Chemotherapy Induced Nausea and Vomiting (CINV) is among the most intensive side effects and critical concerns for patients with cancer. Most of these patients experience nausea and vomiting after chemotherapy. Sometimes, this is so annoying that it may prevent them from continuing the therapy. With the recent advances, a variety of therapeutic methods are innovated and applied to control CINV. Among them, the main methods include medicinal therapy, relaxation, and herbal therapy. Yet, using dexamethasone together with massage therapy and ginger is identified as the most effective method.
Aqueous solutions of the tinidazole (TNZ) have been treated by applying the combination of ultrasound irradiation and H2O2. Based on the results, the maximum removal efficiency of 75% was achieved under the optimum operating conditions (pH 3, 120 kHz frequency, 333 mM/L of H2O2 and 150 min of operating time) while, under the same conditions the minimum removal efficiency was found to be 8.5 by ultrasound radiation in the absence of H2O2. The results also revealed that the degradation of TNZ was enhanced with decreasing both TNZ initial concentrations and pH. Furthermore, TNZ removal efficiency in the case of actual wastewater was less than of synthetic wastewater (75% and 68% of synthetic and actual, respectively). According to the chromatographic analyses, no harmful intermediate compounds were observed. The chemical oxygen demand analysis (65% reduction) confirmed our findings.
With the popularity of Location-based Social Networks, Pointof-Interest (POI) recommendation has become an important task, which learns the users' preferences and mobility patterns to recommend POIs. Previous studies show that incorporating contextual information such as geographical and temporal influences is necessary to improve POI recommendation by addressing the data sparsity problem. However, existing methods model the geographical influence based on the physical distance between POIs and users, while ignoring the temporal characteristics of such geographical influences. In this paper, we perform a study on the user mobility patterns where we find out that users' check-ins happen around several centers depending on their current temporal state. Next, we propose a spatio-temporal activity-centers algorithm to model users' behavior more accurately. Finally, we demonstrate the effectiveness of our proposed contextual model by incorporating it into the matrix factorization model under two different settings: i) static and ii) temporal. To show the effectiveness of our proposed method, which we refer to as STACP, we conduct experiments on two well-known real-world datasets acquired from Gowalla and Foursquare LBSNs. Experimental results show that the STACP model achieves a statistically significant performance improvement, compared to the state-of-the-art techniques. Also, we demonstrate the effectiveness of capturing geographical and temporal information for modeling users' activity centers and the importance of modeling them jointly.
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