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This paper proposed a novel design for a high-speed adaptive filter, specifically a new filter structure employing a convex combination architecture incorporating systolic architecture. The performance of convex architecture and systolic architecture are analyzed for LMS and RLS filters in two different orders. Systolic architecture improves the speed of convergence and SNR of the filters. Additionally convex combination architecture significantly improves the overall filter performance, especially in steady-state conditions. The combination of these two filter design techniques results in significantly improved convergence speed and steady-state performance of filters. The performance of the proposed filters combined two architectures outperforms the conventional filters in several metrics for filter design, including convergence speed, SNR, Steady-state MSE, learning capability, and filtering capability in terms of residual noise. Simulation results demonstrate the effectiveness of proposed filters in denoising ECG signals from PLI noise. The suggested filter configurations undergo evaluation for the purpose of ECG noise elimination for a set of ten ECG signals, acquired from the MIT-BIH database. [ABSTRACT FROM AUTHOR]

This study aims to evaluate whether there is a correlation and unidirectional or bidirectional causality between terrorist incidents and foreign direct investment in Türkiye. Toda Yamamoto elucidates that there is no long-term relationship. RLS, on the other hand, reveals that there is a short-term relationship, and that produces a more fascinating result. While foreign direct investment does not increase terrorist incidents, terrorist incidents possess a positive effect on foreign direct investment. In this case, terrorist actions increase foreign direct investment to Türkiye in the short term. It also appears that terrorist incidents concentrated in big cities do not possess a deterrent effect on foreign direct investments concentrated in big cities. [ABSTRACT FROM AUTHOR]

Ramularia leaf spot of barley, caused by Ramularia collo-cygni has become a serious disease threat to barley production. By inducing necrotic lesions, the disease expiates the onset of leaf senescence, which can result in significant grain yield and quality losses. In addition to surviving between seasons in trash, stubble and volunteers, R. collo-cygni can be transmitted from seed to emerging seedlings. As a potential additional means of transmission it may also pose a risk for the spread of specific traits such as fungicide resistance. In the present study, we screened Irish barley seed stocks harvested in the years 2015–2017 for the presence of R. collo-cygni using qPCR, and subsequently using an amplicon sequencing assay screened for the presence of alleles in the SDHC gene of R. collo-cygni associated with SDHI resistance. Over the three year period > 60% of the samples screened had detectable levels of R. collo-cygni present. In a subsample of these five alleles associated with resistance (SDHC—N87S, G91R, H145R, R152M and H153R) were detected. The prevalence of the key alleles C-N87S and C-H146R remained stable over the three year period, and was similar to that detected amongst an isolate collection established in 2022. [ABSTRACT FROM AUTHOR]

To solve the problem that the poor accuracy of long-term prediction relying on model accuracy for the filter-based prediction method, a fusion fault prediction algorithm based on filter and long short-term memory (LSTM) network is proposed to achieve the prediction of spacecraft flywheel slowly growing faults. Firstly, a mini-batch normalization LSTM network and a trend recognition module are designed, which are connected in series to form a neural network predictor to improve the time series prediction accuracy. Then, the Kalman filter update process is improved by the recursive least square (RLS) parameter estimation principle to enhance the robustness for time series prediction error. On this basis, the predicted values output by the neural network predictor are fused with the improved filter. Prediction residual can be obtained for iterative updating and prediction, overcoming the dependence of the filter algorithm on the model and improving prediction accuracy. Finally, the time series predictive performance of three neural network predictors are analyzed by simulation experiments. And considering the degradation fault of flywheel bearing performance, the proposed fusion prediction algorithm is used to determine that the flywheel degrades to a threshold at 856 s, with a prediction time error of 36 s. The simulation results verify the effectiveness of the proposed algorithm for slowly growing fault.

The integration of renewable energy sources results in an increase in balancing corporate and industry load, which presents a number of power quality issues in the current environment. Highly sensitive, linear, non-linear, or imbalanced loads among grid-connected commercial utilities are among the demands operated by hospitals. The priority and diversity of these loads also result in significant issues with the quality of electricity within a distribution system for the neighborhood. Its broad divergence causes the injection of harmonics or reactive power imbalance. To address reactive electrical inequalities, neutral current reimbursement, load balancing, and harmonic abatement, the use of Distribution Static Comp (DSTATCOM) is suggested. The current study intends to improve the quality of electricity in the City Distributed Grid by generating pulses of switching during IGBT switching at the DSTATCOM using an Irregular Type-2 Fuzzy Logical Controllers (IT2FLC) equipped with a Recurrent Least Square () filter. In regard to harmonic reduction, the suggested approach performs better than the Type 1 fuzzy algorithm controller and the Conventional PI controller. The suggested system is simulated using MATLAB software for efficient implementation. However, this proposed method has not looked at the first order, second order, or fifth order harmonics. Consequently, even if the system's efficiency will rise, we can say that the fuzzy in SV PWM technique is what's causing the system to perform better. [ABSTRACT FROM AUTHOR]

