Optimization is an entire field that aims to improve efficiency and effectiveness across various domains. Its primary objective is to minimize costs, time, and risks while maximizing gains, quality, and efficiency. In this context, the 0/1 1-D bin packing problem is one of combinatorial optimization’s most challenging and extensively studied problems. This problem holds significant practical applications in supply chain management, packaging design, and resource optimization. This work solves the 0/1 1-D bin packing problem using a nature-inspired golden eagle optimizer. The hunting behavior of golden eagles inspires this bio-solver, and it employs swarm intelligence-based strategies to approximate solutions. We perform a comparative analysis of the bio-inspired algorithm to evidence its yield. We use twenty instances of the 1-D bin packing problem. Computational results show that the golden eagle optimizer exhibits better results in convergence time than well-known bio-inspired algorithms.

The Quintero Bay, located along the central coast of Chile, has suffered different oil spills during the past 5 years, impacting marine ecosystems. This report describes the microbial community structure of seawater samples obtained from the Quintero Bay through 16S rRNA amplicon sequencing.

Wildfire is a major concern worldwide and particularly in Australia. The 2019–2020 wildfires in Australia became historically significant as they were widespread and extremely severe. Linking climate and vegetation settings to wildfires can provide insightful information for wildfire prediction, and help better understand wildfires behavior in the future. The goal of this research was to examine the relationship between the recent wildfires, various hydroclimatological variables, and satellite-retrieved vegetation indices. The analyses performed here show the uniqueness of the 2019–2020 wildfires. The near-surface air temperature from December 2019 to February 2020 was about 1 °C higher than the 20-year mean, which increased the evaporative demand. The lack of precipitation before the wildfires, due to an enhanced high-pressure system over southeast Australia, prevented the soil from having enough moisture to supply the demand, and set the stage for a large amount of dry fuel that highly favored the spread of the fires.

The β3 adrenergic receptor is raising as an important drug target for the treatment of pathologies such as diabetes, obesity, depression, and cardiac diseases among others. Several attempts to obtain selective and high affinity ligands have been made. Currently, Mirabegron is the only available drug on the market that targets this receptor approved for the treatment of overactive bladder. However, the FDA (Food and Drug Administration) in USA and the MHRA (Medicines and Healthcare products Regulatory Agency) in UK have made reports of potentially life-threatening side effects associated with the administration of Mirabegron, casting doubts on the continuity of this compound. Therefore, it is of utmost importance to gather information for the rational design and synthesis of new β3 adrenergic ligands. Herein, we present the first combined 2D-QSAR (two-dimensional Quantitative Structure-Activity Relationship) and 3D-QSAR/CoMSIA (three-dimensional Quantitative Structure-Activity Relationship/Comparative Molecular Similarity Index Analysis) study on a series of potent β3 adrenergic agonists of indole-alkylamine structure. We found a series of changes that can be made in the steric, hydrogen-bond donor and acceptor, lipophilicity and molar refractivity properties of the compounds to generate new promising molecules. Finally, based on our analysis, a summary and a regiospecific description of the requirements for improving β3 adrenergic activity is given.

Malaria is an infectious disease caused by Plasmodium spp. parasites, with widespread drug resistance to most antimalarial drugs. We report the development of two 3D-QSAR models based on comparative molecular field analysis (CoMFA), comparative molecular similarity index analysis (CoMSIA), and a 2D-QSAR model, using a database of 349 compounds with activity against the P. falciparum 3D7 strain. The models were validated internally and externally, complying with all metrics (q2 > 0.5, r2test > 0.6, r2m > 0.5, etc.). The final models have shown the following statistical values: r2test CoMFA = 0.878, r2test CoMSIA = 0.876, and r2test 2D-QSAR = 0.845. The models were experimentally tested through the synthesis and biological evaluation of ten quinoline derivatives against P. falciparum 3D7. The CoMSIA and 2D-QSAR models outperformed CoMFA in terms of better predictive capacity (MAE = 0.7006, 0.4849, and 1.2803, respectively). The physicochemical and pharmacokinetic properties of three selected quinoline derivatives were similar to chloroquine. Finally, the compounds showed low cytotoxicity (IC50 > 100 µM) on human HepG2 cells. These results suggest that the QSAR models accurately predict the toxicological profile, correlating well with experimental in vivo data.

