Samir Safi | Islamic University of Gaza (original) (raw)

Papers by Samir Safi

Research Square (Research Square), Dec 3, 2023

This study investigates the impact of senior management's nancial intelligence on the nancial per... more This study investigates the impact of senior management's nancial intelligence on the nancial performance of banks and insurance companies operating in the Gaza Strip. The study followed the descriptive analytical approach to measure nancial performance by relying on the nancial reports of the sample companies for 2022. The results indicated that the dimensions of nancial intelligence were available. Banks and insurance companies have good nancial performance. Furthermore, nancial intelligence signi cantly affected the performance of banks and insurance companies, as measured by return on investment (ROI) and return on assets (ROA). The dimensions of nancial intelligence also signi cantly impacted the performance of banks and insurance companies as measured by return on equity (ROE). Simultaneously, there was no signi cant effect of nancial intelligence on the performance of banks and insurance companies as measured by ROE. To the best of the author's knowledge, the current study is among the rst research efforts in Palestine. The ndings indicate that senior management should pay greater attention to developing nancial intelligence skills among employees in supervisory positions, especially those in the administrative, nancial, and planning elds, due to their impact on improving companies' performance.

F1000Research

Background: Reducing global food waste is an international environmental, health, and sus-tainabi... more Background: Reducing global food waste is an international environmental, health, and sus-tainability priority. Although significant reductions have been achieved across the food chain, progress by UAE households and consumers remain inadequate. This study seeks to understand the association between consumer attitudes, knowledge, and awareness relating to food waste practice of residents living in the UAE. to help inform policy and action for addressing this national priority. Methods: A cross-sectional study was conducted using a validated semi-structured online questionnaire through stratified sampling (n =1052). The Spearman correlation coefficient was performed to determine the correlations. Two independent regression analysis were used to determine the association between food waste practice with: 1) knowledge and awareness and attitude subdomains, and 2) sociodemographic characteristics. Respondents (n=1072) largely reflect the socio-demographic characteristics and population ...

British Journal of Multidisciplinary and Advanced Studies, Oct 21, 2023

The Autoregressive Integrated Moving Average (ARIMA) model cannot capture the nonlinear patterns ... more The Autoregressive Integrated Moving Average (ARIMA) model cannot capture the nonlinear patterns exhibited by the 2019 coronavirus (COVID-19) in terms of daily growth factor. As a result, Artificial Neural Networks (ANNs) and Hybrid ARIMA-ANN models have been successfully applied to resolve problems with nonlinear estimation. We compare the forecasting performance of these models using real, worldwide, daily COVID-19 data. The best forecasting model selected was compared using the forecasting assessment criterion known as mean absolute error. The main finding results show that the ANN model is more efficient than the ARIMA and Hybrid ARIMA-ANN models. The main finding from the ANN model analysis indicates that the magnitude of the increase in growth factor over time is rising in general while the percentage change in the growth factor is declining. This may be the result of the social distancing, safety, and cautionary measures mandated by governments worldwide.

American Journal of Theoretical and Applied Statistics, 2016

Time series of quarterly observations on Gross Domestic Product (GDP) is collected and used in th... more Time series of quarterly observations on Gross Domestic Product (GDP) is collected and used in this study. Forecasting results of ANNs are compared with those of the Autoregressive Integrated Moving Average (ARIMA) and regression as benchmark methods. Using Root Mean Square Error (RMSE), the empirical results show that ANN performs better than the traditional methods in forecasting GDP.

IUG Journal of Natural Studies, Dec 2, 2015

IUG Journal of Natural Studies, Dec 12, 2015

Advances in Decision Sciences, 2022

The primary objective of this paper is to identify the best forecasting model for China exports, ... more The primary objective of this paper is to identify the best forecasting model for China exports, especially during the spread of the COVID-19 pandemic. Methodology: We used the data of China exports to the United States and different economic regions from January 2014 to January 2021 to compare models using various criteria and selected the best exports forecast model. The hybrid model is employed to conduct the analysis. The combination of the hybrid model consists of six different models: ARIMA, ETS, Theta, NNAR, seasonal and trend decomposition, and TBATS model. Findings: Our results showed that the hybrid and ANN outperformed the remaining models in forecasting China exports to the world, considering the shock created by the ongoing coronavirus pandemic. This paper underscores the importance of using the specified models in forecasting exports during this period. The results also demonstrate that the magnitude of China exports to all groups decreased and will continue to decline for the next few months. Practical Implication: Forecasting of the export data is presented for the subsequent nine months, thereby providing insights to all policymakers, governments, and investors to be proactive in designing their strategies to avoid any delay/disruption in the imports from China, which could enhance the smooth flow of raw material and sustain industrial production.

