Intensive exercise and a patient in acute phase of polymyositis

Background and objective: Polymyositis (PM) is an idiopathic inflammatory myopathy manifested by proximal limb muscles weakness, elevated creatinin kinase, electromyography changes, and muscle inflammation in biopsy. We report an instance of intensive rehabilitation therapy in a patient with clinically active polymyositis. Case report: A 19-year-old female patient, diagnosed with 'electromyography and biopsy proven' polymyositis for 5 years, suffered from worsening limbs weakness and dysphagia. In her history, she had upper and lower limbs weakness accompanied by dysphagia which was further complicated by right bronchial aspiration 9 months ago. A four-week trial of intensive training and exercise rehabilitation, concurrently accompanied by medications was prescribed for this patient. At the end of therapy she achieved significant improvement in muscle strength, activities of daily living, and ambulation without any disease exacerbation. Conclusion: We concluded that short-term intensive training and exercise may lead to improvements in patients with PM, without causing a progress in the disease. Due to the rarity of PM and difficulty of conducting well-controlled studies to examine the risks and benefits of exercise in these patients, further research is necessary to investigate benefits of exercise training in active phase of disease

Metabolic and immunologic alterations of ginger rhizome among streptozotocin-nicotinamide induced diabetic rats

Introduction: This study was conducted to determine immunological and metabolic effects of different concentrations of ginger rhizome (Zingiber officinale Roscoe) in streptozotocin (STZ)-nicotinamide (NA) induced diabetic rats. Methods: Forty-eight fasted male Sprague-Dawley rats were induced diabetes using a single intraperitoneal injection of NA(110 mg/kg b.w.) and STZ (65 mg/kg b.w, 15 min after NA). Diabetic rats orally received either different concentrations (250, 500 and 750 mg/kg body weight) of ginger rhizome suspension or glibenclamide (10 mg/kg body weight) for 6 weeks. Two control diabetic and normal groups were gavaged with only distilled water as a vehicle. Results: The results indicated that the lower concentrations of ginger modulated body weight, fasting blood glucose, level of triglyceride and tumor necrosis factor-α (TNF-α) (p0.05). Conclusion: Ginger indicated better impact on metabolic and immunologic parameters in lower doses of supplementation compared with high doses of treatment

Optimal Operation of Energy Storage in Power Transmission and Distribution

In this thesis, we investigate optimal operation of energy storage units in power transmission and distribution grids. At transmission level, we investigate the problem where an investor-owned independently-operated energy storage system seeks to offer energy and ancillary services in the day-ahead and real-time markets. We specifically consider the case where a significant portion of the power generated in the grid is from renewable energy resources and there exists significant uncertainty in system operation. In this regard, we formulate a stochastic programming framework to choose optimal energy and reserve bids for the storage units that takes into account the fluctuating nature of the market prices due to the randomness in the renewable power generation availability. At distribution level, we develop a comprehensive data set to model various stochastic factors on power distribution networks, with focus on networks that have high penetration of electric vehicle charging load and distributed renewable generation. Furthermore, we develop a data-driven stochastic model for energy storage operation at distribution level, where the distribution of nodal voltage and line power flow are modelled as stochastic functions of the energy storage unit’s charge and discharge schedules. In particular, we develop new closed-form stochastic models for such key operational parameters in the system. Our approach is analytical and allows formulating tractable optimization problems. Yet, it does not involve any restricting assumption on the distribution of random parameters, hence, it results in accurate modeling of uncertainties. By considering the specific characteristics of random variables, such as their statistical dependencies and often irregularly-shaped probability distributions, we propose a non-parametric chance-constrained optimization approach to operate and plan energy storage units in power distribution girds. In the proposed stochastic optimization, we consider uncertainty from various elements, such as solar photovoltaic , electric vehicle chargers, and residential baseloads, in the form of discrete probability functions. In the last part of this thesis we address some other resources and concepts for enhancing the operation of power distribution and transmission systems. In particular, we proposed a new framework to determine the best sites, sizes, and optimal payment incentives under special contracts for committed-type DG projects to offset distribution network investment costs. In this framework, the aim is to allocate DGs such that the profit gained by the distribution company is maximized while each DG unit’s individual profit is also taken into account to assure that private DG investment remains economical

