Distributed Sequential Detection for Cooperative Spectrum Sensing in Cognitive Internet of Things.

The rapid development of wireless communication technology has led to an increasing number of internet of thing (IoT) devices, and the demand for spectrum for these devices and their related applications is also increasing. However, spectrum scarcity has become an increasingly serious problem. Therefore, we introduce a collaborative spectrum sensing (CSS) framework in this paper to identify available spectrum resources so that IoT devices can access them and, meanwhile, avoid causing harmful interference to the normal communication of the primary user (PU). However, in the process of sensing the PUs signal in IoT devices, the issue of sensing time and decision cost (the cost of determining whether the signal state of the PU is correct or incorrect) arises. To this end, we propose a distributed cognitive IoT model, which includes two IoT devices independently using sequential decision rules to detect the PU. On this basis, we define the sensing time and cost functions for IoT devices and formulate an average cost optimization problem in CSS. To solve this problem, we further regard the optimal sensing time problem as a finite horizon problem and solve the threshold of the optimal decision rule by person-by-person optimization (PBPO) methodology and dynamic programming. At last, numerical simulation results demonstrate the correctness of our proposal in terms of the global false alarm and miss detection probability, and it always achieves minimal average cost under various costs of each observation taken and thresholds.

Atorvastatin Ameliorates Doxorubicin-Induced Cardiomyopathy by Regulating the Autophagy-Lysosome Pathway and Its Upstream Regulatory Factor Transcription Factor EB.

ABSTRACT: Doxorubicin is a widely used anticancer drug in clinical practice, and its myocardial toxicity is the main concern in oncotherapy. Statins are commonly used as hypolipidemic drugs. Recent studies have also focused on the effects of statins on autophagy. Autophagy is a process in which cells consume their own cytoplasm or organelles after stimulation and finally degrade the phagosome in the lysosome. Transcription factor EB (TFEB) is the main factor regulating lysosomal gene transcription and function. We found that atorvastatin (ATO) increased TFEB protein levels and the ratio of lysosomal-associated membrane protein 2/LC3B in the myocardial tissue of mice with doxorubicin-induced cardiomyopathy (DIC). Therefore, we speculated that ATO may improve cardiac function in mice with DIC by increasing the expression of TFEB to enhance lysosomal function and autophagy. This study explored the role of TFEB in DIC and the possible mechanism of ATO in improving DIC and used statins to prevent and treat DIC; various dilated cardiomyopathy and heart failure diseases provide more experimental evidence. All relevant data are within the article and its supporting information files.

Meta-analysis of risk factors associated with suicidal ideation after stroke.

BACKGROUND: Over the past decade, increasing attention has been paid on post stroke suicide (PSS), which is one of complications of stroke. The rates of stroke and suicide are relatively high, especially in Asian populations. Thus, a deeper understanding of the prevalence and epidemiological impact of suicide after stroke is urgently needed. Clinical diagnosis and prevention of PSS are at the incipient stage, but the risk factors responsible for the occurrence of PSS in different regions and stages of the disease remain largely unknown. The present meta-analysis aimed to determine the incidence of PSS at different stages and time courses, and to identify the underlying risk factors for PSS. METHODS: We systematically searched the Cochrane library, Embase, PubMed, CNKI and Web of Science databases from their inception until April 2019.The research articles reporting on the risk factor for PSS were screened and included in the meta-analysis. The data from the included studies were extracted according to the predefined criteria. RESULTS: A total of 12 studies (n = 2,693,036) were included for meta-analyses. Of these studies, 7 reporting suicide prevalence were meta-analyzed. The pooled estimate of suicidal ideation rates after stroke was 12%, which could be influenced by multiple risk factors, including sex, smoking, depression, sleep disorders, previous stroke and low household income. Studies conducted in Asia demonstrated higher suicide prevalence (approximately 15%) compared to other regions. Smoking, low family income, depression, heart disease and sleep disorders were important risk factors for PSS. When compared to PSS of more than 1 year, the incidence of suicide within 1 year after stroke was more likely to be statistically significant. It was found that 4 out of every 1000 stroke survivors tended to commit suicide. The results of this meta-analysis showed that depression (OR = 2.32; p < 0.01) was significantly associated with suicidal ideation, regardless of stroke duration. CONCLUSION: PSS is one of the common complications of stroke. Despite some limitations, we successfully identified the risk factors associated with suicidal ideation after stroke. Notably, depression was significantly associated with suicidal ideation, regardless of stroke duration. Targeting this risk factor may be helpful to improve stroke patient care and prevent suicidal ideation after stroke. Future research will be carried out to assess whether suicidal ideation or thoughts and actual suicide attempts are strongly predictive of suicide deaths after stroke (Registration No. CRD42019128813).

Existence and Uniqueness of the Local Smooth Solution to 3D Stochastic MHD Equations without Diffusion.

In this paper, we consider the existence of local smooth solution to stochastic magneto-hydrodynamic equations without diffusion forced by additive noise in R 3 . We first transform the system into a random system via a simple change of variable and borrow the result obtained for classical magneto-hydrodynamic equations, then we show that this random transformed system is measurable with respect to the stochastic element. Finally we extend the solution to the maximality solution. Due to the coupled construction of this system, we need more elaborate and complicated estimates with respect to stochastic Euler equation.

Developing a regional scale construction and demolition waste landfill landslide risk rapid assessment approach.

