Design of multimodal hub-and-spoke transportation network for emergency relief under COVID-19 pandemic: A meta-heuristic approach.

When COVID-19 suddenly broke out, the epidemic areas are short of basic emergency relief which need to be transported from surrounding areas. To make transportation both time-efficient and cost-effective, we consider a multimodal hub-and-spoke transportation network for emergency relief schedules. Firstly, we establish a mixed integer nonlinear programming (MINLP) model considering multi-type emergency relief and multimodal transportation. The model is a bi-objective one that aims at minimizing both transportation time consumption and transportation costs. Due to its NP-hardness, devising an efficient algorithm to cope with such a problem is challenging. This study thus employs and redesigns Grey Wolf Optimizer (GWO) to tackle it. To benchmark our algorithm, a real-world case is tested with three solution methods which include other two state-of-the-art meta-heuristics. Results indicate that the customized GWO can solve such a problem in a reasonable time with higher accuracy. The research could provide significant practical management insights for related government departments and transportation companies on designing an effective transportation network for emergency relief schedules when faced with the unexpected COVID-19 pandemic.

RNAi-mediated disruption of neuropeptide genes, nlp-3 and nlp-12, cause multiple behavioral defects in Meloidogyne incognita.

Owing to the current deficiencies in chemical control options and unavailability of novel management strategies, root-knot nematode (M. incognita) infections remain widespread with significant socio-economic impacts. Helminth nervous systems are peptide-rich and appear to be putative drug targets that could be exploited by antihelmintic chemotherapy. Herein, to characterize the novel peptidergic neurotransmitters, in silico mining of M. incognita genomic and transciptomic datasets revealed the presence of 16 neuropeptide-like protein (nlp) genes with structural hallmarks of neuropeptide preproproteins; among which 13 nlps were PCR-amplified and sequenced. Two key nlp genes (Mi-nlp-3 and Mi-nlp-12) were localized to the basal bulb and tail region of nematode body via in situ hybridization assay. Mi-nlp-3 and Mi-nlp-12 were greatly expressed (in qRT-PCR assay) in the pre-parasitic juveniles and adult females, suggesting the association of these genes in host recognition, development and reproduction of M. incognita. In vitro knockdown of Mi-nlp-3 and Mi-nlp-12 via RNAi demonstrated the significant reduction in attraction and penetration of M. incognita in tomato root in Pluronic gel medium. A pronounced perturbation in development and reproduction of NLP-silenced worms was also documented in adzuki beans in CYG growth pouches. The deleterious phenotypes obtained due to NLP knockdown suggests that transgenic plants engineered to express RNA constructs targeting nlp genes may emerge as an environmentally viable option to manage nematode problems in crop plants.

Computer-aided Molecular Design of Water Compatible Visible Light Photosensitizers for Dental Adhesive.

Dental adhesive resin undergoes phase separation during its infiltration through the wet demineralized dentin and it has been observed previously that the hydrophilic-rich phase is a vulnerable region for failure due to the lack of photo-polymerization and crosslinking density. The lack of photo-polymerization is mostly due to the partitioning of photo-initiators in low concentrations within this phase. Here, a computational approach has been employed to design candidate water compatible visible light photosensitizers which could improve the photo-polymerization of the hydrophilic-rich phase. This study is an extension of our previous work. QSPRs were developed for properties related to the photo-polymerization reaction of the adhesive monomers and hydrophilicity of the photosensitizer using connectivity indices as descriptors. QSPRs and structural constraints were formulated into an optimization problem which was solved stochastically via Tabu Search. Four candidate photosensitizer molecules have been proposed here which have the iminium ion as a common feature.

Optimal synthesis and design of the number of cycles in the leaching process for surimi production.

Water consumption required during the leaching stage in the surimi manufacturing process strongly depends on the design and the number and size of stages connected in series for the soluble protein extraction target, and it is considered as the main contributor to the operating costs. Therefore, the optimal synthesis and design of the leaching stage is essential to minimize the total annual cost. In this study, a mathematical optimization model for the optimal design of the leaching operation is presented. Precisely, a detailed Mixed Integer Nonlinear Programming (MINLP) model including operating and geometric constraints was developed based on our previous optimization model (NLP model). Aspects about quality, water consumption and main operating parameters were considered. The minimization of total annual costs, which considered a trade-off between investment and operating costs, led to an optimal solution with lesser number of stages (2 instead of 3 stages) and higher volumes of the leaching tanks comparing with previous results. An analysis was performed in order to investigate how the optimal solution was influenced by the variations of the unitary cost of fresh water, waste treatment and capital investment.