International Journal of Financial Studies, Feb 15, 2023

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

International Journal of Energy Economics and Policy

This paper critically analyses the predictability of exchange rates using oil prices. Extant lite... more This paper critically analyses the predictability of exchange rates using oil prices. Extant literature that investigates the significance of oil prices in forecasting exchange rates remains largely inconclusive due to limitations arising from methodological issues. As such, this study uses deep learning approaches such as Multi-Layer Perceptron (MLP), Convolution Neural Network (CNN), and Long Short-Term Memory (LSTM) to predict exchange rates. In addition, the Empirical Mode Decomposition (EMD) of time series dataset was utilized to ascertain its effect on the quality of prediction. To examine the efficacy of using oil prices in forecasting exchange rates, bivariate models were also built. Of the three bivariate models developed, the EMD-CNN model has the best predictive performance. Results obtained show that oil price information has a strong influence on forecasting exchange rates.

Islamic Countries Society of Statistical Sciences, 2011

ABSTRACT

IUG Journal of Natural Studies, Dec 1, 2015

Jordan Journal of Economic Sciences

The study sheds light on the role of foreign aid to secondary education on the development of hum... more The study sheds light on the role of foreign aid to secondary education on the development of human capital from the perspective of teachers and educational experts, based on staff capacity-building programs and the assistance provided to the infrastructure of educational institutions (schools) in the Gaza Strip, Palestine. Teachers’ questionnaires and the interviews of educational experts showed the role of foreign aid to secondary education on the development of human capital in the Gaza Strip. Experts (higher level) agreed that foreign aid contributes significantly to human capital development through training and capacity building of employees and improving infrastructure. In contrast, teachers (operational level) found that foreign aid has limited benefits in these areas. Moreover, frequencies and percentages related to demographic characteristics, and standard deviations, indicate that experience, training, and education are the three main mechanisms for acquiring and developi...

Agricultural and Resource Economics: International Scientific E-Journal

Purpose. This study highlights the specific and accurate methods for forecasting prices of common... more Purpose. This study highlights the specific and accurate methods for forecasting prices of commonly consumed grains or legumes in Nigeria based on data from January 2017 to June 2020. Methodology / approach. Different models that include autoregressive integrated moving average (ARIMA), artificial neural networks (ANN), seasonal decomposition of time series by loess method (STLM), and a combination of these three models (hybrid model) were proposed to forecast the sample grain price data. This study uses price data on widely consumed grains, such as white maize, local rice, imported rice, and white beans, in Nigeria from January 2017 to June 2020. Results. Our result indicates that ARIMA is the best applicable model for white maize and imported rice because it is well fitted to stationary data, as demonstrated in the sample period. The STLM is more appropriate in forecasting white beans. As white beans are highly seasonal in Nigeria, it further explains why the STLM model fits bette...

Chemistry - A European Journal, 2017

Better understanding of uranyl-protein interactions is a prerequisite to predict uranium chemical... more Better understanding of uranyl-protein interactions is a prerequisite to predict uranium chemical toxicity in cells. The EF-hand motif of the calmodulin site I is about thousand times more affine for uranyl than for calcium, and threonine phosphorylation increases the uranyl affinity by two orders of magnitude at pH 7. In this study, we confront X-ray absorption spectroscopy with Fourier transform infrared (FTIR) spectroscopy, time-resolved laser-induced fluorescence spectroscopy (TRLFS), and structural models obtained by molecular dynamics simulations to analyze the uranyl coordination in the native and phosphorylated calmodulin site I. For the native site I, extended X-ray absorption fine structure (EXAFS) data evidence a short U−Oeq distance, in addition to distances compatible with mono-and bidentate coordination by carboxylate groups. Further analysis of uranyl speciation by TRLFS and thorough investigation of the fluorescence decay kinetics strongly support the presence of a hydroxide uranyl ligand. For a phosphorylated site I, the EXAFS and FTIR data support a monodentate uranyl coordination by the phosphoryl group and strong interaction with mono-and bidentate carboxylate ligands. This study confirms the important role of a phosphoryl ligand in the stability of uranyl-protein interactions. By evidencing a hydroxide uranyl ligand in calmodulin site I, this study also highlights the possible role of less studied ligands as water or hydroxide ions in the stability of protein-uranyl complexes.

