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The expansion of offshore wind farms into deeper waters presents economic challenges due to the rising costs of operation and maintenance. To address this issue, the development of targeted opportunistic maintenance strategies for offshore wind farms is crucial. Existing strategies often overlook the interconnections between maintenance activities across wind farms, focusing solely on individual wind farms. However, joint maintenance of wind farms has been acknowledged as an effective approach to reducing maintenance costs at the operational level. This paper introduces an opportunistic maintenance strategy that considers the joint maintenance of two wind farms and establishes reliability thresholds to guide maintenance decisions. Furthermore, the developed maintenance model is improved by incorporating more realistic vessel models and considering various types of maintenance costs. Conclusions can be drawn from a comprehensive analysis of optimization results across multiple scenarios involving maintenance costs, wind farm parameters, and uncertainties. The comparative analysis demonstrates that, in the farthest offshore distance case, the proposed strategy exhibits a reduction in maintenance costs of 15.67% and 4.87% when compared to two traditional strategies, respectively. This effect is further amplified in scenarios where offshore wind farms are located in close proximity to each other and encounter substantial uncertainties. • Developing a novel joint maintenance strategy for two offshore wind farms. • New decision-making structure is established using three decision thresholds. • The strategy is evaluated considering cost, wind farm parameters, and uncertainty. • The proposed strategy saves 15.67% and 4.87% in LCC versus traditional strategies. [ABSTRACT FROM AUTHOR]

The DECT standard cipher (DSC) is a 64-bit stream cipher, which was used in the digital enhanced cordless telecommunications (DECT) standard to protect the privacy of users. The Coisel–Sanchez (CS) attack proposed by Coisel and Sanchez in CHES 2015 is the most effective cryptanalysis against the DSC cipher up to now. Based on the CS attack, we present an improved attack in which an information collection method with unknown memory is used to improve the utilization of information and the message classification technique is adopted to reduce the computation time of evaluation indexes. Meanwhile, we present an improved pre-sieving technique to avoid the computation of evaluation indexes of a large number of incorrect candidates. Our known plaintext attack requires only 2 min of communication compared to 3 min for the CS attack. By analyzing 213 keystreams, our approach is able to increase the success probability of retrieving secret key from 55–89%. As far as we know, the attack we present in this paper is the most effective one among the existing attacks to the DSC cipher. [ABSTRACT FROM AUTHOR]

Background Brachial plexus injury (BPI) is a serious peripheral nerve injury, and clinical outcomes are generally unsatisfactory. It has been reported that cortical plasticity could influence the restoration of motor function. However, the neurologic mechanism of BPI is unclear, which provides a basis for further investigation. The supplementary motor area (SMA) plays an important role in the regulation of motor function. This study aims to explore SMA–whole brain functional connectivity after deafferentation of the brachial plexus. Methods Study subjects included 16 patients with BPI and 8 healthy volunteers. The seed region was defined by a block-design functional magnetic resonance imaging program that used unilateral imaginary hand grasp motion as a task stimulus. Next, the voxel–wise functional connectivity between the predefined region and the other regions of the brain was calculated. Results We discovered decreased voxel–wise functional connectivity between the SMA and multiple brain regions, including precuneus, posterior cingulum cortex, and anterior cingulum cortex, that are closely associated with information integration or motor processing in patients with BPI. Conclusions Patients with BPI showed weakened functional connectivity between hand grasp–related areas and the SMA and multiple regions associated with motor processing or information integration. A clear image of the functional status of the brain after deafferentation was provided. On the basis of this discovery, a relationship between changes in neuroimaging measurements and clinical outcomes can be determined in future studies. [ABSTRACT FROM AUTHOR]

Brachial plexus injury (BPI) causes functional changes in the brain, but the structural changes resulting from BPI remain unknown. In this study, we compared grey matter volume between nine BPI patients and ten healthy controls by means of voxel-based morphometry. This was the first study of cortical morphology in BPI. We found that brain regions including the cerebellum, anterior cingulate cortex, bilateral inferior, medial, superior frontal lobe, and bilateral insula had less grey matter in BPI patients. Most of the affected brain regions of BPI patients are closely related to motor function. We speculate that the loss of grey matter in multiple regions might be the neural basis of the difficulties in the motor rehabilitation of BPI patients. The mapping result might provide new target regions for interventions of motor rehabilitation. [ABSTRACT FROM AUTHOR]