Restless legs syndrome (RLS) has been associated with anxiety, depression, insomnia, lifestyle factors and infections. We aimed to study the prevalence of symptoms of RLS during the COVID-19 pandemic versus pre-pandemic. We hypothesized that pre-existing RLS symptoms worsened and pandemic-related factors may have triggered new symptoms of RLS. Adults (≥18 years) from fifteen countries across four continents participated in an online survey between May and August 2020. The harmonized questionnaire included a validated single question on RLS with response alternatives from 1 to 5 on a scale from never to every/almost every evening or night. Other measures were the Insomnia Severity Index (ISI), measures of symptoms of anxiety (GAD-2) and depression (PHQ-2), and questions on different pandemic-related factors. Altogether, 17 846 subjects (63.8 % women) were included in the final analyses. The mean age was 41.4 years (SD 16.1). During the pandemic, symptoms of RLS (≥3 evenings/nights per week) were more common 9.1 % (95 % CI 8.7–10.1) compared to 5.4 % (95 % CI 4.9–6.0) before the pandemic (P < 0.0001). Alltogether 1.3 % (95 % CI 1.1–1.6) respondents had new-onset symptoms (≥3 evenings/nights per week). Moderate-severe insomnia was strongly associated with RLS symptoms. The occurrences of new-onset RLS symptoms were 5.6 % (95 % CI 0.9–13.0) for participants reporting COVID-19 and 1.1 % (95 % CI 0.7–1.5) for non-COVID-19 participants. In the fully adjusted logistic regression model, the occurrence of new-onset RLS symptoms was associated with younger age, social restrictions and insomnia severity. In a similar analysis, RLS symptoms (≥3 evenings/nights per week) were associated with lower education, financial hardship, sleep apnea symptoms, use of hypnotics, insomnia severity, symptoms of depression and possible post-traumatic stress disorder. Our findings indicate that RLS symptoms were more common during the pandemic than before. Usually, the prevalence of RLS increases with age. However, during the pandemic, new-onset symptoms of RLS were more common in younger age groups. This may be due to the pandemic-related factors being more pronounced in the younger compared to the older. The association between insomnia, psychiatric symptoms and RLS warrants clinical attention. • Restless legs symptoms were more prevalent during the COVID-19 pandemic than before. • Insomnia and psychiatric symptoms were associated with RLS symptoms. • New-onset RLS symptoms were more common among younger subjects. [ABSTRACT FROM AUTHOR]

Background and purpose: Sleep disorders are increasingly implicated as risk factors for stroke, as well as a determinant of stroke outcome. They can also occur secondary to the stroke itself. In this review, we describe the variety of different sleep disorders associated with stroke and analyze their effect on stroke risk and outcome. Methods: A search term-based literature review ("sleep," "insomnia," "narcolepsy," "restless legs syndrome," "periodic limb movements during sleep," "excessive daytime sleepiness" AND "stroke" OR "cerebrovascular" in PubMed; "stroke" and "sleep" in ClinicalTrials.gov) was performed. English articles from 1990 to March 2023 were considered. Results: Increasing evidence suggests that sleep disorders are risk factors for stroke. In addition, sleep disturbance has been reported in half of all stroke sufferers; specifically, an increase is not only sleep-related breathing disorders but also periodic limb movements during sleep, narcolepsy, rapid eye movement (REM) sleep behavior disorder, insomnia, sleep duration, and circadian rhythm sleep–wake disorders. Poststroke sleep disturbance has been associated with worse outcome. Conclusion: Sleep disorders are risk factors for stroke and associated with worse stroke outcome. They are also a common consequence of stroke. Recent guidelines suggest screening for sleep disorders after stroke. It is possible that treatment of sleep disorders could both reduce stroke risk and improve stroke outcome, although further data from clinical trials are required. [ABSTRACT FROM AUTHOR]

Deadbeat predictive current control (DPCC) is an effective model-based motor control method. However, due to the unbalanced inductance and parameter variations of the segmented powered linear motor stator, the conventional model of linear motors is not accurate, which ultimately affect the performance of the control. This paper proposes a novel DPCC based on the recursive least squares (RLS) method to identify the parameters of the dual three-phase segmented powered linear motor (SP-LM) model. First, the influence of unbalanced inductance caused by the segmented motor stator and parameter variations of the conventional DPCC are analyzed. Second, a discrete RLS model of the dual three-phase SP-LM is established, which is a common model for both linear induction motors (LIMs) and linear synchronous motors (LSMs). Finally, the model parameters are identified by the RLS method and the deadbeat principle is used to predict the current. The proposed method effectively eliminates the influence of unbalanced inductance and the parameter variation, improves the current control performance and reduces the thrust fluctuation. Experiments based on hardware-in-the-loop verify the proposed method. [ABSTRACT FROM AUTHOR]

