The Relationship between Osteogenesis Imperfecta and Spinal Muscular Atrophy

ObjectiveA 4-month-old female with osteogenesis imperfecta (OI) type II was admitted in PICU of our center due to severe respiratory distress and fever with a diagnosis of severe pneumonia, and mechanical ventilation was initiated. Due to severe hypotonia, NCV and EMG were performed, and spinal muscular atrophy (SMA) type I was diagnosed.Keywords: Osteogenesis imperfecta; spinal muscular atrophy; hypotoni

Strain Selection and Statistical Optimization of Culture Conditions for 19F Polysaccharide Production from Pneumococcus

Introduction:  Capsular polysaccharides of pneumococci are principle antigenic constituents of vaccines against pneumococci. Enhancing the yield of capsule production decreases costs of these vaccines and increases the vaccine coverage in developing countries. In this study therefore, we aim to optimize the capsule production from serotype 19F pneumococcus in terms of the applied pneumococcal strain and environmental culture conditions.Materials and Methods:  Thirteen serotype 19F Streptococcus pneumoniae strains were screened for the capsule production in modified Hoeprich culture medium using the stains all assay. The optimal ranges of environmental culture conditions for the selected strain were determined using single factor at a time (SFAT) strategy and utilized for the design of experiments based on the response surface methodology (RSM).Results:  S. pneumoniae 82218 showed the highest capsule production, and thus used for further studies. The maximum capsule production (1.364 mg/ml) was attained under optimal conditions (pH 7.26, 35.5 ºC, 30 rpm) predicted by the RSM derived quadratic model. The capsule production under the optimal conditions increased to 1.9 mg/ml using the buffered culture medium. Conclusion:  These results are much higher than those reported for pneumococcal capsule production in published studies [1, 2] and thus can be used to design suitable systems for the serotype 19F capsule production in the vaccine manufacturing process.

Prevalence of Nephrolithiasis in 7-11 year-old Students: A Multicenter Study

Introduction: Renal diseases can be asymptomatic even in progressive disorders; therefore, detecting urine and ultrasound abnormalities may help facilitate early diagnosis and prevention of renal diseases. This study was conducted to investigate random urine parameters and urinary system ultrasonography findings in 7-11 year-old students.Materials and Methods: Healthy students from Tehran and Qom, Iran were enrolled in a prospective descriptive study and their sex, age, weight, height, and BMI were measured. Then, a fresh clean urine sample was collected and ultrasonography of the urinary tract was done. The urine specimen was tested for urine Ca/Cr, urine oxalate/Cr, and urine citrate/Cr.Results: Of 932 students, 47.9% were female and 52.1% were male. The age range of the students was 7-11 years with a mean age of9.08 years. A history of renal disease and UTI was positive in 1.1% and 9.9% of the students, respectively. Ultrasound was normal in78% and abnormal in 22% of the students. Abnormal findings included hydronephrosis in 1.1%, fullness of the urinary tract in 0.1%, urinary system duplication in 3%, urolithiases in 0.7%, decreased kidney size in 0.4%, increased bladder thickness in 8.9%, and other abnormal findings in 7.8% of the subjects. Abnormal urine findings included hypercalciuria, in 10.9%, urine hyperuricosuria in 5.4%, urine hyperoxaluria in 12.8%, and hypocitraturia in 96.9% of the students.Conclusions: According to the results, nephrolithiasis may be due to hyperoxaluria, hypercalciuria, and hyperuricosuria in a normal population. Genetics and nutrition are more important risk factors. Therefore, some nutritional interventions for decreasing urine oxalate, calcium, and uric acid may be beneficial. Keywords: Urinalysis; Ultrasonography; Hypercalciuria; Hyperuricosuria; Hyperoxaluria; Child

A novel comprehensive energy management model for multi-microgrids considering ancillary services

