS) sleeping strategy and radio resource allocation can effectively improve the EE for the wireless communication networks. Meanwhile, renewable energy is important to decrease the carbon emissions which is also subject to the need for green communication. In this paper, we jointly optimize the BS sleeping strategy, resource allocation and renewable energy procurement scheme to maximize the profit of the network operators and minimize the carbon emissions. We formulate the joint optimization problem as a mixed integer programming problem which is difficult to tackle in general. In the first, we adopt the bi-velocity discrete particle swarm optimization (BVDPSO) algorithm to optimize the BS sleeping strategy. When the BS sleeping strategy is fixed, the original optimization problem only consists of the power allocation, subcarrier assignment and energy procurement which is also difficult to solve due to the combinatorial nature of the subcarrier assignment. Then, we propose an optimal algorithm based on Lagrange dual domain method to optimize the power allocation, subcarrier assignment and energy procurement. Numerical results illustrate the effectiveness of our proposed scheme and algorithm.

Resource allocation for orthogonal frequency division multiplexing OFDM system, as a core technology for the 4th generation mobile communication system, has shown significant importance in the improvement of system transmission rate. At present, the two-step algorithm is described as a main method to deal with resource allocation for OFDM system. As carrier allocation and power allocation are not independent of each other, the two-step algorithm may result in lower complexity but poor convergence. This paper proposes a mixed evolutionary algorithm to allocate sub-carrier and power at the same time. It combines the simulated annealing algorithm and evolutionary algorithm. This paper also improves the definition of fairness in OFDM system. It can achieves perfect fairness among users while maintaining the efficient capacity performance. Simulation results demonstrate that the proposed resource allocation algorithm can achieve perfect performance, and increase the convergence rate greatly meanwhile efficiently improving the system capacity.

Planning of base stations (BS) is one of the fundamental problems in the fourth generation (4G) wireless network design. A new mathematical model for the 4G BS planning is proposed in this paper. The co-channel interference, orthogonal frequency division multiplexing (OFDM), the cell edge rate, the reference signal received power (RSRP) and the base station density are considered in detail in this model. The objectives of the model are to minimum construction cost, maximum coverage and capacity at the same time. It's a multiobjective optimization problem with constraints. So a multiobjective algorithm with a local search operator is designed to solve this model. Simulation results show that the according algorithm with local search operator can give a set of solutions with relatively lower cost, larger coverage, larger capacity and faster speed than the algorithm without local search operator. The effective and efficient of the proposed model can also be identified by the comparison of the solutions.

The mutation strategy and control parameter have a significant influence on the performance of differential evolution. An local and global mutation operator based subregion and external set strategies are proposed in this paper. They use the idea of direct simplex method of mathematical programming. It is advantageous to search the better solutions in the search space. The local mutation operator is applied to improve the local search performance of the algorithm and accelerate convergence speed. The global mutation operator is used to exploit a wider area and jump out of the local optima. An adaptive strategy for assigning mutation strategies and control parameters is proposed in this paper. The more successful is a mutation strategy and control parameter setting in the previous search, the more chance it will be used in the further search. Moreover, a novel crossover operator based subregion and external set strategy also is introduced. In order to demonstrate the performance of the proposed algorithm, it is compared with the MOEA/D-DE and the hybrid-NSGA-II-DE. The result indicates that the proposed algorithm is efficient.

The ultra-high voltage transmission line is long, which needs to pass through the various complex terrains. In order to ensure the safe structural system, understanding and grasping the wind-induced response and reliability of double column suspended guyed tower (DCSGT) in strong wind area of Gobi Desert are basic requirements. Through combining the conditional probability density of wind speed and frequency function of wind direction, the successive-type joint distribution model of wind speed and direction is obtained by multiplication theorem. In accordance with the principle of equivalent extreme value event, through utilizing the statistical moment point estimation method, the first four-order statistical moment information of equivalent extreme value variables is solved. The probability density function (PDF) of DCSGT response is calculated in accordance with these statistical moments. The dynamic reliability index and failure probability of DCSGT are solved by the moment method.

