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Leishmaniasis is one of the most important neglected tropical diseases according to the World Health Organization. The available drugs are expensive, not sufficiently effective, have serious cytotoxic effects and parasitic resistance has increased in the last years. In the present work, a virtual screening protocol was used to identify new natural compounds potentially active against Leishmania spp. using machine learning-based models. Three vegetable origin compounds were selected by using a multiclassifier composed by models developed with k-nearest neighbor, classification tree, Multilayer perceptron and Support Vector Machine; all these models for Leishmania amazonensis promastigote form were developed with WEKA software. The selected compounds showed in vitro activity against L. amazonensis (MHOM/BR/77/LTB0016) promastigotes with CI₅₀ lower than 1 µg/mL using 96-well plates and resazurine fluorescence method.

NSGA-type algorithms are widely used in mathematical optimization to power up diverse strategies for hyper-heuristics methodologies in problem-solving, combinations of these genetic algorithms and other techniques are also well documented in the literature related to the use of alternative ways to solve situations in the leading industries. In critical welding processes like armouring, it is essential to achieve excellent solutions due to the nature of the usage of the piece, whose principal objective is the protection and preservation of life. In armouring applications, the weakest point of the entire piece is the soldering strip itself.

In this particular application, using a robotic-guided welding process, the idea is to combine the NSGA-II with neuronal networks to find out the most impacting and optimal welding variables values to preserve the best mechanical properties of the resulting prototypes, by the use of the design of experiments (DOE), neuronal networks and multiobjective genetic algorithms the aim of this work is to present the pulsed Gas Tungsten Arc Welding (P-GTAW) process optimization by using a new approach combining the aforementioned to find the optimal values of three process key variables.

After intensive experimentation using different techniques of parameter estimations (traditional for this process, and the by the utilisation of the new approach), the results of the analysis are presented and compared, visual inspection and other inspections point to "attractive solutions" for welding with the new methodology

The Concrete Delivery Problem consists of the delivery of this product to different construction sites, defined as clients. These demands a certain amount which has to be totally satisfied and for each one of them there is a time window within concrete must be delivered. The product is transported by a heterogeneous fleet of trucks and due to their capacity limits, deliveries must be split. On the other hand, to assure the proper bonding of concrete layers, a maximum time lag between consecutive deliveries to the same client is required. The objective, in this case, is to maximize the number of satisfied customers, according to their demand. As distributors of concrete possess a finite capacity it is expected some clients might not be served during the operation. In this project, formulations proposed in the literature for this problem are studied, and based on them a new and more compact formulation is proposed as well as a metaheuristic approach capable of deal with larger instances and give solutions in shorter computation times.

In optimal packing problems, there is a set of small elements (load) to be arranged in one or more large objects (containers), fulfilling the non-overlapping conditions between the small elements and the containment conditions (the load does not exceed the dimensions of the container), in addition, there is an objective to optimize.

The main objective of this investigation is to find acceptable solutions in a reasonable time to the instances of the problem.
of packing convex polygons in convex containers (circular and circular sections) of variable dimensions, using an exact mathematical nonlinear programming model, defining polygons or items/loads by their vertices using a Lagrangian approach and convexity conditions. In addition to determining the effect on the packaging, having as control parameters the number of elements to be packaged, the type of element, and the type of container.

The service planning problem is motivated by a situation faced by a telecommunications company where the following problem arises: how to select a subset of service orders to be performed by a set of available crews as well as determine the sequences in which they must be carried out such that the wage between the crews is balanced, taking into account restrictions on compatibility service-crew and working hours.

After a detailed literature review, we conclude that the problem can be modeled as a mixed-integer linear programming model and as a constraint programming model. The formulations are coded in C++ and solved through the CPLEX optimizer. In this work, we analyzed results over a set of 100 instances adapted from the literature.

Bin packing problems (BPP) are finding a position layout for a set of objects inside a container. The optimization objective of this problem can be minimizing the wasted area, maximizing the occupied space, or a group of functions related to the container or objects. The current formulations for these problems need to be more representative of reality's problems. The optimal solutions can be found for small problems. Moreover, formulations are only generalized to represent some possible cases. The approaches to solving these problems depend on specific rules related to the instances of use, a consequence of the complexity of solving a model with these characteristics. For those reasons, it is necessary to emerge a common way to represent these different approaches. So, it is essential to investigate and develop new optimization strategies and representations to obtain better results closer to the industrial needs. This work intends to use a series of well-known tools in operations research but little used in BPP to study packaging problems. This study presents a hybrid method for solving packing problems: This method represents the combination between an exact model and an approximate algorithm. It uses the benefits and strengths of both ways while complementing their weaknesses. Also, we present a general formulation for BPP representing convex objects by their sides (inequalities) or by a set of vertices.

Ambulance location and allocation have been studied to improve Emergency Medical Service Systems in the past decades. The problem in Mexico and other Latin American countries mains that few ambulances are available for the system, and more than one service provider is involved in attending accidents. These service providers are generally uncoordinated; each locates their ambulances where they think it's better.

The investigation's objective is to obtain an optimal location and allocation for all service providers' ambulances if necessary, considering constraints for each service provider, e.g., different potential sites, coverage radii, or ambulance available for each service provider when an emergency enters the system; and looking for maximize accident coverage.

Leishmaniasis is a vector-borne parasite that affects 700,000–1 million people and kills 26,000–65,000 annually. This work presents a brief, comprehensive review of AI/ML studies performed in leishmania drug discovery. In this study, research was carried out using the Scopus and Web of Science (WoS) databases from 2013 to 2022. Between the two databases, 28 documents were found, eight of which were duplicates; hence, a total of 20 articles were analysed. Of these, nine were research articles, 10 review articles, and one editorial document. Current Topics in Medicinal Chemistry was the only journal that received more than one paper (three papers in total). The available literature on the topic is limited. The most relevant articles selected from the two databases, WoS and Scopus, provided an overview of the scientific topic (stages of drug discovery for leishmaniasis using AI/ML). This brief review can also serve as a starting point for integrating knowledge in this field through research and suggest future research avenues for AI and ML applications in protozoan infectious diseases

Recent advances in nanomaterials and nanotechnology have been attracting intense attention worldwide due to their bright prospects in nanomedicine and other areas. The synthesis of nanoparticles (NPs) is usually done by conventional techniques that produce a lot of harmful and toxic by-products. Therefore, greener ways are needed to develop eco-friendly, clean, and less toxic nanoparticles than conventional methods. In this paper, we present a brief review of recent advances in NP biosynthesis. We include a wide variety of plant, bacterial, fungal, algal, and viral species that have been used to synthesise NPs for biomedical applications.