Swarm intelligence is an emerging area in the field of optimization and researchers have developed various algorithms by modeling the behaviors of different swarm of animals and insects such as ants, termites, bees, birds, fishes. In 1990s, Ant Colony Optimization based on ant swarm and Particle Swarm Optimization based on bird flocks and fish schools have been introduced and they have been applied to solve optimization problems in various areas within a time of two decade. However, the intelligent behaviors of bee swarm have inspired the researchers especially during the last decade to develop new algorithms. This work presents a survey of the algorithms described based on the intelligence in bee swarms and their applications.

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A survey: algorithms simulating bee swarm intelligence

September 19-20 International Student Orientation September 22 New Student check-In for Undergraduates September 22-29 New Student Orientation for Undergraduates September 23 New Student Check-In for Graduates September 23-27 New Student Orientation for Graduates September 26 Undergraduate Academic Standards and Honors Committee – 1:00 p.m. September 27 Undergraduate Academic Standards and Honors Committee Appeals– 1:00 p.m. September 30 Rosh Hashanah – classes excused October 1 Beginning of instruction 8:00 a.m. October 18 Last day for adding courses and removing conditions and incompletes October 30Midterm examination period November 5 November 11 Midterm deficiency notices due 9:00 a.m. November 20 Last day for dropping courses, exercising pass/fail option, and changing sections November 21Registration for winter term 2019-20 December 6 November 28-29 Thanksgiving (Institute holiday) December 6 Last day of classes Last day to register for winter term 2019-20 without a $50 late fee December 7-10 Study period December 11*-13 Final examinations, fall term 2019-20 December 13 End of fall term 2019-20 December 14Winter recess January 5 December 18 Instructors' final grade reports due 9:00 a.m. *First due date for final examinations

カリフォルニア工科大学 (California Institute of technology) 滞在記

Biotic interactions underlie ecosystem structure and function, but predicting interaction outcomes is difficult We tested the hypothesis that biotic interaction strength increases toward the equator, using a global experiment with model caterpillars to measure predation risk Across an 11,660-kilometer latitudinal gradient spanning six continents, we found increasing predation toward the equator, with a parallel pattern of increasing predation toward lower elevations Patterns across both latitude and elevation were driven by arthropod predators, with no systematic trend in attack rates by birds or mammals These matching gradients at global and regional scales suggest consistent drivers of biotic interaction strength, a finding that needs to be integrated into general theories of herbivory, community organization, and life-history evolution

/pdf/higher-predation-risk-for-insect-prey-at-low-latitudes-and-1ouzwpy7na.pdf

Higher predation risk for insect prey at low latitudes and elevations

Big data and machine learning are having an impact on most aspects of modern life, from entertainment, commerce, and healthcare. Netflix knows which films and series people prefer to watch, Amazon knows which items people like to buy when and where, and Google knows which symptoms and conditions people are searching for. All this data can be used for very detailed personal profiling, which may be of great value for behavioral understanding and targeting but also has potential for predicting healthcare trends. There is great optimism that the application of artificial intelligence (AI) can provide substantial improvements in all areas of healthcare from diagnostics to treatment. It is generally believed that AI tools will facilitate and enhance human work and not replace the work of physicians and other healthcare staff as such. AI is ready to support healthcare personnel with a variety of tasks from administrative workflow to clinical documentation and patient outreach as well as specialized support such as in image analysis, medical device automation, and patient monitoring. In this chapter, some of the major applications of AI in healthcare will be discussed covering both the applications that are directly associated with healthcare and those in the healthcare value chain such as drug development and ambient assisted living.

The rise of artificial intelligence in healthcare applications

. Despite recent research identifying a clear anthropogenic impact on glacier
recession, the effect of recent climate change on glacier-related hazards is
at present unclear. Here we present the first global spatio-temporal
assessment of glacial lake outburst floods (GLOFs) focusing explicitly on
lake drainage following moraine dam failure. These floods occur as mountain
glaciers recede and downwaste. GLOFs can have an enormous impact on
downstream communities and infrastructure. Our assessment of GLOFs associated
with the rapid drainage of moraine-dammed lakes provides insights into the
historical trends of GLOFs and their distributions under current and future
global climate change. We observe a clear global increase in GLOF frequency
and their regularity around 1930, which likely represents a lagged response
to post-Little Ice Age warming. Notably, we also show that GLOF frequency and
regularity – rather unexpectedly – have declined in recent decades
even during a time of rapid glacier recession. Although previous studies have
suggested that GLOFs will increase in response to climate warming and glacier
recession, our global results demonstrate that this has not yet clearly
happened. From an assessment of the timing of climate forcing, lag times in
glacier recession, lake formation and moraine-dam failure, we predict
increased GLOF frequencies during the next decades and into the 22nd
century.

