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Sexual harassment is a recurring problem around the globe. Different nations have taken measures to deal with the consequences of such a problem. Continuous development of policies is observed. Nations or companies are dealing with the topic by either reacting or pro-acting to the salient situations. Lebanon is not different than other countries. Sexual harassment occurs in the workplace, but unfortunately there are no public records of the cases. Therefore, this research attempts to close such a gap. This paper explores sexual harassment in the workplace within selected Lebanese organizations. A questionnaire has been developed and circulated to that purpose. The organizations contacted comprised of one hotel, several restaurants and night clubs, and a university. The high response rate (67%) has enabled a comprehensive and reliable information resource to be created, hopefully to enable application in terms of on-the-ground practice and levels of awareness and action in the stated regions. This information is considered particularly timely as it reflects the current position in Lebanon. The expected outcome of this paper is the development of policies and creation of awareness which build on the findings of this research. Findings are also expected to contribute to defining future research work.

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The availability of short-term forecast weather model for a particular country or region is essential for operation planning of energy systems. This paper presents the first step by a group of researchers at UAE University to establish a weather model for the UAE using the weather data for at least 10 years and employing various models such as classical empirical models, artificial neural network (ANN) models, and time-series regression models with autoregressive integrated moving-average (ARIMA). This work uses time-series regression with ARIMA modeling to establish a model for the mean daily and monthly global solar radiation (GSR) for the city of Al-Ain, United Arab Emirates. Time-series analysis of solar radiation has shown to yield accurate average long-term prediction performance of solar radiation in Al-Ain. The model was built using data for 10 years (1995–2004) and was validated using data of three years (2005–2007), yielding deterministic coefficients (R2) of 92.6% and 99.98% for mean daily and monthly GSR data, respectively. The low corresponding values of mean bias error (MBE), mean absolute bias error (MABE), mean absolute percentage error (MAPE), and root-mean-square error (RMSE) confirm the adequacy of the obtained model for long-term prediction of GSR data in Al-Ain, UAE.