Patients having COPD share some factors, e.g., chronic hypoxemia, anemia of chronic disease and nicotine use, which are also the risk factors for RLS hence predispose them to experience RLS in higher then general population. There are limited studies with methodological constraints evaluating the prevalence and/or correlates of RLS among patients with COPD. Consecutive adult patients of either gender, having stable COPD as per GOLD guidelines 2021, were assessed for RLS using IRLSSG (2014) criteria (excluding RLS mimics) and the severity of RLS was determined in participants having RLS. Phenomenology of RLS, past medical history and laboratory parameters were gathered. Insomnia and depression were assessed using the insomnia severity index and PHQ-9, respectively. Participants' (N = 210) mean age was 63.02 ± 8.19 years, and 83.8% of subjects were men. 12.9% of participants were found to have RLS. Among those having RLS, nearly half (51.9%) had moderate symptoms, and 18.5% experienced severe symptoms. RLS was more prevalent among younger, females, those having severe COPD, participants having exacerbation of COPD in the previous year, lower post-bronchodilator FEV1, higher dyspnea and COPD assessment test score. Multivariate analysis showed that younger age, female gender, lower post-bronchodilator FEV1, lower FEV1/FVC ratio and higher serum creatinine increased the odds of having RLS. Depressive symptoms were more frequent in participants having RLS. The present study found that the prevalence of RLS among patients with stable COPD was higher than the general population. Female gender, younger age, higher airflow limitation and higher serum creatinine (though in the physiological range) increase the odds of having RLS. Stable patients with COPD having these characteristics must be screened for RLS. • 210 stable COPD patients were evaluated using IRLSSG (2014) criteria and the IRLS severity rating scale. • 12.9% of patients were found to have RLS, which is six times higher than the general population. • 51.9% of patients had moderate RLS, while 18.5% experienced severe symptoms. • Younger age, female gender, higher airflow obstruction and higher serum creatinine increased the odds of having RLS. [ABSTRACT FROM AUTHOR]

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Background: Epidemiological evidence indicates a high correlation and comorbidity between Attention Deficit Hyperactivity Disorder (ADHD) and Restless Legs Syndrome (RLS). Objective: We aimed to investigate the causal relationship and shared genetic architecture between ADHD and RLS, as well as explore potential causal associations between both disorders and peripheral iron status. Methods: We performed two-sampleMendelian randomization (MR) analyses using summary statistics from genome-widemeta-analyses of ADHD, RLS, and peripheral iron status (serumiron, ferritin, transferrin saturation, and total iron binding capacity). Additionally, we employed linkage disequilibrium score regression (LDSC) to assess genetic correlations between ADHD and RLS using genetic data. Results: Our MR results supports a causal effect from ADHD (as exposure) to RLS (as outcome) (inverse variance weighted OR = 1.20, 95% CI: 1.08-1.34, p = 0.001). Conversely, we found no a causal association from RLS to ADHD (inverse variance weighted OR = 1.04, 95% CI: 0.99-1.09, p = 0.11). LDSC analysis did not detect a significant genetic correlation between RLS and ADHD (Rg = 0.3, SE = 0.16, p = 0.068). Furthermore, no evidence supported a causal relationship between peripheral iron deficiency and the RLS or ADHD onset. However, RLS may have been associated with a genetic predisposition to reduced serum ferritin levels (OR = 1.20, 95% CI: 1.00-1.04, p = 0.047). Conclusion: This study suggests that ADHD is an independent risk factor for RLS, while RLS may confer a genetic predisposition to reduced serum ferritin levels. Limitations: The GWAS summary data utilized originated from populations of European ancestry, limiting the generalizability of conclusions to other populations. Clinical implications: The potential co-occurrence of RLS in individuals with ADHD should be considered during diagnosis and treatment. Moreover, iron supplementation may be beneficial for alleviating RLS symptoms. [ABSTRACT FROM AUTHOR]

