Search

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.

Objective: Radiodermatitis (RD) is a frequent adverse event of radiotherapy (RT). Currently, there is no consensus and approved protocol for the treatment of RD. Curcumin (CUR) is a natural polyphenol obtained from turmeric and it has low intrinsic toxicity in humans. The aim of this systematic review was to explore the efficacy of CUR for prevention and treatment of RD.Materials and Methods: A systematic literature review was performed in the following online databases: Cochrane library, PubMed, Scopus, Web of Science, MEDLINE, and EMBASE. Among the 5 selected records, 3 had a randomized clinical trial (RCT)-design and the other had a pilot and controlled study designed. The included studies were performed on breast cancer (N=3), head and neck cancers (N=1) and different types of cancer (N=1).Results: Four of the studies reported that the application of curcumin in cancer patients undergoing radiotherapy is associated with decreased intensity of radiodermatitis. However, one study did not report any significant effect of CUR on radiodermatitis. This review provides substantial evidence which confirm the clinical value of CUR in cancer supportive care.Conclusion: Further prospective clinical trials in larger scales are warranted in order to determine the " supplemental form and dose of CUR" for RD prevention and treatment in patients receiving radiotherapy.

Vehicle-to-grid (V2G) systems play a key role in the integration of electric vehicles (EVs) into smart grids by enabling bidirectional energy flows between EVs and the grid. Optimizing V2G operations poses significant challenges due to the dynamic nature of energy demand, grid constraints, and user preferences. This paper addresses the optimization challenges in V2G systems and explores the use of artificial intelligence (AI) methods to tackle these challenges. The paper provides a comprehensive analysis of existing work on optimization in V2G systems and identifies gaps where AI-driven algorithms, machine learning, metaheuristic extensions, and agile optimization concepts can be applied. Case studies and examples demonstrate the efficacy of AI-driven algorithms in optimizing V2G operations, leading to improved grid stability, cost optimization, and user satisfaction. Furthermore, agile optimization concepts are introduced to enhance flexibility and responsiveness in V2G optimization. The paper concludes with a discussion on the challenges and future directions for integrating AI-driven methods into V2G systems, highlighting the potential for these intelligent algorithms and methods.

In this study, 11 designs with sand-to-cement ratios of 4.5, 4.75, 5, 5.25, 5.5, 5.75, 6, 6.25, 6.5, 6.75, and 7 were used to investigate the impact of increasing the sand-to-cement ratio in reactive powder concrete. Laboratory results indicate a decrease in compressive and bending strength, following an increase in the ratio of sand to cement. Sample S7 with the highest ratio of sand to cement has the lowest bending strength and compressive strength at the ages of 1, 7, 14, 28, 42 and 56 days.In the second process, 1 % polymer resin was added to the sand-to-cement ratio from the first step. By analyzing the results, it was observed that the addition of 1 % of polymer resin causes a decrease of 40.8 % in compressive strength at the age of 1 day. The biggest one-day pressure drop is S6.75 with a drop of 68.1 %. As the samples age, adding 1 % of polymer resin increases the compressive strength at 11 sand-cement ratios, so that the 90-day compressive strength of the S4.75 sample reaches 59.7 MPa. The addition of 1 % polymer resin also increased the bending strength by 9.1 %. After examining the results of the first and second steps of the study, the sand to cement ratios of 5.5, 6, 6.25, 6.5 and 7 were selected as optimal and suitable samples. In the third step of the study, coarse aggregate with percentages of 10, 20, 30, 40 and 50 was added to the selected samples. The results showed that the addition of coarse aggregate caused a decrease of 41.1 % in the compressive strength of reactive powder concrete at the age of 1 day, compared to the control sample. In the third step, the maximum compressive strength at the ages of 28, 42, 56 and 90 days for sand-cement ratios of 5.5, 6, 6.25, 6.5 and 7 corresponds to the sample containing 50 % of coarse aggregate. The best samples of the third step in flexural strength are those with 50 % gravel, which increases flexural strength with increasing sand-cement ratio. On the other hand, with the addition of coarse aggregate, an increase in flexural strength was observed compared to the control sample of reactive powder concrete. In the fourth step of the study, 1 % of polymer resin was added to the samples of the third step. The results of this step show that, like the third step, the maximum compressive strength of the ages of 28, 42, 56 and 90 days for the sand-cement ratios of 5.5, 6, 6.25, 6.5 and 7 for the sample with 50 % coarse aggregate. In the fourth step, by increasing the ratio of sand to cement, samples with a lower percentage of sand have higher bending strength.

