Freeze v/s air-dried alginate-pectin gel beads modified with sodium dodecyl sulphate for enhanced removal of copper ions.

This work presents a novel polymer-based adsorbent, Sodium Dodecyl Sulphate modified alginate-pectin gel beads (APS221) prepared via controlled freeze drying & air drying, for the removal of copper ions from the aqueous solution. This work also critically discusses the role played by various components and their concentrations in the success of APS221. Addition of pectin to alginate resulted into approximately 150 % increase in the metal removal performance of the adsorbent while addition of SDS into alginate-pectin complex enhanced the performance by 14 % approximately, taking the maximum adsorption capacity of final complex APS221 to 111.11 mg/g. Our characterization studies revealed that the adsorption happened predominantly by complexation and ion-exchange mechanisms, and hence despite having a higher surface area, freeze-dried variant showed lesser adsorption capacity than air-dried variant as there was a loss of ion-exchange sites resulting from breakage of crosslinking bonds due to chain elongation. The adsorption process was found to follow Langmuir isotherm and pseudo-second order kinetics with a good fit of experimental data. Further, operating parameters have been optimized via RSM to, simultaneously, maximize the utilization of the adsorbent and minimize the cost of the process. Stability studies showed that APS221 beads could be used up to eight cycles.

Adsorptive removal of Cd2+ ions using dolochar at an industrial-scale process optimization by response surface methodology.

In this work, performance of laboratory-synthesized dolochar has been investigated for adsorption of Cd2+ ions in a large-scale process with the application of Aspen Adsorption. Moreover, the optimum values of the operating parameters (namely, flow rate, bed height, and inlet metal ion concentration) that would result into maximum amount of cadmium ion adsorption (high exhaustion capacity) in minimum time (less exhaustion time) for a fixed mass of dolochar have been calculated via the application of response surface methodology. It was found that, at optimum values of bed height (3.48 m), flow rate (76.31 m3/day), and inlet concentration (10 ppm), the optimized value of exhaustion capacity and exhaustion time for cadmium ion adsorption in dolochar packed bed is equal to 1.85 mg/g and 11.39 h, respectively. The validity of these simulation experiments can be proven by the fact that the obtained exhaustion capacity of dolochar packed bed always remained in close proximity of the experimentally obtained value of adsorption capacity of the dolochar in batch process mode (equal to 2.1 mg/g).

Large-scale randomized experiments reveals that machine learning-based instruction helps people memorize more effectively.

We perform a large-scale randomized controlled trial to evaluate the potential of machine learning-based instruction sequencing to improve memorization while allowing the learners the freedom to choose their review times. After controlling for the length and frequency of study, we find that learners for whom a machine learning algorithm determines which questions to include in their study sessions remember the content over ~69% longer. We also find that the sequencing algorithm has an effect on users' engagement.

Recent advances in heavy metal removal by chitosan based adsorbents.

Chitosan, a low cost polymer, has been used in many studies for adsorption of heavy metal ions. This review covers the performance of all those adsorbents which were derived from chitosan for the adsorption of heavy metal ions in recent past. Further, the common chitosan modifications methods have been discussed in this paper among which crosslinking and grafting were found to be the most popular methods. Adsorption equilibrium and kinetics were also studied and Pseudo-second order kinetic model and Langmuir isotherm were found to be successful for modeling of most of the chitosan derivatives-metal adsorption experiments. Moreover, with focus on chitosan derivatives, effect of adsorbent structure on metal removal, adsorption mechanism, effect of co-existing ions on adsorption, adsorbent synthesis protocols and regeneration methods have been also discussed in great detail. Finally, fixed bed column design for industrial wastewater treatment was elaborated along with efficiency of chitosan based adsorbents in columns.

RecipeDB: a resource for exploring recipes.

Cooking is the act of turning nature into the culture, which has enabled the advent of the omnivorous human diet. The cultural wisdom of processing raw ingredients into delicious dishes is embodied in their cuisines. Recipes thus are the cultural capsules that encode elaborate cooking protocols for evoking sensory satiation as well as providing nourishment. As we stand on the verge of an epidemic of diet-linked disorders, it is eminently important to investigate the culinary correlates of recipes to probe their association with sensory responses as well as consequences for nutrition and health. RecipeDB (https://cosylab.iiitd.edu.in/recipedb) is a structured compilation of recipes, ingredients and nutrition profiles interlinked with flavor profiles and health associations. The repertoire comprises of meticulous integration of 118 171 recipes from cuisines across the globe (6 continents, 26 geocultural regions and 74 countries), cooked using 268 processes (heat, cook, boil, simmer, bake, etc.), by blending over 20 262 diverse ingredients, which are further linked to their flavor molecules (FlavorDB), nutritional profiles (US Department of Agriculture) and empirical records of disease associations obtained from MEDLINE (DietRx). This resource is aimed at facilitating scientific explorations of the culinary space (recipe, ingredient, cooking processes/techniques, dietary styles, etc.) linked to taste (flavor profile) and health (nutrition and disease associations) attributes seeking for divergent applications. Database URL:  https://cosylab.iiitd.edu.in/recipedb.

Enhancing human learning via spaced repetition optimization.

Spaced repetition is a technique for efficient memorization which uses repeated review of content following a schedule determined by a spaced repetition algorithm to improve long-term retention. However, current spaced repetition algorithms are simple rule-based heuristics with a few hard-coded parameters. Here, we introduce a flexible representation of spaced repetition using the framework of marked temporal point processes and then address the design of spaced repetition algorithms with provable guarantees as an optimal control problem for stochastic differential equations with jumps. For two well-known human memory models, we show that, if the learner aims to maximize recall probability of the content to be learned subject to a cost on the reviewing frequency, the optimal reviewing schedule is given by the recall probability itself. As a result, we can then develop a simple, scalable online spaced repetition algorithm, MEMORIZE, to determine the optimal reviewing times. We perform a large-scale natural experiment using data from Duolingo, a popular language-learning online platform, and show that learners who follow a reviewing schedule determined by our algorithm memorize more effectively than learners who follow alternative schedules determined by several heuristics.