Background: The functional connectivity (FC) has emerged as a valuable tool for comprehending the cerebral operational mechanism. Understanding the FC changes in patients with chronic ankle instability (CAI) helps reveal the underlying central nervous system mechanisms of the disease and provides clues for developing personalized treatment plans. Objectives: To explore differences between low‐ and high‐order FC in patients with CAI and healthy controls, as well as the correlation among the feature connections and clinical data. Methods: In our study, we recruited 40 patients with CAI and 42 healthy individuals who had not experienced ankle injuries. All participants underwent clinical assessments of ankle joints, collected the number of ankle sprains within the past 6 months, and performed resting‐state functional magnetic resonance imaging (rs‐fMRI) scans. Pearson correlation and matrix variate normal distribution (MVND) were used to construct low‐order and high‐order FC networks, respectively. Feature selections between groups were performed by two‐sample t‐tests, and a multi‐kernel support vector machine (MK‐SVM) was subsequently applied to combine the multiple connection patterns for the classification. Using leave‐one‐out cross‐validation (LOOCV) to assess classification performance and identify the consensus connections contributing most to classification. Results: FC was reduced in certain brain regions of CAI patients. More consensus connections were recognized in low‐order FC network than in high‐order FC network. The highest classification accuracy of 91.30% was achieved by combining three connection patterns. The most discriminating functional connections were primarily centered on the default mode network and spanned the visual network, sensorimotor network, ventral attention network, and central executive network. In addition, FC strength in the left cingulate and paracingulate gyrus (DCG.L) and right superior temporal gyrus (STG.R) was negatively correlated with the number of ankle sprains in the past 6 months in all FC networks (p < 0.05). Conclusions: Abnormalities in connectivity in patients with CAI were observed in both low‐ and high‐order FC networks. The adaptive changes in the brain related to CAI may extend beyond the sensorimotor networks, primarily involving higher‐level default mode networks associated with attention. Moreover, the FC strength between DCG.L and STG.R may predict the risk of ankle re‐sprains and help clinicians develop personalized treatment plans. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the stochastic resonance (SR) signal processing method enhances the characteristic signal while enhancing the interference signal, which leads to a lack of desired results, this paper proposed a hybrid resonant sparse (RS) decomposition and tri-stable SR (RSSR) signal method. Firstly, the feasibility and superiority of the RSSR method have been demonstrated by analyzing and comparing the performance of the SR method, the SR method, and the RSSR method in handling single-frequency and multi-frequency signals. Subsequently, the impact of the high-quality factor and the scale transformation parameter on the output performance of the RSSR method was investigated. Additionally, an analysis was conducted on the influence of selecting high-energy sub-bands on the output results in the process of directly applying the RS method to the original signal and in the process of applying the RS method to the output signal of the RSSR method. Finally, a rotor shaft bending fault signal was analyzed using the SR method, the SR method, and the RSSR method, further confirming the superiority of the RSSR method. Experimental data processing results demonstrate that the RSSR method can effectively reduce interference components in the signal, enhance characteristic signals, and improve the signal-to-noise ratio by 59.7% compared to the original signal, by 79.05% compared to the RS method, and by 9.38% compared to the SR method. The results of this study can provide reference and guidance for the extraction of weak characteristic signals. [ABSTRACT FROM AUTHOR]

