A Survey on Secure Wireless Body Area Networks

Combining tiny sensors and wireless communication technology, wireless body area network (WBAN) is one of the most promising fields. Wearable and implantable sensors are utilized for collecting the physiological data to achieve continuously monitoring of people’s physical conditions. However, due to the openness of wireless environment and the significance and privacy of people’s physiological data, WBAN is vulnerable to various attacks; thus, strict security mechanisms are required to enable a secure WBAN. In this article, we mainly focus on a survey on the security issues in WBAN, including securing internal communication in WBAN and securing communication between WBAN and external users. For each part, we discuss and identify the security goals to be achieved. Meanwhile, relevant security solutions in existing research on WBAN are presented and their applicability is analyzed

MARVEL: A Randomized Double‐Blind, Placebo‐Controlled Trial in Patients Undergoing Endovascular Therapy: Study Rationale and Design

BACKGROUND Steroids have pleiotropic neuroprotective actions including the regulation of inflammation and apoptosis which may influence the effects of ischemia on neurons, glial cells, and blood vessels. The effect of low‐dose methylprednisolone in patients with acute ischemic stroke in the endovascular therapy era remains unknown. This trial investigates the efficacy and safety of low‐dose methylprednisolone (2 mg/kg IV for 3 days) as adjunctive therapy for patients with acute ischemic stroke undergoing endovascular therapy within 24 hours from symptom onset. METHODS The MARVEL(Methylprednisolone as Adjunctive Therapy for Acute Large Vessel Occlusion: A Randomized Double‐Blind, Placebo‐Controlled Trial in Patients Undergoing Endovascular Therapy) trial is an investigator‐initiated, prospective, randomized, double‐blind, placebo‐controlled multicenter clinical trial. Up to 1672 eligible patients with anterior circulation large‐vessel occlusion stroke presenting within 24 hours from symptom onset are planned to be consecutively randomized to receive methylprednisolone or placebo in a 1:1 ratio across 82 stroke centers in China. RESULTS The primary outcome is the ordinal shift in the modified Rankin scale score at 90 days. Secondary outcomes include 90‐day functional independence (modified Rankin scale score, 0–2). The primary safety end points include mortality rate at 90 days and symptomatic intracerebral hemorrhage within 48 hours of endovascular therapy. CONCLUSION The MARVEL trial will provide evidence of the efficacy and safety of low‐dose methylprednisolone as adjunctive therapy for patients with anterior circulation large‐vessel occlusion stroke undergoing endovascular therapy

Methylprednisolone as Adjunct to Endovascular Thrombectomy for Large-Vessel Occlusion Stroke

Importance It is uncertain whether intravenous methylprednisolone improves outcomes for patients with acute ischemic stroke due to large-vessel occlusion (LVO) undergoing endovascular thrombectomy. Objective To assess the efficacy and adverse events of adjunctive intravenous low-dose methylprednisolone to endovascular thrombectomy for acute ischemic stroke secondary to LVO. Design, Setting, and Participants This investigator-initiated, randomized, double-blind, placebo-controlled trial was implemented at 82 hospitals in China, enrolling 1680 patients with stroke and proximal intracranial LVO presenting within 24 hours of time last known to be well. Recruitment took place between February 9, 2022, and June 30, 2023, with a final follow-up on September 30, 2023.InterventionsEligible patients were randomly assigned to intravenous methylprednisolone (n = 839) at 2 mg/kg/d or placebo (n = 841) for 3 days adjunctive to endovascular thrombectomy. Main Outcomes and Measures The primary efficacy outcome was disability level at 90 days as measured by the overall distribution of the modified Rankin Scale scores (range, 0 [no symptoms] to 6 [death]). The primary safety outcomes included mortality at 90 days and the incidence of symptomatic intracranial hemorrhage within 48 hours. Results Among 1680 patients randomized (median age, 69 years; 727 female [43.3%]), 1673 (99.6%) completed the trial. The median 90-day modified Rankin Scale score was 3 (IQR, 1-5) in the methylprednisolone group vs 3 (IQR, 1-6) in the placebo group (adjusted generalized odds ratio for a lower level of disability, 1.10 [95% CI, 0.96-1.25]; P = .17). In the methylprednisolone group, there was a lower mortality rate (23.2% vs 28.5%; adjusted risk ratio, 0.84 [95% CI, 0.71-0.98]; P = .03) and a lower rate of symptomatic intracranial hemorrhage (8.6% vs 11.7%; adjusted risk ratio, 0.74 [95% CI, 0.55-0.99]; P = .04) compared with placebo. Conclusions and Relevance Among patients with acute ischemic stroke due to LVO undergoing endovascular thrombectomy, adjunctive methylprednisolone added to endovascular thrombectomy did not significantly improve the degree of overall disability.Trial RegistrationChiCTR.org.cn Identifier: ChiCTR210005172