Journal of inorganic biochemistry, Jul 1, 2017

The threat of a dirty bomb which could cause internal contamination has been of major concern for... more The threat of a dirty bomb which could cause internal contamination has been of major concern for the past decades. Because of their high chemical toxicity and their presence in the nuclear fuel cycle, uranium and neptunium are two actinides of high interest. Calmodulin (CaM) which is a ubiquitous protein present in all eukaryotic cells and is involved in calcium-dependent signaling pathways has a known affinity for uranyl and neptunyl ions. The impact of the complexation of these actinides on the physiological response of the protein remains, however, largely unknown. An isothermal titration calorimetry (ITC) was developed to monitor in vitro the enzymatic activity of the phosphodiesterase enzyme which is known to be activated by CaM and calcium. This approach showed that addition of actinyl ions (AnO2(n+)), uranyl (UO2(2+)) and neptunyl (NpO2(+)), resulted in a decrease of the enzymatic activity, due to the formation of CaM-actinide complexes, which inhibit the enzyme and alter it...

Inorganic Chemistry, 2016

Because of their presence in the nuclear fuel cycle, neptunium and uranium are two actinides of m... more Because of their presence in the nuclear fuel cycle, neptunium and uranium are two actinides of main interest in case of internal contamination. Complexation of U(VI) and Np(V) by the target protein calmodulin (CaM(WT)) was therefore studied herein. Both actinides have two axial oxygen atoms, which, charge aside, makes them very similar structurally wise. This work combines spectroscopy and theoretical density functional theory (DFT) calculations. Structural characterization was performed by extended X-ray absorption fine structure (EXAFS) at the L(III)-edge for each studied actinide. Models for the binding site of the protein were developed and then refined by using DFT to fit the obtained experimental EXAFS data. The effect of hydrolysis was also considered for both actinides (the uranyl experiment was performed at pH 3 and 6, while the neptunyl experiment was conducted at pH 7 and 9). The effect of the pH variation was apparent on the coordination sphere of the uranyl complexes, while the neptunyl complex characteristics remained stable under both studied conditions. The DFT calculations showed that at near physiological pH the complex formed by CaM(WT) with the neptunium ion is more stable than the one formed with uranyl.

Inorganic Chemistry, 2016

In case of a nuclear event, contamination (broad or limited) of the population or of specific wor... more In case of a nuclear event, contamination (broad or limited) of the population or of specific workers might occur. In such a senario, the fate of actinide contaminants may be of first concern, in particular with regard to human target organs like the skeleton. To improve our understanding of the toxicological processes that might take place, a mechanistic approach is necessary. For instance, ∼50% of Pu(IV) is known from biokinetic data to accumulate in bone, but the underlining mechanisms are almost unknown. In this context, and to obtain a better description of the toxicological mechanisms associated with actinides(IV), we have undertaken the investigation, on a molecular scale, of the interaction of thorium(IV) with osteopontin (OPN) a hyperphosphorylated protein involved in bone turnover. Thorium is taken here as a simple model for actinide(IV) chemistry. In addition, we have selected a phosphorylated hexapeptide (His-pSer-Asp-Glu-pSer-Asp-Glu-Val) that is representative of the peptidic sequence involved in the bone interaction. For both the protein and the biomimetic peptide, we have determined the local environment of Th(IV) within the bioactinidic complex, combining isothermal titration calorimetry, attenuated total reflectance Fourier transform infrared spectroscopy, theoretical calculations with density functional theory, and extended X-ray absorption fine structure spectroscopy at the Th LIII edge. The results demonstrate a predominance of interaction of metal with the phosphate groups and confirmed the previous physiological studies that have highlighted a high affinity of Th(IV) for the bone matrix. Data are further compared with those of the uranyl case, representing the actinyl(V) and actinyl(VI) species. Last, our approach shows the importance of developing simplified systems [Th(IV)-peptide] that can serve as models for more biologically relevant systems.