This article proposes a novel comprehensive multi-layer power management system (PMS) along with its smart distribution network (SDN) constraints as bi-level optimization to address the participation of multi-microgrids (MMGs) in day-ahead energy and ancillary services markets. In the first layer of the proposed model, optimal programming of MMG-connected SDN is considered, in which Microgrids (MGs) participation in the markets is performed to bidirectionally coordinate sources and active loads along with the operator of MGs. In the second layer, the bidirectional coordination of operators of MGs and SDN, that is PMS, is executed in which energy loss, voltage security, and expected energy not-supplied (EENS) are minimized as weighted sum functions. The problem of the difference between costs and revenues of MGs in markets is minimized subject to constraints of linearized AC-power flow, reliability, security, and flexibility of the MGs. To obtain a single-level model, the Karush–Kuhn–Tucker method is applied, and a hybrid stochastic-robust programming is implemented to model uncertainties associated with the load, renewable power, energy price, mobile storage energy demand, and network equipment accessibility. The contributions of this paper include the simultaneous modelling of several economic indicators, multi-layer energy management modelling, and stochastic mixed modelling of uncertainties. The efficiency of this method is validated by simultaneously evaluating the optimum condition of technical and economic indices of several SDNs and MGs. Flexibility of 0.022 MW is obtained for the proposed scheme, which is close to zero (100% flexibility). The voltage security index is increased to 22 by the mentioned scheme, which is close to its normal value, that is, 24. The voltage deviation is below 0.07 p.u. Energy losses are reduced by about 30% compared with that in power flow studies, and the EENS reaches roughly 3 MWh, that is, close to zero (100% reliability).© 2022 The Authors. IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.fi=vertaisarvioitu|en=peerReviewed

A Framework of Electricity Market based on Two-Layer Stochastic Power Management for Microgrids

This article develops a novel multi-microgrids (MMGs) participation framework in the day-ahead energy and ancillary services, i.e. services of reactive power and reserve regulation, markets incorporating the smart distribution network (SDN) objectives based on two-layer power management system (PMS). A bi-level optimization structure is introduced wherein the upper level models optimal scheduling of SDN in the presence of MMGs while considering the bilateral coordination between microgrids (MGs) and SDN’s operators, i.e. second layer’s PMS. This layer is responsible for minimizing energy loss, expected energy not-supplied, and voltage security as the sum of weighted functions. In addition, the proposed problem is subject to linearized AC optimal power flow (LAC-OPF), reliability and security constraints to make it more practical. Lower level addresses participation of MGs in the competitive market based on bilateral coordination among sources, active loads and MGs’ operator (first layer’s PMS). The problem formulation then tries to minimize the difference between MGs’ cost and revenue in markets while satisfying constraints of LAC-OPF equations, reliability, security, and flexibility of the MGs. Karush–Kuhn–Tucker method is exploited to achieve a single-level model. Moreover, a stochastic programming model is introduced to handle the uncertainties of load, renewable power, energy price, the energy demand of mobile storage, and availability of network equipment. The simulation results confirm the capabilities of the suggested stochastic two-layer scheme in simultaneous evaluation of the optimal status of different technical and economic indices of the SDN and© 2022 Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

A Survey on Deep Learning Role in Distribution Automation System : A New Collaborative Learning-to-Learning (L2L) Concept

This paper focuses on a powerful and comprehensive overview of Deep Learning (DL) techniques on Distribution Automation System (DAS) applications to provide a complete viewpoint of modern power systems. DAS is a crucial approach to increasing the reliability, quality, and management of distribution networks. Due to the importance of development and sustainable security of DAS, the use of DL data-driven technology has grown significantly. DL techniques have blossomed rapidly, and have been widely applied in several fields of distribution systems. DL techniques are suitable for dynamic, decision-making, and uncertain environments such as DAS. This survey has provided a comprehensive review of the existing research into DL techniques on DAS applications, including fault detection and classification, load and energy forecasting, demand response, energy market forecasting, cyber security, network reconfiguration, and voltage control. Comparative results based on evaluation criteria are also addressed in this manuscript. According to the discussion and results of studies, the use and development of hybrid methods of DL with other methods to enhance and optimize the configuration of the techniques are highlighted. In all matters, hybrid structures accomplish better than single methods as hybrid approaches hold the benefit of several methods to construct a precise performance. Due to this, a new smart technique called Learning-to-learning (L2L) based DL is proposed that can enhance and improve the efficiency, reliability, and security of DAS. The proposed model follows several stages that link different DL algorithms to solve modern power system problems. To show the effectiveness and merit of the L2L based on the proposed framework, it has been tested on a modified reconfigurable IEEE 32 test system. This method has been implemented on several DAS applications that the results prove the decline of mean square errors by approximately 12% compared to conventional LSTM and GRU methods in terms of prediction fields.©2022 Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

IoT-Enabled Operation of Multi Energy Hubs Considering Electric Vehicles and Demand Response

