Penerapan Strategi Pembelajaran Crossword Puzzle Sebagai Upaya Meningkatkan Keaktifan Siswa Dalam Proses Pembelajaran Ekonomi Kelas VIII Di Madrasah Tsanawiyah Negeri Bekonang Filial Kartasura Tahun Pelajaran 2012/2013

Tujuan Penelitian Tindakan Kelas Ini adalah dengan penerapan strategi pembelajaran Croosword Puzzle untuk meningkatkan keaktifan dalam pembelajaranekonomi pada siswa kelasVIII. MTs Negeri Bekonang Filial Kartasura Tahun Ajaran 2012/2013. Penelitian ini menggunakan metode Penelitian Tindakan Kelas (PTK). Teknik pengumpulan data yang digunakan dalam penelitian ini adalah observasi, wawancara, dokumentasi. Teknik analisis yang digunakan adalah dengan model interaktif yang terdiri 3 kegiatan yaitu reduksi data, pengumpulan data, dan penarikan kesimpulan. Prosedur dalam Penelitian ini terdapat empat tahap yaitu perencanaan, pelaksanaan, pengamatan, dan refleksi. Penelitian ini dilakukan dengan dua siklus pertemuan yang bertujuan untuk memperoleh data peningkatan keaktifan dalam pembelajaran ekonomi siswa. Hasil penelitian dapat disimpulkan bahwa penerapan srtategi pembelajaran Crossword Puzzle dapat meningkatkan keaktifan pembelajaran ekonomi siswa. Hasil penelitian menunjukkan bahwa sebelum tindakan diperoleh rata – rata tingkat keaktifan sebesar 10%. Pada siklus I tingkat rata – rata keaktifan siswa meningkat menjadi 41,66%. Pada siklus II tingkat rata – rata keaktifan siswa meningkat menjadi 85%. Berdasarkan data hasil penelitian tindakan kelas tersebut maka dapat disimpulkan bahwa dengan penerapan srtategi pembelajaran Crossword Puzzle dapat meningkatkan keaktifan dalam pembelajaran ekonomi pada siswa kelas VIII. MTs Negeri Bekonang Filial Kartasura Tahun Ajaran 2012/2013. Kata kunci: Strategi pembelajaranCrossword Puzzle, Keaktifan, Ekonom

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

Background: Many patients with COVID-19 have been treated with plasma containing anti-SARS-CoV-2 antibodies. We aimed to evaluate the safety and efficacy of convalescent plasma therapy in patients admitted to hospital with COVID-19. Methods: This randomised, controlled, open-label, platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]) is assessing several possible treatments in patients hospitalised with COVID-19 in the UK. The trial is underway at 177 NHS hospitals from across the UK. Eligible and consenting patients were randomly assigned (1:1) to receive either usual care alone (usual care group) or usual care plus high-titre convalescent plasma (convalescent plasma group). The primary outcome was 28-day mortality, analysed on an intention-to-treat basis. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936. Findings: Between May 28, 2020, and Jan 15, 2021, 11558 (71%) of 16287 patients enrolled in RECOVERY were eligible to receive convalescent plasma and were assigned to either the convalescent plasma group or the usual care group. There was no significant difference in 28-day mortality between the two groups: 1399 (24%) of 5795 patients in the convalescent plasma group and 1408 (24%) of 5763 patients in the usual care group died within 28 days (rate ratio 1·00, 95% CI 0·93–1·07; p=0·95). The 28-day mortality rate ratio was similar in all prespecified subgroups of patients, including in those patients without detectable SARS-CoV-2 antibodies at randomisation. Allocation to convalescent plasma had no significant effect on the proportion of patients discharged from hospital within 28 days (3832 [66%] patients in the convalescent plasma group vs 3822 [66%] patients in the usual care group; rate ratio 0·99, 95% CI 0·94–1·03; p=0·57). Among those not on invasive mechanical ventilation at randomisation, there was no significant difference in the proportion of patients meeting the composite endpoint of progression to invasive mechanical ventilation or death (1568 [29%] of 5493 patients in the convalescent plasma group vs 1568 [29%] of 5448 patients in the usual care group; rate ratio 0·99, 95% CI 0·93–1·05; p=0·79). Interpretation: In patients hospitalised with COVID-19, high-titre convalescent plasma did not improve survival or other prespecified clinical outcomes. Funding: UK Research and Innovation (Medical Research Council) and National Institute of Health Research

