Complexity science for sleep stage classification from EEG

Automatic sleep stage classification is an important paradigm in computational intelligence and promises consider- able advantages to the health care. Most current automated methods require the multiple electroencephalogram (EEG) chan- nels and typically cannot distinguish the S1 sleep stage from EEG. The aim of this study is to revisit automatic sleep stage classification from EEGs using complexity science methods. The proposed method applies fuzzy entropy and permutation entropy as kernels of multi-scale entropy analysis. To account for sleep transition, the preceding and following 30 seconds of epoch data were used for analysis as well as the current epoch. Combining the entropy and spectral edge frequency features extracted from one EEG channel, a multi-class support vector machine (SVM) was able to classify 93.8% of 5 sleep stages for the SleepEDF database [expanded], with the sensitivity of S1 stage was 49.1%. Also, the Kappa’s coefficient yielded 0.90, which indicates almost perfect agreement

Liquid Metal Bandwidth-Reconfigurable Antenna

This letter shows how slugs of liquid metal can be used to connect/disconnect large areas of metalization and achieve a radiation performance not possible by using conventional switches. The proposed antenna can switch its operating bandwidth between ultrawideband and narrowband by connecting/disconnecting the ground plane for the feedline from that of the radiator. This could be achieved by using conventional semiconductor switches. However, such switches provide point-like contacts. Consequently, there are gaps in electrical contact between the switches. Surface currents, flowing around these gaps, lead to significant back radiation. In this letter, the slugs of a liquid metal are used to completely fill the gaps. This significantly reduces the back radiation, increases the bore-sight gain, and produces a pattern identical to that of a conventional microstrip patch antenna. Specifically, the realized gain and total efficiency are increased by 2 dBi and 24%, respectively. The antenna has potential applications in wireless systems employing cognitive radio (CR) and spectrum aggregation

Dietary Supplementation with Different ω-6 to ω-3 Fatty Acid Ratios Affects the Sustainability of Performance, Egg Quality, Fatty Acid Profile, Immunity and Egg Health Indices of Laying Hens

The supplementation of different ω-6/ω-3 ratio to the diets of the laying hens has been studied to evaluate the effects on performance, egg quality, egg health indices, egg fatty acid profiles, and immune response. One-hundred and twenty, 50-weeks-old hens were divided into three groups fed diets with different ω-6/ω-3 polyunsaturated fatty acids (PUFA) at ratio: 16.7:1, 9.3:1, and 5.5:1, respectively. Each group contained eight replicates of five hens. Hens fed the diet with the highest ω-6/ω-3 ratio had significantly increased weight gain compared to those fed the 5.5:1 and 9.3:1 ω-6/ω-3 ratios. In contrast, hens fed the 9.3:1 ω-6/ω-3 ratios laid significantly more eggs, had increased egg mass, greater livability, and a better FCR than the control group. However, hens fed a ratio of 5.5:1 ω-6/ω-3 PUFA showed improved thrombogenic, atherogenic, hypocholesteremia, and hypocholesteremia/hypercholesteremia indices. In conclusion, laying hens of the 9.3:1 ω-6/ω-3 PUFA group showed improved laying performance, while a ratio of 5.5:1 enriched the ω-3 PUFA in eggs and boosted the immune response of hens

An integration of enhanced social force and crowd control models for high-density crowd simulation

Social force model is one of the well-known approaches that can successfully simulate pedestrians’ movements realistically. However, it is not suitable to simulate high-density crowd movement realistically due to the model having only three basic crowd characteristics which are goal, attraction, and repulsion. Therefore, it does not satisfy the high-density crowd condition which is complex yet unique, due to its capacity, density, and various demographic backgrounds of the agents. Thus, this research proposes a model that improves the social force model by introducing four new characteristics which are gender, walking speed, intention outlook, and grouping to make simulations more realistic. Besides, the high-density crowd introduces irregular behaviours in the crowd flow, which is stopping motion within the crowd. To handle these scenarios, another model has been proposed that controls each agent with two different states: walking and stopping. Furthermore, the stopping behaviour was categorized into a slow stop and sudden stop. Both of these proposed models were integrated to form a high-density crowd simulation framework. The framework has been validated by using the comparison method and fundamental diagram method. Based on the simulation of 45,000 agents, it shows that the proposed framework has a more accurate average walking speed (0.36 m/s) compared to the conventional social force model (0.61 m/s). Both of these results are compared to the real-world data which is 0.3267 m/s. The findings of this research will contribute to the simulation activities of pedestrians in a highly dense population

