(1) Background: Carbohydrates are the most important source of nutritional energy for the human body. Carbohydrate digestion, metabolism, and their role in the gut microbiota modulation are the focus of multiple studies. The objective of this weight of evidence systematic review is to investigate the potential relationship between ingested carbohydrates and the gut microbiota composition at different taxonomic levels. (2) Methods: Weight of evidence and information value techniques were used to evaluate the relationship between dietary carbohydrates and the relative abundance of different bacterial taxa in the gut microbiota. (3) Results: The obtained results show that the types of carbohydrates that have a high information value are: soluble fiber with Bacteroides increase, insoluble fiber with Bacteroides and Actinobacteria increase, and Firmicutes decrease. Oligosaccharides with Lactobacillus increase and Enterococcus decrease. Gelatinized starches with Prevotella increase. Starches and resistant starches with Blautia decrease and Firmicutes increase. (4) Conclusions: This work provides, for the first time, an integrative review of the subject by using statistical techniques that have not been previously employed in microbiota reviews.

Coronavirus disease 2019 (COVID-19) has been the most recent disease, with millions of deaths worldwide. Fortunately, vaccines have been developed to immunize the population. However, the distribution of the vaccine is also a significant challenge. Generally, each country defines its strategy to bring it closer to the target population, considering the adequate transport for their conservation, depending on the type of vaccines defined. In this work, a mathematical model is proposed for the distribution of different COVID-19 vaccines in Mexico. Our results suggest an efficient strategy to meet demand in a given period.

Creating robust and explainable statistical learning models is essential in credit risk management. For this purpose, equally spaced or frequent discretization is the de facto choice when building predictive models. The methods above have limitations, given that when the discretization procedure is constrained, the underlying patterns are lost. This study introduces an innovative approach by combining traditional discretization techniques with clustering-based discretization, specificallykmeans and Gaussian mixture models. The study proposes two combinations: Discrete Competitive Combination (DCC) and Discrete Exhaustive Combination (DEC). Discrete Competitive Combination selects features based on the discretization method that performs better on each feature, whereas Discrete Exhaustive Combination includes every discretization method to complement the information not captured by each technique. The proposed combinations were tested on 11 different credit risk datasets by fitting a logistic regression model using the weight of evidence transformation over the training partition and contrasted over the validation partition. The experimental findings showed that both combinations similarly outperform individual methods for the logistic regression without compromising the computational efficiency. More importantly, the proposed method is a feasible and competitive alternative to conventional methods without reducing explainability.

Introducción. La lesión medular afecta la calidad de vida y el estado de salud de la persona que la padece. Por su parte, el uso de una silla de ruedas adecuada a las necesidades y características particulares de cada individuo puede aumentar los cuidados en la salud, prevenir complicaciones como las úlceras por presión e incrementar la calidad de vida. Objetivo: Valorar la percepción de la calidad de vida y dar seguimiento a la formación de úlceras por presión como indicadores del impacto que tiene un programa basado en las “Pautas de suministro de sillas de ruedas manuales en entornos de menores recursos” de la OMS. Material y Métodos. A un grupo piloto de lesionados medulares se les otorgó una silla de ruedas adecuada; se les aplicaron los cuestionarios CHART-SF y WHOQOL-BREF para medir su calidad de vida y se les hizo seguimiento del desarrollo de úlceras por presión durante 12 meses. Resultados. Los principales hallazgos fueron una mejor percepción de la movilidad en el CHART-SF, mientras que en el WHOQOL -BREF el grupo no percibió ningún cambio, ni positivo ni negativo, en su calidad de vida. Los pacientes no desarrollaron úlceras por presión durante el estudio. Conclusiones. Los cuestionarios aplicados no son suficientemente sensibles para medir la calidad de vida en pacientes con lesión medular.

Despite the advancements witnessed in the realm of quality improvement, Statistical Process Control (SPC) perseveres as a universally embraced and extensively employed tool within manufacturing companies across the globe. This enduring popularity can be attributed to its adherence to the time-tested principles established by Shewhart, which have withstood the test of time since its inception in 1924. Over the years, SPC has consistently proven its efficacy in optimizing manufacturing processes, enhancing product quality, and fostering continuous improvement initiatives. Particularly in the automotive industry quality is an utmost necessity. In Mexico this industry ranks as the 3rd most significant, and globally, is the 5th in terms of volume and transactional activity. This research focuses on a successful real case study that demonstrates the enhancement of capability in a critical machining operation within a Mexican automotive plant, specifically dedicated to truck engine remanufacturing. This improvement was achieved through the analysis of x and R control charts, followed by the implementation of the Plan-Do-Check-Act (PDCA) cycle. The results obtained were derived from a comparative analysis of data from two distinct periods in the machining process of bushing diameters. This analysis revealed a significant increase in process capacity, with a 13.08 % improvement in the Process Capability Index (Cpk) and a 17.2 % enhancement in the Process Performance Index (Ppk). The decrease in expected out-of-specification parts showed a remarkable improvement of 72.7 %. Additionally, there was an enhancement in process variability, with a notable 8.33 % reduction in the mean range. The significance of this research lies in the empirical identification of gaps and opportunities within the realms of quality assurance and quality control, utilizing methodologies for quality improvement. By uncovering these gaps and opportunities, this study provides valuable insights that can guide future efforts to enhance quality practices and drive continuous improvement in the field.

In the capacitated arc routing problem (CARP), a fleet of capacitated vehicles based at a depot must cover the streets of a network where the demand is located at the lowest possible total cost. Waste collection, street sweeping, winter gritting, and mail delivery are among its best-known applications. This work introduces the CARP with vehicle dependence (CARP-VD), an extension in which the cost of servicing an edge, and that of traversing it without service, are specific to each vehicle type and formulates it as a mixed-integer linear program. A two-stage matheuristic is proposed: the first stage distributes the required edges among the vehicles without exceeding their capacities, and the second builds the route of each vehicle. A bound is derived that limits the optimality loss of this decomposition by its own deadheading cost. Both approaches are evaluated on 47 benchmark instances adapted from the literature under a common one-hour budget, and their robustness is assessed over six scenarios that vary the parameters of the adaptation. The matheuristic returns good-quality solutions in a fraction of the time on the smaller instances, and on those in which almost every edge requires service it improves the best solutions found by a commercial solver applied to the complete model by up to 44%.