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This study investigates the influence of infector location and classroom size on indoor air quality, utilizing CO2 as a tracer gas. We employ Computational Fluid Dynamics (CFD) simulations, focusing on the local air quality index as a crucial evaluation parameter. Our research underscores the intricate relationship between infector location, classroom size, and ventilation effectiveness. In a learning environment comprising 16 students and one teacher, larger classrooms, approximately 2.3 times in size, demonstrated the potential to reduce the number of vulnerable students. However, the occupants in the proximity to the infector may still face an increased risk of exposure. The CFD results exhibit that the increasing size (dimensions) of the teaching space significantly reduced the number of vulnerable occupants in all simulated test cases. Moreover, our study also reveals that, when the infector is located near return openings, it not only enhances air quality in its immediate vicinity but also positively influences the overall classroom space.

This paper focuses on estimating the Self-Exciting Threshold Autoregressive (SETAR) type time-series model under right-censored data. As is known, the SETAR model is used when the underlying function of the relation-ship between the time-series itself (Yt), and its p delays (Yt−j)j=1p$$({Y_{t - j}})_{j = 1}^p$$ violates the lin-earity assumption and this function is formed by multiple behaviors that called regime. This paper addresses the right-censored dependent time-series problem which has a serious negative effect on the estimation performance. Right-censored time series cause biased coefficient estimates and unqualified predictions. The main contribution of this paper is solving the censorship problem for the SETAR by three different techniques that are kNN imputation which represents the imputation techniques, Kaplan-Meier weights that is applied based on the weighted least squares, synthetic data transformation which adds the effect of censorship to the modeling process by manipulating dataset. Then, these solutions are combined by the SETAR-type model estimation process. To observe the behavior of the nonlinear estimators in practice, a simulation study and a real data example are carried out. The Covid-19 dataset collected in China is used as real data. Results prove that although the three estimators show satisfying performance, the quality of the estimate SETAR model based on the kNN imputation technique dominates the other two estimators.

In the literature, new machine learning algorithms are dynamically produced in the field of artificial intelligence engineering and the algorithms are constantly updated with new parameter estimations. The performance of existing algorithms in various business areas is still an important topic of discussion. Also, machine learning algorithms are frequently used in long-term credit ratings, which is an crucially important sub-branch of finance. This study was conducted to determine which popular machine learning model performs better in credit scoring. Artificial Neural Network, Random Forest, Support Vector Machine and K Nearest Neighbor were used to determine the algorithm that is suitable for the structure, attribute content and distribution of the data, and the operating logic of the models. In the study, the long-term credit rating is the target variable and the remaining variables are the features, the prediction performances of these 4 algorithm, which are frequently used in previous studies such as credit rating, credit risk, fraud analysis were compared. After data preprocessing, a classification study was carried out using the features included in the model. The metrics used in the comparison are MSE, RMSE, MAE and accuracy. According to the metrics, RF algorithm showed the best performance in the credit scoring.

In this paper, we improved the efficiency of parameter estimation in partially linear models, where subspace information is available. We proposed linear shrinkage and shrinkage pretest estimation strategies. The asymptotic distributional risk of the proposed estimators was examined. We also conducted a Monte Carlo simulation to evaluate the risk performance of the estimators. The proposed estimators performed better than the unrestricted estimator. A real data example was used to illustrated the application of the proposed estimators.

Currency depreciation as a channel of output management has been a hot and controversial topic in both developed and developing economies. In Pakistan’s case, relevant research would require study of annual data available for the period 1972 to 2010. The stationarity of variables under consideration at different orders require the application of the bounds testing approach to cointegration. The findings based on open economy IS-LM framework induce a negative effect of currency depreciation on output levels. This is consistent with the long-run estimates of the autoregressive distributive lag (ARDL) model. The short-run estimates of error-correction model (ECM) may lead to significant increment in output levels because of the depreciation of Pakistan rupee. Government spending may cause to reduce the output in the short-run, as well as, in long-run which furnishes strong support to crowding out hypothesis. The terms of trade, positive in the short-run, are negatively related to output in the long-run. However, surprise money has been insignificant in both long-run and short-run ECM. The country would need a clear long-term policy regime that inspires trust of the international community and restores the exporters’ confidence.

This study investigated the Landsat-8 Operational Land Imager (OLI) to determine its suitability for change detection analysis and mapping applications due to its enhanced signal-to-noise ratio (SNR), spectral band configuration, technical superiority, improved system design and high radiometric resolution. Earlier Landsat series had a lower SNR, comparatively smaller radiometric resolution and limitations in spectral band configurations. Pre-classification methods e.g., image differencing; image rationing, vegetation indices and principal components used for change detection analysis do not indicate type of the change due to its limitations. Therefore, better technique of post classification change detection was used on OLI data in the study area which provided information of the type of the change i.e., from-to change detection. This paper evaluated the OLI support vector machines (SVM) classification suitability using data covering four different seasons (i.e., spring, autumn, winter, and summer) after pre-processing and atmospheric correction. After classification, the major change detection results of OLI SVM-classified data for the four seasons were compared to change detection results of six cases: (1) winter to spring; (2) winter to summer; (3) winter to autumn; (4) spring to summer; (5) spring to autumn; and (6) summer to autumn. Seasonal change in the shoreline resulted with the corresponding change in categories. The OLI data classifications were made by applying SVM classifier and due to its improved features, OLI data is found suitable for seasonal land cover classification and post classification change detection analysis.