There are two main tasks involved in addressing a multiobjective optimization problem (MOP) by evolutionary multiobjective (EMO) algorithms: 1) make the population converge close to the Pareto-optimal front and 2) maintain adequate population diversity. However, most state-of-the-art EMO algorithms are designed based on the “convergence first and diversity second” principle. It has been observed that although these EMO algorithms have been successful in optimizing many real-world MOPs, they fail to solve certain problems that feature a severe imbalance between diversity preservation and achieving convergence. This paper characterizes an imbalanced MOP by clearly defining properties and indicating the reasons for the existing EMO algorithms’ difficulties in solving them. We then present 14 imbalanced problems, with and without constraints. Computational results using four existing EMO algorithms—elitist non-dominated sorting genetic algorithm (NSGA-II), multiobjective evolutionary algorithm based on decomposition (MOEA/D), strength Pareto evolutionary algorithm 2 (SPEA2), and S metric selection EMO algorithm (SMS-EMOA) and a proposed generalized vector-evaluated genetic algorithm are then presented. It is seen that these EMO algorithms cannot solve these imbalanced problems, but they are able to solve the problems when augmented by multiobjective to multiobjective (M2M), an approach that decomposes the population into several interacting subpopulations. These results and the successful application of the EMO methods with the M2M approach even on standard so-called balanced problems indicate the usefulness of using the M2M approach.

Brain storm optimization (BSO) is a new kind of swarm intelligence algorithm inspired by human creative problem solving process. Human being is the most intelligent organism in the world and the brainstorming process popularly used by them has been demonstrated to be a significant and promising way to create great ideas for problem solving. BSO transplants the brainstorming process in human being into optimization algorithm design and gains successes. BSO generally uses the grouping, replacing, and creating operators to produce ideas as many as possible to approach the problem global optimum generation by generation. In this paper, we propose two novel designs to enhance the conventional BSO performance. The first design of the modified BSO (MBSO) is that it uses a simple grouping method (SGM) in the grouping operator instead of the clustering method to reduce the algorithm computational burden. The second design is that MBSO uses a novel idea difference strategy (IDS) in the creating operator instead of the Gaussian random strategy. The IDS not only contains open minded element to avoid the ideas being trapped by local optima, but also can match the search environment to create better new ideas for problem solving. Experiments have been conducted to illustrate the effectiveness and efficiency of the MBSO algorithm. Moreover, the contributions of SGM and IDS are investigated to show how and why MBSO can perform better than BSO.

A novel multiobjective DE algorithm using the subregion and external set strategy (MOEA/S-DE) is proposed in this paper, in which the objective space is divided into some subregions and then independently optimize each subregion. An external set is introduced for each subregion to save some individuals ever found in this subregion. An alternative of mutation operators based the idea of direct simplex method of mathematical programming are proposed: local and global mutation operator. The local mutation operator is applied to improve the local search performance of the algorithm and the global mutation operator to explore a wider area. Additionally, a reusing strategy of difference vector also is proposed. It reuses the difference vector of the better individuals according to a given probability. Compared with traditional DE, the crossover operator also is improved. In order to demonstrate the performance of the proposed algorithm, it is compared with the MOEA/D-DE and the hybrid-NSGA-II-DE. The result indicates that the proposed algorithm is efficient.

In this paper, we presents an augmented K-means clustering-weighted hyper line clustering(K-WHLC) approach to solve the problem of mixing matrix estimation for Bernoulli-Gaussian sources. This algorithm employs the K-means clustering method as the stage of initialization and then uses a recursive weighted approach to robustly localize the direction of hyper lines with the PCA technology. Furthermore, a valid probabilistic criteria is proposed to detect true vectors of basis matrix from the hyper lines set. The advantage of weighting strategy lies in that it can suppress the effect of outliers and strengthen the precision of algorithm. A series of numerical simulations demonstrate its high performance on the task of mixing matrix estimation under the medium and large-scale cases.