Introduction: Colorectal cancer (CRC) is a common cancer associated with poor outcomes, underscoring a need for the identification of novel prognostic and therapeutic targets to improve outcomes. This study aimed to identify genetic variants and differentially expressed genes (DEGs) using genome-wide DNA and RNA sequencing followed by validation in a large cohort of patients with CRC. Methods: Whole genome and gene expression profiling were used to identify DEGs and genetic alterations in 146 patients with CRC. Gene Ontology, Reactom, GSEA, and Human Disease Ontology were employed to study the biological process and pathways involved in CRC. Survival analysis on dysregulated genes in patients with CRC was conducted using Cox regression and Kaplan–Meier analysis. The STRING database was used to construct a protein–protein interaction (PPI) network. Moreover, candidate genes were subjected to ML-based analysis and the Receiver operating characteristic (ROC) curve. Subsequently, the expression of the identified genes was evaluated by Real-time PCR (RT-PCR) in another cohort of 64 patients with CRC. Gene variants affecting the regulation of candidate gene expressions were further validated followed by Whole Exome Sequencing (WES) in 15 patients with CRC. Results: A total of 3576 DEGs in the early stages of CRC and 2985 DEGs in the advanced stages of CRC were identified. ASPHD1 and ZBTB12 genes were identified as potential prognostic markers. Moreover, the combination of ASPHD and ZBTB12 genes was sensitive, and the two were considered specific markers, with an area under the curve (AUC) of 0.934, 1.00, and 0.986, respectively. The expression levels of these two genes were higher in patients with CRC. Moreover, our data identified two novel genetic variants—the rs925939730 variant in ASPHD1 and the rs1428982750 variant in ZBTB1—as being potentially involved in the regulation of gene expression. Conclusions: Our findings provide a proof of concept for the prognostic values of two novel genes—ASPHD1 and ZBTB12—and their associated variants (rs925939730 and rs1428982750) in CRC, supporting further functional analyses to evaluate the value of emerging biomarkers in colorectal cancer.

Carsharing practices are spreading across many cities in the world. This paper analyzes real-life data obtained from a private carsharing company operating in the city of Barcelona, Spain. After describing the main trends in the data, machine learning and time-series analysis methods are employed to better understand citizens’ needs and behavior, as well as to make predictions about the evolution of their demand for this service. In addition, an original proposal is made regarding the location of the pick-up points. This proposal is based on a capacitated dispersion algorithm, and aims at balancing two relevant factors, including scattering of pick-up points (so that most users can benefit from the service) and efficiency (so that areas with higher demand are well covered). Our aim is to gain a deeper understanding of citizens’ needs and behavior in relation to carsharing services. The analysis includes three main components: descriptive, predictive, and prescriptive, resulting in customer segmentation and forecast of service demand, as well as original concepts for optimizing parking station location.

Mobility and transportation activities in smart cities require an increasing amount of energy. With the frequent energy crises arising worldwide and the need for a more sustainable and environmental friendly economy, optimizing energy consumption in these growing activities becomes a must. This work reviews the latest works in this matter and discusses several challenges that emerge from the aforementioned social and industrial demands. The paper analyzes how collaborative concepts and the increasing use of electric vehicles can contribute to reduce energy consumption practices, as well as intelligent x-heuristic algorithms that can be employed to achieve this fundamental goal. In addition, the paper analyzes computational results from previous works on mobility and transportation in smart cities applying x-heuristics algorithms. Finally, a novel computational experiment, involving a ridesharing example, is carried out to illustrate the benefits that can be obtained by employing these algorithms.