Restless legs syndrome (RLS), also called Willis–Ekbom disease (WED), affects approximately 3% of the global population, with higher incidence in Caucasians and women. This review explores the latest advancements in the diagnosis, pathophysiology, and management of RLS, and it highlights the recent changes in diagnostic criteria that improve specificity. The diagnostic spectrum now includes atypical presentations involving the upper limbs and other body parts. The new diagnostic specifiers categorize RLS more effectively into chronic-persistent or intermittent types, and a clinical significance specifier has been introduced to better address the disorder's effects on daily life. RLS management has shifted toward a balanced approach with non-pharmacological strategies and lifestyle changes, supported by updated pharmacological treatment protocols including iron therapy designed to mitigate risks such as augmentation. Notably, gabapentinoids have emerged as a superior treatment option over dopamine agonists owing to their lower risk of augmentation and superior safety profile. Innovative treatments, such as the FDA-approved tonic motor activation device and new pharmacological agents, serve as alternatives for treating medication-refractory RLS, thereby expanding the therapeutic landscape. Additionally, this review addresses the specific considerations for managing RLS in special groups, including pregnant women, individuals with chronic kidney disease, and patients with comorbid conditions such as Parkinson's disease and multiple sclerosis. We emphasizes the dynamic nature of RLS research and highlight the critical need for ongoing studies to further elucidate the complex mechanisms underlying RLS, as well as to refine the treatment modalities to enhance patient outcomes. [ABSTRACT FROM AUTHOR]

Background: The coronavirus disease 2019 (COVID-19) is critically affecting not only the physical health but also mental health globally. This study aimed to define the frequency of sleep disorders in COVID-19 positive patients in Aswan University Hospitals, Aswan Governorate, Egypt to clarify the different clinical types of them and to identify factors associated with sleep changes during the COVID-19. Methods: Our study recruited 280 positive COVID-19 patients. Their sleep disorders were diagnosed by using Global Sleep Assessment questionnaire (GSAQ). Their socioeconomic status was determined by using Revised Kuppuswamy Socioeconomic Status Scale (RK SSS). The severity of anxiety symptoms and insomnia were determined by using Hamilton Anxiety Rating Scale (HARS) and Insomnia Severity Index (ISI) respectively. Diagnosis of Restless Leg Syndrome (RLS) was completed according to Restless Leg Syndrome Diagnostic Index (RLS-DI). Results: Sleep disorders were documented in 35.7% of our patients. They were categorized into insomnia (24.6%), RLS (15%) and poor sleep quality (17.4%). Younger age and female sex were the predominate age and sex. Most of them had severe anxiety. Upper class of socioeconomic status was the commonest SES. Most of patients had CORADS I and II respectively. Conclusion: The present study recognized that female gender, younger population, COVID- 19 related stressors were the major factors associated with sleep disorder. Further investigation is needed to understand the changes in sleep pattern among the patients of the COVID-19 pandemic.

【Objective】Optical fiber dispersion, nonlinear impairments, and bandwidth limitation in high-speed passive optical access networks result in significant power budget loss with traditional intensity modulation and direct detection schemes, failing to meet the requirements of high-speed passive optical access networks.【Methods】In order to enhance the rate and performance of the intensity modulation and direct detection optical access system, this study explores the channel equalization method of VDFE-RLS based on Recursive Least Squares (RLS) algorithm, building upon the Volterra Decision Feedback Equalizer (VDFE), This equalizer uses RLS algorithm to update its tap coefficients. It includes a three-order Volterra series, allowing it to compensate for both linear and nonlinear impairments. VDFE-RLS is applied to the downlink optical access system with a single wavelength of 200 Gbit/s based on O-band intensity modulation and direct detection scheme after 20 km transmission.【Results】The experimental results show that the RLS outperforms the conventional Least Mean Square (LMS) algorithm in the equalization process. Moreover, VDFE-RLS achieves a power budget greater than 29 dB. When the length of the equalizer is the same, VDFE-RLS can increase the power budget by 2.2 dB compared with the conventional Volterra Feed Forward Equalization (VFFE)-RLS equalizer. When the length of VDFE-RLS equalizer is half that of VFFE-RLS, the VDFE-RLS can increase the power budget by 0.5 dB compared with the VFFE-RLS equalizer.【Conclusion】Compared with other traditional methods, the proposed method can shorten the length of the equalizer and increase the power budget of the system, ultimately restoring signals with better performance.

Major depressive disorder (MDD) and Restless legs syndrome (RLS) present complex clinical challenges, often coexisting and complicating treatment strategies. While the relationship between MDD and RLS remains somewhat elusive, emerging evidence suggests a potential interplay between antidepressant medications and the worsening of RLS symptoms. This case report illuminates an instance where mirtazapine, a tetracyclic antidepressant commonly used in MDD, precipitated a resurgence of RLS symptoms in a patient with a previously controlled presentation. [ABSTRACT FROM AUTHOR]