Association of Pro12Ala polymorphism in peroxisome proliferator activated receptor gamma with proliferative diabetic retinopathy

Contains fulltext : 118015.pdf (publisher's version ) (Open Access)PURPOSE: The association of non-synonymous substitution polymorphism rs1801282 (c.34C>G, p.Pro12Ala) in exon 4 of the peroxisome proliferator activated receptor gamma gene with diabetic retinopathy (DR) has been reported inconsistently. Therefore, the purpose of the present study was to understand the population-specific role of the Pro12Ala polymorphism in DR susceptibility in Pakistani subjects. METHODS: A total of 180 subjects with DR, 193 subjects with type 2 diabetes mellitus (T2DM) with no diabetic retinopathy, and 200 healthy normoglycemic non-retinopathic Pakistani individuals were genotyped for the rs1801282 (c.34C>G) polymorphism using polymerase chain reaction-restriction fragment length polymorphism. RESULTS: We found the individuals with T2DM carrying 12Ala were at a reduced risk of developing DR (odds ratio [OR]=0.53; 95% confidence interval [CI]=0.33-0.87). Upon stratified analysis regarding disease severity, we observed this protective effect was confined to proliferative DR (OR=0.4; 95% CI=0.2-0.8) with non-significant effects on the susceptibility of non-proliferative DR (OR=0.67; 95% CI=0.37-1.19). CONCLUSIONS: We report a protective role of the 12Ala polymorphism against proliferative DR in individuals with T2DM in Pakistan

Online damage detection in plates via vibration measurements

In this work, we propose a new framework for the online detection of damage in plates via vibration measurements. To this end, a finite element model of the plate is handled by a recursive Bayesian filter for simultaneous state and parameter estimation. To drastically reduce the computational costs and enhance the robustness of the filter, such model is projected onto a (sub-) space spanned by a few vibration modes only, which are provided by a snapshot-based proper orthogonal decomposition (POD) method. A challenge in using such approach for damaging structures stems from the fact that vibration modes can be adjusted only during the training stage of the analysis; if damage occurs or grows when the reduced-order model is at work, the training stage has to be re-started. Here, an alternate method is proposed to concurrently update the sub-space spanned by the modes and to provide estimates of damage location and amplitude. The robustness and accuracy of the proposed approach are ascertained through an ad-hoc pseudo-experimental campaign. © The Society for Experimental Mechanics, Inc. 201

Fatigue monitoring and remaining lifetime prognosis using operational vibration measurements

A framework is presented for real-time monitoring of fatigue damage accumulation and prognosis of the remaining lifetime at hotspot locations of new or existing structures by combining output-only vibration measurements from a permanently installed, optimally located, sparse sensor network with the information build into high-fidelity computational mechanics models. To produce fatigue damage accumulation maps at component and/or system level, valid for the monitoring period, the framework integrates developments in (a) fatigue damage accumulation (FDA) and (b) stress time histories predictions under loading and structural modeling uncertainties based on monitoring information (Papadimitriou et al., Struct Control Health Monit 18(5):554–573, 2011). Methods and computational tools include, but are not limited to, the use of Kalman-type filters for state and stress response reconstruction based on the sensor information (Eftekhar Azam et al., Mech Syst Signal Process 60:866–886, 2015; Lourens et al., Mech Syst Signal Process 29:310–327, 2012), as well as stress cycle counting techniques, S-N curves and fatigue damage accumulation laws (Miner, Appl Mech Trans (ASME) 12(3):159–164, 1945; Palmgren, VDI-Z 68(14):339–341, 1924) to estimate fatigue from the reconstructed stress time histories at numerous hot spot locations. The FDA maps provide realistic fatigue estimates consistent with the actual operational conditions experienced by an individual structure. Combined with models of future loading events and their uncertainties, assumed or rationally estimated during the long-term monitoring period, the continuously updated FDA maps can be used to predict the remaining fatigue lifetime maps and associated uncertainties. Developments are valuable for planning cost-effective maintenance strategies, eventually reducing the life-cycle maintenance cost. © The Society for Experimental Mechanics, Inc. 2019

