on 17-Sep-2019 (Tue)

if X is a set of airports and xRy means "there is a direct flight from airport x to airport y" (for x and y in X), then the [...] of R on X is the relation R⁺ such that x R⁺ y means "it is possible to fly from x to y in one or more flights". Informally, the [...] gives you the set of all places you can get to from any starting place.

ばねとは、力が加わると変形し、力を取り除くと元に戻るという、物体の弾性という性質を利用する機械要素である^[1]。広義には、弾性の利用を主な目的とするものの総称ともいえる^[2]。ばねの形状や材質は様々で、日用品から車両、電気電子機器、構造物に至るまで、非常に多岐にわたって使用される。

Zentralvenöser Katheter (ZVK, Cava-Katheter) ” Indikationen PatientInnen, bei denen mit peripheren Zugängen nicht das Auslangen gefunden wird, die parenteral ernährt werden müs- sen oder viele Medikamente gleichzei- tig erhalten; außerdem zur Messung des zentralen Venendrucks und bei häufigen Blutabnahmen

It is clear there was little change in the control group, while the control system maintained a much more even temperature profile in the treatment group. Although the overall mean temperature is prac- tically the same, overnight temperatures are up to 1°C warmer than previously.

The remaining five households, however, reported problems with the temperatures they had experienced. The most significant of these appeared to be night-time overheating

Overnight heating occurred for two reasons – to opti- mize the cost-efficiency of the heat pump and to facilitate DSR by pre-heating when (simulated) prices were lower overnight.

Furthermore, participants who had air-source heat pumps reported an increase in fan activity overnight:

While new tariffs and optimization by HEMS devices can avoid some of the problems associ- ated with legacy systems (e.g. by allowing more top-up heat during the day), unless additional storage is installed, they will be limited by the amount of pre- heating that can be achieved without undue impact on consumers’ thermal comfort, as found here.

These profiles show a much slower ramping up of demand overnight as the controller attempts to optimize the heat pump operation trading- off increased heat loss caused by higher MITs and improved heat pump energy efficiency through smoother operation. This change in the operation of the heat pump to a ‘smoother’ mode of operation is intended to improve the coefficient of performance (COP) of the heat pump.

The temperature evolution graph in particular illustrates preheating in advance of the 5 a.m. high price period which is repeated again before 4 p.m. (hour 16) and 10 p.m. (hour 22).

When such a price signal is in operation, the controller attempts to operate the heat pump in a man- ner that minimizes predicted energy costs by using the thermal inertia of the building to store energy.

In this case, the tariff was sent to the homes minutes before the high price period came into force, so there is little evidence of a demand spike before the tariff transition. During the high price period there is a dramatic reduction in demand with little impact on the temperature profile (Figure 10). It seems likely that this sort of short-term adjustment of temperature would be less impactful on consumers than that observed with the Economy 10 tariff.

This is a good example of the conflict between encouraging users to update their schedules reg- ularly in order to avoid unnecessary heating and save money and the need to be able to predict temperature demands in order to respond to time-varying prices.

There is evidence that occupants’ pre- existing heating routines (concerning their operation of the heating system, TRVs etc., but also use of doors and windows) will influence how acceptable the kind of intervention tested here will be.

Firstly, it was noted that almost all participants reported experiencing some kind of techni- cal problem with the controller during the trial.

In some cases, partici- pants reported technical problems, but no evidence was found of them on inspection during the interview. On observing the participants using the controller, it seemed likely they were not using it as the designers intended.

Participants’ views on whether this objec- tive were accomplished were mixed. The main factor tended to be whether or not participants considered themselves to be ‘tech savvy’.

When the outcome of actions is unclear, there appears to be lack of a connection between actions and outcomes (or contingency), diminishing the sense of control which was one of the main aims of the new control interface

recognizing, however, that combining innovative con- trols with heat pump technology that is not yet fully mature in the UK (e.g. concerning installation; Energy Saving Trust, 2010) is likely to mean that technical issues of the kind experienced here remain a reality for some time.

The analyses of the measured data in 31 homes showed that the HEMS delivered a more even internal temperature in their efforts to maximize the COP of the heat pumps by slight ly increasing late-night and daytime temperatures.

The Economy 10 test was less successful; consumption during high price periods was greater than expected and new peaks in demand were created in advance of price increases. Analysis of the case study data has indi- cated that this was partly due to participant interven- tion and partly d ue to the physical nature of the dwelling and heating systems.

The time-of-use tariff exper- iments showed mixed results. The CPP test resulted in a marked demand reduction of demand with no evidence of ‘crowding’ before the price increase.

During the trial, pre-heating in advance of high price periods led to overheating and noise which was unaccep- table to participants; however, short-term demand reductions could be delivered with minimal impact on internal temperatures.

No dedicated heat storage was present in the trial homes, which may be expected to be a common situation in the UK if the current trend of hot water tank removal continues. This will severely limit the level of demand flexibility available from heat pumps

Secondly, as Fischer and Madani (2017) point out, they assume perfect control of theflexibleheatpumpdemand without recourse to real-world interactions with consumers or the performance of the technology being deployed. This is understandable, because although there are many trials of demand shifting with heat pumps (e.g. the EcoGrid EU, PowerMatching City and eFlex projects discussed below), limited information is available on the performance of demand shifting in the field.

All recurrent neural networks have the form of a chain of repeating modules of neural network. In standard RNNs, this repeating module will have a very simple structure, such as a single tanh layer.

The repeating module in a standard RNN contains a single layer.

LSTMs also have this chain like structure, but the repeating module has a different structure. Instead of having a single neural network layer, there are four, interacting in a very special way.

The repeating module in an LSTM contains four interacting layers.

Don’t worry about the details of what’s going on. We’ll walk through the LSTM diagram step by step later. For now, let’s just try to get comfortable with the notation we’ll be using.

A slightly more dramatic variation on the LSTM is the Gated Recurrent Unit, or GRU, introduced by Cho, et al. (2014) . It combines the forget and input gates into a single “update gate.” It also merges the cell state and hidden state, and makes some other changes. The resulting model is simpler than standard LSTM models, and has been growing increasingly popular.

’s fast + wide + stable use amiodarone. 3 - If the rhythm’s slow + stable use atropine (epi drips can also be used in the new ACLS roll out). 4 - If the rhythm’s Afib/Aflutter (note this is the only rhythm that actually had to be identified to do the right intervention), rate control is preferred. If they were unstable shock them since