In the present study, to address the issue of flow rate instability in the flow boiling experimental system, a flow rate adaptive control system is developed using a single-neuron PID adaptive algorithm, enhanced with the whale optimization algorithm (WOA) for parameter tuning. A recursive least-squares online identification method is integrated to adapt to varying operating conditions. The simulation results demonstrate that in step response the WOA-improved single-neuron PID significantly mitigates the overshoot, with a mere 0.31% overshoot observed, marking a reduction of 98.27% compared to the traditional PID control. The output curve of the WOA-improved single-neuron PID closely aligns with the sinusoidal signal, exhibiting an average absolute error of 0.120, which is lower than that of the traditional PID (0.209) and fuzzy PID (0.296). The WOA-improved single-neuron PID (1.01 s) exhibited a faster return to a stable state compared to the traditional PID (2.46 s) and fuzzy PID (1.28 s). Finally, the effectiveness of the algorithm is validated through practical application. The results demonstrate that, compared to traditional PID and single-neuron PID algorithms, the WOA-improved single-neuron PID algorithm achieves an average flow stability of 9.9848 with a standard error of 0.0914394. It exhibits superior performance, including faster rise and settling times, and higher stability.

In this paper, an improved high-speed adaptive filter is proposed and implemented using a field-programmable gate array platform. Specifically, a new filter structure combining systolic and convex architectures has been analyzed and compared with conventional filter architectures. The new filter structure efficiently removes power line interference noise from electrocardiogram signals at high convergence speeds. The systolic architecture is used to improve the convergence speed of the filter, and the convex architecture is used in combination to improve the signal-to-noise ratio of the filter. One fast filter was designed using the retimed delay recursive least square algorithm (recursive least square filter design with systolic architecture), and it was combined with one slow filter designed using the least mean square algorithm based on convex combination architecture. The proposed filter architectures are assessed for electrocardiogram noise cancellation, obtained from the MIT-BIH database, and the performance is compared with various filter structures in terms of the signal-to-noise ratio, convergence speed, learning behaviors, and complexity. The results show an improvement in signal-to-noise ratio of 24.2% and an increase in convergence speed of 50% when compared with conventional filter structures. [ABSTRACT FROM AUTHOR]

Background: The coronavirus disease 2019 (COVID-19) is critically affecting not only the physical health but also mental health globally. This study aimed to define the frequency of sleep disorders in COVID-19 positive patients in Aswan University Hospitals, Aswan Governorate, Egypt to clarify the different clinical types of them and to identify factors associated with sleep changes during the COVID-19. Methods: Our study recruited 280 positive COVID-19 patients. Their sleep disorders were diagnosed by using Global Sleep Assessment questionnaire (GSAQ). Their socioeconomic status was determined by using Revised Kuppuswamy Socioeconomic Status Scale (RK SSS). The severity of anxiety symptoms and insomnia were determined by using Hamilton Anxiety Rating Scale (HARS) and Insomnia Severity Index (ISI) respectively. Diagnosis of Restless Leg Syndrome (RLS) was completed according to Restless Leg Syndrome Diagnostic Index (RLS-DI). Results: Sleep disorders were documented in 35.7% of our patients. They were categorized into insomnia (24.6%), RLS (15%) and poor sleep quality (17.4%). Younger age and female sex were the predominate age and sex. Most of them had severe anxiety. Upper class of socioeconomic status was the commonest SES. Most of patients had CORADS I and II respectively. Conclusion: The present study recognized that female gender, younger population, COVID-19 related stressors were the major factors associated with sleep disorder. Further investigation is needed to understand the changes in sleep pattern among the patients of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

In this paper, Recursive Least Square (RLS) and Affine Projection (AP) adaptive filters are designed using Xilinx System Generator and implemented on the Spartan6 xc6slx16- 2csg324 FPGA platform. FPGA platform utilizes the non-restoring division algorithm and the COordinate Rotation DIgital Computer (CORDIC) division algorithm to perform the division task of the RLS and AP adaptive filters. The Non-restoring division algorithm demonstrates efficient performance in terms of convergence speed and signal-to-noise ratio. In contrast, the CORDIC division algorithm requires 31 cycles for division initialization, whereas the non-restoring algorithm initializes division in just one cycle. To validate the effectiveness of the proposed filters, a set of ten ECG records from the BIT-MIT database is used to test their ability to remove Power Line Interference (PLI) noise from the ECG signal. The proposed adaptive filters are compared with various adaptive algorithms in terms of Signal-to-Noise Ratio (SNR), convergence speed, residual noise, steady-state Mean Square Error (MSE), and complexity.