It is well known that the ordinary least squares (OLS) estimates in the regression model are effi... more It is well known that the ordinary least squares (OLS) estimates in the regression model are efficient when the disturbances have mean zero, constant variance and are uncorrelated. In problems concerning time series, it is often the case that the disturbances are, in fact, correlated. It is known that OLS may not be optimal in this context. We consider the robustness of various estimators, including estimated generalized least squares. We found that if the disturbance structure is autoregressive and the dependent variable is nonstochastic and linear or quadratic, the OLS performs nearly as well as its competitors. For other forms of the dependent variable, we have developed rules of thumb to guide practitioners in their choice of estimators

Research Square (Research Square), Dec 3, 2023

This study investigates the impact of senior management's nancial intelligence on the nancial per... more This study investigates the impact of senior management's nancial intelligence on the nancial performance of banks and insurance companies operating in the Gaza Strip. The study followed the descriptive analytical approach to measure nancial performance by relying on the nancial reports of the sample companies for 2022. The results indicated that the dimensions of nancial intelligence were available. Banks and insurance companies have good nancial performance. Furthermore, nancial intelligence signi cantly affected the performance of banks and insurance companies, as measured by return on investment (ROI) and return on assets (ROA). The dimensions of nancial intelligence also signi cantly impacted the performance of banks and insurance companies as measured by return on equity (ROE). Simultaneously, there was no signi cant effect of nancial intelligence on the performance of banks and insurance companies as measured by ROE. To the best of the author's knowledge, the current study is among the rst research efforts in Palestine. The ndings indicate that senior management should pay greater attention to developing nancial intelligence skills among employees in supervisory positions, especially those in the administrative, nancial, and planning elds, due to their impact on improving companies' performance.

F1000Research

Background: Reducing global food waste is an international environmental, health, and sus-tainabi... more Background: Reducing global food waste is an international environmental, health, and sus-tainability priority. Although significant reductions have been achieved across the food chain, progress by UAE households and consumers remain inadequate. This study seeks to understand the association between consumer attitudes, knowledge, and awareness relating to food waste practice of residents living in the UAE. to help inform policy and action for addressing this national priority. Methods: A cross-sectional study was conducted using a validated semi-structured online questionnaire through stratified sampling (n =1052). The Spearman correlation coefficient was performed to determine the correlations. Two independent regression analysis were used to determine the association between food waste practice with: 1) knowledge and awareness and attitude subdomains, and 2) sociodemographic characteristics. Respondents (n=1072) largely reflect the socio-demographic characteristics and population ...

British Journal of Multidisciplinary and Advanced Studies, Oct 21, 2023

The Autoregressive Integrated Moving Average (ARIMA) model cannot capture the nonlinear patterns ... more The Autoregressive Integrated Moving Average (ARIMA) model cannot capture the nonlinear patterns exhibited by the 2019 coronavirus (COVID-19) in terms of daily growth factor. As a result, Artificial Neural Networks (ANNs) and Hybrid ARIMA-ANN models have been successfully applied to resolve problems with nonlinear estimation. We compare the forecasting performance of these models using real, worldwide, daily COVID-19 data. The best forecasting model selected was compared using the forecasting assessment criterion known as mean absolute error. The main finding results show that the ANN model is more efficient than the ARIMA and Hybrid ARIMA-ANN models. The main finding from the ANN model analysis indicates that the magnitude of the increase in growth factor over time is rising in general while the percentage change in the growth factor is declining. This may be the result of the social distancing, safety, and cautionary measures mandated by governments worldwide.

American Journal of Theoretical and Applied Statistics, 2016

Time series of quarterly observations on Gross Domestic Product (GDP) is collected and used in th... more Time series of quarterly observations on Gross Domestic Product (GDP) is collected and used in this study. Forecasting results of ANNs are compared with those of the Autoregressive Integrated Moving Average (ARIMA) and regression as benchmark methods. Using Root Mean Square Error (RMSE), the empirical results show that ANN performs better than the traditional methods in forecasting GDP.

IUG Journal of Natural Studies, Dec 2, 2015

IUG Journal of Natural Studies, Dec 12, 2015

Advances in Decision Sciences, 2022

The primary objective of this paper is to identify the best forecasting model for China exports, ... more The primary objective of this paper is to identify the best forecasting model for China exports, especially during the spread of the COVID-19 pandemic. Methodology: We used the data of China exports to the United States and different economic regions from January 2014 to January 2021 to compare models using various criteria and selected the best exports forecast model. The hybrid model is employed to conduct the analysis. The combination of the hybrid model consists of six different models: ARIMA, ETS, Theta, NNAR, seasonal and trend decomposition, and TBATS model. Findings: Our results showed that the hybrid and ANN outperformed the remaining models in forecasting China exports to the world, considering the shock created by the ongoing coronavirus pandemic. This paper underscores the importance of using the specified models in forecasting exports during this period. The results also demonstrate that the magnitude of China exports to all groups decreased and will continue to decline for the next few months. Practical Implication: Forecasting of the export data is presented for the subsequent nine months, thereby providing insights to all policymakers, governments, and investors to be proactive in designing their strategies to avoid any delay/disruption in the imports from China, which could enhance the smooth flow of raw material and sustain industrial production.

