Cognitive bias, intuitive attributes and investment decision quality in commercial real estate in Uganda

PurposeThis study examines the relationship between investor cognitive bias, investor intuitive attributes and investment decision quality in commercial real estate in Uganda.Design/methodology/approachA cross-sectional research survey was used in this study, and data were collected from 200 investors of commercial real estate in Uganda using a structured questionnaire. Hierarchical regression analysis was used to test the hypotheses derived under this study.FindingsThe results indicate that investor cognitive bias and investor intuitive attributes are positive and significant determinants of investment decision quality in commercial real estate. In addition, the two components of Investor cognitive bias (framing variation and cognitive heuristics) are positive and significant determinants of investment decision quality, whereas mental accounting is a negative and significant determinant of investment decision quality. For investor intuitive attributes, confidence degree and loss aversion are positive and significant determinants of investment decision quality, whereas herding behavior is a negative and significant determinant of investment decision quality in commercial real estate in Uganda.Practical implicationsFor practitioners in commercial real estate sector should emphasize independent evaluation of investment opportunities (framing variation), simplify information regarding investments (Cognitive heuristics), believe in own abilities (Confidence degree), be risk averse (loss aversion) and avoid making decisions based on subjective visual mind (mental accounting) and group think/herding in order to make quality investment decisions. For policymakers, the study has illuminated factors such as provision of reliable information that ought to be taken into account when promulgating policies for regulation of the commercial real estate sector. This will help investors to come up with investment decisions which are plausible.Originality/valueFew studies have focused on investor cognitive bias and investor intuitive attributes on investment decision quality in commercial real estate. This study is the first to examine the relationship, especially in the commercial real estate sector in a developing country like Uganda.

Interval analysis-based Bi-iterative algorithm for robust TDOA-FDOA moving source localisation

In this study, an interval extension method of a bi-iterative is proposed to determine a moving source. This method is developed by utilising the time difference of arrival and frequency difference of arrival measurements of a signals received from several receivers. Unlike the standard Gaussian noise model, the time difference of arrival - frequency difference of arrival measurements are obtained by interval enclosing, which avoids convergence and initialisation problems in the conventional Taylor-series method. Using the bi-iterative strategy, the algorithm can alternately calculate the position and velocity of the moving source in interval vector form. Simulation results indicate that the proposed scheme significantly outperforms other methods, and approaches the Cramer-Rao lower bound at a sufficiently high noise level before the threshold effect occurs. Moreover, the interval widths of the results provide the confidence degree of the estimate.

Long-term target tracking combined with re-detection

Long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, the performances of most existing methods have been limited in the long-term tracking tasks. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art learning adaptive discriminative correlation filters (LADCF) tracking algorithm with a re-detection component based on the support vector machine (SVM) model. The LADCF tracking algorithm localizes the target in each frame, and the re-detector is able to efficiently re-detect the target in the whole image when the tracking fails. We further introduce a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to-correlation energy (APCE) to judge the confidence level of the predicted target. When the confidence degree is generally high, the SVM is updated accordingly. If the confidence drops sharply, the SVM re-detects the target. We perform extensive experiments on the OTB-2015 and UAV123 datasets. The experimental results demonstrate the effectiveness of our algorithm in long-term tracking.

Long-term target tracking combined with re-detection

The long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, the performances of most existing methods have been limited in the long-term tracking tasks. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art Learning Adaptive Discriminative Correlation Filters (LADCF) tracking algorithm with a re-detection component based on the SVM model. The LADCF tracking algorithm localizes the target in each frame and the re-detector is able to efficiently re-detect the target in the whole image when the tracking fails. We further introduce a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to correlation energy (APCE) to judge the confidence level of the predicted target. When the confidence degree is generally high, the SVM is updated accordingly. If the confidence drops sharply, the SVM re-detects the target. We perform extensive experiments on the OTB-2015 and UAV123 datasets. The experimental results demonstrate the effectiveness of our algorithm in long-term tracking.