Autonomous remote sensing systems establish communication links between nodes. Ensuring coverage and seamless communication in highly dense environments is not a trivial task as localization, separation, and tracking of targets, as well as interference suppression, are challenging. Therefore, smart antenna systems fulfill these requirements by employing beamforming algorithms and are considered a key technology for autonomous remote sensing applications. Among many beamforming algorithms, the recursive least square (RLS) algorithm has proven superior convergence and convergence rate performances. However, the tracking performance of RLS degrades in the case of dynamic targets. The forgetting factor in RLS needs to be updated constantly for fast target tracking. Additionally, multiple beamforming algorithms can be combined to increase tracking performance. An improved hybrid constant modulus RLS beamforming algorithm with an adaptive forgetting factor and a variable regularization factor is proposed. The forgetting factor is updated using the low-complexity yet robust adaptive moment estimation method (ADAM). The sliding-window technique is applied to the proposed algorithm to mitigate the steady-state noise. The proposed algorithm is compared with existing RLS-based algorithms in terms of convergence, convergence rate, and computational complexity. Based on the results, the proposed algorithm has at least 10 times better convergence (accuracy) and a convergence rate two times faster than the compared RLS-based algorithms. [ABSTRACT FROM AUTHOR]

Sleep disorders are highly prevalent during pregnancy and significantly affect women's health and quality of life. Gestational diabetes mellitus (GDM) is one of the most common metabolic complications during pregnancy and constitutes a significant risk factor for both mother and fetus in the short and the long term. While the association between sleep disorders and type 2 diabetes mellitus (T2DM) is indisputable, it is not clear whether there is a link between sleep disorders and GDM. The aim of this article was to investigate the association between sleep disorders and GDM and whether the treatment of sleep disorders may prevent GDM development. Insomnia, obstructive sleep apnea (OSA), restless legs syndrome (RLS), and narcolepsy were the most common sleep disorders identified during pregnancy and were related to poor sleep quality and short or prolonged sleep duration. They were all associated with an increased risk of GDM. The ideal sleep duration for pregnant women was determined at 8–9 h daily. In conclusion, sleep disorders constitute a risk factor for GDM. It is imperative that prospective studies be conducted to evaluate the effect of the early management of sleep disorders on GDM manifestation and control. Healthcare providers should highlight the importance of sufficient sleep to reinforce pregnancy outcomes. [ABSTRACT FROM AUTHOR]

This study explores the application of LMS and RLS algorithms in adaptive beamforming for a linear array of half wavelength dipole (HWD) antennas with known mutual coupling. The research initiates by deriving analytical expressions for the covariance matrix of signal impinging on an HWD antenna array, accounting for mutual coupling effects. Leveraging these expressions, we calculate the weights for both LMS and RLS algorithms, enabling the evaluation of the overall far-field radiation patterns of the HWD antenna array. The results demonstrate that these algorithms effectively steer the main lobe of the radiation pattern towards the desired user while creating nulls in the direction of undesired users. Notably, simulations indicate that the proposed compensation method enhances the performance of beamforming algorithms, particularly when considering mutual coupling effects. Specifically, the LMS algorithm outperforms the RLS algorithm in reducing interference, resulting in lower side lobe levels (SLL) in the radiation pattern. [ABSTRACT FROM AUTHOR]

This paper proposes an improved Recursive Least Square (RLS) algorithm to address the limitations of the conventional RLS algorithm, which suffers from poor performance when dealing with non-stationary noise. The proposed algorithm, along with various algorithms, have been designed using Xilinx System Generator (XSG). These algorithms have been tested to denoise ECG signals that have been corrupted with four types of real noise obtained from the MIT-BIH dataset. The improved algorithm utilizes a systolic architecture that enables faster processing and better noise reduction capabilities. The experimental results demonstrate that the proposed algorithm with and without systolic architecture outperforms the conventional RLS algorithm and other state-of-the-art algorithms in terms of signal-to-noise ratio (SNR), mean squared error (MSE), and convergence speed.

IntroductionRestless legs syndrome (RLS) is a debilitating condition characterized by uncomfortable sensations in the legs, typically occurring during periods of rest or sleep. It is more prevalent during pregnancy and is linked to sleep disturbances, diminished quality of life, and pregnancy complications. However, previous studies yielded inconsistent findings among pregnant women in middle-income countries. Consequently, this systematic review and meta-analysis sought to determine the pooled prevalence of restless legs syndrome and its associated factors in these populations.MethodA systematic review and meta-analysis was conducted on published studies from middle-income countries until May 2023. The review strictly adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Relevant search terms were used to search for studies in PubMed, MEDLINE, EMBASE, and Google Scholar. Data extraction was performed using the Joanna Briggs Institute tool for prevalence studies. The meta-analysis was conducted using STATA 17 software, and heterogeneity was assessed using the I2 test, while publication bias was evaluated using Egger's test. Forest plots were also used to present the pooled prevalence and odds ratio (OR) with a 95% confidence interval (CI) using the random-effects model.ResultThis review included 22 studies from nine countries with a total of 17, 580 study participants. The overall pooled prevalence of RLS among pregnant women in middle-income countries was 13.82% (95% CI: 13.31, 14.32), and having low hemoglobin level (AOR: 1.68, 95% CI: 1.29, 2.18), history of RLS (AOR: 7.54, 95% CI: 3.02, 18.79), muscle cramps (AOR: 3.58, 95% CI: 1.21, 10.61), excessive day time sleepiness (AOR: 4.02, 95% CI: 1.34, 12.04), preeclampsia (AOR: 2.06, 95% CI: 1.28, 3.30), and taking prophylactic iron supplementation (AOR: 0.59, 95% CI: 0.50, 0.69) were the identified factors associated with it.ConclusionGenerally, nearly one in every eight pregnant women in middle-income countries develop restless legs syndrome during pregnancy. Having low hemoglobin level, a history of RLS, muscle cramps, excessive daytime sleepiness, preeclampsia, and taking prophylactic iron supplementation were the identified factors associated with it. These findings underscore the importance of addressing the identified factors associated with RLS in order to effectively mitigate its occurrence among pregnant women.

