Regional research priorities in brain and nervous system disorders

The characteristics of neurological, psychiatric, developmental and substance-use disorders in low-and middle-income countries are unique and the burden that they have will be different from country to country. Many of the differences are explained by the wide variation in population demographics and size, poverty, conflict, culture, land area and quality, and genetics. Neurological, psychiatric, developmental and substance-use disorders that result from, or are worsened by, a lack of adequate nutrition and infectious disease still afflict much of sub-Saharan Africa, although disorders related to increasing longevity, such as stroke, are on the rise. In the Middle East and North Africa, major depressive disorders and post-traumatic stress disorder are a primary concern because of the conflict-ridden environment. Consanguinity is a serious concern that leads to the high prevalence of recessive disorders in the Middle East and North Africa and possibly other regions. The burden of these disorders in Latin American and Asian countries largely surrounds stroke and vascular disease, dementia and lifestyle factors that are influenced by genetics. Although much knowledge has been gained over the past 10 years, the epidemiology of the conditions in low-and middle-income countries still needs more research. Prevention and treatments could be better informed with more longitudinal studies of risk factors. Challenges and opportunities for ameliorating nervous-system disorders can benefit from both local and regional research collaborations. The lack of resources and infrastructure for health-care and related research, both in terms of personnel and equipment, along with the stigma associated with the physical or behavioural manifestations of some disorders have hampered progress in understanding the disease burden and improving brain health. Individual countries, and regions within countries, have specific needs in terms of research priorities.Fil: Ravindranath, Vijayalakshmi. Indian Institute of Science; IndiaFil: Dang, Hoang Minh. Vietnam National University; VietnamFil: Goya, Rodolfo Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Bioquímicas de La Plata ; ArgentinaFil: Mansour, Hader. University of Pittsburgh; Estados Unidos. Mansoura University; EgiptoFil: Nimgaonkar, Vishwajit L.. University of Pittsburgh; Estados UnidosFil: Russell, Vivienne Ann. University of Cape Town; SudáfricaFil: Xin, Yu. Peking University; Chin

Determination of Genetic Diversity Based on RAPD molecular Marker and ParARF3 Gene Expressions in some Apricot Genotypes in Iraq

Employing DNA markers allowed determining genetic diversity and relationships amongst five apricot genotypes. In this study, two relative gene expressions pertaining to ParARF3 gene, which could be distinguished from the genotypes that were exposed to various concentrations of marine alga treatments. As per our results, screening of seven primers with the DNA of 5 apricot genotypes was done, and six primers were generated while the primer OPN–16 gave negative results. The total quantity of fragments generated by 6 primers was 80 at an average of 13.33 fragments ̸primer. The highest unique percentage band depicted in U-17 touched 23%, and the total number of polymorphic bands touched 17 fragments with the average reaching 2.83 fragments ̸primer. The number of monomorphic lied in the range of 5 to 10, with a total of 47 monomorphic. Primer M 32 yielded the highest number of monomorphic bands reaching 10. Between Zaghenia and Zinni, a maximum genetic distance value of 0.8 was reached with less similarity value of 20%. A minimum genetic distance value of 0.44721 was noted between Kaisy and Baia while the high similarity value touched 55.3%. According to the cluster tree analysis, the genotypes were generally split into two key groups: A and B. The Zinni group, which included one apricot genotype, showed genetic similarity of 20% with the other genotypes present in B group. The B group was split into two sub-clusters B1 and B2 and the genetic similarity reached 44%. The ParARF3 relative gene expression pertaining to Zinni genotypes was second as well as convergent with that of gene expression for Zaghenia genotype results. Baia and Kaisy genotypes lied in between the lowest and highest ParARF3 value gene expression exposed to Marine Alga. These outcomes showed that RAPD markers offer an effectual alternative for the plant species genetic characterisation.</jats:p

A new Internet of Things based optimization scheme of residential demand side management system

The steady increase in the energy demand and the growing carbon footprint has forced electricity-based utilities to shift from their use of non-renewable energy sources to renewable energy sources. Furthermore, there has been an increase in the integration of renewable energy sources in the electric grid. Hence, one needs to manage the energy consumption needs of the consumers, more effectively. Consumers can connect all the devices and houses to the internet by using Internet of Things (IoT) technology. In this study, the researchers have developed and proposed a novel 2-stage hybrid method that schedules the power consumption of the houses possessing a distributed energy generation and storage system. Stage 1 modeled the non-identical Home Energy Management Systems (HEMSs) that can contain the DGS like WT and PV. The HEMS organise the controllable appliances after taking into consideration the user preferences, electricity prices and the amount of energy produced /stored. The set of optimal consumption schedules for every HEMS was estimated using a BPSO and BSA. On the other hand, Stage 2 includes a Multi-Agent-System (MAS) based on the IoT. The system comprises two portions: software and hardware. The hardware comprises the Base Station Unit (BSU) and many Terminal Units (TUs)