/pdf/climate-change-and-the-global-pattern-of-moraine-dammed-51swzy4gi4.pdf

Climate Change and the Global Pattern of Moraine-Dammed Glacial Lake Outburst Floods

In Grids scheduling decisions are often made on the basis of jobs being either data or computation intensive: in data intensive situations jobs may be pushed to the data and in computation intensive situations data may be pulled to the jobs This kind of scheduling, in which there is no consideration of network characteristics, can lead to performance degradation in a Grid environment and may result in large processing queues and job execution delays due to site overloads In this paper we describe a Data Intensive and Network Aware (DIANA) meta-scheduling approach, which takes into account data, processing power and network characteristics when making scheduling decisions across multiple sites Through a practical implementation on a Grid testbed, we demonstrate that queue and execution times of data-intensive jobs can be significantly improved when we introduce our proposed DIANA scheduler The basic scheduling decisions are dictated by a weighting factor for each potential target location which is a calculated function of network characteristics, processing cycles and data location and size The job scheduler provides a global ranking of the computing resources and then selects an optimal one on the basis of this overall access and execution cost The DIANA approach considers the Grid as a combination of active network elements and takes network characteristics as a first class criterion in the scheduling decision matrix along with computations and data The scheduler can then make informed decisions by taking into account the changing state of the network, locality and size of the data and the pool of available processing cycles

Data intensive and network aware (DIANA) grid scheduling

The objective of the study was to evaluate the performance of three different aquatic macrophytes for treatment of municipal wastewater collected from Taxila (Pakistan). A physical model of treatment plant was constructed and was operated for six experimental runs with each species of macrophyte. Every experimental run consist of thirty days period. Regular monitoring of influent and effluent concentrations were made during each experimental run. For the treatment locally available macrophyte species i.e. water hyacinth, duckweed & water lettuce were selected to use. To evaluate the treatment performance of each macrophyte, BOD5, COD, and Nutrients (Nitrogen and Phosphorus) were monitored in effluent from model at different detention time of every experimental run after ensuring steady state conditions. The average reduction of effluent value of each parameter using water hyacinth were 50.61% for BOD5, 46.38% for COD, 40.34% for Nitrogen and 18.76% for Phosphorus. For duckweed the average removal efficiency for selected parameters were 33.43% for BOD5, 26.37% for COD, 17.59% for Nitrogen and 15.25% for Phosphorus and for Water Lettuce the average removal efficiency were 33.43% for BOD5, 26.37% for COD, 17.59% for Nitrogen and 15.25% for Phosphorus. The mechanisms of pollutant removal in this system include both aerobic and anaerobic microbiological conversions, sorption, sedimentation, volatilization and chemical transformations. The rapid growth of the biomass was measured within first ten days detention time. It was also observed that performance of macrophytes is influenced by variation of pH and Temperature. A pH of 6-9 and Temperature of 15-38°C is most favorable for treatment of wastewater by macrophytes. The option of macrophytes for treatment of Municipal sewage under local environmental conditions can be explored by further verifying the removal efficiency under variation of different environmental conditions. Also this is need of time that macrophyte system should be used for treatment of wastewater because their performance is comparable to conventional wastewater treatment plants and also the system has very low O&M costs.

https://link.springer.com/content/pdf/10.1186%2F2052-336X-12-106.pdf

Performance assessment of aquatic macrophytes for treatment of municipal wastewater

E-learning can be loosely defined as a wide set of applications and processes, which uses available electronic media (and tools) to deliver vocational education and training. With its increasing recognition as an ubiquitous mode of instruction and interaction in the academic as well as corporate world, the need for a scaleable and realistic model is becoming important. In this paper we introduce SELF; a Semantic grid-based E-Learning Framework. SELF aims to identify the key-enablers in a practical grid-based E-learning environment and to minimize technological reworking by proposing a well-defined interaction plan among currently available tools and technologies. We define a dichotomy with E-learning specific application layers on top and semantic grid-based support layers underneath. We also map the latest open and freeware technologies with various components in SELF.

A Semantic Grid-based E-Learning Framework (SELF)

SAGE: Next generation multi-agent system

Multi agent systems (MAS) are expected to be involved in futuristic technologies. Agents require some execution environment in which they can publish their service interfaces and can provide services to other agent. Such execution environment is called agent platform (AP). From a technical point of view any abnormal behavior of platform can distress agents residing on that platform. That's why it is necessary to provide a suitable architecture for the AP which should not only provide fault tolerance but also scalability features. There also exist some management components within the platform, which provide services to application agents. All the agents within MAS are managed by agent management system (AMS) which is the mandatory supervisory authority of any AP. To be more scalable, a single agent platform can be distributed over several machines which not only provides load balancing but also fault tolerance depending upon the distributed architecture of the AP. In existing systems, AMS is centralized i.e. it exists on one machine. With centralized AMS, this infrastructure lacks fault tolerance, which is a key feature of high assurance. Absence of fault tolerance is the main reason for the small number of deployments of MAS. Failure of AMS leads towards abnormal behavior in the distributed platform. This paper proposes virtual agent cluster (VAC) paradigm which strongly supports decentralized AMS to achieve fault tolerance in distributed AP. VAC provides fault tolerance by using separate communication layers among different machines. Experiments show that it improves performance, brings autonomy and supports fault recovery along with load balancing in distributed AP.

Arshad Ali

Papers

Data intensive and network aware (DIANA) grid scheduling

Performance assessment of aquatic macrophytes for treatment of municipal wastewater

A Semantic Grid-based E-Learning Framework (SELF)

SAGE: Next generation multi-agent system

Decentralized architecture for fault tolerant multi agent system