The aim of this study is to examine patients with restless legs syndrome (RLS) in terms of difficulty in emotion regulation and insomnia. A total of 52 patients with RLS and 57 healthy volunteers were enrolled. Difficulties in Emotion Regulation Scale Short Form (DERS-16), Insomnia Severity Index (ISI), Hospital Anxiety and Depression Scale (HADS), and International Restless Legs Syndrome rating scale were applied to participants. The mean age was 34.00 ± 8.27 years in patients and 31.70 ± 9.12 years in control. The scores of DERS-16, ISI, HADS-A, and HADS-D were significantly higher in the patients than the controls (p = 0.000). The DERS-16 total score showed a significant correlation with age (r = 0.404, p = 0.003), ISI (r = 0.281, p = 0.043), IRLS score (r = 0.422, p = 0.002), HADS-A (r = 0.409, p = 0.003), and HADS-D (r = 0.416, p = 0.002). The factors (age, gender, ferritin, ISI, and IRLS scores) that may be associated with the DERS-16 total scores were assessed with stepwise regression analysis. It was seen that the IRLSs variable had the most ability and could predict 45% (β = 0.625, p < 0.001, ∆R2 = 0.450) of difficulty in emotion regulation. In addition, the variable of insomnia could predict difficulty in emotion regulation by 17% (β = 0.097, p = 0.001, ∆R2 = 0.170). Patients with RLS may experience more emotion regulation difficulties than healthy controls. RLS severity and insomnia may be factors affecting emotion regulation difficulties in patients with RLS. According to our knowledge and our literature review, this finding seems to be the first report in the literature. [ABSTRACT FROM AUTHOR]

Background Restless legs syndrome (RLS) is a prevalent sensorimotor disorder that can dramatically impair sleep quality, daytime function, and quality of life. Although many patients benefit from standard pharmacological therapy, some patients suffer from insufficient treatment response or medication intolerance. Novel treatment approaches are therefore necessary. Objective Given the overlap between RLS and pain syndromes in both pathophysiological mechanisms and certain treatment options, we aimed to perform a scoping review of the available evidence on spinal cord stimulation (SCS) for RLS and discuss potential mechanistic implications. Methods We identified a total of 16 cases of patients with RLS who underwent SCS, all from case reports or case series. Discussion The published evidence is insufficient to assess SCS efficacy in patients with RLS, but SCS remains a promising investigational therapy in RLS on the basis of its potential mitigatory effects in the central hyperexcitability of the sensorimotor cortex through neuromodulation of spinal, subcortical, and cortical areas. A call for further research in this field is presented, with suggestions for future directions and trial designs. [ABSTRACT FROM AUTHOR]

Purpose: To examine the association between long‐term use of dopamine agonists (DAs) and the risk of lung cancer in patients with restless legs syndrome (RLS). Methods: We conducted a retrospective cohort study using Optum Clinformatics® database. We included adults ≥40 years diagnosed with RLS during the study period (1/2006–12/2016). Follow‐up started with the first RLS diagnosis and ended on the earliest of: incident diagnosis of lung cancer, end of enrollment in the database or end of the study period. The exposure of interest was cumulative duration of DAs use, measured in a time‐varying manner. We constructed a multivariable Cox regression model to estimate HRs and 95% CIs for the association between lung cancer and cumulative durations of DA use, adjusting for potential confounding variables. Results: We identified 295 042 patients with a diagnosis of RLS. The mean age of the cohort was 62.9; 66.6% were women and 82.3% were white. The prevalence of any DA exposure was 40.3%. Compared to the reference group (no use and ≤1 year), the crude HRs for lung cancer were 1.16 (95% CI 0.99–1.36) and 1.14 (95% CI 0.86–1.51) for 1–3 years and >3 years of cumulative DA use, respectively. The adjusted HR for lung cancer was 1.05 (95% CI 0.88–1.25) for 1–3 years and 1.02 (95% CI 0.76–1.37) for >3 years of cumulative DA use, respectively. Conclusions: At typical doses for the clinical management of RLS, long‐term DA use was not associated with risk of lung cancer. [ABSTRACT FROM AUTHOR]