The prevalence of diabetes and prediabetes in the adult population of Jeddah, Saudi Arabia- a community-based survey

BACKGROUND: Type 2 (T2DM) is believed to be common in Saudi Arabia, but data are limited. In this population survey, we determined the prevalence of T2DM and prediabetes. MATERIALS AND METHODS: A representative sample among residents aged ≥ 18 years of the city of Jeddah was obtained comprising both Saudi and non-Saudi families (N = 1420). Data on dietary, clinical and socio-demographic characteristics were collected and anthropometric measurements taken. Fasting plasma glucose and glycated hemoglobin (HbA1c) were used to diagnose diabetes and prediabetes employing American Diabetes Association criteria. Multiple logistic regression analysis was used to identify factors associated with T2DM. RESULTS: Age and sex standardized prevalence of prediabetes was 9.0% (95% CI 7.5-10.5); 9.4% (7.1-11.8) in men and 8.6% (6.6-10.6) in women. For DM it was 12.1% (10.7-13.5); 12.9% (10.7-13.5) in men and 11.4% (9.5-13.3) in women. The prevalence based on World Population as standard was 18.3% for DM and 11.9% for prediabetes. The prevalence of DM and prediabetes increased with age. Of people aged ≥50 years 46% of men and 44% of women had DM. Prediabetes and DM were associated with various measures of adiposity. DM was also associated with and family history of dyslipidemia in women, cardiovascular disease in men, and with hypertension, dyslipidemia and family history of diabetes in both sexes. DISCUSSION: Age was the strongest predictor of DM and prediabetes followed by obesity. Of people aged 50 years or over almost half had DM and another 10-15% had prediabetes leaving only a small proportion of people in this age group with normoglycemia. Since we did not use an oral glucose tolerance test the true prevalence of DM and prediabetes is thus likely to be even higher than reported here. These results demonstrate the urgent need to develop primary prevention strategies for type 2 diabetes in Saudi Arabia

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

Safe correction of severe hyponatremia in patient with severe renal failure using continuous venovenous hemofiltration with modified sodium content in the replacement fluid

Optimal treatment of severe hyponatremia in patients requiring dialysis is not known. Rapid correction with the use of different dialysis modalities can lead to osmotic demyelination syndrome. We described a safe correction of severe hyponatremia in a 42-year-old male patient requiring dialysis, who was treated with continuous venovenous hemofiltration using hypotonic replacement fluid which was prepared and adjusted on a daily basis

Hearables: automatic overnight sleep monitoring with standardised in-ear EEG sensor

Objective: Advances in sensor miniaturisation and computational power have served as enabling technologies for monitoring human physiological conditions in real-world scenarios. Sleep disruption may impact neural function, and can be a symptom of both physical and mental disorders. This study proposes wearable in-ear electroencephalography (ear- EEG) for overnight sleep monitoring as a 24/7 continuous and unobtrusive technology for sleep quality assessment in the community. Methods: Twenty-two healthy participants took part in overnight sleep monitoring with simultaneous ear-EEG and conventional full polysomnography (PSG) recordings. The ear- EEG data were analysed in the both structural complexity and spectral domains; the extracted features were used for automatic sleep stage prediction through supervised machine learning, whereby the PSG data were manually scored by a sleep clinician. Results: The agreement between automatic sleep stage prediction based on ear-EEG from a single in-ear sensor and the hypnogram based on the full PSG was 74.1% in the accuracy over five sleep stage classification; this is supported by a Substantial Agreement in the kappa metric (0.61). Conclusion: The in-ear sensor is both feasible for monitoring overnight sleep outside the sleep laboratory and mitigates technical difficulties associated with scalp-EEG. It therefore represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring. Significance: The ‘standardised’ one-size-fits-all viscoelastic in-ear sensor is a next generation solution to monitor sleep - this technology promises to be a viable method for readily wearable sleep monitoring in the community, a key to affordable healthcare and future eHealth