Your search

This work proposes a new approach in addressing Economic Load Dispatch (ELD) optimization problem in power unit systems using nature-inspired metaheuristics search algorithms. Solving such a problem requires a degree of maximization of the economic pact of a power network system, where this is possible with some existing population-based metaheuristic search algorithms. The key issue to be handled here is how to maximize the economic benevolence of a power network under a variety of operational constraints, taking into account the reduction in the generated fuel cost as well as the aggregate power loss in the transmission power network system. Some nature-inspired metaheuristics will be explored. Meanwhile, we shall focus our attention on a newly developed nature-inspired search algorithm, referred to as the Crow Search Algorithm or CSA for short, as well as the Differential Evolution (DE) that is commonly known as a metaheuristic. The CSA emerged to light by simulating the intelligent flocking behavior of crows. The practicability of the proposed approach-based CSA was conducted to common types of power generators, including three and six buses (nodes) in addition to the IEEE 30-bus standard system. The results of the presented approaches were compared to other results developed using existing nature-inspired metaheuristic algorithms like particle swarm optimization and genetic algorithms and also compared to traditional approaches such as quadratic programming method. The results reported here support that CSA has achieved an outstanding performance in solving the problem of ELD in power systems, demonstrating their good optimization capabilities through arriving at a combination of power loads that consummate the constraints of the ELD problem while simultaneously lessening the entire fuel cost. The experimental results also showed that the CSA solutions were capable of maximizing the reliability of the power supplied to the customers, and also reducing both the generated power cost and the loss of power in the transmission power systems.

Regrettably, a large proportion of likely patients with sleep apnea are underdiagnosed. Obstructive sleep apnea (OSA) is one of the main causes of hypertension, type II diabetes, stroke, coronary artery disease, and heart failure. OSA affects not only adults but also children where it forms one of the sources of learning disabilities for children. This study aims to provide a classification model for one of the well-known sleep disorders known as OSA, which causes a serious malady that affects both men and women. OSA affects both genders with different scope. Men versus women diagnosed with OSA are about 8:1. In this research, logistic regression (LR) and artificial neural networks were applied successfully in several classification applications with promising results, particularly in the bio-statistics area. LR was used to derive a membership probability for a potential OSA system from a range of anthropometric features including weight, height, body mass index (BMI), hip, waist, age, neck circumference, modified Friedman, snoring, Epworth sleepiness scale (ESS), sex, and daytime sleepiness. We developed two models to predict OSA, one for men and one for women. The proposed sleep apnea diagnosis model has yielded accurate classification results and possibly a prototype software module that can be used at home. These findings shall reduce the patient’s need to spend a night at a laboratory and make the study of sleep apnea to implement at home.

The Inside-Out Prison Exchange Program is an experiential learning program that involves teaching college courses inside correctional facilitates to classes that include incarcerated and nonincarcerated students. This teaching note describes the program and argues that its congruence with social work values makes it a valuable tool for preparing students to take on social justice work. The impact of the program’s pedagogy and structure on students’ and faculty’s capacity for critical thinking and self-reflection is described. Logistical issues to consider in developing programs are discussed.

For the last 93 years mayfly diversity has been studied at all taxonomic levels from families to populations and all spatial and temporal scales. This has resulted in the description of almost every possible pattern with regard to latitude, elevation, habitat, climate, and season. However, if the focus is on species and sampling restricted to nymphs and the spatial scale limited to drainage networks, then a single recurring pattern appears—the hump-shaped Mid-network Mayfly Maxima (MMM). Past studies have attempted to explain this hump-shaped pattern with regards to some combination of ecologic, hydrologic, climatic, anthropogenic, or historic variables. There is wide agreement that some of these variables are important to the occurrence and distribution of individual species, but there is no consensus as to what combination of these variables comprises the best general explanation of the humped-shaped pattern. Because of the lack of agreement on how to explain this recurring pattern for mayflies and an unwillingness to acknowledge it as a general feature of drainage networks, where mayflies occur, we have become stuck trying to tease out a cause and have failed to recognize that although knowing a cause is important it may not be relevant to the broader use of this information for biomonitoring, climate monitoring, habitat management, and conservation now. I believe that there is sufficient evidence that the MMM exists and occurs in almost every major drainage system and is reasonably predictable using basic attributes of drainage networks pertinent to the aquatic life stage (nymphs). Herein I will summarize pertinent studies that support this contention; reexamine some results from one of the most comprehensive recent studies that included mayfly taxa along altitudinal gradients in North America by Gill et al. (2014), as well as a test dataset from a river in western Maine, USA; comment on information needed to estimate the location of MMM in any drainage network and how the MMM can be used; and discuss how the extent of common vs. rare species affect the perceptions of the regional species pool and the MMM.

This study sought to understand why college students are food insecure and what the impact is on their academics. A generic qualitative study design was utilized that consisted of written surveys and semi-structured interviews. Participants (n = 19) self-identified as food insecure based upon their answers to the 6-item Short Form Food Security Survey Module. Two researchers independently coded the data for themes and patterns. Risk factors for food insecurity included finances, time, and lack of resources/skills to provide for healthy eating. Time and money put commuter students at risk for food insecurity. While students within this convenience sample reported negative impacts on their academic achievement from food insecurity, their coping mechanisms prioritized cost and convenience over food quality, possibly exacerbating their situation.College food insecurity is a complex issue and raw measurements may not paint a complete picture. Conflicting priorities in addition to income and time pressures mean that interventions should be tailored from current approaches. Colleges may need to find unique ways to ensure food security for at risk students within financial aid packages and/or university programs to ensure student success.

Coastal ecosystems experience substantial natural fluctuations in pCO2 and dissolved oxygen (DO) conditions on diel, tidal, seasonal and interannual timescales. Rising carbon dioxide emissions and anthropogenic nutrient input are expected to increase these pCO2 and DO cycles in severity and duration of acidification and hypoxia. How coastal marine organisms respond to natural pCO2 × DO variability and future climate change remains largely unknown. Here, we assess the impact of static and cycling pCO2 × DO conditions of various magnitudes and frequencies on early life survival and growth of an important coastal forage fish, Menidia menidia. Static low DO conditions severely decreased embryo survival, larval survival, time to 50% hatch, size at hatch and post-larval growth rates. Static elevated pCO2 did not affect most response traits, however, a synergistic negative effect did occur on embryo survival under hypoxic conditions (3.0 mg L-1). Cycling pCO2 × DO, however, reduced these negative effects of static conditions on all response traits with the magnitude of fluctuations influencing the extent of this reduction. This indicates that fluctuations in pCO2 and DO may benefit coastal organisms by providing periodic physiological refuge from stressful conditions, which could promote species adaptability to climate change.