Community forestry in Nepal: a policy innovation for local livelihoods

millions fed, food security, Community forestry, Civil society, CFUG,

Parallel Bayesian Optimization of Agent-based Transportation Simulation

MATSim (Multi-Agent Transport Simulation Toolkit) is an open source large-scale agent-based transportation planning project applied to various areas like road transport, public transport, freight transport, regional evacuation, etc. BEAM (Behavior, Energy, Autonomy, and Mobility) framework extends MATSim to enable powerful and scalable analysis of urban transportation systems. The agents from the BEAM simulation exhibit 'mode choice' behavior based on multinomial logit model. In our study, we consider eight mode choices viz. bike, car, walk, ride hail, driving to transit, walking to transit, ride hail to transit, and ride hail pooling. The 'alternative specific constants' for each mode choice are critical hyperparameters in a configuration file related to a particular scenario under experimentation. We use the 'Urbansim-10k' BEAM scenario (with 10,000 population size) for all our experiments. Since these hyperparameters affect the simulation in complex ways, manual calibration methods are time consuming. We present a parallel Bayesian optimization method with early stopping rule to achieve fast convergence for the given multi-in-multi-out problem to its optimal configurations. Our model is based on an open source HpBandSter package. This approach combines hierarchy of several 1D Kernel Density Estimators (KDE) with a cheap evaluator (Hyperband, a single multidimensional KDE). Our model has also incorporated extrapolation based early stopping rule. With our model, we could achieve a 25% L1 norm for a large-scale BEAM simulation in fully autonomous manner. To the best of our knowledge, our work is the first of its kind applied to large-scale multi-agent transportation simulations. This work can be useful for surrogate modeling of scenarios with very large populations.Comment: LOD'2022 (Nature Springer Computer Science Proceedings - LNCS

Evaluation of the financial and technical impacts of changing commercial-scale pharmaceutical manufacturing processes.

Growing pressures in the pharmaceutical industry are driving the need to optimise processes used for the manufacture of drugs at commercial-scale, in order to improve cost of goods, product throughput and production times. Evaluating the impacts of process optimisation upon these metrics presents a challenge due to complexities and trade-offs that are often encountered when developing a typical bioprocess. Such factors have resulted in a range of novel simulation- and experimental- based techniques being developed which enable rapid, accurate and cost effective assessment of manufacturing options for commercial-scale production. This thesis proposes a combination of modelling and experimental methods for evaluating the business- and process-related impacts of implementing changes to pre-existing commercial-scale pharmaceutical manufacturing processes. The approaches are illustrated through an industrial case study, focusing upon a process operated by Protherics U.K. Limited for the manufacture of the FDA-approved rattlesnake anti-venom CroFab (Crotalidae Polyvalent Immune Fab (Ovine)). The novel methods developed and illustrated in this thesis include: Investigating the effects of process changes upon calculated yields and processing times within the production framework for a pre-existing FDA-approved bio-manufacturing process Evaluating the impacts of both developing and implementing process changes, combining output metrics into a single value to simplify the assessment Developing a multi-layered simulation methodology for the rapid and efficient evaluation of bio- manufacturing process options Applying advanced sensitivity analysis techniques to identify the most critical factors that influence product yield and throughput Evaluating a novel synthetic Protein A matrix for the recovery and purification of polyclonal antibodies from hyperimmunised ovine serum Developing decision-support software to aid the design of chromatography steps for antibody purification at industrial scale Demonstrating the utility of such models by application to data and constraints derived from a full-scale industrial facility

Rural institutions, social networks, and self-organized adaptation to climate change

Support for rural livelihoods to adapt to climate change is a top policy priority around the world. We advance the concept of "self-organized adaptation" to analyze how long-term pathways of transformation come about as the organic outcome of farmers' incremental and continuous responses to climate and other challenges. Through an analysis of four decades' responses to changing climate conditions in an agricultural system of the Indian Himalayas, we show how several key policy interventions – institutional support for the dissemination of agricultural knowledge, investments in infrastructure, and strengthening of market linkages – have produced favorable conditions for successful, long-term self-organized adaptation to climate change. This has led to the transformation of an agricultural system specialized in apple production to one with a great diversity of fruit, vegetable, and food grain crops. We find that farmers growing these crops cluster into five distinct agricultural portfolios that reflect the constraints and opportunities that different farmers face, and which are patterned by interaction with rural institutions and household social networks. We highlight the role of distributed decision-making in shaping broader trajectories of systemic transformation, and we argue for the need to move beyond pre-defined climate interventions toward the identification of policy mechanisms that can support more effective self-organization over the long-term

