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The German Weather Service (DWD) releases a heat warning, when the weather forecast provides a warm, humid, sunny, and windless weather condition during the next days. The heat stress is calculated by the so called Klima-Michel model. If the apparent air temperature exceeds ca. 32°C / 38°C, there is a strong / extreme heat stress. The smallest forecast area is each administrative district. As people (and especially the vulnerable population) stay most of the time indoors, the heat health warning system was extended by the prediction of heat stress in typical rooms. Therewith it is feasible to forecast the heat stress using a combination of the outdoor and indoor heat stress. The prediction for the indoor heat stress is based on the same weather forecast like the Heat Health Warning Systems (HHWS).and calculates the heat stress by the PMV-model (predicted mean vote). Based on a sophisticated data analysis and simulation study, realistic but summer-critical living situations were defined and implemented in the building simulation program ESP-r. As the simulation runs especially for extreme weather conditions, a simplified building model cannot be used. Standardized input/output routines and an adaptive handover of start values provide for short run times for each forecast area. Good building designs and urban planning provide effective measures to reduce heat stress in cities. However, we have to also pay attention to the present building stock under climate change and a higher heat-wave risk. The extended German HHWS provide information for the emergency services to support the social assistants during heat waves.
This study presents some results from a monitoring project with night ventilation and earthto-air heat exchanger. Both techniques refer to air-based low-energy cooling. As these technologies are limited to specific boundary conditions (e.g. moderate summer climate, low temperatures during night, or low ground temperatures, respectively), water-based low-energy cooling may be preferred in many projects. A comparison of the night-ventilated building with a ground-cooled building shows major differences in both concepts.
Surface and interface acoustic waves are two-dimensionally guided waves, as their displacement field is plane-wave like regarding its dependence on the spatial coordinates parallel to the guiding plane, while it decays exponentially along the axis normal to that plane. When propagating at the planar surface or interface of homogeneous media, they are non-dispersive. Another type of non-dispersive acoustic waves which is, however, one-dimensionally guided, has displacement fields localized near the apex of a wedge made of an elastic material. In this short review, their propagation properties are described as well as theoretical and experimental methods which have been used for their analysis. Experimental findings are discussed in comparison with corresponding theoretical work and potential applications of this fascinating type of acoustic waves are presented.
Significance of new electrocardiographic parameters to improve cardiac resynchronization therapy
(2011)
Introduction: Oesophageal left heart electrogram (LHE) is a valuable tool providing electrocardiographic parameters for cardiac resynchronization therapy (CRT). It can be utilized to measure left ventricular (LVCD) and intra-leftventricular conduction delays (ILVCD) in heart failure patients to justify implantation of CRT systems. In the follow-up, LHE enables measurement of implant-related interatrial conduction times (IACT) which are the key intervals defining the hemodynamically optimal AV delay (AVD).
Methods: By TOSlim oesophageal electrode and Rostockfilter (Osypka AG, Rheinfelden, Germany), LHE was recorded in 39 heart failure patients (10f, 29m, 65±8yrs., QRS=163±21ms) after implantation of CRT systems according to guidelines. In position of maximal left ventricular deflection, LVCD and ILVCD were measured and compared with QRS width. In position of maximal left atrial deflection (LA), IACT was determined in VDD and DDD operation as interval As-LA and Ap-LA between atrial sense event (As) or stimulus (Ap), resp., and onset of LA. AVD was individualized using SAV =As-LA + 50ms for VDD and PAV=Ap-LA + 50ms for DDD operation.
Results: The CRT patients were characterized by minimal transoesophageal LVCD of 40ms but 73±20ms, at mean, ILVCD of 90±24ms and QRS/LVCD ratio of 2.4±0.6. The measured As-LA of 39±24ms and Ap-LA of 124±26ms resulted into SAV of 89±24ms and PAV of 174±26ms. In case of empirical AVD programming using 120ms for SAV and 180ms for PAV, the LHE revealed inverse sequences of LA and Vp in 4 patients (10%) during VDD and 13 patients (33%) in DDD pacing. In these patients, Vp preceded LA as IACT exceeded the programmed AVD.
Conclusion: Guideline indication of CRT systems is associated with LVCD of 40ms or more. Therefore, individual LVCD offers the minimal target interval that should be reached during left ventricular electrode placement to increase responder rate. Postoperatively, AV delay optimization respecting implant-related IACTs excludes adverse hemodynamic effects.
Electrical velocimetry to optimize VV delay in biventricular VVIR and DDD pacing for heart failure
(2011)
Introduction: VV delay (VVD) is the only parameter to hemodynamically optimize cardiac resynchronization therapy (CRT) for patients with atrial fibrillation (AF). Electrical velocimetry (EV) has been established to monitor thoracic electrical conductivity and to calculate hemodynamic surrogate parameters. We compared the response of this method to hemodynamic parameter changes between CRT patients with sinus rhythm (SR) and patients with AF.
Methods: VVD was individualized in 17 CRT patients in SR (12m, 5f, 67.0±7.2yrs.) after echo AV delay optimization and in 11 CRT patients in AF (10m, 1f, 69.8±9.6yrs.) using the Aesculon Cardiovascular Monitor (Osypka Medical, Berlin, Germany). Serial 30s EV recordings were accomplished, decreasing the VVD stepwise by 10ms from +60ms to -60ms between right and left ventricular stimulus. Optimal VVD was determined by the maximum of at least two of the three averaged parameters stroke volume (SV), cardiac output (CO) and cardiac index (CI). The response of SV, CO and CI was tested comparing their values in optimal VVD and suboptimal VVD. Suboptimal VVD was defined by optimal VVD±20ms.
Results: In all 28 patients in SR and AF, EV recordings resulted in optimal VVD. Between suboptimal and optimal mean VVD of 18.6±30.8ms between left and right ventricular stimulus, SV increased by 7.2±6.8%, CO by 7.8±7.2% and CI by 10.0±13.3% (all p<0.02). In the SR group with VVD of 18.8± 29.6ms, SV increased by 4.6±2.9%, CO by 5.0±2.9% and CI by 4.9±2.9% (all p<0.02). In the AF group with VVD of 18.2±4.0ms, SV increased by 10.4±8.9%, CO by 11.3±9.5% and CI by 16.4±18.2% (all p<0.02). Significant differences were not found between optimal VVD in SR and AF patients.
Conclusion: EV is a feasible serial method to individualize VVD in DDD and VVIR pacing for heart failure. Its response to hemodynamic changes demonstrates the value of EV for VVD fine-tuning.