Application of exhaust gas fuel reforming in diesel and homogeneous charge compression ignition (HCCI) engines fuelled with biofuels

This is the post-print version of the final paper published in Energy. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2007 Elsevier B.V.This paper documents the application of exhaust gas fuel reforming of two alternative fuels, biodiesel and bioethanol, in internal combustion engines. The exhaust gas fuel reforming process is a method of on-board production of hydrogen-rich gas by catalytic reaction of fuel and engine exhaust gas. The benefits of exhaust gas fuel reforming have been demonstrated by adding simulated reformed gas to a diesel engine fuelled by a mixture of 50% ultra low sulphur diesel (ULSD) and 50% rapeseed methyl ester (RME) as well as to a homogeneous charge compression ignition (HCCI) engine fuelled by bioethanol. In the case of the biodiesel fuelled engine, a reduction of NOx emissions was achieved without considerable smoke increase. In the case of the bioethanol fuelled HCCI engine, the engine tolerance to exhaust gas recirculation (EGR) was extended and hence the typically high pressure rise rates of HCCI engines, associated with intense combustion noise, were reduced

Effect of inlet valve timing and water blending on bioethanol HCCI combustion using forced induction and residual gas trapping

This is the post-print version of the final paper published in Fuel. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2007 Elsevier B.V.It has been shown previously that applying forced induction to homogeneous charge compression ignition (HCCI) combustion of bioethanol with residual gas trapping, results in a greatly extended engine load range compared to normal aspiration operation. However, at very high boost pressures, very high cylinder pressure rise rates develop. The approach documented here explores two ways that might have an effect on combustion in order to lower the maximum pressure rise rates and further improve the emissions of oxides of nitrogen (NOx); inlet valve timing and water blending. It was found that there is an optimal inlet valve timing. When the timing was significantly advanced or retarded away from the optimal, the combustion phasing could be retarded for a given lambda (excess air ratio). However, this would result in higher loads and lower lambdas for a given boost pressure, with possibly higher NOx emissions. Increasing the water content in ethanol gave similar results as the non-optimal inlet valve timing