In recent years, construction and demolition waste (CDW) landfills landslide accidents have occurred globally, with consequences varying due to surrounding environmental factors. Risk monitoring is crucial to mitigate these risks effectively. Existing studies mainly focus on improving risk assessment accuracy for individual landfills, lacking the ability to rapidly assess multiple landfills at a regional scale. This study proposes an innovative approach utilizing deep learning models to quickly locate suspected landfills and develop risk assessment models based on surrounding environmental factors. Shenzhen, China, with significant CDW disposal pressure, is chosen as the empirical research area. Empirical findings from this study include: (1) the identification of 52 suspected CDW landfills predominantly located at the administrative boundaries within Shenzhen, specifically in the Longgang, Guangming, and Bao'an districts; (2) landfills at the lower risk of landslides are typically found near the northern borders adjacent to cities like Huizhou and Dongguan; (3) landfills situated at the internal administrative junctions generally exhibit higher landslide risks; (4) about 70 % of these landfills are high-risk, mostly located in densely populated areas with substantial rainfall and complex topographies. This study advances landfill landslide risk assessments by integrating computer vision and environmental analysis, providing a robust method for governments to rapidly evaluate risks at CDW landfills regionally. The adaptable models can be customized for various urban and broadened to general landfills by adjusting specific indicators, enhancing environmental safety protocols and risk management strategies effectively.

The risk of new vertebral fracture after percutaneous vertebral augmentation in patients suffering from single-level osteoporotic vertebral compression fractures: A meta-analysis and systematic review.

BACKGROUND: To investigate the effect of Vertebral augmentation (VA) in the treatment of single-level osteoporotic vertebral compression fractures (OVCFs) on new vertebral fractures. METHODS: Electronic databases Pubmed, Embase, and the Cochrane Central Register of Controlled Trials were searched from database creation to 5 September 2022. Eligible studies had to use VA as an intervention and conservative treatment as a control group. Studies had to explicitly report whether new vertebral fractures occurred during follow-up. Data were extracted by multiple investigators. Data were pooled using random or fixed effects models depending on the degree of heterogeneity. RESULTS: Of the 682 articles screened, 7 met the inclusion criteria and were included in the analysis, giving a total of 1240 patients. Meta-analysis showed that VA (OR = 2.10, 95% CI: 1.35-3.28, P = .001) increased the risk of new postoperative vertebral fractures compared with conservative treatment. Subgroup analyses showed that the risk was greater in the group with a follow-up time greater than 1 year (OR = 2.57, 95% CI: 1.06-6.26, P = .001). Compared with conservative treatment, VA (OR = 2.17, 95% CI: 1.23-3.82, P = .007) increased the risk of postoperative adjacent vertebral fracture. CONCLUSION SUBSECTIONS: VA is associated with an increased risk of new vertebral fractures and adjacent vertebral fractures following single-level OVCFs. With longer follow-ups, new vertebral fractures may be more significant. Clinical surgeons should pay attention to long-term postoperative complications and choose treatment carefully.

Optimized Motor Imagery Paradigm Based on Imagining Chinese Characters Writing Movement.

BACKGROUND: motor imagery (MI) is a mental representation of motor behavior. The MI-based brain computer interfaces (BCIs) can provide communication for the physically impaired. The performance of MI-based BCI mainly depends on the subject's ability to self-modulate electroencephalogram signals. Proper training can help naive subjects learn to modulate brain activity proficiently. However, training subjects typically involve abstract motor tasks and are time-consuming. METHODS: to improve the performance of naive subjects during motor imagery, a novel paradigm was presented that would guide naive subjects to modulate brain activity effectively. In this new paradigm, pictures of the left or right hand were used as cues for subjects to finish the motor imagery task. Fourteen healthy subjects (11 male, aged 22-25 years, and mean 23.6±1.16) participated in this study. The task was to imagine writing a Chinese character. Specifically, subjects could imagine hand movements corresponding to the sequence of writing strokes in the Chinese character. This paradigm was meant to find an effective and familiar action for most Chinese people, to provide them with a specific, extensively practiced task and help them modulate brain activity. RESULTS: results showed that the writing task paradigm yielded significantly better performance than the traditional arrow paradigm (p < 0.001). Questionnaire replies indicated that most subjects thought that the new paradigm was easier. CONCLUSION: the proposed new motor imagery paradigm could guide subjects to help them modulate brain activity effectively. Results showed that there were significant improvements using new paradigm, both in classification accuracy and usability.

A parameter estimation algorithm for LFM/BPSK hybrid modulated signal intercepted by Nyquist folding receiver.

Nyquist folding receiver (NYFR) is a novel ultra-wideband receiver architecture which can realize wideband receiving with a small amount of equipment. Linear frequency modulated/binary phase shift keying (LFM/BPSK) hybrid modulated signal is a novel kind of low probability interception signal with wide bandwidth. The NYFR is an effective architecture to intercept the LFM/BPSK signal and the LFM/BPSK signal intercepted by the NYFR will add the local oscillator modulation. A parameter estimation algorithm for the NYFR output signal is proposed. According to the NYFR prior information, the chirp singular value ratio spectrum is proposed to estimate the chirp rate. Then, based on the output self-characteristic, matching component function is designed to estimate Nyquist zone (NZ) index. Finally, matching code and subspace method are employed to estimate the phase change points and code length. Compared with the existing methods, the proposed algorithm has a better performance. It also has no need to construct a multi-channel structure, which means the computational complexity for the NZ index estimation is small. The simulation results demonstrate the efficacy of the proposed algorithm.