Designing liquid repellent surfaces for fabrics, feathers and fog

Thesis (Ph. D. in Chemical Engineering Practice)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, February 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis. "December 2012."Includes bibliographical references.Omniphobicity refers to a property of surfaces which are not wetted by water, oils, alcohols and other low surface tension liquids. Robust omniphobic surfaces can be applied in many areas including fabrics with chemical / biological protection and dirt / fingerprint resistant touch screens. The main aim of this thesis is to develop rules for the systematic design of omniphobic surfaces with a focus on textiles. First, a design chart is developed to help us understand the impact of surface chemistry and surface topography on the wettability of a textured surface. A smaller characteristic length scale of a re-entrant surface topography, tighter weave and a coating with inherently low wettability are better for producing omniphobic surfaces that resist wetting by liquids with low surface tension. This framework is applied to textile fabrics and bird feathers to test their wettability. Using this framework, wettability of low surface tension liquids on a polyester fabric is tuned or switched using either thermal annealing or biaxial stretching. Army Combat Uniform fabrics are rendered oleophobic, thus opening the way to optimize omniphobic army uniforms. The wettability of molecules similar to fluorodecyl POSS is investigated by measuring the contact angles with liquids of a broad range of surface tension and polarity. Of the molecules tested so far, fluorodecyl POSS has the lowest solid surface energy (9.3 mN/m) and the lowest increment in solid surface energy (7 mN/m). The wetting aspects of the hierarchical topography of bird feathers are captured using contact angle measurements in terms of a spacing ratio. Thermodynamics of the wetting of feathers and the robustness against wetting during the course of a dive are correlated to the wing spreading behavior. Our understanding of surface wettability of woven meshes is applied to optimize their fog collection ability. A business case for fog harvesting is developed and strategies to decrease asset and cash flow risks are proposed. The contributions presented here provide means to better characterize surfaces with complex topography, tune and a priori predict their wettability and recommend a design strategy both at a molecular and a macroscopic level to maximize their non-wettability.by Shreerang S. Chhatre.Ph.D.in Chemical Engineering Practic

Solution spraying of poly(methyl methacrylate) blends to fabricate microtextured, superoleophobic surfaces

We describe a simple technique to prepare superhydrophobic and superoleophobic microtextured surfaces by spray coating a blend of poly(methyl methacrylate) (PMMA) and the low surface energy molecule 1H,1H,2H,2H-heptadecafluorodecyl polyhedral oligomeric silsesquioxane (fluorodecyl POSS, γ[subscript sv] ≈ 10 mN/m) using an air brush with a pressurized nitrogen stream. Scanning electron micrographs show the formation of microtextured surfaces possessing re-entrant curvature; a critical feature for obtaining liquid repellency with low surface tension liquids. The surface morphology can be tuned systematically from a corpuscular or spherical microstructure to a beads-on-string structure and finally to bundled fibers by controlling the solution concentration and molecular weight of the sprayed polymer. The oleophobicity of the resulting structures is characterized by advancing and receding contact angle measurements with liquids of a range of surface tensions.United States. Army Research Office (Contract W911NF-07-D-0004)Air Force Research Laboratory (Wright-Patterson Air Force Base, Ohio). Propulsion DirectorateUnited States. Air Force Office of Scientific Researc

The Simplex Algorithm for the Rapid Identification of Operating Conditions During Early Bioprocess Development: Case Studies in FAb' Precipitation and Multimodal Chromatography

This study describes a data-driven algorithm as a rapid alternative to conventional Design of Experiments (DoE) approaches for identifying feasible operating conditions during early bioprocess development. In general, DoE methods involve fitting regression models to experimental data, but if model fitness is inadequate then further experimentation is required to gain more confidence in the location of an optimum. This can be undesirable during very early process development when feedstock is in limited supply and especially if a significant percentage of the tested conditions are ultimately found to be sub-optimal. An alternative approach involves focusing solely upon the feasible regions by using the knowledge gained from each condition to direct the choice of subsequent test locations that lead towards an optimum. To illustrate the principle, this study describes the application of the Simplex algorithm which uses accumulated knowledge from previous test points to direct the choice of successive conditions towards better regions. The method is illustrated by two case studies; a two variable precipitation example investigating how salt concentration and pH affect FAb' recovery from E. coli homogenate and a three-variable chromatography example identifying the optimal pH and concentrations of two salts in an elution buffer used to recover ovine antibody bound to a multimodal cation exchange matrix. Two-level and face-centered central composite regression models were constructed for each study and statistical analysis showed that they provided a poor fit to the data, necessitating additional experimentation to confirm the robust regions of the search space. By comparison, the Simplex algorithm identified a good operating point using 50% and 70% fewer conditions for the precipitation and chromatography studies, respectively. Hence, data-driven approaches have significant potential for early process development when material supply is at a premium