The Important Herbal Pair for the Treatment of COVID-19 and Its Possible Mechanisms

Background: Coronavirus Disease 2019 (COVID-19) is an unprecedented disaster for people around the world. Many studies have shown that traditional Chinese medicine (TCM) are effective in treating COVID-19. However, it is difficult to find the most effective combination herbal pair among numerous herbs, as well as identifying its potential mechanisms. Herbal pair is the main form of a combination of TCM herbs, which is widely used for the treatment of diseases. It can also help us to better understand the compatibility of TCM prescriptions, thus improving the curative effects. The purpose of this article is to explore the compatibility of TCM prescriptions and identify the most important herbal pair for the treatment of COVID-19, and then analyze the active components and potential mechanisms of this herbal pair.Methods: We first systematically sorted the TCM prescriptions recommended by the leading experts for treating COVID-19, and the specific herbs contained in these prescriptions across different stages of the disease. Next, the association rule approach was employed to examine the distribution and compatibility among these TCM prescriptions, and then identify the most important herbal pair. On this basis, we further investigated the active ingredients and potential targets in the selected herbal pair by a network pharmacology approach, and analyzed the potential mechanisms against COVID-19. Finally, the main active compounds in AE were selected for molecular docking with severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) 3CLpro and angiotensin converting enzyme II (ACE2).Result: We obtained 32 association rules for the herbal combinations in the selection of TCM treatment for COVID-19. The results showed that the combination of Amygdalus Communis Vas (ACV) and Ephedra sinica Stapf (ESS) had the highest confidence degree and lift value, as well as high support degree, which can be used in almost all the stages of COVID-19, so ACV and ESS (AE) were selected as the most important herbal pair. There were 26 active ingredients and 44 potential targets, which might be related to the herbal pair of AE against COVID-19. The main active ingredients of AE against COVID-19 were quercetin, kaempferol, luteolin, while the potential targets were Interleukin 6 (IL-6), Mitogen-activated Protein Kinase 1 (MAPK)1, MAPK8, Interleukin-1β (IL-1β), and Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) p65 subunit (RELA). The protein-protein interaction (PPI) cluster demonstrated that IL-6 was the seed in the cluster, which plays an important role in connecting other nodes in the PPI network. The potential pathways mainly involved tumor necrosis factor (TNF), Toll-like receptor (TLR), hypoxia-inducible factor-1 (HIF-1), and nucleotide-binding oligomerization domain (NOD)-like receptor (NLRs). The molecular docking results showed that the main active ingredients of AE had good affinity with SARS-COV-2 3CLpro and ACE2.Conclusion: The combination of ACV and EAS was the most important herbal pair for the treatment of COVID-19. AE with multicomponents might have therapeutic effects against COVID-19 by affecting the inflammatory and immune responses, cell apoptosis, hypoxia damage and other pathological processes through multiple components, targets and pathways.

The Important Herbal Pair for the Treatment of COVID-19 and Its Possible Mechanisms

Abstract Background: Coronavirus Disease 2019 (COVID-19) is an unprecedented disaster for people around the world. Many studies have shown that traditional Chinese medicine (TCM) are effective in treating COVID-19. However, it is difficult to find the most effective combination herbal pair among numerous herbs, as well as identifying its potential mechanisms. Herbal pair is the main form of a combination of TCM herbs, which is widely used for the treatment of diseases. It can also help us to better understand the compatibility of TCM prescriptions, thus improving the curative effects. The purpose of this article is to explore the compatibility of TCM prescriptions and identify the most important herbal pair for the treatment of COVID-19, and then analyze the active components and potential mechanisms of this herbal pair. M ethods: We first systematically sorted the TCM prescriptions recommended by the leading experts for treating COVID-19, and the specific herbs contained in these prescriptions across different stages of the disease. Next, the association rule approach was employed to examine the distribution and compatibility among these TCM prescriptions, and then identify the most important herbal pair. On this basis, we further investigated the active ingredients and potential targets in the selected herbal pair by a network pharmacology approach, and analyzed the potential mechanisms against COVID-19. R esult: We obtained 32 association rules for the herbal combinations in the selection of TCM treatment for COVID-19. The results showed that the combination of Amygdalus Communis Vas (ACV) and Ephedra sinica Stapf (ESS) had the highest confidence degree and lift value, as well as high support degree, which can be used in almost all the stages of COVID-19, so ACV and ESS (AE) were selected as the most important herbal pair. There were 26 active ingredients and 44 potential targets, which might be related to the herbal pair of AE against COVID-19. The main active ingredients of AE against COVID-19 were quercetin, kaempferol, luteolin, while the potential targets were Interleukin 6 (IL-6), Mitogen-activated Protein Kinase 1 (MAPK)1, MAPK8, Interleukin-1β (IL-1β), and Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) p65 subunit (RELA). The protein-protein interaction (PPI) cluster demonstrated that IL-6 was the seed in the cluster, which plays an important role in connecting other nodes in the PPI network. The potential pathways mainly involved tumor necrosis factor (TNF), Toll-like receptor (TLR), hypoxia-inducible factor-1 (HIF-1), and nucleotide-binding oligomerization domain (NOD)-like receptor (NLRs). C onclusion: The combination of ACV and EAS was the most important herbal pair for the treatment of COVID-19. AE might have therapeutic effects against COVID-19 by affecting the inflammatory and immune responses, cell apoptosis, hypoxia damage and other pathological processes through multiple components, targets and pathways..