WSNs Microseismic Signal Subsection Compression Algorithm Based on Compressed Sensing

For wireless network microseismic monitoring and the problems of low compression ratio and high energy consumption of communication, this paper proposes a segmentation compression algorithm according to the characteristics of the microseismic signals and the compression perception theory (CS) used in the transmission process. The algorithm will be collected as a number of nonzero elements of data segmented basis, by reducing the number of combinations of nonzero elements within the segment to improve the accuracy of signal reconstruction, while taking advantage of the characteristics of compressive sensing theory to achieve a high compression ratio of the signal. Experimental results show that, in the quantum chaos immune clone refactoring (Q-CSDR) algorithm for reconstruction algorithm, under the condition of signal sparse degree higher than 40, to be more than 0.4 of the compression ratio to compress the signal, the mean square error is less than 0.01, prolonging the network life by 2 times

eFCM: An Enhanced Fuzzy C-Means Algorithm for Longitudinal Intervention Data

Clustering methods become increasingly important in analyzing heterogeneity of treatment effects, especially in longitudinal behavioral intervention studies. Methods such as K-means and Fuzzy C-means (FCM) have been widely endorsed to identify distinct groups of different types of data. Build upon our MIFuzzy [1], our goal is to concurrently handle multiple methodological issues in studying high dimensional longitudinal intervention data with missing values. Particularly, this paper focuses on the initialization issue of FCM and proposes a new initialization method to overcome the local optimal problem and decrease the convergence time in handling high-dimensional data with missing values for overlapping clusters. Based on the idea of K-means++ [9], we proposed an enhanced Fuzzy C-means clustering (eFCM) and incorporated it into our MIFuzzy. This method was evaluated using real longitudinal intervention data, classic and generic datasets. Compared to conventional FCM, our findings indicate eFCM can improve computational efficiency and avoid the local optimization

Limit cycle theory of temporal current self-oscillations in sequential tunneling of superlattices

A unified theory of the temporal current self-oscillations is presented. We establish these oscillations as the manifestations of limit cycles, around unstable steady-state solutions caused by the negative differential conductance. This theory implies that both the generation and the motion of an electric-field domain boundary are universal in the sense that they do not depend on the initial conditions. Under an extra weak ac bias with a frequency Wac, the frequency must be either Wac or an integer fractional of Wac if the tunneling current oscillates periodically in time, indicating the periodic doubling for this non-linear dynamical system

Intrusion Detection Based on Parallel Intelligent Optimization Feature Extraction and Distributed Fuzzy Clustering in WSNs

Aiming at large-scale, high dimensional data, and variable intrusion behavior in wireless sensor networks (WSNs), an intrusion detection algorithm based on parallel intelligent optimization feature extraction and distributed fuzzy clustering for WSNs is proposed. First, in order to effectively reduce the data dimensionality and improve the robustness of the feature extraction process, the parallel intelligent optimization feature extraction framework is constructed on the basis of defining the optimal feature evaluation index, for which the theoretical analysis shows that the index can eliminate feature redundancy and maintain the diversity of original data. Second, the spider cluster optimization algorithm evolution rule is redefined by introducing local search and adaptive multi strategy update method, and it proves that the improved social spider optimization (ISSO) algorithm has global convergence. The ISSO is used to solve the feature extraction framework, and through the parallel feature subset selection process, the best feature combination is extracted. Finally, WSNs intrusion detection is carried out by using the best feature subset and the distributed fuzzy clustering technology, and intelligent iterative evolution method and adaptive clustering strategy are introduced in order to improve the fuzzy clustering algorithm performance. Experimental results show that the intrusion detection algorithm can effectively give the results of intrusion detection, and moreover, compared with the other detection algorithms, the intrusion detection rate is improved by about 13.1%, and the false detection rate is decreased by about 8.5%