Performance analysis of a two-level polling control system based on LSTM and attention mechanism for wireless sensor networks.

A continuous-time exhaustive-limited (K = 2) two-level polling control system is proposed to address the needs of increasing network scale, service volume and network performance prediction in the Internet of Things (IoT) and the Long Short-Term Memory (LSTM) network and an attention mechanism is used for its predictive analysis. First, the central site uses the exhaustive service policy and the common site uses the Limited K = 2 service policy to establish a continuous-time exhaustive-limited (K = 2) two-level polling control system. Second, the exact expressions for the average queue length, average delay and cycle period are derived using probability generating functions and Markov chains and the MATLAB simulation experiment. Finally, the LSTM neural network and an attention mechanism model is constructed for prediction. The experimental results show that the theoretical and simulated values basically match, verifying the rationality of the theoretical analysis. Not only does it differentiate priorities to ensure that the central site receives a quality service and to ensure fairness to the common site, but it also improves performance by 7.3 and 12.2%, respectively, compared with the one-level exhaustive service and the one-level limited K = 2 service; compared with the two-level gated- exhaustive service model, the central site length and delay of this model are smaller than the length and delay of the gated- exhaustive service, indicating a higher priority for this model. Compared with the exhaustive-limited K = 1 two-level model, it increases the number of information packets sent at once and has better latency performance, providing a stable and reliable guarantee for wireless network services with high latency requirements. Following on from this, a fast evaluation method is proposed: Neural network prediction, which can accurately predict system performance as the system size increases and simplify calculations.

A chaos-based adaptive equilibrium optimizer algorithm for solving global optimization problems.

The equilibrium optimizer (EO) algorithm is a newly developed physics-based optimization algorithm, which inspired by a mixed dynamic mass balance equation on a controlled fixed volume. The EO algorithm has a number of strengths, such as simple structure, easy implementation, few parameters and its effectiveness has been demonstrated on numerical optimization problems. However, the canonical EO still presents some drawbacks, such as poor balance between exploration and exploitation operation, tendency to get stuck in local optima and low convergence accuracy. To tackle these limitations, this paper proposes a new EO-based approach with an adaptive gbest-guided search mechanism and a chaos mechanism (called a chaos-based adaptive equilibrium optimizer algorithm (ACEO)). Firstly, an adaptive gbest-guided mechanism is injected to enrich the population diversity and expand the search range. Next, the chaos mechanism is incorporated to enable the algorithm to escape from the local optima. The effectiveness of the developed ACEO is demonstrated on 23 classical benchmark functions, and compared with the canonical EO, EO variants and other frontier metaheuristic approaches. The experimental results reveal that the developed ACEO method remarkably outperforms the canonical EO and other competitors. In addition, ACEO is implemented to solve a mobile robot path planning (MRPP) task, and compared with other typical metaheuristic techniques. The comparison indicates that ACEO beats its competitors, and the ACEO algorithm can provide high-quality feasible solutions for MRPP.

Adaptive Guided Equilibrium Optimizer with Spiral Search Mechanism to Solve Global Optimization Problems.

The equilibrium optimizer (EO) is a recently developed physics-based optimization technique for complex optimization problems. Although the algorithm shows excellent exploitation capability, it still has some drawbacks, such as the tendency to fall into local optima and poor population diversity. To address these shortcomings, an enhanced EO algorithm is proposed in this paper. First, a spiral search mechanism is introduced to guide the particles to more promising search regions. Then, a new inertia weight factor is employed to mitigate the oscillation phenomena of particles. To evaluate the effectiveness of the proposed algorithm, it has been tested on the CEC2017 test suite and the mobile robot path planning (MRPP) problem and compared with some advanced metaheuristic techniques. The experimental results demonstrate that our improved EO algorithm outperforms the comparison methods in solving both numerical optimization problems and practical problems. Overall, the developed EO variant has good robustness and stability and can be considered as a promising optimization tool.

Orthogonal pinhole-imaging-based learning salp swarm algorithm with self-adaptive structure for global optimization.

