top of page

Blackcaviarbangkok Group

Public·8 members
Alexander Richardson
Alexander Richardson

Monitoring Za 7 Klass

In order to assess the real-world representativeness of the CO2 emissions and of the fuel or energy consumption determined at type-approval, as well as to prevent the growing of the gap between emissions tested in the laboratory and real-world emissions, the Commission will collect real-world data of cars and vans, starting with those vehicles placed on the market in 2021. These data will be collected using on-board fuel consumption monitoring (OBFCM) devices.

monitoring za 7 klass

The best way to reduce absenteeism is by closely monitoring attendance and acting quickly once a pattern is noticed. Daily school attendance should be monitored for all students, including students participating in in-person and distance learning. Schools should use multi-tiered strategies to proactively support student attendance for all students. Additionally, schools should implement strategies to identify and differentiate interventions to support those at higher risk for absenteeism. Local data should be used to define priority groups whose attendance has been most deeply impacted during the pandemic. Schools are encouraged to create an attendance action plan with a central emphasis on family engagement throughout any school year.14

On platforms that support the concept of a thread name on their native threads, the java.lang.Thread.setName() method will also set that native thread name. However, this will only occur when called by the current thread, and only for threads started through the java.lang.Thread class (not for native threads that have attached via JNI). The presence of a native thread name can be useful for debugging and monitoring purposes. Some platforms may limit the native thread name to a length much shorter than that used by the java.lang.Thread, which may result in some threads having the same native name.

A klass that has been considered unreachable by the concurrent marking of G1, can be looked up in the ClassLoaderData/SystemDictionary, and its _java_mirror or _class_loader fields can be stored in a root or any other reachable object making it alive again. Whenever a klass is resurrected in this manner, the SATB part of G1 needs to be notified about this, otherwise, the concurrent marking remark phase will erroneously unload that klass.

Interest in monitoring honeybee colonies on a continuous basis, defined here as data gathered from the colony (as opposed to individual bees) hourly or more often for periods exceeding 2 days, is not new. Gates (1914), for example, reported hourly temperature data over several days collected from a beehive in 1907. However, sensor technology has changed a great deal, and its application to both bee research and general beekeeping is increasing. Smaller, cheaper, and more accurate sensors, along with easier connections to computers and the Internet (Faludi 2010), have made it possible for bee researchers and beekeepers to monitor many physical aspects of bee colonies continuously, remotely, and with little manpower. Once sensors have been installed, hives can be monitored without disturbance, including during periods when invasive hive inspections are contraindicated, such as during winter or times of colony stress.

Here, we present early and recent studies employing continuous monitoring of physical parameters of honeybee colonies and discuss method application and data interpretation. Continuous monitoring provides longitudinal data that allows correlation of hive events, such as changes in forager activity, with changes in hive health, phenology, and queen status, and with external factors, such as weather, nectar flow, or pesticide exposure, and it provides an important perspective to studies on the interactions between colony health and the environment.

The objectives, methods, location (field or laboratory), and duration of studies involving continuous monitoring of bee colonies have varied among researchers (Table I). Sensors have been grouped here into four main types: (1) weight; (2) temperature, humidity, and gas; (3) sound and vibration; and (4) forager traffic. The variables examined in these studies can be considered either state variables (weight, temperature, humidity, gases) or rate variables (forager traffic), which offer different options for statistical analysis and biological inference. Among the state variables, colony weight at any particular moment is merely a physical characteristic with little information on colony status per se, but first- and second-order changes in weight over time are informative. Temperature, humidity, and respiratory gas concentrations are somewhat different since these variables are directly related to the metabolism of the bee colony (Kronenberg and Heller 1982; Van Nerum and Buelens 1997). There are often temperature and gas gradients within a hive, so data depend on the location and precision of the sensor. Vibration and sound are difficult to categorize as state or rate as they can be considered in both time and frequency domains. Bees use vibration and sound to communicate, but they also produce vibrations and sounds they likely do not use (Atauri Mezquida and Llorente Martínez 2009) and may not even detect, although those may provide information about the hive. A final section discusses the application of wireless networks to continuously monitored systems.

Sensors are often used in environmental and agricultural monitoring, and data collection in those fields is often managed using wireless networks (Aqeel-ur-Rehman et al. 2014; Hart and Martinez 2006; Ruiz-Garcia et al. 2009; Zerger et al. 2010). Advances in technology and the increasing availability of wireless network access even in comparatively remote locations have made the use of such networks a commonplace among researchers, farmers, and land and wildlife managers. Where wireless phone networks are not available, transmitters are available to access satellite phone networks. Aqeel-ur-Rehman et al. (2014) compare different kinds of networks in terms of, among other aspects, frequency band, cost, energy consumption, and security. Zerger et al. (2010) provided examples of how sensor networks have been employed in three main application domains: vegetation, animals, and soils.

Methods and technology for continuous monitoring of beehives have changed since Gates (1914), Milner (1921), and Hambleton (1925), although their dedication to the task was impressive. Modern studies involve more electronics with a higher sampling frequency for more hive parameters. The use of sensors also permits hive observation without disturbance; gathering field data on colony growth and phenology from frequent, invasive hive inspections, for example, to assess treatment effects, can provoke bee defensive behavior (Breed et al. 2004), enable robbing by other colonies (Gary 1992), and lose, injure, or kill the queen in addition to disrupting the hive environment. Small, autonomous sensors, particularly those linked to wireless networks, can provide much information in real time with no disturbance.

Continuous monitoring involves, by definition, conducting observations over time. While we restricted the scope of this paper to studies with two or more days of data collected at least hourly, the length of time a variable could be measured in a practical sense was in many ways determined by the technology available and by the variable itself. Continuous monitoring of any kind is limited when it depends on constant human attention. Gates (1914) and Hambleton (1925) exerted what was surely considerable effort to monitor hives hourly without interruption for just a few days at a time, while Meikle et al. (2006, 2008) produced hourly weight data for 16 months simply using electronic data loggers. The quantity of data produced increases rapidly with sampling frequency and the number of sensors and colonies involved. Managing, and extracting useful information from, continuous data from experiments using large numbers of hives over extended periods of time can be challenging. Analyses can be simplified by exploiting patterns. Some variables, such as weight, temperature, and humidity, have been found to have strong sinusoidal components (Human et al. 2006; Meikle et al. 2008) owing to circadian rhythmicity; statistical analyses can be conducted on curve parameters.

Continuous monitoring will very likely become a more common tool as both for research and in practical apiculture as electronic components become easier to deploy in the field, owing to small, accurate, and robustly designed sensors. Data on frequently measured variables, such as weight and temperature, will likely be more thoroughly exploited for information. Monitoring hive weight or forager traffic prior to crop pollination would allow beekeepers to observe hive health, and during pollination, those data could provide a record of quality control. Monitoring multiple variables offers the possibility of synergy, as the information that one method validates or augments information gained simultaneously via another method. Combining methods may also provide a way of exploiting data from variables such as vibration, for which the biological interpretation is not always evident. Sound cues have already been the focus of commercially available diagnostic tools for beehives. Other anticipated improvements include the following: 041b061a72


Welcome to the group! You can connect with other members, ge...


bottom of page