International Journal of Financial Studies, Feb 15, 2023

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

International Journal of Energy Economics and Policy

This paper critically analyses the predictability of exchange rates using oil prices. Extant lite... more This paper critically analyses the predictability of exchange rates using oil prices. Extant literature that investigates the significance of oil prices in forecasting exchange rates remains largely inconclusive due to limitations arising from methodological issues. As such, this study uses deep learning approaches such as Multi-Layer Perceptron (MLP), Convolution Neural Network (CNN), and Long Short-Term Memory (LSTM) to predict exchange rates. In addition, the Empirical Mode Decomposition (EMD) of time series dataset was utilized to ascertain its effect on the quality of prediction. To examine the efficacy of using oil prices in forecasting exchange rates, bivariate models were also built. Of the three bivariate models developed, the EMD-CNN model has the best predictive performance. Results obtained show that oil price information has a strong influence on forecasting exchange rates.

Islamic Countries Society of Statistical Sciences, 2011

ABSTRACT

IUG Journal of Natural Studies, Dec 1, 2015

Jordan Journal of Economic Sciences

The study sheds light on the role of foreign aid to secondary education on the development of hum... more The study sheds light on the role of foreign aid to secondary education on the development of human capital from the perspective of teachers and educational experts, based on staff capacity-building programs and the assistance provided to the infrastructure of educational institutions (schools) in the Gaza Strip, Palestine. Teachers’ questionnaires and the interviews of educational experts showed the role of foreign aid to secondary education on the development of human capital in the Gaza Strip. Experts (higher level) agreed that foreign aid contributes significantly to human capital development through training and capacity building of employees and improving infrastructure. In contrast, teachers (operational level) found that foreign aid has limited benefits in these areas. Moreover, frequencies and percentages related to demographic characteristics, and standard deviations, indicate that experience, training, and education are the three main mechanisms for acquiring and developi...

Agricultural and Resource Economics: International Scientific E-Journal

Purpose. This study highlights the specific and accurate methods for forecasting prices of common... more Purpose. This study highlights the specific and accurate methods for forecasting prices of commonly consumed grains or legumes in Nigeria based on data from January 2017 to June 2020. Methodology / approach. Different models that include autoregressive integrated moving average (ARIMA), artificial neural networks (ANN), seasonal decomposition of time series by loess method (STLM), and a combination of these three models (hybrid model) were proposed to forecast the sample grain price data. This study uses price data on widely consumed grains, such as white maize, local rice, imported rice, and white beans, in Nigeria from January 2017 to June 2020. Results. Our result indicates that ARIMA is the best applicable model for white maize and imported rice because it is well fitted to stationary data, as demonstrated in the sample period. The STLM is more appropriate in forecasting white beans. As white beans are highly seasonal in Nigeria, it further explains why the STLM model fits bette...

Chemistry - A European Journal, 2017

Better understanding of uranyl-protein interactions is a prerequisite to predict uranium chemical... more Better understanding of uranyl-protein interactions is a prerequisite to predict uranium chemical toxicity in cells. The EF-hand motif of the calmodulin site I is about thousand times more affine for uranyl than for calcium, and threonine phosphorylation increases the uranyl affinity by two orders of magnitude at pH 7. In this study, we confront X-ray absorption spectroscopy with Fourier transform infrared (FTIR) spectroscopy, time-resolved laser-induced fluorescence spectroscopy (TRLFS), and structural models obtained by molecular dynamics simulations to analyze the uranyl coordination in the native and phosphorylated calmodulin site I. For the native site I, extended X-ray absorption fine structure (EXAFS) data evidence a short U−Oeq distance, in addition to distances compatible with mono-and bidentate coordination by carboxylate groups. Further analysis of uranyl speciation by TRLFS and thorough investigation of the fluorescence decay kinetics strongly support the presence of a hydroxide uranyl ligand. For a phosphorylated site I, the EXAFS and FTIR data support a monodentate uranyl coordination by the phosphoryl group and strong interaction with mono-and bidentate carboxylate ligands. This study confirms the important role of a phosphoryl ligand in the stability of uranyl-protein interactions. By evidencing a hydroxide uranyl ligand in calmodulin site I, this study also highlights the possible role of less studied ligands as water or hydroxide ions in the stability of protein-uranyl complexes.