Salp swarm algorithm (SSA) is a simple and effective bio-inspired algorithm that is gaining popularity in global optimization problems. In this paper, first, based on the pinhole imaging phenomenon and opposition-based learning mechanism, a new strategy called pinhole-imaging-based learning (PIBL) is proposed. Then, the PIBL strategy is combined with orthogonal experimental design (OED) to propose an OPIBL mechanism that helps the algorithm to jump out of the local optimum. Second, a novel effective adaptive conversion parameter method is designed to enhance the balance between exploration and exploitation ability. To validate the performance of OPLSSA, comparative experiments are conducted based on 23 widely used benchmark functions and 30 IEEE CEC2017 benchmark problems. Compared with some well-established algorithms, OPLSSA performs better in most of the benchmark problems.

Velocity clamping-assisted adaptive salp swarm algorithm: balance analysis and case studies.

Salp swarm algorithm (SSA) is a recently proposed, powerful swarm-intelligence based optimizer, which is inspired by the unique foraging style of salps in oceans. However, the original SSA suffers from some limitations including immature balance between exploitation and exploration operators, slow convergence and local optimal stagnation. To alleviate these deficiencies, a modified SSA (called VC-SSA) with velocity clamping strategy, reduction factor tactic, and adaptive weight mechanism is developed. Firstly, a novel velocity clamping mechanism is designed to boost the exploitation ability and the solution accuracy. Next, a reduction factor is arranged to bolster the exploration capability and accelerate the convergence speed. Finally, a novel position update equation is designed by injecting an inertia weight to catch a better balance between local and global search. 23 classical benchmark test problems, 30 complex optimization tasks from CEC 2017, and five engineering design problems are employed to authenticate the effectiveness of the developed VC-SSA. The experimental results of VC-SSA are compared with a series of cutting-edge metaheuristics. The comparisons reveal that VC-SSA provides better performance against the canonical SSA, SSA variants, and other well-established metaheuristic paradigms. In addition, VC-SSA is utilized to handle a mobile robot path planning task. The results show that VC-SSA can provide the best results compared to the competitors and it can serve as an auxiliary tool for mobile robot path planning.

Rank-driven salp swarm algorithm with orthogonal opposition-based learning for global optimization.

Salp swarm algorithm (SSA) is a relatively new and straightforward swarm-based meta-heuristic optimization algorithm, which is inspired by the flocking behavior of salps when foraging and navigating in oceans. Although SSA is very competitive, it suffers from some limitations including unbalanced exploration and exploitation operation, slow convergence. Therefore, this study presents an improved version of SSA, called OOSSA, to enhance the comprehensive performance of the basic method. In preference, a new opposition-based learning strategy based on optical lens imaging principle is proposed, and combined with the orthogonal experimental design, an orthogonal lens opposition-based learning technique is designed to help the population jump out of a local optimum. Next, the scheme of adaptively adjusting the number of leaders is embraced to boost the global exploration capability and improve the convergence speed. Also, a dynamic learning strategy is applied to the canonical methodology to improve the exploitation capability. To confirm the efficacy of the proposed OOSSA, this paper uses 26 standard mathematical optimization functions with various features to test the method. Alongside, the performance of the proposed methodology is validated by Wilcoxon signed-rank and Friedman statistical tests. Additionally, three well-known engineering optimization problems and unknown parameters extraction issue of photovoltaic model are applied to check the ability of the OOSA algorithm to obtain solutions to intractable real-world problems. The experimental results reveal that the developed OOSSA is significantly superior to the standard SSA, currently popular SSA-based algorithms, and other state-of-the-artmeta-heuristic algorithms for solving numerical optimization, real-world engineering optimization, and photovoltaic model parameter extraction problems. Finally, an OOSSA-based path planning approach is developed for creating the shortest obstacle-free route for autonomous mobile robots. Our introduced method is compared with several successful swarm-based metaheuristic techniques in five maps, and the comparative results indicate that the suggested approach can generate the shortest collision-free trajectory as compared to other peers.

microRNA-130a Promotes Human Keratinocyte Viability and Migration and Inhibits Apoptosis Through Direct Regulation of STK40-Mediated NF-κB Pathway and Indirect Regulation of SOX9-Meditated JNK/MAPK Pathway: A Potential Role in Psoriasis.

Psoriasis is a chronic inflammatory skin disorder. The aim of this study was to determine a potential role of microRNA (miR)-130a in psoriasis, and underlying mechanism. Expression levels of miR-130a in psoriasis specimens and normal skin tissues were analyzed. MiR-130a mimic, inhibitor, miR-control, small interfering RNA (siRNA) specific serine/threonine kinase 40 (STK40), or sex-determining region Y chromosome-box 9 (SOX9) were transfected to human keratinocyte HaCaT cells, respectively. After transfection, the cell viability, apoptosis, and migration were determined. Luciferase reporter assay, quantitative reverse transcription-polymerase chain reaction, and western blot were performed to explore whether STK40 was a target of miR-130a. The effects of aberrant expressions of miR-130a, STK40, or SOX9 on key proteins of NF-κB and c-Jun N-terminal kinase (JNK)/mitogen-activated protein kinase (MAPK) pathway were assessed. The miR-130a levels were significantly higher in patients with psoriasis compared to the healthy controls (p < 0.01). Overexpressing miR-130a strikingly promoted HaCaT cell viability and migration and inhibited apoptosis (p < 0.01 or p < 0.05). We confirmed that STK40 was a direct target of miR-130a, and STK40 was involved in miR-130a-induced cell functions. Overexpressing miR-130a significantly upregulated NF-κB p65, SOX9, p-c-Jun, p-JNK, and p-p38MAPK proteins and silencing miR-130a downregulated them. In addition, silencing STK40 alleviated the effects of anti-miR-130a on SOX9 expression. Furthermore, silencing SOX9 also decreased levels of p-c-Jun, p-JNK, and p-p38MAPK proteins. MiR-130a regulates human keratinocyte HaCaT viability, migration and apoptosis might be by direct regulation of STK40-mediated NF-κB pathway and by indirect regulation of SOX9-mediated downstream JNK/MAPK signaling pathway.

Haptoglobin protein and mRNA expression in psoriasis and its clinical significance.

The present study aimed to explore the association between haptoglobin protein and mRNA expression and psoriasis. A total of 138 patients with psoriasis that were undergoing therapy at Linyi People's Hospital (Linyi, China) between January 2011 and January 2015 were enrolled in the present study. The mRNA expression levels of haptoglobin were detected by in situ hybridization; immunohistochemistry was used to detect haptoglobin protein expression; and double­labeling immunofluorescence was used to count Langerhans cells; western blotting was also conducted to determine protein expression. A receiver operating characteristic (ROC) curve was generated to assess the diagnostic value of haptoglobin for psoriasis. Compared with the normal and negative control (NC) groups, the mRNA expression levels of haptoglobin were markedly increased in the experimental group (P<0.05). Haptoglobin protein expression was also markedly increased in the experimental group compared with in the normal and NC groups (P<0.05). Conversely, there was no significant difference in haptoglobin expression between the NC group and the normal group (P>0.05). The critical value of haptoglobin mRNA in the diagnosis of psoriasis was 2.93, and sensitivity and specificity were 91.3 and 73.6%, respectively. The area under the ROC curve was 0.883 [95% confidence interval (CI)=0.837­0.929]. The critical value of haptoglobin protein in the diagnosis of psoriasis was 0.995, and sensitivity and specificity were 76.1 and 99.9%, respectively. The area under the ROC curve was 0.926 (95% CI=0.837­0.929). The present study demonstrated that the mRNA and protein expression levels of haptoglobin were increased in patients with psoriasis. Haptoglobin mRNA and protein expression were closely associated with the occurrence of psoriasis; therefore, haptoglobin may be considered a promising novel clinical indicator for the diagnosis of psoriasis.