Efficiently handling constraints in mixed-integer nonlinear programming problems using gradient-based repair differential evolution.

Mixed integer nonlinear programming (MINLP) addresses optimization problems that involve continuous and discrete/integer decision variables, as well as nonlinear functions. These problems often exhibit multiple discontinuous feasible parts due to the presence of integer variables. Discontinuous feasible parts can be analyzed as subproblems, some of which may be highly constrained. This significantly impacts the performance of evolutionary algorithms (EAs), whose operators are generally insensitive to constraints, leading to the generation of numerous infeasible solutions. In this article, a variant of the differential evolution algorithm (DE) with a gradient-based repair method for MINLP problems (G-DEmi) is proposed. The aim of the repair method is to fix promising infeasible solutions in different subproblems using the gradient information of the constraint set. Extensive experiments were conducted to evaluate the performance of G-DEmi on a set of MINLP benchmark problems and a real-world case. The results demonstrated that G-DEmi outperformed several state-of-the-art algorithms. Notably, G-DEmi did not require novel improvement strategies in the variation operators to promote diversity; instead, an effective exploration within each subproblem is under consideration. Furthermore, the gradient-based repair method was successfully extended to other DE variants, emphasizing its capacity in a more general context.

Designing dynamic reverse logistics network for post-sale service.

The paper addresses the problem of designing a multi-country production-distribution network that also provides services such as repairs and remanufacturing. The proposed work concentrates primarily on post-sale service provided by the firm under warranty returns. The proposed model assumes that existing warehouses can also serve as collection centres or repair centres for reverse logistics. In addition, the model also explores the possibility of establishing a new facility. Hybrid facilities are considered because of their huge cost-cutting potential due to equipment sharing and space sharing. The capacity of hybrid facilities can be expanded to a predefined limit to process returned products without hampering forward logistics operations. However, if a product cannot be repaired at the warehouse, it is transported to the plant for remanufacturing. The model optimizes the overall configuration and operation cost of the production-distribution network. The production-distribution model developed in the paper is a mixed-integer nonlinear program (MINLP) that is later transformed to a mixed-integer linear program to reduce the solution time. The usefulness of the model is illustrated using a randomly generated dataset. The model identifies (a) the optimal locations/allocations of the existing/new facilities, (b) the distribution of returned products for refurbishing and remanufacturing, and (c) the capacity expansion of the existing plants and warehouses to facilitate remanufacturing and repair services.

Optimal Design and Competitive Spans of Timber Floor Joists Based on Multi-Parametric MINLP Optimization.

This study investigates the optimization of the design of timber floor joists, taking into account the self-manufacturing costs and the discrete sizes of the structure. This non-linear and discrete class of optimization problem was solved with the multi-parametric mixed-integer non-linear programming (MINLP). An MINLP optimization model was developed. In the model, an accurate objective function of the material and labor costs of the structure was subjected to design, strength, vibration and deflection (in)equality constraints, defined according to Eurocode regulations. The optimal design of timber floor joists was investigated for different floor systems, different materials (sawn wood and glulam), different load sharing systems, different vertical imposed loads, different spans, and different alternatives of discrete cross-sections. For the above parameters, 380 individual MINLP optimizations were performed. Based on the results obtained, a recommended optimal design for timber floor joists was developed. Engineers can select from the recommendations the optimal design system for a given imposed load and span of the structure. Economically suitable spans for timber floor joists structures were found. The current knowledge of competitive spans for timber floor joists is extended based on cost optimization and Eurocode standards.

Energy and environmental sustainability of waste personal protective equipment (PPE) treatment under COVID-19.

Combating the COVID-19 pandemic has raised the demand for and disposal of personal protective equipment in the United States. This work proposes a novel waste personal protective equipment processing system that enables energy recovery through producing renewable fuels and other basic chemicals. Exergy analysis and environmental assessment through a detailed life cycle assessment approach are performed to evaluate the energy and environmental sustainability of the processing system. Given the environmental advantages in reducing 35.42% of total greenhouse gas emissions from the conventional incineration and 43.50% of total fossil fuel use from landfilling processes, the optimal number, sizes, and locations of establishing facilities within the proposed personal protective equipment processing system in New York State are then determined by an optimization-based site selection methodology, proposing to build two pre-processing facilities in New York County and Suffolk County and one integrated fast pyrolysis plant in Rockland County. Their optimal annual treatment capacities are 1,708 t/y, 8,000 t/y, and 9,028 t/y. The proposed optimal personal protective equipment processing system reduces 31.5% of total fossil fuel use and 35.04% of total greenhouse gas emissions compared to the personal protective equipment incineration process. It also avoids 41.52% and 47.64% of total natural land occupation from the personal protective equipment landfilling and incineration processes.

