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Global efforts to achieve climate neutrality increasingly rely on innovative urban planning and design strategies. This study focuses on the identification and application of key performance indicators (KPIs) to support policymakers and local authorities in driving sustainable urban transitions. Using a real-life case study of European cities and countries, this research leverages data analytics and machine learning to inform decision-making processes. Specifically, the k-means clustering algorithm was employed to group countries based on socioeconomic and environmental KPIs, while principal component analysis was used to rank the most influential indicators in shaping these clusters. The analysis highlighted GDP per capita, corruption perception, and climate-related expenditure as key drivers of clustering. Additionally, time series analysis of KPI trends demonstrated the impact of policy decisions over time. This study showcases how machine learning and data-driven approaches can provide valuable insights for urban planners, offering a robust framework for evaluating and improving climate-neutrality strategies at both city and country levels. [ABSTRACT FROM AUTHOR]

In urban logistics, effective maintenance is crucial for maintaining the reliability and efficiency of energy supply systems, impacting both asset performance and operational stability. This paper addresses the scheduling and routing plans for maintenance of power generation assets over a multi-period horizon. We model this problem as a multi-period team orienteering problem. To address this multi-period challenge, we propose a dual approach: a novel reinforcement learning (RL) framework and a biased-randomized heuristic algorithm. The RL-based method dynamically learns from real-time operational data and evolving asset conditions, adapting to changes in asset health and failure probabilities to optimize decision making. In addition, we develop and apply a biased-randomized heuristic algorithm designed to provide effective solutions within practical computational limits. Our approach is validated through a series of computational experiments comparing the RL model and the heuristic algorithm. The results demonstrate that, when properly trained, the RL-based model is able to offer equivalent or even superior performance compared to the heuristic algorithm. [ABSTRACT FROM AUTHOR]

A fundamental assumption in addressing real-world problems is acknowledging the presence of uncertainty and dynamism. Dismissing these factors can lead to the formulation of an optimal solution for an entirely different problem. This paper presents a novel variant of the capacitated dispersion problem (CDP) referred to as the stochastic and non-static CDP. The main objective of this problem is to strategically position facilities to achieve maximum dispersion while meeting the capacity demand constraint. The proposed approach combines stochastic and non-static elements, introducing a new paradigm to address the problem. This innovation allows us to consider more realistic and flexible environments. To solve this challenging problem, a novel sim-learnheuristic algorithm is proposed. This algorithm combines a biased-randomized metaheuristic (optimization component) with a simulation component (to model the uncertainty) and a machine learning component (to model non-static behavior). The non-static part works by using black box and white box mechanisms to learn the uncertainty with some related facilities’ variables. Based on an extended set of traditional benchmarks for the CDP, a series of computational experiments were carried out. The results demonstrate the effectiveness of the proposed sim-learnheuristic approach for solving the CDP under non-static and stochastic scenarios. [ABSTRACT FROM AUTHOR]

In the last few years, there have been notable developments in organic solar cells using both small molecule donor and acceptor. It has been noted that adding halogens to the end groups of small molecules could enhance the film structure and, consequently, the performance of the devices. In this study, three novel small molecule donors are created. The molecules include a vinyl‐CPDT oligomer with three units, with end‐caps made up of indanedione groups and containing four H, four Cl, and four F substituents. The purpose of the study is to investigate how the halogen substituent affects the photovoltaic characteristics of binary devices made with the non‐fullerene acceptor (NFA) TOCR2 as the acceptor. Having the halogen in the device enhances its effectiveness, and FG5, which has 4‐Cl substituents in the end groups, shows the highest efficiency among all devices with a PCE of 14.39%. Incredibly, the ternary device that is created in normal atmospheric conditions with chloro‐substituted FG5 as the donor, TOCR2 as the acceptor, and the wide band gap NFA DICTF as the third element shows significantly improved efficiency, achieving PCE values of up to 16.35%. [ABSTRACT FROM AUTHOR]

The team orienteering problem (TOP) is an NP‐hard optimization problem with an increasing number of potential applications in smart cities, humanitarian logistics, wildfire surveillance, etc. In the TOP, a fixed fleet of vehicles is employed to obtain rewards by visiting nodes in a network. All vehicles share common origin and destination locations. Since each vehicle has a limitation in time or traveling distance, not all nodes in the network can be visited. Hence, the goal is focused on the maximization of the collected reward, taking into account the aforementioned constraints. Most of the existing literature on the TOP focuses on its deterministic version, where rewards and travel times are assumed to be predefined values. This paper focuses on a more realistic TOP version, where travel times are modeled as random variables, which introduces reliability issues in the solutions due to the route‐length constraint. In order to deal with these complexities, we propose a simheuristic algorithm that hybridizes biased‐randomized heuristics with a variable neighborhood search and MCS. To test the quality of the solutions generated by the proposed simheuristic approach, we employ the well‐known sample average approximation (SAA) method, as well as a combination model that hybridizes the metaheuristic used in the simheuristic approach with the SAA algorithm. The results show that our proposed simheuristic outperforms the SAA and the hybrid model both on the objective function values and computational time. [ABSTRACT FROM AUTHOR]