The existence of mutually correlated thin and rotating planes of satellite galaxies around both the Milky Way (MW) and Andromeda (M31) calls for an explanation. Previous work in Milgromian dynamics (MOND) indicated that a past MW–M31 encounter might have led to the formation of these satellite planes. We perform the first-ever hydrodynamical MOND simulation of the Local Group using phantom of ramses. We show that an MW–M31 encounter at z ≈ 1, with a perigalactic distance of about 80 kpc, can yield two disc galaxies at z = 0 oriented similarly to the observed galactic discs and separated similarly to the observed M31 distance. Importantly, the tidal debris are distributed in phase space similarly to the observed MW and M31 satellite planes, with the correct preferred orbital pole for both. The MW–M31 orbital geometry is consistent with the presently observed M31 proper motion despite this not being considered as a constraint when exploring the parameter space. The mass of the tidal debris around the MW and M31 at z = 0 compare well with the mass observed in their satellite systems. The remnant discs of the two galaxies have realistic radial scale lengths and velocity dispersions, and the simulation naturally produces a much hotter stellar disc in M31 than in the MW. However, reconciling this scenario with the ages of stellar populations in satellite galaxies would require that a higher fraction of stars previously formed in the outskirts of the progenitors ended up within the tidal debris, or that the MW–M31 interaction occurred at z > 1.

Objective The aim of the present study was to evaluate, after 5 years, the efficacy of proximal microinvasive sealing of permanent teeth on the risk for caries lesion development. Methods Children aged 8 to 10 y at baseline, at high caries risk, were studied. In the preventive (P) group the children had caries lesions on the distal surface of primary second molars (05d) but sound mesial surfaces of the approximating permanent first molars (6m). In the therapeutic (T) group the children had initial caries lesions on 6m that abutted lesions on 05d. Each child in the two groups had one 05d/6m pair. Using a split-mouth design, one 6m surface in each pair was randomly assigned to receive sealing while the other pair served as an unsealed control. Results Of the 61 children at baseline 42 could be blindly examined clinically and radiographically both at baseline and after 5 years. In the P group, 8 of 28 (28.6%) sealed and 15 of 28 (53.6 %) unsealed sound 6m surfaces had developed caries lesions ( p = 0.04). In the T group, the progression of the carious lesions on 6m was observed in 4 of 14 sealed (28.6%) and 8 of 14 (57.1%) unsealed caries control surfaces ( p = 0.29). Pooling the data from the two groups, the difference between sealed and non-sealed surfaces was significant ( p = 0.013). Conclusion Both preventive and therapeutic sealant to 6m adjacent to a lesion on 05d has effectiveness in caries reduction in high caries risk children Clinical Significance The beneficial effect of sealing is observed for at least 5 years after a single sealant treatment.

We present the first volume-limited sample of spectroscopically confirmed hot subluminous stars out to 500 pc, defined using the accurate parallax measurements from the Gaia space mission data release 3 (DR3). The sample comprises a total of 397 members, with 305 (~77%) identified as hot subdwarf stars, including 83 newly discovered systems. Of these, we observe that 178 (~58%) are hydrogen-rich sdBs, 65 are sdOBs (~21%), 32 are sdOs (~11%), and 30 are He-sdO/Bs (~10%). Among them, 48 (~16%) exhibit an infrared excess in their spectral energy distribution fits, suggesting a composite binary system. The hot subdwarf population is estimated to be 90% complete, assuming that most missing systems are these composite binaries located within the main sequence (MS) in the Gaia colour-magnitude diagram. The remaining sources in the sample include cataclysmic variables, blue horizontal branch stars, hot white dwarfs, and MS stars. We derived the mid-plane density ρ0 and scale height hz for the non-composite hot subdwarf star population using a hyperbolic sechant profile (sech2). The best-fit values are ρ0 = 5.17 ± 0.33 × 10−7 stars pc−3 and hz = 281 ± 62 pc. When accounting for the composite-colour hot subdwarfs and their estimated completeness, the mid-plane density increases to ρ0 = 6.15−0.53+1.16 × 10−7 stars pc−3. This corrected space density is an order of magnitude lower than predicted by population synthesis studies, supporting previous observational estimates.