Optimal mixed control of Axial Flux Permanent Magnet Synchronous generator wind turbines with modular stator structure.

Wind turbine systems are constructed using different types of generators, aero-mechanical components and control systems. Due to their ability to work in low speed, Axial Flux Permanent Magnet (AFPM) generators are becoming widespread in wind energy systems which contributes to eliminating the gearbox from the system, noticeable increase in efficiency and decrease in system weight. Due to the modular nature of the stator in AFPM generators, it is possible to control each module independently. In this paper, in addition to obtain the dynamic model of the turbine and AFPM generator, a control strategy is designed based on Mixed Integer Nonlinear Programming (MINLP) to incorporate both pitch angle and the number of active stator modules as control input signals. These control signals are used in order to maximize system efficiency and regulate output voltage in different wind speeds and electrical loads. Simulation results for a typical generator shows the effectiveness of the proposed method in speed control of the generator.

Design and optimization of poly(hydroxyalkanoate)s production plants using alternative substrates.

In this work, we propose a Mixed Integer Nonlinear Programming (MINLP) model to determine the optimal design of a poly(hydroxyalkanoate)s (PHAs) production plant configuration. The superstructure based optimization model considers different carbon sources as raw material: glycerol (crude and purified), corn starch, cassava starch, sugarcane sucrose and sugarcane molasses. The PHA extraction section includes four alternatives: the use of enzyme, solvent, surfactant-NaOCl or surfactant-chelate. Model constraints include detailed capital cost for equipment, mass and energy balances, product specifications and operating bounds on process units. The resulting MINLP model maximizes the project net present value (NPV) as objective function and it is implemented in an equation oriented environment. Optimization results show the sugarcane-enzyme option as the most promising alternative (NPV = 75.01 million USD) for PHAs production with an energy consumption of 22.56 MJ/kg PHA and a production cost of 3.02 USD/kg PHA. Furthermore, an economic sensitivity analysis is performed.

Identification of regulatory variables for state transition of biological networks.

Attractors represent steady states of biological networks. Recent studies have shown that regulatory variables can be used to steer a network state transition from an undesired attractor, such as a cancerous state, to a desired healthy one. Therefore, it is important to identify the regulatory variables and determine their time-dependent profile for state transition of a given network. However, this is a challenging task since regulatory variables have to be identified among numerous candidates in a large-scale biological network. In this study, we developed a new method for identifying regulatory variables in large-scale biological networks for the purpose of state transition. As a result, a set of optimal regulatory variables can be determined based on formulating and solving a mixed-integer nonlinear dynamic optimization problem. A relaxation scheme is used to overcome the difficulties in solving this complex problem containing a large number of binary variables. The solution to this problem simultaneously identifies the optimal regulatory variables, provides strength of regulatory interactions, and obtains the minimal control time to realize the required state transition. In addition, by adjusting the objective function, various combinations of the strength of regulatory interactions and the transition time can be achieved according to the requirement for disease therapy. Results of three case studies (a myeloid differentiation regulatory network, a cancer gene regulatory network, and a T-LGL signaling network) demonstrate the efficacy of the proposed approach. Therefore, this study establishes an appropriate framework for identifying the regulatory variables for state transition of complex biological networks.

Selection of process alternatives for lignocellulosic bioethanol production using a MILP approach.

This work proposes a decision-making framework for the selection of processes and unit operations for lignocellulosic bioethanol production. Process alternatives are described by its capital and operating expenditures, its contribution to process yield and technological availability information. A case study in second generation ethanol production using Eucalyptus globulus as raw material is presented to test the developed process synthesis tool. Results indicate that production cost does not necessarily decrease when yield increases. Hence, optimal processes can be found at the inflexion point of total costs and yield. The developed process synthesis tool provides results with an affordable computational cost, existing optimization tools and an easy-to-upgrade description of the process alternatives. These features made this tool suitable for process screening when incomplete information regarding process alternatives is available.