In the financial sector, insurance companies generate large volumes of data, including policy transactions, customer interactions, and risk assessments. These historical data on established customers provide opportunities to enhance decision-making processes and offer more customized services. However, data on potential new customers are often limited, due to a lack of historical records and to legal constraints on personal data collection. Despite these limitations, accurately predicting whether a potential new customer will generate benefits (high-performance) or incur losses (low-performance) is crucial for many service companies. This study used a real-world dataset of existing car insurance customers and introduced advanced machine learning models, to predict the performance of potential new customers for whom available data are limited. We developed and evaluated approaches based on traditional binary classification models and on more advanced boosting classification models. Our computational experiments show that accurately predicting the performance of potential new customers can significantly reduce operation costs and improve the customization of services for insurance companies. [ABSTRACT FROM AUTHOR]

On March 19, 2020, Argentina's government decided to close academic institutions for an indefinite time in an attempt to contain the spread of the COVID-19 pandemic. Since the University established that, when the quarantine ends, 5 weeks will be reserved for face-to-face activities that could not be carried out virtually (laboratory lessons and final exams), the main focus on the online activities was in the theoretical lessons. For the Instrumental Analytical Chemistry course both synchronous strategy such as questions and answer sessions and asynchronous communication such as Microsoft PowerPoint presentations with explicative audios, simulated assays, and assignments were adopted. A survey conducted with our students indicated that although synchronous lessons provided face-to-face interaction, the lack of good Internet connectivity was the main weakness of this strategy. On the other hand, asynchronous lessons did not require very high bandwidth networks and had the possibility of stopping the lesson and continuing it later. However, students' lack of interaction with their teachers was the main drawback of this type of communication. Most of the students pointed out that a blend of synchronous and asynchronous communication is more desirable for Instrumental Analytical Chemistry lessons than asynchronous or synchronous communication separately.

Introduction: Stafne's bone cavity is a rare, radiolucent, well-demarcated anatomic variant that usually occurs in the molar region near the mandibular angle and below the canal for the inferior dental nerve. It is frequently misdiagnosed with other pathological entities. Objective: To determine the frequency of Stafne's bone cavity in panoramic radiographs of the Oral and Maxillofacial Radiology Service of the Teaching Dental Care Center “Cayetano Heredia”, from 2015 to 2019. Methods: An observational, descriptive, cross-sectional and retrospective study was performed on a sample of 17875 panoramic radiographs. Demographic variables such as gender, age, location and shape were considered; subsequently tables of contents were performed for data analysis. Results: Among the 17875 patients, only 24 (0.13 %) had Stafne's bone cavity, including 16 males and 8 females. The eighth decade of life presented the highest number of cases with 6 (0.4 %). The right posterior location accounted for 13 (54.17 %), the left posterior with 7 (29.17 %) and the anterior with 4 (16.67 %). The oval shape with 23 (95.83 %) and round with only 1 (4.17 %). Conclusions: The frequency of Stafne's bone cavity was 0.13 % with male sex predilection, eighth decade of life, right posterior location and oval shape.

México importa el 99 % de la semilla de sorgo Sorghum bicolor. La producción de semillas representa menos del 1 % procedente de genotipos sobresalientes y adaptados. Esto incrementa costos de producción y genera problemas de adaptación de genotipos. La tecnología de producción debe adecuarse a los progenitores específicos y región del país, para incrementar la semilla. Este estudio evaluó la calidad de semillas de catorce líneas de sorgo (progenitoras de siete híbridos), de INIFAP. En el ciclo primavera-verano (P-V) del 2018, en Celaya Guanajuato, México, se establecieron tres fechas de siembra, 12 de mayo, 6 y 28 de junio, bajo un diseño de bloques al azar y tres repeticiones. Se evaluaron los días/floración, el peso de mil semillas, la germinación al momento de la cosecha y después de seis meses de almacenamiento. El análisis de varianza indicó diferencias significativas entre líneas (G), fechas de siembra (FS) en todas las variables y en la interacción G x FS para días a floración; donde las líneas androestériles (líneas A), presentaron diferencia de 8 a 10 días en días a floración con los progenitores masculinos (líneas restauradoras R) en la FS del 6 de junio lo que dificultaría la producción de semilla híbrida. En calidad de semilla a pesar de que se presentaron fuertes precipitaciones durante el llenado de grano y madurez fisiológica las líneas SBA-25, LBA-98, LBA-101 y la cruza SBA25 x SBR-31 sobresalieron por su alto porcentaje (%) de germinación y mantuvieron su calidad después de seis meses de almacenamiento.