Optimal Operation of Energy Storage in Power Transmission and Distribution

In this thesis, we investigate optimal operation of energy storage units in power transmission and distribution grids. At transmission level, we investigate the problem where an investor-owned independently-operated energy storage system seeks to offer energy and ancillary services in the day-ahead and real-time markets. We specifically consider the case where a significant portion of the power generated in the grid is from renewable energy resources and there exists significant uncertainty in system operation. In this regard, we formulate a stochastic programming framework to choose optimal energy and reserve bids for the storage units that takes into account the fluctuating nature of the market prices due to the randomness in the renewable power generation availability. At distribution level, we develop a comprehensive data set to model various stochastic factors on power distribution networks, with focus on networks that have high penetration of electric vehicle charging load and distributed renewable generation. Furthermore, we develop a data-driven stochastic model for energy storage operation at distribution level, where the distribution of nodal voltage and line power flow are modelled as stochastic functions of the energy storage unit’s charge and discharge schedules. In particular, we develop new closed-form stochastic models for such key operational parameters in the system. Our approach is analytical and allows formulating tractable optimization problems. Yet, it does not involve any restricting assumption on the distribution of random parameters, hence, it results in accurate modeling of uncertainties. By considering the specific characteristics of random variables, such as their statistical dependencies and often irregularly-shaped probability distributions, we propose a non-parametric chance-constrained optimization approach to operate and plan energy storage units in power distribution girds. In the proposed stochastic optimization, we consider uncertainty from various elements, such as solar photovoltaic , electric vehicle chargers, and residential baseloads, in the form of discrete probability functions. In the last part of this thesis we address some other resources and concepts for enhancing the operation of power distribution and transmission systems. In particular, we proposed a new framework to determine the best sites, sizes, and optimal payment incentives under special contracts for committed-type DG projects to offset distribution network investment costs. In this framework, the aim is to allocate DGs such that the profit gained by the distribution company is maximized while each DG unit’s individual profit is also taken into account to assure that private DG investment remains economical

Comparing the mental health and quality of life in patients with irritable bowel syndrome and healthy subjects in Kashan, Iran

Background: Irritable bowel syndrome (IBS) is a functional gastrointestinal (GI) disorder that is characterized by abdominal pain, diarrhea or constipation. The severity of IBS is associated with mental health and quality of life. Thus, the present study aimed to compare the mental health and the quality of life in IBS patients and healthy subjects in Kashan, Iran. Materials and Methods: In this analytical cross-sectional study, 71 patients with IBS meeting the diagnostic criteria of Rom- III and 69 healthy subjects were selected through convenience sampling. Data were collected using the demographic questionnaire, general health questionnaire (GHQ-28) and quality of life questionnaire (QOL-34) and then analyzed. Results: Results showed that the mean scores for quality of life (101.21) and its sub-scales in IBS patients were lower than those in healthy subjects (47.31), but the mean scores for mental health (30.11) and its sub-scales were higher than those in healthy subjects (19.57). Moreover, the signs, anxiety and depression in IBS patients were more severe than healthy subjects. Conclusion: It seems that mental health and the quality of life are lower for IBS patients compared to healthy subjects in this city

Concurrent Peripheral Pathologies and Complex Regional Pain Syndrome Type 1 as Contributors to Acute Post-Stroke Shoulder Pain: A Case Report

Post-stroke shoulder pain is associated with either a peripheral or central pathology. However, most of the time, it is challenging to establish a cause-and-effect relationship between the suggested pathology and shoulder pain reported. We report a 66 year-old man who developed a right hemiplegic shoulder pain two months post stroke with initial investigations suggestive of peripheral pathologies. Pharmacological and non-pharmacological treatment did not improve his shoulder pain. Later he developed complex regional pain syndrome (CRPS) of the right hand and the initial shoulder pain subsequently relieved following resolution of the CRPS

Power systems big data analytics: An assessment of paradigm shift barriers and prospects

Electric power systems are taking drastic advances in deployment of information and communication technologies; numerous new measurement devices are installed in forms of advanced metering infrastructure, distributed energy resources (DER) monitoring systems, high frequency synchronized wide-area awareness systems that with great speed are generating immense volume of energy data. However, it is still questioned that whether the today’s power system data, the structures and the tools being developed are indeed aligned with the pillars of the big data science. Further, several requirements and especial features of power systems and energy big data call for customized methods and platforms. This paper provides an assessment of the distinguished aspects in big data analytics developments in the domain of power systems. We perform several taxonomy of the existing and the missing elements in the structures and methods associated with big data analytics in power systems. We also provide a holistic outline, classifications, and concise discussions on the technical approaches, research opportunities, and application areas for energy big data analytics. Keywords: Energy, Big data analytics, Internet of energy, Smart gri