In some application fields, series are affected by two different types of effects: abrupt changes (or change-points) and functional effects. We propose here a Bayesian approach that allows us to estimate these two parts. Here, the underlying piecewise-constant part (associated to the abrupt changes) is expressed as the product of a lower triangular matrix by a sparse vector and the functional part as a linear combination of functions from a large dictionary where we want to select the relevant ones. This problem can thus lead to a global sparse estimation and a stochastic search variable selection approach is used to this end. The performance of our proposed method is assessed using simulation experiments. Applications to three real datasets from geodesy, agronomy and economy fields are also presented.

Exchange rates are determined by factors such as interest rates, political stability, confidence, the current account on balance of payments, government intervention, economic growth and relative inflation rates, among other variables. In October 2019, an increased climate of citizen discontent with current social policies resulted in a series of massive protests that ignited important political changes in Chile. This event along with the global COVID-19 pandemic were two major factors that affected the value of the US dollar and produced sudden changes in the typically stable USD/CLP (Chilean Peso) exchange rate. In this paper, we use a Bayesian approach to detect and locate change points in the currency exchange rate process in order to identify and relate these points with the important dates related to the events described above. The implemented method can successfully detect the onset of the social protests, the beginning of the COVID-19 pandemic in Chile and the economic reactivation in the US and Europe. In addition, we evaluate the performance of the proposed MCMC algorithms using a simulation study implemented in Python and R.

The progress of metaheuristic techniques, big data, and the Internet of things generates opportunities to performance improvements in complex industrial systems. This article explores the application of Big Data techniques in the implementation of metaheuristic algorithms with the purpose of applying it to decision‐making in industrial processes. This exploration intends to evaluate the quality of the results and convergence times of the algorithm under different conditions in the number of solutions and the processing capacity. Under what conditions can we obtain acceptable results in an adequate number of iterations? In this article, we propose a cuckoo search binary algorithm using the MapReduce programming paradigm implemented in the Apache Spark tool. The algorithm is applied to different instances of the crew scheduling problem. The experiments show that the conditions for obtaining suitable results and iterations are specific to each problem and are not always satisfactory.

Optimization techniques, specially metaheuristics, are constantly refined in order to decrease execution times, increase the quality of solutions, and address larger target cases. Hybridizing techniques are one of these strategies that are particularly noteworthy due to the breadth of applications. In this article, a hybrid algorithm is proposed that integrates the k-means algorithm to generate a binary version of the cuckoo search technique, and this is strengthened by a local search operator. The binary cuckoo search algorithm is applied to the NP-hard Set-Union Knapsack Problem. This problem has recently attracted great attention from the operational research community due to the breadth of its applications and the difficulty it presents in solving medium and large instances. Numerical experiments were conducted to gain insight into the contribution of the final results of the k-means technique and the local search operator. Furthermore, a comparison to state-of-the-art algorithms is made. The results demonstrate that the hybrid algorithm consistently produces superior results in the majority of the analyzed medium instances, and its performance is competitive, but degrades in large instances.

We introduce an algebra of braids and ties (or bt-algebra) of type B . In analogy to the construction of the bt-algebra of type A, we define this bt–algebra of type B through a framization of the Hecke algebra of type B. We find a basis for it, a faithful tensorial representation, and we prove that it supports a Markov trace, from which we derive invariants of classical links in the solid torus.

Industry 4.0 is a new industrial paradigm that brings new challenges for workers as they have to actively collaborate with robots in an interconnected environment. The main purpose of this paper is to give a brief overview of how collaborative robots can be used to support human workers in Industry 4.0 manufacturing environments. The use of collaborative robots certainly brings many advantages as these machines enable more efficient product systems by supporting workers with both physical and cognitive tasks, as is the case of exoskeletons. On the other hand, human–robot interaction might also have some risks if human factors considerations are not well thought through throughout the process. Moreover, it becomes clear that the role that humans have been playing so far in a manufacturing environment is rapidly changing. Human workers will have to adapt to these new systems by acquiring and improving a set of skills that have sometime been neglected until nowadays.