Soft set theory and topology

[EN] In this paper we study and discuss the soft set theory giving new definitions, examples, new classes of soft sets, and properties for mappings between different classes of soft sets. Furthermore, we investigate the theory of soft topological spaces and we present new definitions, characterizations, and properties concerning the soft closure, the soft interior, the soft boundary, the soft continuity, the soft open and closed maps, and the soft homeomorphism.Georgiou, DN.; Megaritis, AC. (2014). Soft set theory and topology. Applied General Topology. 15(1):93-109. doi:http://dx.doi.org/10.4995/agt.2014.2268.93109151Aktaş, H., & Çağman, N. (2007). Soft sets and soft groups. Information Sciences, 177(13), 2726-2735. doi:10.1016/j.ins.2006.12.008Ali, M. I., Feng, F., Liu, X., Min, W. K., & Shabir, M. (2009). On some new operations in soft set theory. Computers & Mathematics with Applications, 57(9), 1547-1553. doi:10.1016/j.camwa.2008.11.009Aygünoğlu, A., & Aygün, H. (2011). Some notes on soft topological spaces. Neural Computing and Applications, 21(S1), 113-119. doi:10.1007/s00521-011-0722-3Çağman, N., & Enginoğlu, S. (2010). Soft set theory and uni–int decision making. European Journal of Operational Research, 207(2), 848-855. doi:10.1016/j.ejor.2010.05.004Çağman, N., & Enginoğlu, S. (2010). Soft matrix theory and its decision making. Computers & Mathematics with Applications, 59(10), 3308-3314. doi:10.1016/j.camwa.2010.03.015Çağman, N., Karataş, S., & Enginoglu, S. (2011). Soft topology. Computers & Mathematics with Applications, 62(1), 351-358. doi:10.1016/j.camwa.2011.05.016Chen, D., Tsang, E. C. C., Yeung, D. S., & Wang, X. (2005). The parameterization reduction of soft sets and its applications. Computers & Mathematics with Applications, 49(5-6), 757-763. doi:10.1016/j.camwa.2004.10.036Feng, F., Jun, Y. B., & Zhao, X. (2008). Soft semirings. Computers & Mathematics with Applications, 56(10), 2621-2628. doi:10.1016/j.camwa.2008.05.011Hussain, S., & Ahmad, B. (2011). Some properties of soft topological spaces. Computers & Mathematics with Applications, 62(11), 4058-4067. doi:10.1016/j.camwa.2011.09.051O. Kazanci, S. Yilmaz and S. Yamak, Soft Sets and Soft BCH-Algebras, Hacettepe Journal of Mathematics and Statistics 39, no. 2 (2010), 205-217.KHARAL, A., & AHMAD, B. (2011). MAPPINGS ON SOFT CLASSES. New Mathematics and Natural Computation, 07(03), 471-481. doi:10.1142/s1793005711002025Maji, P. K., Roy, A. R., & Biswas, R. (2002). An application of soft sets in a decision making problem. Computers & Mathematics with Applications, 44(8-9), 1077-1083. doi:10.1016/s0898-1221(02)00216-xMaji, P. K., Biswas, R., & Roy, A. R. (2003). Soft set theory. Computers & Mathematics with Applications, 45(4-5), 555-562. doi:10.1016/s0898-1221(03)00016-6P. K. Maji, R. Biswas and A. R. Roy, Fuzzy soft sets, J. Fuzzy Math. 9, no. 3 (2001), 589-602.MAJUMDAR, P., & SAMANTA, S. K. (2008). SIMILARITY MEASURE OF SOFT SETS. New Mathematics and Natural Computation, 04(01), 1-12. doi:10.1142/s1793005708000908Min, W. K. (2011). A note on soft topological spaces. Computers & Mathematics with Applications, 62(9), 3524-3528. doi:10.1016/j.camwa.2011.08.068Molodtsov, D. (1999). Soft set theory—First results. Computers & Mathematics with Applications, 37(4-5), 19-31. doi:10.1016/s0898-1221(99)00056-5D. A. Molodtsov, The description of a dependence with the help of soft sets, J. Comput. Sys. Sc. Int. 40, no. 6 (2001), 977-984.D. A. Molodtsov, The theory of soft sets (in Russian), URSS Publishers, Moscow, 2004.D. A. Molodtsov, V. Y. Leonov and D. V. Kovkov, Soft sets technique and its application, Nechetkie Sistemy i Myagkie Vychisleniya 1, no. 1 (2006), 8-39.D. Pei and D. Miao, From soft sets to information systems, In: X. Hu, Q. Liu, A. Skowron, T. Y. Lin, R. R. Yager, B. Zhang, eds., Proceedings of Granular Computing, IEEE, 2 (2005), 617-621.Shabir, M., & Naz, M. (2011). On soft topological spaces. Computers & Mathematics with Applications, 61(7), 1786-1799. doi:10.1016/j.camwa.2011.02.006Shao, Y., & Qin, K. (2011). The lattice structure of the soft groups. Procedia Engineering, 15, 3621-3625. doi:10.1016/j.proeng.2011.08.678I. Zorlutuna, M. Akdag, W. K. Min and S. Atmaca, Remarks on soft topological spaces, Annals of Fuzzy Mathematics and Informatics 3, no. 2 (2012), 171-185.Zou, Y., & Xiao, Z. (2008). Data analysis approaches of soft sets under incomplete information. Knowledge-Based Systems, 21(8), 941-945. doi:10.1016/j.knosys.2008.04.00

Effect of portable noninvasive ventilation on thoracoabdominal volumes in recovery from intermittent exercise in patients with COPD