Journal of inorganic biochemistry, Jul 1, 2017

The threat of a dirty bomb which could cause internal contamination has been of major concern for... more The threat of a dirty bomb which could cause internal contamination has been of major concern for the past decades. Because of their high chemical toxicity and their presence in the nuclear fuel cycle, uranium and neptunium are two actinides of high interest. Calmodulin (CaM) which is a ubiquitous protein present in all eukaryotic cells and is involved in calcium-dependent signaling pathways has a known affinity for uranyl and neptunyl ions. The impact of the complexation of these actinides on the physiological response of the protein remains, however, largely unknown. An isothermal titration calorimetry (ITC) was developed to monitor in vitro the enzymatic activity of the phosphodiesterase enzyme which is known to be activated by CaM and calcium. This approach showed that addition of actinyl ions (AnO2(n+)), uranyl (UO2(2+)) and neptunyl (NpO2(+)), resulted in a decrease of the enzymatic activity, due to the formation of CaM-actinide complexes, which inhibit the enzyme and alter it...

Inorganic Chemistry, 2016

Because of their presence in the nuclear fuel cycle, neptunium and uranium are two actinides of m... more Because of their presence in the nuclear fuel cycle, neptunium and uranium are two actinides of main interest in case of internal contamination. Complexation of U(VI) and Np(V) by the target protein calmodulin (CaM(WT)) was therefore studied herein. Both actinides have two axial oxygen atoms, which, charge aside, makes them very similar structurally wise. This work combines spectroscopy and theoretical density functional theory (DFT) calculations. Structural characterization was performed by extended X-ray absorption fine structure (EXAFS) at the L(III)-edge for each studied actinide. Models for the binding site of the protein were developed and then refined by using DFT to fit the obtained experimental EXAFS data. The effect of hydrolysis was also considered for both actinides (the uranyl experiment was performed at pH 3 and 6, while the neptunyl experiment was conducted at pH 7 and 9). The effect of the pH variation was apparent on the coordination sphere of the uranyl complexes, while the neptunyl complex characteristics remained stable under both studied conditions. The DFT calculations showed that at near physiological pH the complex formed by CaM(WT) with the neptunium ion is more stable than the one formed with uranyl.

Inorganic Chemistry, 2016

In case of a nuclear event, contamination (broad or limited) of the population or of specific wor... more In case of a nuclear event, contamination (broad or limited) of the population or of specific workers might occur. In such a senario, the fate of actinide contaminants may be of first concern, in particular with regard to human target organs like the skeleton. To improve our understanding of the toxicological processes that might take place, a mechanistic approach is necessary. For instance, ∼50% of Pu(IV) is known from biokinetic data to accumulate in bone, but the underlining mechanisms are almost unknown. In this context, and to obtain a better description of the toxicological mechanisms associated with actinides(IV), we have undertaken the investigation, on a molecular scale, of the interaction of thorium(IV) with osteopontin (OPN) a hyperphosphorylated protein involved in bone turnover. Thorium is taken here as a simple model for actinide(IV) chemistry. In addition, we have selected a phosphorylated hexapeptide (His-pSer-Asp-Glu-pSer-Asp-Glu-Val) that is representative of the peptidic sequence involved in the bone interaction. For both the protein and the biomimetic peptide, we have determined the local environment of Th(IV) within the bioactinidic complex, combining isothermal titration calorimetry, attenuated total reflectance Fourier transform infrared spectroscopy, theoretical calculations with density functional theory, and extended X-ray absorption fine structure spectroscopy at the Th LIII edge. The results demonstrate a predominance of interaction of metal with the phosphate groups and confirmed the previous physiological studies that have highlighted a high affinity of Th(IV) for the bone matrix. Data are further compared with those of the uranyl case, representing the actinyl(V) and actinyl(VI) species. Last, our approach shows the importance of developing simplified systems [Th(IV)-peptide] that can serve as models for more biologically relevant systems.

It is well known that the ordinary least squares (OLS) estimates in the regression model are effi... more It is well known that the ordinary least squares (OLS) estimates in the regression model are efficient when the disturbances have mean zero, constant variance and are uncorrelated. In problems concerning time series, it is often the case that the disturbances are, in fact, correlated. It is known that OLS may not be optimal in this context. We consider the robustness of various estimators, including estimated generalized least squares. We found that if the disturbance structure is autoregressive and the dependent variable is nonstochastic and linear or quadratic, the OLS performs nearly as well as its competitors. For other forms of the dependent variable, we have developed rules of thumb to guide practitioners in their choice of estimators