Enhanced insecticidal activity of Bacillus thuringiensis using a late embryogenesis abundant peptide co-expression system.

Bacillus thuringiensis (Bt) is a ubiquitous, gram positive, spore-forming bacterium that synthesizes parasporal crystalline inclusions containing crystal protein, some of which are toxic against a wide range of insect orders like caterpillars, beetles, and flies, including mosquitoes. Regarding the biological control of insects, Bt is the mostly used microorganism worldwide and also alternatives to chemical insecticides for environmental conservation. Some strains of Bt are showing a promising activity against a wide variety of mosquito like Aedes, Culex, and Anopheles and so on with extremely damages in the larval midgut and ultimate death. Here, we introduced a late embryogenesis abundant (LEA) peptide co-expression system based on the expression vector pHT01 with a strong σA-dependent promoter to enhance the expression of insecticidal crystal proteins in native Bt. Two types of LEA peptide (LEA-II and LEA-K) were designed based on the sequence of group-3 LEA protein, which consists of a repetitive sequence of 11 amino acids. The LEA-II mediated co-expression system enhanced the production of crystal protein 3-fold after 12 h of induction of the peptide with 0.5 mM IPTG. Enhanced expression of crystal protein was confirmed by bioassay using 4th instar Aedes albopictus larvae. This unique approach has great potential to produce bio-pesticides by enhanced crystal protein expression not only for mosquitoes but also for other insects.

Capacitated vehicle-routing problem model for scheduled solid waste collection and route optimization using PSO algorithm.

Waste collection widely depends on the route optimization problem that involves a large amount of expenditure in terms of capital, labor, and variable operational costs. Thus, the more waste collection route is optimized, the more reduction in different costs and environmental effect will be. This study proposes a modified particle swarm optimization (PSO) algorithm in a capacitated vehicle-routing problem (CVRP) model to determine the best waste collection and route optimization solutions. In this study, threshold waste level (TWL) and scheduling concepts are applied in the PSO-based CVRP model under different datasets. The obtained results from different datasets show that the proposed algorithmic CVRP model provides the best waste collection and route optimization in terms of travel distance, total waste, waste collection efficiency, and tightness at 70-75% of TWL. The obtained results for 1 week scheduling show that 70% of TWL performs better than all node consideration in terms of collected waste, distance, tightness, efficiency, fuel consumption, and cost. The proposed optimized model can serve as a valuable tool for waste collection and route optimization toward reducing socioeconomic and environmental impacts.

Backtracking search algorithm in CVRP models for efficient solid waste collection and route optimization.

Waste collection is an important part of waste management that involves different issues, including environmental, economic, and social, among others. Waste collection optimization can reduce the waste collection budget and environmental emissions by reducing the collection route distance. This paper presents a modified Backtracking Search Algorithm (BSA) in capacitated vehicle routing problem (CVRP) models with the smart bin concept to find the best optimized waste collection route solutions. The objective function minimizes the sum of the waste collection route distances. The study introduces the concept of the threshold waste level (TWL) of waste bins to reduce the number of bins to be emptied by finding an optimal range, thus minimizing the distance. A scheduling model is also introduced to compare the feasibility of the proposed model with that of the conventional collection system in terms of travel distance, collected waste, fuel consumption, fuel cost, efficiency and CO2 emission. The optimal TWL was found to be between 70% and 75% of the fill level of waste collection nodes and had the maximum tightness value for different problem cases. The obtained results for four days show a 36.80% distance reduction for 91.40% of the total waste collection, which eventually increases the average waste collection efficiency by 36.78% and reduces the fuel consumption, fuel cost and CO2 emission by 50%, 47.77% and 44.68%, respectively. Thus, the proposed optimization model can be considered a viable tool for optimizing waste collection routes to reduce economic costs and environmental impacts.