We previously showed that use of portable noninvasive ventilation (pNIV) during recovery periods within intermittent exercise improved breathlessness and exercise tolerance in patients with COPD compared with pursed-lip breathing (PLB). However, in a minority of patients recovery from dynamic hyperinflation (DH) was better with PLB, based on inspiratory capacity. We further explored this using Optoelectronic Plethysmography to assess total and compartmental thoracoabdominal volumes. Fourteen patients with COPD (means ± SD) (FEV1: 55% ± 22% predicted) underwent, in a balanced order sequence, two intermittent exercise protocols on the cycle ergometer consisting of five repeated 2-min exercise bouts at 80% peak capacity, separated by 2-min recovery periods, with application of pNIV or PLB in the 5 min of recovery. Our findings identified seven patients showing recovery in DH with pNIV (DH responders) whereas seven showed similar or better recovery in DH with PLB. When pNIV was applied, DH responders compared with DH nonresponders exhibited greater tidal volume (by 0.8 ± 0.3 L, P = 0.015), inspiratory flow rate (by 0.6 ± 0.5 L/s, P = 0.049), prolonged expiratory time (by 0.6 ± 0.5 s, P = 0.006), and duty cycle (by 0.7 ± 0.6 s, P = 0.007). DH responders showed a reduction in end-expiratory thoracoabdominal DH (by 265 ± 633 mL) predominantly driven by reduction in the abdominal compartment (by 210 ± 494 mL); this effectively offset end-inspiratory rib-cage DH. Compared with DH nonresponders, DH responders had significantly greater body mass index (BMI) by 8.4 ± 3.2 kg/m2, P = 0.022 and tended toward less severe resting hyperinflation by 0.3 ± 0.3 L. Patients with COPD who mitigate end-expiratory rib-cage DH by expiratory abdominal muscle recruitment benefit from pNIV application. NEW & NOTEWORTHY Compared with the pursed-lip breathing technique, acute application of portable noninvasive ventilation during recovery from intermittent exercise improved end-expiratory thoracoabdominal dynamic hyperinflation (DH) in 50% of patients with COPD (DH responders). DH responders, compared with DH nonresponders, exhibited a reduction in end-expiratory thoracoabdominal DH predominantly driven by the abdominal compartment that effectively offset end-expiratory rib cage DH. The essential difference between DH responders and DH nonresponders was, therefore, in the behavior of the abdomen

Correction to: Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium (<em>Journal of NeuroEngineering and Rehabilitation</em>, (2023), 20, 1, (78), 10.1186/s12984-023-01198-5)

\ua9 The Author(s) 2024.Following publication of the original article [1], the author noticed the errors in Table 1, and in Discussion section. In Table 1 under Metric (Gait sequence detection) column, the algorithms GSDB was updated with wrong description, input, output, language and citation and GSDc with wrong description has been corrected as shown below: (Table presented.) Description of algorithms for each metric: gait sequence detection (GSD), initial contact event detection (ICD), cadence estimation (CAD) and stride length estimation (SL) Metric Name Description Input Output Language References GSDA Based on a frequency-based approach, this algorithm is implemented on the vertical and anterior–posterior acceleration signals. First, these are band pass filtered to keep frequencies between 0.5 and 3 Hz. Next, a convolution of a 2 Hz sinewave (representing a template for a gait cycle) is performed, from which local maxima will be detected to define the regions of gait acc_v: vertical acceleration acc_ap: anterior–posterior acceleration WinS = 3 s; window size for convolution OL = 1.5 s; overlap of windows Activity_thresh = 0.01; Motion threshold Fs: sampling frequency Start: beginning of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 7 N vector End: termination of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 7 N vector Matlab\uae Iluz, Gazit [40] GSDB This algorithm, based on a time domain-approach, detects the gait periods based on identified steps. First, the norm of triaxial acceleration signal is low-pass filtered (FIR, fc = 3.2 Hz), then a peak detection procedure using a threshold of 0.1 [g] is applied to identify steps. Consecutive steps, detected using an adaptive step duration threshold are associated to gait sequences acc_norm: norm of the 3D-accelerometer signal Fs: sampling frequency th: peak detection threshold: 0.1 (g) Start: beginning of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 7 N vector End: termination of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 7 N vector Matlab\uae Paraschiv-Ionescu, Newman [41] GSDc This algorithm utilizes the same approach as GSDBthe only difference being a different threshold for peak detection of 0.15 [g] acc_norm: norm of the 3D-accelerometer signal Fs: sampling frequency th: peak detection threshold: 0.15 (g) Start: beginning of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 7 N vector End: termination of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 7 N vector Matlab\uae Paraschiv-Ionescu, Newman [41] In Discussion section, the paragraph should read as "Based on our findings collectively, we recommend using GSDB on cohorts with slower gait speeds and substantial gait impairments (e.g., proximal femoral fracture). This may be because this algorithm is based on the acceleration norm (overall accelerometry signal rather than a specific axis/direction (e.g., vertical), hence it is more robust to sensor misalignments that are common in unsupervised real-life settings. Moreover, the use of adaptive threshold, that are derived from the features of a subject’s data and applied to step duration for detection of steps belonging to gait sequences, allows increased robustness of the algorithm to irregular and unstable gait patterns" instead of “Based on our findings collectively, we recommend using GSDB on cohorts with slower gait speeds and substantial gait impairments (e.g., proximal femoral fracture). This may be because this algorithm is based on the acceleration norm (overall accelerometry signal rather than a specific axis/direction (e.g., vertical), hence it is more robust to sensor misalignments that are common in unsupervised real-life settings [41]. Moreover, the use of adaptive thresholds, that are derived from the features of a subject’s data and applied to the amplitude of acceleration norm and to step duration for detection of steps belonging to gait sequences, allows increased robustness of the algorithm to irregular and unstable gait patterns”