Background and Objectives: Effective relationship between teacher and student is one of the most important indicators of effective teaching and a strong stimulus for learning. The present research was conducted with the objective of designing validating the questionnaire of factors affecting teacher - student relationship from the viewpoint of students of Qom University of Medical Sciences in 2015. Methods: In this exploratory study, the questionnaire of the relationship between teachers and students, was designed with the initial list of 46 questions and the participation of 20 students and 5 faculty members. Questionnaire Validation was performed using face validity and content validity and determination of the construct validity, was carried out using exploratory and confirmatory factor analysis. Cronbach alpha coefficient and intra-class correlation coefficient tests were used to determine the reliability of the questionnaire. Data were analyzed by exploratory and confirmatory factor analysis and using SPSS version 16 and Lisrel 8.80 soft wares. Results: The results showed that 35 questions of the Teacher-Student Relationship questionnaire had an acceptable content validity (0.68 to 0.97). Measures of the Cronbach’s alpha coefficient and intra-class correlation coefficient, were reported to be 0.90 and 0.85, respectively. Exploratory factor analysis reduced the questions to 22 questions and categorized the items to five factors (individual ability, academic ability, communication, professional ethics, religion, and belief, and overall, explained a total of 54% of all variance of these variables. Conclusion: The findings of this research indicated that the factors affecting the relationship between teachers and students, have acceptable validity and reliability and can be used in research activities.

Background: Uncertainty conditions have been increasingly considered in optimization problems arising in real-life transportation and logistics activities. Generally, the analysis of complex systems in these non-deterministic environments is approached with simulation techniques. However, simulation is not an optimization tool. Hence, it must be combined with optimization methods when our goal is to: (i) minimize operating costs while guaranteeing a given quality of service; or (ii) maximize system performance using limited resources. When solving NP-hard optimization problems, the use of metaheuristics allows us to deal with large-scale instances in reasonable computation times. By adding a simulation layer to the metaheuristics, the methodology becomes a simheuristic, which allows the optimization element to solve scenarios under uncertainty. Methods: This paper reviews the indexed documents in Elsevier Scopus database of both initial as well as recent applications of simheuristics in the logistics and transportation field. The paper also discusses open research lines in this knowledge area. Results: The simheuristics approaches to solving NP-hard and large-scale combinatorial optimization problems under uncertainty scenarios are discussed, as they frequently appear in real-life applications in logistics and transportation activities. Conclusions: The way in which the different simheuristic components interact puts a special emphasis in the different stages that can contribute to make the approach more efficient from a computational perspective. There are several lines of research that are still open in the field of simheuristics.

Background: DNA vaccination with plasmid encoding bacterial, viral, and parasitic immunogens has been shown to be an attractive method to induce efficient immune responses. Bacteria of the genus Brucella are facultative intracellular pathogens for which new and efficient vaccines are needed. Methods: To evaluate the use of a DNA immunization strategy for protection against brucellosis, a plasmid containing the DNA encoding the Brucella melitensis (B. melitensis) 31 kDa outer membrane protein, as a potent immunogenic target, was constructed. Results: The constructed plasmid, pcDNA3.1+omp31, was injected intramuscularly into mice and the expression of omp31 RNA was assessed by RT-PCR. The integrity of the pcDNA3.1+omp31 construct was confirmed with restriction analysis and sequencing. Omp31 mRNA expression was verified by RT-PCR. Conclusion: Our results indicate that the pcDNA3.1+omp31 eukaryotic expression vector expresses omp31 mRNA and could be useful as a vaccine candidate.

Urban logistics consumes a large portion of energy resources worldwide. Thus, optimization algorithms are used to define mobility modes, vehicle fleets, routing plans, and last-mile delivery operations to reduce energy consumption such as metaheuristics. With the emergence of smart cities, new opportunities were defined, such as carsharing and ridesharing. In addition to last-mile delivery, these opportunities form a challenging problem because of the dynamism they possess. New orders or ride requests could be placed or canceled at any time. Further, transportation times might evolve due to traffic conditions. These dynamic changes challenge traditional optimization methods to propose solutions in real-time to large-scale energy-optimization problems. Thus, a more `agile optimization’ approach is required to provide fast solutions to optimization problems when these changes occur. Agile optimization combines biased randomization and parallelism. It provides `good’ solutions compared to solutions found by traditional optimization methods, such as in-team orienteering problems. Additionally, these solutions are found in short wall clock, real-time.