A novel robust smart energy management and demand reduction for smart homes based on internet of energy

In residential energy management (REM), Time of Use (ToU) of devices scheduling based on user-defined preferences is an essential task performed by the home energy management con-troller. This paper devised a robust REM technique capable of monitoring and controlling residential loads within a smart home. In this paper, a new distributed multi-agent framework based on the cloud layer computing architecture is developed for real-time microgrid economic dispatch and monitoring. In this paper the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm-based Time of Use (ToU) pricing model is proposed to define the rates for shoulder-peak and on-peak hours. The results illustrate the effectiveness of the proposed the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm based ToU pricing scheme. A Raspberry Pi3 based model of a well-known test grid topology is modified to support real-time communication with open-source IoE platform Node-Red used for cloud computing. Two levels communication system connects microgrid system, implemented in Raspberry Pi3, to cloud server. The local communication level utilizes IP/TCP and MQTT is used as a protocol for global communication level. The results demonstrate and validate the effectiveness of the proposed technique, as well as the capability to track the changes of load with the interactions in re-al-time and the fast convergence rate

A Novel Robust Smart Energy Management and Demand Reduction for Smart Homes Based on Internet of Energy

In residential energy management (REM), Time of Use (ToU) of devices scheduling based on user-defined preferences is an essential task performed by the home energy management controller. This paper devised a robust REM technique capable of monitoring and controlling residential loads within a smart home. In this paper, a new distributed multi-agent framework based on the cloud layer computing architecture is developed for real-time microgrid economic dispatch and monitoring. In this paper the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm-based Time of Use (ToU) pricing model is proposed to define the rates for shoulder-peak and on-peak hours. The results illustrate the effectiveness of the proposed the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm based ToU pricing scheme. A Raspberry Pi3 based model of a well-known test grid topology is modified to support real-time communication with open-source IoE platform Node-Red used for cloud computing. Two levels communication system connects microgrid system, implemented in Raspberry Pi3, to cloud server. The local communication level utilizes IP/TCP and MQTT is used as a protocol for global communication level. The results demonstrate and validate the effectiveness of the proposed technique, as well as the capability to track the changes of load with the interactions in real-time and the fast convergence rate

A Novel Robust Smart Energy Management and Demand Reduction for Smart Homes Based on Internet of Energy

In residential energy management (REM), Time of Use (ToU) of devices scheduling based on user-defined preferences is an essential task performed by the home energy management controller. This paper devised a robust REM technique capable of monitoring and controlling residential loads within a smart home. In this paper, a new distributed multi-agent framework based on the cloud layer computing architecture is developed for real-time microgrid economic dispatch and monitoring. In this paper the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm-based Time of Use (ToU) pricing model is proposed to define the rates for shoulder-peak and on-peak hours. The results illustrate the effectiveness of the proposed the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm based ToU pricing scheme. A Raspberry Pi3 based model of a well-known test grid topology is modified to support real-time communication with open-source IoE platform Node-Red used for cloud computing. Two levels communication system connects microgrid system, implemented in Raspberry Pi3, to cloud server. The local communication level utilizes IP/TCP and MQTT is used as a protocol for global communication level. The results demonstrate and validate the effectiveness of the proposed technique, as well as the capability to track the changes of load with the interactions in real-time and the fast convergence rate.</jats:p

A Novel Internet of Energy Based Optimal Multi-Agent Control Scheme for Microgrid including Renewable Energy Resources

The increasing integration of Renewable Energy Resources (RERs) in distribution networks forms the Networked Renewable Energy Resources (NRERs). The cooperative Peer-to-Peer (P2P) control architecture is able to fully exploit the resilience and flexibility of NRERs. This study proposes a multi-agent system to achieve P2P control of NRERs based Internet of Things (IoT). The control system is fully distributed and contains two control layers operated in the agent of each RER. For primary control, a droop control is adopted by each RER-agent for localized power sharing. For secondary control, a distributed diffusion algorithm is proposed for arbitrary power sharing among RERs. The proposed levels communication system is implemented to explain the data exchange between the distribution network system and the cloud server. The local communication level utilizes the Internet Protocol (IP)/Transmission Control Protocol (TCP), and Message Queuing Telemetry Transport (MQTT) is used as the protocol for the global communication level. The effectiveness of the proposed system is validated by numerical simulation with the modified IEEE 9 node test feeder. The controller proposed in this paper achieved savings of 20.65% for the system, 25.99% for photovoltaic, 35.52 for diesel generator, 24.59 for batteries, and 52.34% for power loss.</jats:p