Emis User
Kizzy Burnworth
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Objectives: Chemsex is a phenomenon highly relevant to public health concerns. Our primary aim is to describe the Czech chemsex scene regarding substances used, sexual behaviour, mental health, sexual life satisfaction, internalization of homonegative attitudes, and prevalent chemsex patterns.
Methods: The data from the European Men Who Have Sex With Men Internet Survey (EMIS) 2017 were used. The mental health of chemsex users was assessed by the Patient Health Questionnaire 4 (PHQ4), internalized homonegativity was measured using the Short Internalized Homonegativity Scale. A sample of 87 men who have sex with men (MSM) chemsex users and a comparison group of 261 MSM were selected from the total sample of 1,688 respondents. Mann-Whitney and χ2 tests were used to compare groups.
Conclusions: In our sample, chemsex use was not associated with a negative impact on health or wellbeing. Our results suggest that chemsex is not a homogeneous phenomenon. Many different patterns and subcultures exist, some of them are riskier, some safer than others.
Before setting up a Task Team to receive Cross Organisation Tasks make sure the CrossOrgTasks Data Sharing Agreement allowing Organisations creating tasks for me has been activated (Configuration > Data Sharing Manager):
Any user who is a member of a team must be enabled to accept workflow tasks from sharing organisations. Go to Configuration > Organisation Configuration and select Users. Search for the user you wish to edit.
Single Sign on for EMIS Web and INPS determines which user is logged into the clinical system and automatically logs you into Docman. If you choose to log out of the clinical system, you will be prompted by Docman to switch user.
Note: In order for Single Sign on to work, your Docman user account must be associated with a Clinical System user account. If these accounts are not linked, Single Sign On will not work. Click here to view the Associating a Clinical User guide.
EMIS capabilities include tracking, understanding, and processing data from utility bills to help users calculate and visualize whole-building energy consumption over time, as well as compare buildings using monthly utility bill data. Some tools take this further into the management of the bills themselves, offering suites of tools for validation, payment processing, and storage.
A core EMIS function is to graph energy consumption over time and calculate energy-related key performance indicators (KPIs). Utility bill data provides valuable high-level insights, such as how much energy the building uses per year or per month, how much that energy costs, and how it is changing over time.
Some organizations use an EMIS to perform a variety of accounting-related tasks to process utility bills. Bill validation features allow users to audit utility bills for errors such as incorrect meter readings or charges.
Example of a billing error discovered using bill validation. EMIS may provide simple visualizations to help the user spot issues, or automated error checking to automatically spot utility bill errors. This screenshot features software detection of a duplicated invoice number.
Payment processing features allow users to streamline accounting processes, such as bill payments and cost reporting, and may include integration with accounts payable software. Budgeting allows for use of historical data to set predictions and budgets for upcoming time periods such as quarters or fiscal years. Internal or tenant billing tools allow users to allocate utility costs among different user groups and produce chargebacks or rebills. In the absence of submeters that show actual usage allocations, bills are typically divided up proportionally by tenant floor area, tenant nominal occupancy, or a similar known proxy metric.
EMIS offer a variety of tools specifically designed to analyze meter data at intervals of one hour or less. These "interval data" offer far more granularity than monthly utility bills, and these tools excel at processing all of that extra data in ways that help users find opportunities for performance improvements.
Interval data allow users to enhance utility bill processing capabilities. Advanced utility bill validation features allow the user to compare totalized data from federal advanced metering infrastructure (AMI) meters to the data reported on the utility bill. Advanced internal or tenant billing tools allow the user to allocate utility costs according to the actual usage of buildings, departments, or tenants.
Heat maps are similar to profiles and are typically used to analyze longer time periods, such as a full year. The advantage of a heat map is the ability to leverage color as a third dimension to present a great deal of data in a very compact space. Heat maps can also be easily combined with heating and cooling degree day data to compare demand data to weather patterns, as shown in the example below.
An example heat map matched with weather patterns. Heat maps are similar to profiles and are typically used to analyze longer time periods, such as a full year. The advantage of a heat map is the ability to leverage color as a third dimension to present a large amount of data in a very compact space.
Some EMIS vendors also offer tools that match daily or weekly profiles to trends from underlying systems, allowing the user to determine the equipment loads that are most significantly impacting usage at the meter.
Another valuable type of interval meter analytics is based on the creation of a mathematical model of expected usage for each meter. The model is then compared to actual usage on a regular basis (e.g., hourly or daily) to uncover various opportunities.
Models are typically formulated using linear regression modeling tools in the EMIS, where the "dependent variable" (i.e., meter usage at a given time), is defined based on the value of the "independent" variables, such as weather or occupancy conditions.
Single-variable change point models, such as those that use a balance point of 65F, have historically been used to determine when either heating or cooling systems are enabled in commercial buildings. These models have been enhanced into three-to-four-point change models to more accurately predict building energy usage. One example of this type of model is the Whole Building Energy Module, a component of the Whole Building Diagnostician, developed by Pacific Northwest National Laboratory, which tracks total building electrical energy consumption, by end use, via AMI interval data, submetered end-use loads, or equipment-level submetering. It provides a graphical record of building- or system-level performance and usage on a daily basis. The history determines constant variables or fluctuations for different times of the year. Over time, the daily performance history allows the user to identify major changes in energy consumption. The Whole Building Energy Module uses time of day, day of year, day of week, outdoor air temperature, relative humidity, and occupancy to predict and diagnose energy consumption.
EMIS can be used to monitor distributed energy resources (DERs) such as photovoltaics, and produce valuable KPIs related to energy production, displaced conventional electricity, and net energy use. Additionally, DER systems can be analyzed to evaluate their overall performance compared to expectations. For example, the performance of a solar array can be evaluated by comparing array output to local solar irradiance data.
Users can leverage the computational power of an EMIS to create virtual points, including virtual meters. Virtual meters calculate an energy consumption value for end uses such as heating, ventilation, and air conditioning (HVAC) or lighting using static equipment data, engineering calculations, and live data from the equipment. Once individual pieces of equipment have been virtually metered, all similar end uses can be rolled up to determine estimated totals by end use.
AFDD is the process of identifying (detecting) deviations from normal or expected operation (faults) and resolving (diagnosing) the type of problem or its location. AFDD is an advanced EMIS capability that uses algorithms to detect equipment- or system-level faults and diagnose their causes. AFDD vastly reduces the time required to find these faults using standard methods, such as trend visualization and analysis. By continuously collecting and analyzing data using rule-based algorithms, the analysis is automated and can be applied to very large data sets on a real-time basis. Several federal agencies such as National Aeronautics and Space Administration and U.S. Army Corps of Engineers also refer to AFDD as condition-based maintenance.
When AFDD software tools are applied to large buildings or a campus with numerous facilities, the sheer number of identified faults can be overwhelming for facilities staff. Further, some faults are drastically more important to operators than others. Fault prioritization is deployed in many AFDD products and is typically based on the estimated energy cost savings of correcting a given fault. The ability to accurately calculate energy savings and energy cost savings helps to prioritize faults for the building operator and is especially useful when deploying AFDD at scale across a large number of HVAC systems and buildings. The non-energy benefits of correcting faults can also be used for prioritization, including equipment criticality, the size of the equipment, the impact on occupant comfort, and other factors.
Supervisory control capabilities are an emerging set of features that allow the EMIS itself to make automated changes to underlying building systems. Following are several emerging types of supervisory control offered by EMIS vendors.
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