Experimental validation of the Kalman-type filters for online and real-time state and input estimation

In this study, a novel dual implementation of the Kalman filter proposed by Eftekhar Azam et al. (2014, 2015) is experimentally validated for simultaneous estimation of the states and input of structural systems. By means of numerical simulations, it has been shown that the proposed method outperforms existing techniques in terms of robustness and accuracy for the estimated displacement and velocity time histories. Herein, dynamic response measurements, in the form of displacement and acceleration time histories from a small-scale laboratory building structure excited at the base by a shake table, are considered for evaluating the performance of the proposed Dual Kalman filter and in order to compare this with available alternatives, such as the augmented Kalman filter (Lourens et al., 2012b) and the Gillijn De Moore filter (GDF) (2007b). The suggested Bayesian approach requires the availability of a physical model of the system in addition to output-only measurements from limited degrees of freedom. Two categories of such physical models are herein studied to evaluate the effect of model error on the filter performances; the first, is a model that comprises identified modal parameters, i.e., natural frequencies, mode shapes, modal damping ratios and modal participation factors; the second, is a model that is extracted from a recently developed subspace identification procedure, namely the transformed stochastic subspace identification method. The results are encouraging for the further use of the dual Kalman filter and its available alternatives for addressing the important problems of full response reconstruction and fatigue estimation in the entire body of linear structures, using a limited number of output-only vibration measurements. © The Author(s) 2015

Output-only fatigue prediction of uncertain steel structures

A fatigue estimation framework for steel structures is proposed in this study, under realistic assump-tions on the sensor network capacity and under the premise of uncertainty in the structural information available throughout the life-cycle of the monitored structure. To this purpose, in a first step, a joint input-state-parameter estimation problem is formulated, which integrates the dual Kalman filter and the unscented Kalman filter. The former aims at estimating the unknown structural excitations, while the latter solves the state and parameter estimation problem that is closely related to the estimation of stresses in critical areas. Accordingly, in a second step, a fatigue estimator is developed using material fatigue data and damage accumulation rules, which evaluates the stresses at all unmeasured hotspot locations of the structure to the fatigue damage accumulation and prognosis of the remaining fatigue life. Numerical simulations under different measurement setups and available structural properties are presented, in order to demonstrate the method's effectiveness

Experimental validation of the dual kalman filter for online and real-time state and input estimation

In this study, a novel dual implementation of the Kalman filter is proposed for simultaneous estimation of the states and input of structures via acceleration measurements. In practice, the uncertainties stemming from the absence of information on the input force, model inaccuracy and measurement errors render the state estimation a challenging task and the research to achieve a robust solution is still in progress. Via the use of numerical simulation, it was shown that the proposed method outperforms the existing techniques in terms of robustness and accuracy of displacement and velocity estimations [8]. The efficacy of the proposed method is validated using the data obtained from a shake table experiment on a laboratory test structure. The measured accelerations of the floors of the structure are fed into the filter, and the estimated time histories of the displacement estimates are cross-compared to the true time histories obtained from the displacement sensors. © The Society for Experimental Mechanics, Inc. 2015

Output-only schemes for joint input-state-parameter estimation of linear systems

The subject of predicting structural response, for control or fatigue assessment purposes, via output only vibration measurements is an emerging topic of Structural Health Monitoring. The subject of estimation of the states of a partially observed dynamic system within a stochastic framework has been studied by many scientists and there are well developed algorithms to manage both linear and nonlinear state-space models. Dealing with structural systems, the system states comprise the response displacements and velocities at the degrees of freedom of the structure. On one hand, in practical cases it is difficult or sometimes impossible to measure structural displacements and velocities for continuous monitoring purposes. On the other hand, recent developments in highly accurate low consumption wireless MEMS accelerometers permit continuous and accurate acceleration measurements when dealing with structural systems. Dealing with operational conditions the uncertainties stemming from the absence of information on the input force, model inaccuracy and measurement errors render the state estimation a challenging task, with research to achieve a robust solution still in progress. Eftekhar Azam et al. [1] have proposed a novel dual Kalman filter to accomplish the task of joint input-state estimation for linear time invariant systems. In this study, the extension of such a scheme is considered for the joint input-state and parameter estimation of linear systems