Research on Sea State Signal Recognition Based on Beluga Whale Optimization-Slope Entropy and One Dimensional-Convolutional Neural Network.

This study introduces a novel nonlinear dynamic analysis method, known as beluga whale optimization-slope entropy (BWO-SlEn), to address the challenge of recognizing sea state signals (SSSs) in complex marine environments. A method of underwater acoustic signal recognition based on BWO-SlEn and one-dimensional convolutional neural network (1D-CNN) is proposed. Firstly, particle swarm optimization-slope entropy (PSO-SlEn), BWO-SlEn, and Harris hawk optimization-slope entropy (HHO-SlEn) were used for feature extraction of noise signal and SSS. After 1D-CNN classification, BWO-SlEn were found to have the best recognition effect. Secondly, fuzzy entropy (FE), sample entropy (SE), permutation entropy (PE), and dispersion entropy (DE) were used to extract the signal features. After 1D-CNN classification, BWO-SlEn and 1D-CNN were found to have the highest recognition rate compared with them. Finally, compared with the other six recognition methods, the recognition rates of BWO-SlEn and 1D-CNN for the noise signal and SSS are at least 6% and 4.75% higher, respectively. Therefore, the BWO-SlEn and 1D-CNN recognition methods proposed in this paper are more effective in the application of SSS recognition.

Multi-Objective Optimization Method for Signalized Intersections in Intelligent Traffic Network.

Urban intersections are one of the most common sources of traffic congestion. Especially for multiple intersections, an appropriate control method should be able to regulate the traffic flow within the control area. The intersection signal-timing problem is crucial for ensuring efficient traffic operations, with the key issues being the determination of a traffic model and the design of an optimization algorithm. So, an optimization method for signalized intersections integrating a multi-objective model and an NSGAIII-DAE algorithm is established in this paper. Firstly, the multi-objective model is constructed including the usual signal control delay and traffic capacity indices. In addition, the conflict delay caused by right-turning vehicles crossing straight-going non-motor vehicles is considered and combined with the proposed algorithm, enabling the traffic model to better balance the traffic efficiency of intersections without adding infrastructure. Secondly, to address the challenges of diversity and convergence faced by the classic NSGA-III algorithm in solving traffic models with high-dimensional search spaces, a denoising autoencoder (DAE) is adopted to learn the compact representation of the original high-dimensional search space. Some genetic operations are performed in the compressed space and then mapped back to the original search space through the DAE. As a result, an appropriate balance between the local and global searching in an iteration can be achieved. To validate the proposed method, numerical experiments were conducted using actual traffic data from intersections in Jinzhou, China. The numerical results show that the signal control delay and conflict delay are significantly reduced compared with the existing algorithm, and the optimal reduction is 33.7% and 31.3%, respectively. The capacity value obtained by the proposed method in this paper is lower than that of the compared algorithm, but it is also 11.5% higher than that of the current scheme in this case. The comparisons and discussions demonstrate the effectiveness of the proposed method designed for improving the efficiency of signalized intersections.

Quantitative Study on the Impact of Energy Consumption Based Dynamic Selfishness in MANETs.

Cooperative communication and resource limitation are two main characteristics of mobile ad hoc networks (MANETs). On one hand, communication among the nodes in MANETs highly depends on the cooperation among nodes because of the limited transmission range of the nodes, and multi-hop communications are needed in most cases. On the other hand, every node in MANETs has stringent resource constraints on computations, communications, memory, and energy. These two characteristics lead to the existence of selfish nodes in MANETs, which affects the network performance in various aspects. In this paper, we quantitatively investigate the impacts of node selfishness caused by energy depletion in MANETs in terms of packet loss rate, round-trip delay, and throughput. We conducted extensive measurements on a proper simulation platform incorporating an OMNeT++ and INET Framework. Our experimental results quantitatively indicate the impact of node selfishness on the network performance in MANETs. The results also imply that it is important to evaluate the impact of node selfishness by jointly considering selfish nodes' mobility models, densities, proportions, and combinations.

Quantifying the spatial nonstationary response of environmental factors on purse seine tuna vessel fishing.

To investigate the spatial and temporal patterns of environmental factors influencing the activity of purse seine tuna fishing vessels, data on fishing efforts of purse seine tuna fleets and environmental factors in the Western and Central Pacific Ocean (WCPO) from 2015 to 2020 were utilized to develop a geographically weighted regression (GWR) model. The results showed that fishing activity was primarily concentrated in the area between 140°E and 175°W, and between 10°S and 5°N. The GWR model showed excellent fitting performance and was suitable for correlation analysis. The environmental factors had a significant spatially heterogeneous effect on the fishing activity of purse seine tuna fishing vessels. The sea surface temperature, primary productivity at 200 m depth, and dissolved oxygen below the surface had the greatest spatially heterogeneous effect and are important environmental variables influencing the activity of purse seine tuna vessels in the WCPO. This study provides new methods for exploring the spatial distribution of fishing vessel activity to support science-based conservation and management.

Long-term stable disease with mFOLFOX6 chemotherapy plus cetuximab for bone marrow metastasis from rectal cancer: A case report.

Bone marrow metastasis from rectal cancer is a rare but severe disease associated with a poor prognosis due to limited treatment options. There is no consensus on therapeutic strategies, and better-tolerated and more effective treatment options are urgently needed. We report a case that one patient with rectal cancer developed pancytopenia 15 months after completion of radical surgery and chemotherapy and was diagnosed with bone marrow metastasis. The patient was treated with mFOLFOX6 chemotherapy plus cetuximab, considering both his poor bone marrow function and a genetic test showing a wild-type of KRAS/NRAS/PIK3CA/BRAF. Twelve cycles were successfully completed with dose modifications and supportive measures. The patient's condition improved markedly based on a comprehensive assessment that included computed tomography images, blood cell counts, tumor markers, and clinical symptoms. The patient remains alive for 11 months at the last follow up. The patient treated with mFOLFOX6 chemotherapy plus cetuximab attained long-term stable disease, suggesting its promising efficacy and safety for bone marrow metastasis from rectal cancer and may hold promise as a treatment strategy for this specific patient population. Consideration can be given to the inclusion of mFOLFOX6 chemotherapy plus cetuximab in first-line treatment regimen for bone marrow metastasis from rectal cancer.

Gabapentin has Longer-Term Efficacy for the Treatment of Chronic Pelvic Pain in Women: A Systematic Review and Pilot Meta-analysis.

INTRODUCTION: Gabapentin has potential analgesic benefits in patients with neuropathic pain, such as post-herpetic neuralgia and diabetic peripheral neuropathy neuropathic pain. However, its efficacy in women with chronic pelvic pain (CPP) remains contradictory. In the present study, we performed a systematic review and meta-analysis of randomized controlled trials (RCTs) to ascertain the efficacy of this treatment. METHODS: We systematically reviewed RCTs published in PubMed, Embase, the Cochrane Library, Web of Science, and Google Scholar databases, up to July 2021. These articles compared gabapentin with placebo or any other active treatment for CPP in women, with 'the change in pain scores from the baseline during the first 3 and 6 months of treatment' taken as the primary outcome. We considered reductions equivalent to 1.0 cm for primary outcomes to be clinically important. RESULTS: Four studies, comprising 469 participants, were included in our meta-analysis. Results revealed that the gabapentin group had significantly higher change in pain intensity scores from baseline to 3 months [weighted mean difference (WMD) - 0.61 cm; 95% confidence interval (CI) - 0.97 to - 0.25; I2 = 0%; p = 0.0009] and 6 months (WMD - 1.38 cm; 95% CI - 1.89 to - 0.88; I2 = 0%; p < 0.00001), relative to the control group. The difference of 6-month pooled result was more clinically important. Results from analysis of secondary outcomes showed that gabapentin had no beneficial efficacy during the first 3 months of treatment. Although gabapentin treatment was associated with a higher risk of dizziness and somnolence, no statistically significant differences were observed with regards to the total incidence of adverse events. CONCLUSIONS: Overall, gabapentin could be a potential treatment option for CPP in women. However, as a pilot study, further studies are needed to explore the longer-term benefits and definite safety of this therapy in the future. REGISTRATION NUMBER: PROSPERO registration number CRD42021249421.