The wind turbine (WT) dynamics are affected by wind variations and system parameter changes as well. To deal with the pitch control problem a Self-Tuning Controller (STC) with Proportional-Derivative (PD) structure has been proposed for the benchmark model (BM). This BM also models some sensors, actuators and system faults. The STC design herein is pivoted on a second-order auto-regressive exogenous (ARX) model. At first, three identification methods for the ARX model are presented: Recursive Least Square method (RLS), RLS with exponential forgetting (RLS-EF) and RLS with adaptive directional forgetting (RLS-ADF). In comparison, the performance of RLS-ADF is better than the rest two. The STC PD controller supersedes contemporary adaptive control approaches to fix up the generated power to its rating for above-rated wind. Secondly, on analysis, the proposed STC PD controller is fault-tolerant for nine types of faults. In the third stage, in extensive Monte Carlo Simulation (MCS), the sensitivity test to system parameter variation is carried out including the sensor faults. The result in the presence of a fault, as well as parameter variation, shows minimal deviation from normal conditions indicating the suitability of the STC PD controller to keep the system intact except in case of multiple faults. [ABSTRACT FROM AUTHOR]

Every year the number of IoT devices is growing, which gives a boost to development of such technology as Power Line Communication (PLC). The term PLC refers to the use of a power supply circuit to transmit data. Full-duplex technology can be applied to increase the data transfer rate in a PLC. The difficulty in using a full duplex is the need to suppress the transmitter's own signal (interference signal). Analog cancellation and digital cancellation are used for this purpose. Common algorithms for digital cancellation are adaptive algorithms. In this paper adaptive algorithms such as Particle Swarm Optimization (PSO) and Recursive Least Squares (RLS) are considered and a modification of the classical RLS is proposed. The proposed algorithm uses precalculated weight coefficients together with periodic reinitialization of the filter. This avoids the influence of significant changes in the input signal on the further process of filter adaptation and provides the highest level of echo suppression. The proposed algorithm provides an improvement of parameters such as Error Vector Magnitude (EVM) and Signal-to-Interference Ratio (SIR) in comparison with classical RLS and PSO, according to the results of the study. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of beam tracking of a multipath channel in millimeter-wave massive MIMO communication system using adaptive filters. We focus on the performance of least-mean-square filter (LMS) and recursive least-squares filter (RLS) algorithms, compared to a reference extended Kalman filter (EKF), in scenarios where the wireless channel is dominated by a single line of sight (LOS) path or a small number of strong paths. The signal direction and channel coefficients are tracked and updated using these filters. Our results recommend that beamforming systems at millimeter-wave bands should consider variable number of paths rather than a single dominant LOS path. Furthermore, we show that the mean squared-error (MSE) of the innovation process gives a better overall view of the tracking performance than the MSE of the state parameters.

Restless Legs Syndrome (RLS) is a common sensorimotor and a sleep disorder that affects 2.5–10% of the European and North American populations. RLS is also often associated with periodic leg movements during sleep (PLMS). Despite ample evidence of genetic contributions, the underlying mechanisms that elicit the sensory and motor symptoms remain unidentified. Clinically, RLS has been correlated with an altered central iron metabolism, particularly in the brain. While several animal models have been developed to determine the outcome of an altered iron homeostasis on brain function, the potential role of an altered iron homeostasis on sleep and sensorimotor circuits has not yet been investigated. Here, we utilize a mouse model to assess the effects of an iron-deficient (ID) but non-anemic state on sleep time and episodes, and sensorimotor reflexes in male and female mice. We found that animals on the ID diet displayed an increased expression of the transferrin receptor in the spinal cord, confirming the results of previous studies that focused only on the impact of ID in the brain. We also demonstrate that the ID diet reduced hematocrit levels compared to controls but not into the anemic range, and that animals on the ID diet exhibited RLS-like symptoms with regard to sleep onset and spinal cord reflex excitability. Interestingly, the effects on the spinal cord were stronger in females than in males, and the ID diet-induced behaviors were rescued by the return of the animals to the control diet. Taken together, these results demonstrate that diet-induced ID changes to CNS function are both inducible and reversible, and that they mimic the sleep and sensorimotor RLS symptoms experienced in the clinic. We therefore propose replacing the commonly used phrase “brain iron deficiency” (BID) hypothesis in the RLS research field with the term “iron deficiency in the central nervous system” (ID-CNS), to include possible effects of altered iron levels on spinal cord function.