• Two single-parameter-adjusting tri-stable stochastic resonance models are proposed. • The spectral amplification is derived as a quantitative analysis indicator. • The influences of key parameters of the two proposed models are investigated. • The proposed models are employed to extract a weak faulty signal successfully. Weak signal detection methods based on stochastic resonance (SR) have been extensively studied due to their capability to utilize noise energy for enhancing weak signals. Among various SR models, the second-order tri-stable SR models have demonstrated their superiority in weak-signal detection with better output performance compared to other SR models. To optimize the output performance of the second-order tri-stable systems, a variety of parameter optimization methods have been proposed to optimize the parameters of the system. However, these optimization methods often require to optimize multiple parameters, which leads to an increase in computational costs and reduces the real-time processing efficiency of signal processing. Such multi-parameter optimization methods cannot meet the demands for timely signal processing in the context of big data. To address this challenge, this paper proposes two single-parameter-adjusting SR models. The proposed models can attain an ideal output performance by adjusting a single parameter in the SR system. The spectral amplification as an indicator is derived to quantitatively analyze the effects of the proposed models on SR output. On this basis, the influences of the proposed models on the SR output under different potential well parameters, noise intensities, signal frequency, and damping ratio are fully investigated through numerical simulations. At last, the proposed models are employed to process an experimental signal with a weak fault feature, and the experimental results verify the feasibility of the proposed models in optimizing the SR output. The research results can guide the design of tri-stable SR models and support the application of the SR-based signal processing model in the context of big data. [ABSTRACT FROM AUTHOR]

Objective Angiomatous meningioma (AM) is a rare subtype of meningioma characterized by highly vascular tumor tissue comprising predominantly variable sized hyalinized blood vessels. The aim of this study is to evaluate the clinical radiologic features of AM and the long-term prognosis in a single neurosurgical center. Methods A total of 93 patients who underwent surgical resection of AMs between 2003 and 2008 were enrolled for analysis. Clinical information, treatment, and radiologic images were collected and analyzed; follow-up was carried out as well. Expression of estrogen receptor, progesterone receptor, and vascular endothelial growth factor was analyzed by immunohistochemistry. Results Forty-eight females and 45 males were identified. Forty-four patients (47.31%) manifested as hypersignal in T1-weighted imaging sequences and 68 (73.12%) as hypersignal in T2-weighted imaging, and a characteristic ringlike signal was observed in 28 patients (30.11%). Eighty-one cases (87.10%) showed different degrees of peritumor brain edema and it was significantly correlated with the vascular endothelial growth factor expression ( P < 0.001). Simpson I resection was achieved in 63 patients (67.74%), grade II in 27 patients (29.03%), and grade III in 3 patients (3.23%). The extent of resection was not associated with the postoperative neurologic function ( P = 0.546). Only 4 patients experienced recurrences during the follow-up and these 4 patients were stable until the last follow-up. Conclusions AMs were a special subtype of meningioma with distinctive radiologic features. AMs manifest benign behavior with a satisfying outcome, which makes Simpson grade II resection an option. [ABSTRACT FROM AUTHOR]

Purpose: MR neurography(MRN) is an advanced imaging technique to visualize peripheral nerves. Our aim was to determine the value of morphological features of lumbosacral nerve roots on MRN in diagnosing chronic inflammatory demyelinating polyradiculoneuropathy(CIDP) and analyze their correlations with electrophysiological parameters.Methods: MRN of lumbosacral plexus was performed in 21 CIDP patients and 21 healthy volunteers. The cross-sectional areas(CSAs) and signal intensities(SI) of L3 to S1 nerve roots were measured and compared between two groups. Receiver operating characteristic(ROC) curves were plotted to assess the diagnostic accuracy. All patients also underwent nerve conduction studies. Correlations between CSAs and SI of lumbosacral nerve roots and electrophysiological parameters were analyzed.Results: Compared with control group, CIDP patients showed significantly increased CSAs and SI from L3 to S1 nerve root (P < 0.001 and P < 0.05 respectively for all nerve roots). The CSAmean and SImean were 28.04 ± 8.55mm2, 1.314 ± 0.199 for patient group and 14.91 ± 2.36mm2,1.155 ± 0.094 for control group. ROC analysis revealed the best diagnostic accuracy for the CSAmean with an area under the curve of 0.968 and optimal cut-off value of 19.20 mm2. CSAs of L5 or S1 nerve root correlated positively with central latency and negatively with conduction velocity of tibial nerve. SI of L5 also had a positive correlation with latency of sural nerve.Conclusions: Evaluation of lumbosacral nerve roots on MRN in a quantitative manner may serve as an important tool to support the diagnosis of CIDP. [ABSTRACT FROM AUTHOR]