Comparative Study of Mineral Content in Different Varieties of Green Tea

Camellia sinensis L. leaves composed of different concentrations of mineral contents play a vital role in human nutrition and health. In this study, locally processed three different green tea varieties i.e. P3, P5 and P9 were used for mineral content determination at National Tea and High Value Crops Research Institute (NTHRI), Shinkiari, Mansehra, Pakistan. Atomic absorption spectrophotometer, flame photometer and Kjehldal apparatus were used for the determination of mineral concentration in all the collected tea samples. Mineral composition of tea samples were identified in the following quantity order: high level of nitrogen (37300 to 41380 mg/L), calcium (515.6 to 522.1 mg/L) and phosphorus (742 to 1220 mg/L) were observed in all tea samples compared to other minerals. Cobalt (Co), molybdenum (Mo), sodium (Na), zinc (Zn), nickel (Ni) and copper (Cu) were highest in P3, while least amount was identified in P5, variety. On the contrary, calcium (Ca), potassium (K) and lead (Pb) contents were maximum in P5, while minimum were in P3. This study revealed that the levels of mineral elements in different varieties of green tea vary from the permissible level but the monitoring of their levels in tea plant is obligatory for future risk measurements.

Update on Cardiovascular Risk and Obesity in Psoriatic Arthritis

PsA is characterized by a high prevalence of cardiovascular (CV) comorbidities. Recognizing these comorbidities is critical due to their influence on the quality of life and the choice of therapy. Imaging techniques also play an important role in the evaluation of the CV risk in psoriatic disease, improving the prediction of CV events when combined with clinical scores as a predictive tool. Meta-analyses point to a significant reduction in the incidence of CV events associated with the suppression of inflammatory activity when using systemic therapies. Consequently, the mortality rate in PsA patients has fallen in the last 40 years and is now similar to that of the general population, including cardiovascular causes. Obesity is an especially relevant CV comorbidity in patients with psoriatic disease, most of whom are overweight/obese. Body mass index (BMI) is a risk factor for PsA and a causal relationship with psoriasis has been demonstrated by Mendelian randomized studies. The study of fat distribution shows that patients with psoriasis are characterized by visceral fat accumulation, which correlates with CV risk measurements. These findings suggest that approaches to the prevention and treatment of psoriatic disease might come from targeting adiposity levels, in addition to the immune pathways. Weight loss treatment with low energy diets in patients with PsA has been associated with significant improvements in disease activity. Novel strategies using a multimorbidity approach, focused more on patients outcomes, are necessary to better address comorbidities, improve clinical outcomes and the quality of life of patients with psoriatic disease.

Characteristics of Successful Securities Crowdfunding Campaigns in the United States

Title III of the Jumpstart Our Business Startups Act (JOBS Act) enacted by the U.S. Congress enables a new crowdfunding source of investment capital for entrepreneurs and a new opportunity for all investors (Regulation CF). Given the information asymmetry, the SEC requires that managers provide information to investors (Form C). Using this information, this research tests whether business attributes, financial risks, and offering characteristics are associated with successful crowdfunding efforts for 277 offerings originating during 2016-2017 and closed as of May 2018. The following attributes are positively correlated with funding success: product idea; prior managerial experience with startups; financial risks reported by management; availability of an independent CPA review; and, especially for companies reporting revenue, accounting risk measurements. Finally, the funding intermediary chosen is important and some were more successful than others. Overall, the results provide new insights concerning characteristics of successful security-based crowdfunding offerings.

Pricing risk and its use in modelling real estate market yields

Purpose In the light of past financial and economic turmoil, there has been a marked increase in the volatility in real estate markets. This has impacted on the pricing of property assets, partly through market sentiment and particularly concerning risk. It also limits modelling accuracy model accuracy. The purpose of this paper is to create a new variable and model to enhance analysis of what drives real estate yields incorporating market sentiment to risk. Design/methodology/approach This paper specifically considers the modelling of property pricing within a volatile economic environment. The theoretical context begins by analysing the relationship between property yields and government bonds. The analytical context then moves on to specifically include a measurement of risk which stresses its role and importance in investment markets since the Global Financial Crisis. The model thus incorporates macroeconomic and real estate data, together with an international risk multiplier, which is calculated within the paper. Findings The paper finds the use of measurements of market sentiment and risk are more powerful tools for modelling yields than previous techniques alone. Research limitations/implications This is an initial paper outlining the creation of sentiment and risk measurements in the financial market and showing an example of its application to a commercial real estate market. The implication is that this could add a major new explanatory variable to modelling of yields. Practical implications The paper highlights the importance of risk in the pricing of commercial real estate, over and above normal variables. It highlights how this can help explain over and undershooting of yields within commercial real estate which would be of great importance in the investment world. Originality/value This paper attempts to explicitly measure market sentiment, pricing of risk and how this impacts real estate pricing.

A Model Augmenting Credit Risk Management in the Banking Industry

A lack of reliable credit risk measurements and poor control of credit risks has caused massive financial losses across a wide spectrum of business. Financial institutions like banks have not been able to control and contain the rapid increases of the credit defaulting. In this paper, we address the credit lending challenges by eliminating credit defaulting faced by the banking industry. Data from bank of previously accepted and rejected loan applicants was used to construct a credit risk evaluation network. The artificial neural network technique with back-propagation algorithm was applied to develop a model that supports the banks in the credit granting decision-making. The model was trained to categorize applicants as either good (credit granted) or bad (credit denied) based on the credit record. The model was able to predict whether a particular applicant is likely or unlikely to repay the credit. The training of neural network model and validation testing was done using data obtained from the bank. The results show a greater performance, classification and prediction accuracy.

A New Model for Pricing Collateralized OTC Derivatives

ABSTRACTThis paper presents a new model for pricing OTC derivatives subject to collateralization. It allows for collateral posting adhering to bankruptcy laws. As such, the model can back out the market price of a collateralized contract. This framework is very useful for valuing outstanding derivatives. Using a unique dataset, we find empirical evidence that credit risk alone is not overly important in determining credit-related spreads. Only accounting for both collateral arrangement and credit risk can sufficiently explain unsecured credit costs. This finding suggests that failure to properly account for collateralization may result in significant mispricing of derivatives. We also empirically gauge the impact of collateral agreements on risk measurements. Our findings indicate that there are important interactions between market and credit risk. https://frenxiv.org/am8zy/download

A New Model for Pricing Collateralized OTC Derivatives

This paper presents a new model for pricing OTC derivatives subject to collateralization. It allows for collateral posting adhering to bankruptcy laws. As such, the model can back out the market price of a collateralized contract. This framework is very useful for valuing outstanding derivatives. Using a unique dataset, we find empirical evidence that credit risk alone is not overly important in determining credit-related spreads. Only accounting for both collateral arrangement and credit risk can sufficiently explain unsecured credit costs. This finding suggests that failure to properly account for collateralization may result in significant mispricing of derivatives. We also empirically gauge the impact of collateral agreements on risk measurements. Our findings indicate that there are important interactions between market and credit risk. https://osf.io/preprints/socarxiv/dh9mr/download