Long-term target tracking combined with re-detection

The long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, the performances of most existing methods have been limited in the long-term tracking tasks. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art Learning Adaptive Discriminative Correlation Filters (LADCF) tracking algorithm with a re-detection component based on the SVM model. The LADCF tracking algorithm localizes the target in each frame and the re-detector is able to efficiently re-detect the target in the whole image when the tracking fails. We further introduce a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to correlation energy (APCE) to judge the confidence level of the predicted target. When the confidence degree is generally high, the SVM is updated accordingly. If the confidence drops sharply, the SVM re-detects the target. We perform extensive experiments on the OTB-2015 and UAV123 datasets. The experimental results demonstrate the effectiveness of our algorithm in long-term tracking.

A novel portfolio selection with prospect value constraint and distance measure of IFSs based on the improved entropy-weighted method

The purpose of this paper is to solve the portfolio selection problem when historical data are unavailable. In this paper, the problem is viewed as a multi-criteria decision making (MCDM) problem under intuitionistic fuzzy circumstances, and the prospect theory is utilized to reflect decision makers’ psychological state, which is always bounded rational. Therefore, a new approach to solve MCDM problems is presented based on the following improvements. (a) The entropy-weighted method with extreme data resistance is proposed instead of weight function to deal with the weight of criteria, because weight stands for the decision maker’s preference of criteria rather than objective probability and should not be distorted. (b) A new entropy-weighted method with confidence degree is presented, which can not only describe the uncertainty of information each criterion provides but also reflect the decision maker’s confidence in the information. (c) To reduce the interference from extreme data, the median is selected as reference point instead of mean or extreme value. (d) Based on the distance measure, the intuitionistic fuzzy prospect value function is presented to capture decision makers’ psychological state. Finally, a novel model with prospect value constraint and risk preference is constructed to allocate investment ratios. For our proposed method and model, two numerical applications are given to verify their validity and the sensitivity analysis is carried out to illustrate their practical significance.

Analysis of Environmental Factors with Chronic Diarrhea in Toddlers in Jambi City in 2019

Chronic diarrhea is defecation with a frequency of 3 or more times in infants and children lasting for 14 days. The impact of diarrheal disease in general causes loss of fluid in the body (dehydration) and chronic diarrhea can cause a child to experience poor nutritional status and experience growth failure. This study uses a case-control design using a retrospective approach. The number of samples in this study was 135 respondents. Instruments for collecting data in the form of questionnaires and observations. Data were analyzed by univariate, bivariate using the Chi-Square test, and multivariate analysis with multiple logistic regression. The statistical test results obtained p-value on the variables of clean water supply (0.007), latrine ownership (0.001), sewerage system (0.04), confidence degree 95% Confidence Interval (95% CI) and p-value ˂ 0, 05, it can be concluded that there is a significant relationship with chronic diarrheal disease in infants. The results of multiple logistic regression tests, on the variable wastewater discharge obtained OR = 3.801, meaning that sewerage is closely related to causing chronic diarrheal disease in infants.

Long-term target tracking combined with re-detection

The long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, most existing methods have not been done and their performances have also been limited. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art Discriminative Correlation Filters (DCF) tracking algorithm with a re-detection component based on the SVM model. The DCF tracking algorithm localizes the target in each frame and the re-detector is able to efficiently re-detect the target in the whole image when the tracking fails. We further introduce a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to correlation energy (APCE) to judge the confidence level of the predicted target. When the confidence degree is generally high, the SVM is updated accordingly. If the confidence drops sharply, the SVM re-detects the target. We perform extensive experiments on the OTB-2015 dataset, the experimental results demonstrate the effectiveness of our algorithm in long-term tracking.