This paper introduces a novel Internet of Thing (IoT) enabled approach for optimizing the operation costs and enhancing the network reliability incorporating the uncertainty effects and energy management in multi-carrier Energy Hub (EH) and integrated energy systems (IES) with renewable resources, Combined Heat and Power (CHP) and Plug-In Hybrid Electric Vehicle (PHEV). In the proposed model, the optimization process of different carriers of Multi Energy Hubs (MEH) energy considers a price-based demand response (DR) program with MEH electrical and thermal demands. During the peak period, energy carrier prices are calculated at high tariffs, and other power hubs can help to reduce hub energy costs. The proposed model can handle the random behavior of renewable sources in a correlated environment and find optimal solution for turbines' communication in EHs. The simulation results show the high performance of the proposed model by considering the dependency between wind turbines in MEH structure, power exchange and heat among the EHs.© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

A multi-agent privacy-preserving energy management framework for renewable networked microgrids

This paper proposes a fully distributed scheme to solve the day-ahead optimal power scheduling of networked microgrids in the presence of different renewable energy resources, such as photovoltaics and wind turbines, considering energy storage systems. The proposed method enables the optimization of the power scheduling problem through local computation of agents in the system and private communication between existing agents, without any centralized scheduling unit. In this paper, a cloud-fog-based framework is also introduced as a fast and economical infrastructure for the proposed distributed method. The suggested optimized energy framework proposes an area to regulate and update policies, detect misbehaving elements, and execute punishments centrally, while the general power scheduling problem is optimized in a distributed manner using the proposed method. The suggested cloud-fog-based method eliminates the need to invest in local databases and computing systems. The proposed scheme is examined on a small-scale microgrid and also a larger test networked microgrid, including 4 microgrids and 15 areas in a 24-h time period, to illustrate the scalability, convergence, and accuracy of the framework. The simulation results substantiate the fast and precise performance of the proposed framework for networked microgrids compared with other existing centralized and distributed methods.© 2023 The Authors. IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

A novel energy management framework incorporating multi-carrier energy hub for smart city

The development of advanced and intelligent measurement instruments in recent years has increased the intelligence of modern energy systems, especially power systems. Besides, with the advancement of energy conversion technologies, these systems benefit from multi-carrier energy resources. Accordingly, this paper presents a model of smart city which considers various components, including smart transportation system (STS), microgrid (MG), and smart energy hub (SEH) with the ability of energy transformation. The proposed model addresses the islanded operation of a smart city that makes it a smart island. This island deploys the energy carriers of electricity, heat, gas and water as well. In addition, STS includes electric vehicle (EV) parking lots as well as metro system (MS) that can interactively exchange energy. More precisely, the different components of the smart island are modelled on the assumption of energy interdependency. In the proposed model, the water supply unit in SEH is provided which can be effective in reducing the cost of components by supplying water to them. In order to exchange energy within STS, metro stations have been optimally allocated using intelligent water drops (IWD) optimization method. In addition to smart island modelling, this paper quantifies the uncertainties within STS and MG using cloud theory. Eventually, the proposed model is simulated to ensure its effectiveness and accuracy.© 2022 The Authors. IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.fi=vertaisarvioitu|en=peerReviewed

Environment effect on diversity in quality and quantity of essential oil of different wild populations of Kerman thyme

Thymus (thyme) is one of the most important genera with regard to the number of species within the family Lamiaceae. Kerman thyme (Thymus carmanicus Jalas) is an endemic Iranian species, intensively utilized because of its wide ranging medicinal and culinary properties. Aerial parts of T. carmanicus collected from various altitudes including 2000-2500, 2500-3000, and 3000- 3500 m above sea level in Zagros Mountains, Kerman province, South Iran. The yellow oil yields ranged between 0.80 to 1.10% (v/w) for populations collected from various elevations and for the populations collected from various regions ranged between 0.55-1.61% (v/w). GC-MS analyses revealed compounds, constituting 92.2-99.9% of total essential oils. The major constituents of essential oils were carvacrol (47.6-57.9%), thymol (8.3-19.0%), α-terpinene (7.3-7.9%) and p-cymene (4.4-7.6%), that monoterpenes, especially oxygenated monoterpenes was the main constituent group in essential oil from the aerial parts of T. carmanicus. The results of current study indicated that increasing elevation decreased thymol content in essential oils of the wild populations of T. carmanicus