Expression and Membrane Topology of Anopheles gambiae Odorant Receptors in Lepidopteran Insect Cells

A lepidopteran insect cell-based expression system has been employed to express three Anopheles gambiae odorant receptors (ORs), OR1 and OR2, which respond to components of human sweat, and OR7, the ortholog of Drosophila's OR83b, the heteromerization partner of all functional ORs in that system. With the aid of epitope tagging and specific antibodies, efficient expression of all ORs was demonstrated and intrinsic properties of the proteins were revealed. Moreover, analysis of the orientation of OR1 and OR2 on the cellular plasma membrane through the use of a novel ‘topology screen’ assay and FACS analysis demonstrates that, as was recently reported for the ORs in Drosophila melanogaster, mosquito ORs also have a topology different than their mammalian counterparts with their N-terminal ends located in the cytoplasm and their C-terminal ends facing outside the cell. These results set the stage for the production of mosquito ORs in quantities that should permit their detailed biochemical and structural characterization and the exploration of their functional properties

Laboratory and free-living gait performance in adults with COPD and healthy controls

Background Gait characteristics are important risk factors for falls, hospitalisations and mortality in older adults, but the impact of COPD on gait performance remains unclear. We aimed to identify differences in gait characteristics between adults with COPD and healthy age-matched controls during 1) laboratory tests that included complex movements and obstacles, 2) simulated daily-life activities (supervised) and 3) free-living daily-life activities (unsupervised). Methods This case–control study used a multi-sensor wearable system (INDIP) to obtain seven gait characteristics for each walking bout performed by adults with mild-to-severe COPD (n=17; forced expiratory volume in 1 s 57±19% predicted) and controls (n=20) during laboratory tests, and during simulated and free-living daily-life activities. Gait characteristics were compared between adults with COPD and healthy controls for all walking bouts combined, and for shorter (≤30 s) and longer (>30 s) walking bouts separately. Results Slower walking speed (−11 cm·s−1, 95% CI: −20 to −3) and lower cadence (−6.6 steps·min−1, 95% CI: −12.3 to −0.9) were recorded in adults with COPD compared to healthy controls during longer (>30 s) free-living walking bouts, but not during shorter (≤30 s) walking bouts in either laboratory or free-living settings. Double support duration and gait variability measures were generally comparable between the two groups. Conclusion Gait impairment of adults with mild-to-severe COPD mainly manifests during relatively long walking bouts (>30 s) in free-living conditions. Future research should determine the underlying mechanism(s) of this impairment to facilitate the development of interventions that can improve free-living gait performance in adults with COPD

Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases

Introduction: The clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings. Methods: Here, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data. Results and discussion: The results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of −0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, −0.07, and <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases