Search Equation Research Articles

A novel upgraded bat algorithm based on cuckoo search and Sugeno inertia weight for large scale and constrained engineering design optimization problems

The bat algorithm (BA) is one of prominent swarm-based algorithm that has the suitability in solving only small dimension engineering problems and suffers from drawback of getting trapped in local minimum with slow convergence for multi-dimensional problems. In the context of improving its applicability in solving large scale and constrained engineering design problems, this paper presents a novel upgraded bat algorithm with cuckoo search and Sugeno inertia weight (UBCSIW). In the proposed UBCSIW algorithm, first, the bat algorithm with its competence to exploit the optimal solutions in search space is combined with cuckoo search with its ability to explore best solution globally using Levy flight in the search space. Secondly, a new velocity and position search equation is incorporated in which the bat searches around the best candidate solution. This step helps in establishing adequate balance between exploration and exploitation capability and improving the performance effectively by employing greedy selection to choose the best candidate solution. Finally, Sugeno fuzzy inertia weight is introduced in the velocity updation equation, boosting the flexibility and diversity of bat population and results in stability of results. The effectiveness of the proposed UBCSIW algorithm is tested on 16 standard benchmark functions (unimodal and multimodal) with different dimensions, 12 CEC2015 test functions and 7 well-known constrained engineering design problems. The outputs of the proposed UBCSIW algorithm are validated by comparison with classical BA and other swarm-based state-of-the art algorithms. The simulation results show that proposed UBCSIW algorithm achieves highly competitive results in terms of higher optimization accuracy and improved convergence that outperforms basic BA in all twenty-eight test functions while performs better than other competitive algorithms in 24 functions (13 benchmark and 11 CEC2015 functions).

Play Therapy as an Intervention in Hospitalized Children: A Systematic Review.

Background: Hospitalization disrupts children’s lives and can produce feelings such as anxiety, fear, or pain. Playing is an important part of children’s lives. Thus, it is necessary to ensure holistic care during the process, including play therapy. The aim of this study was to analyze the effect of therapeutic play in hospitalized children. Methods: A systematic review was performed. The search was conducted in CINAHL (Cumulative Index of Nursing and Allied Health Literature), CUIDEN, and PubMed (Medline). The search equation was “pediatric nurs* AND play therapy”. The search was performed in March 2020. Results: n = 14 studies were included in the review. The studies reveal that the application of therapeutic play in hospitalized children decreases postoperative pain, improves behavior and attitude, and reduces anxiety during the hospital stay. Conclusions: play therapy has a beneficial impact on the care of hospitalized children and should be implemented in pediatric units after assessing the resources and training needed for pediatric nurses.

Analysis of the Anti-Vaccine Movement in Social Networks: A Systematic Review.

The aim of this study was to analyze social networks’ information about the anti-vaccine movement. A systematic review was performed in PubMed, Scopus, CINAHL and CUIDEN databases. The search equations were: “vaccine AND social network” and “vaccine AND (Facebook[title] OR Twitter[title] OR Instagram[title] OR YouTube[title])”. The final sample was n = 12, including only articles published in the last 10 years, in English or Spanish. Social networks are used by the anti-vaccine groups to disseminate their information. To do this, these groups use different methods, including bots and trolls that generate anti-vaccination messages and spread quickly. In addition, the arguments that they use focus on possible harmful effects and the distrust of pharmaceuticals, promoting the use of social networks as a resource for finding health-related information. The anti-vaccine groups are able to use social networks and their resources to increase their number and do so through controversial arguments, such as the economic benefit of pharmaceuticals or personal stories of children to move the population without using reliable or evidence-based content.

Enhancing artificial bee colony algorithm with multi-elite guidance

Artificial bee colony (ABC) algorithm is a relatively new paradigm of swarm intelligence based optimization technique, which has attracted a lot of attention for its simple structure and good performance. For some complex optimization problems, however, the performance of ABC is challenged due to its solution search equation that has strong explorative ability but poor exploitative ability. To solve this defect, in this work, we propose an improved ABC algorithm by using multi-elite guidance, which has the benefits of utilizing valuable information from elite individuals to guide search while without losing population diversity. First, we construct an elite group by selecting some elite individuals, and then introduce two improved solution search equations into the employed bee phase and onlooker bee phase based on the elite group, respectively. Last, we develop a modified neighborhood search operator by utilizing the elite group as well, which aims to achieve a better tradeoff between explorative and exploitative abilities. To verify our approach, 50 well-known test functions and one real-world optimization problem are used in the experiments, including 22 scalable basic test functions and 28 complex CEC2013 test functions. Seven different well-established ABC variants are involved in the comparison and the results show that our approach can achieve better or at least comparable performance on most of the test functions.

Evolution of ubiquitous computing

Ubiquitous computing understood as the integration of the machine to man is applied in interactive environments where tools such as mobile devices produce this continuous change. This article presents an analysis on the evolution of the internet of things, based on the research found on the science websites, whose objective was based on knowing in depth the conceptual evolution of the internet of things and the context in which it has developed. It is chosen to use a methodology of science mapping that allowed an analysis of scientific productions through cluster analysis and multidimensional scaling, bibliometrics is a scientific discipline that is based on the search for statistically regular behaviors over time in the different elements related to scientific production. A conglomerate of articles was found that were adjusted to the search equations used in the science website, which within the analysis carried out can be used as a reference for other researchers in their research, which that allows enriching the field of research discussed here, this research is important insofar as it allowed to imply in a greater degree of depth the IoT and its evolution over the years.

An improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentation

The artificial bee colony (ABC) algorithm is a relatively new algorithm inspired by nature and has been shown to be efficient in contrast to other optimization algorithms. Nonetheless, ABC has some similar drawbacks to the optimization algorithms in terms of the unbalanced search behavior. The original ABC algorithm shows strong exploration capability with ineffective exploitation due to the unbalanced search model. In this paper, a new ABC algorithm called MeanABC is introduced to achieve the search behavior balance via a modified search equation based on the information of the mean of the previous best solutions. To evaluate the performance of the proposed algorithm, experiments were divided into two parts: First, the proposed algorithm was tested on a comprehensive set of 14 benchmark functions. The results show that the proposed MeanABC enhances the performance of the original ABC in terms of faster global convergence speed, solution quality, and better robustness when compared to other ABC variants. Secondly, the proposed algorithm was applied as a hybrid with the FCM algorithm as a segmentation technique to a set of 20 volumes of real brain MRI images with 20 images for each volume. All of these images have several characteristics, levels of difficulty, and cover different domains. The obtained results are promising, especially when the performance of the proposed algorithm was compared to other state-of-the-art segmentation techniques.

Burnout in Nursing Managers: A Systematic Review and Meta-Analysis of Related Factors, Levels and Prevalence.

Burnout syndrome is a major problem in occupational health, which also affects nursing managers. The main aim was to analyze the level, prevalence and risk factors of burnout among nursing managers. A systematic review with meta-analysis was conducted. The databases used were Medline (Pubmed), PsycINFO, CINAHL, LILACS, Scielo and Scopus. The search equation was “burnout AND nurs* AND (health manager OR case managers)”. Nursing managers present high levels of emotional exhaustion and a high degree of depersonalization. Some studies show that variables like age, gender, marital status, having children or mobbing and other occupational factors are related with burnout. The prevalence estimation of emotional exhaustion with the meta-analysis was high; 29% (95% CI = 9–56) with a sample of n = 780 nursing managers. The meta-analytical estimation of the correlation between burnout and age was r = −0.07 (95% CI = −0.23–0.08). Work overload, the need to mediate personnel conflicts, lack of time and support from superior staff, contribute to the development of burnout among nursing managers.

A Novel Optimization Algorithm Combing Gbest-Guided Artificial Bee Colony Algorithm with Variable Gradients

The artificial bee colony (ABC) algorithm, which has been widely studied for years, is a stochastic algorithm for solving global optimization problems. Taking advantage of the information of a global best solution, the Gbest-guided artificial bee colony (GABC) algorithm goes further by modifying the solution search equation. However, the coefficient in its equation is based only on a numerical test and is not suitable for all problems. Therefore, we propose a novel algorithm named the Gbest-guided ABC algorithm with gradient information (GABCG) to make up for its weakness. Without coefficient factors, a new solution search equation based on variable gradients is established. Besides, the gradients are also applied to differentiate the priority of different variables and enhance the judgment of abandoned solutions. Extensive experiments are conducted on a set of benchmark functions with the GABCG algorithm. The results demonstrate that the GABCG algorithm is more effective than the traditional ABC algorithm and the GABC algorithm, especially in the latter stages of the evolution.

Artifacts related to cone beam computed tomography technology (CBCT) and their significance for clinicians: illustrated review of medical literature.

Objective: to explain the meaning and to illustrate technical artifacts (aliasing as well as the ring artifact) and beam hardening (metal artifact) that can be present in the dentomaxillofacial cone beam computed tomography (CBCT), and to check the accessibility of free illustrations of these artifacts in medical publications.
 Material and methods: One observer applied five search equations using database PubMed. The exclusion criteria were: experimental studies, animal studies, studies not related to dentomaxillofacial area, and articles with closed access. There was no time limit for the search of articles. We limited our search to English and French language.
 Results: Only 3 articles out of 434 publications were retained after application of inclusion/exclusion criteria. In these articles only 4 annotated figures were freely accessible in medical publications from PubMed.
 In this paper we presented examples of aliasing, ring artifact, and beam artifacts from I-CAT, Carestream 9000 3D (Kodak), and Planmeca Promax 3D Mid CBCT. The intensity of beam hardening artifact varies from major degradation of image (i.e., subperiosteal implants, bridges, crowns, dental implants, and orthodontic fix appliances), through mean degradation (screws securing titanium mesh, head of mini-implant) to no beam hardening on metallic devices (orthodontic anchorage, orthodontic contention wire) or on dense objects (endodontic treatments, impression materials, Lego box). Some beam hardening artifacts arising from nasal piercing, hairs, or hearing aid device may be present on the image but they will not disturb the evaluation of the field of view.
 Conclusions: reduction of aliasing artifact is related with the improvement of detectors quality. The presence of ring artifact means that CBCT device has lost its calibration. The field of view (FOV) needs to be reduce in order to avoid scanning regions susceptible to beam hardening (e.g., metallic restorations, dental implants). Finally, the accessibility to open knowledge on technique -related CBCT artifacts seems extremely limited when searching at PubMed database.

A modified Sine Cosine Algorithm with novel transition parameter and mutation operator for global optimization

Inspired by the mathematical characteristics of sine and cosine trigonometric functions, the Sine Cosine Algorithm (SCA) has shown competitive performance among other meta-heuristic algorithms. However, despite its sufficient global search ability, its low exploitation ability and immature balance between exploitation and exploration remain weaknesses. In order to improve Sine Cosine Algorithm (SCA), this paper presents a modified version of the SCA called MSCA. Firstly, a non-linear transition rule is introduced instead of a linear transition to provide comparatively better transition from the exploration to exploitation. Secondly, the classical search equation of the SCA is modified by introducing the leading guidance based on the elite candidate solution. When the above proposed modified search mechanism fails to provide a better solution, in addition, a mutation operator is used to generate a new position to avoid the situation of getting trapped in locally optimal solutions during the search. Thus, the MSCA effectively maximizes the advantages of proposed strategies in maintaining a comparatively better balance of exploration and exploitation as compared to the classical SCA. The validity of the MSCA is tested on a set of 33 benchmark optimization problems and employed for training multilayer perceptrons. The numerical results and comparisons among several algorithms show the enhanced search efficiency of the MSCA.

A hybrid multiverse optimisation algorithm based on differential evolution and adaptive mutation

ABSTRACT Multiverse optimisation (MVO) algorithm is an excellent meta-heuristic algorithm based on laws of physics. However, it is easy to fall into local optimum when solving complex multimodal optimisation problems with high dimensions. The global optimisation performance of the algorithm is still unsatisfactory. In this paper, we propose a hybrid multiverse optimisation (DE-SMVO) algorithm. First, in order to increase the ability of information exchange and global exploration, a differential mutation strategy is introduced to improve the search equations of MVO based on the differential evolution (DE) theory, enabling the algorithm to explore more unknown space. In addition, the adaptive mutation is carried out on the current global optimal universe in each iteration process, and excellent mutation universe is retained, which enhance the exploitation ability and improve the convergence accuracy of MVO algorithm. In order to investigate the performance of the proposed DE-SMVO algorithm, it is evaluated on 23 benchmark functions in this paper. The experimental results prove that the optimisation performance is significantly better than that of the MVO algorithm, and its performance is also superior compared with nine state-of-the-art meta-heuristic optimisation algorithms.

Systematic Review and Study of S Curves for Biomass Quantification in Solid-state Fermentation (SSF) and Digital Image Processing (DIP) Applied to Biomass Measurement in Food Processes.

There are several methods for the quantification of biomass in SSF, such as glucosamine measurement, ergosterol content, protein concentration, change in dry weight or evolution of CO2 production. However, all have drawbacks when obtaining accurate data on the progress of the SSF due to the dispersion in cell growth on the solid substrate, and the difficulty encountered in separating the biomass. Studying the disadvantages associated with the process of biomass quantification in SSF, the monitoring of the growth of biomass by a technique known as digital image processing (DIP), consists of obtaining information on the production of different compounds during fermentation, using colorimetric methods based on the pixels that are obtained from photographs. The purpose of this study was to know about the state of the technology and the advantages of DIP. The methodology employed four phases; the first describes the search equations for the SSF and the DIP. A search for patents related to SSF and DIP carried out in the Free Patents Online and Patent inspiration databases. Then there is the selection of the most relevant articles in each of the technologies. As a third step, modifications for obtaining the best adjustments were also carried out. Finally, the analysis of the results was done and the inflection years were determined by means of six mathematical models widely studied. For these models, the inflection years were 2018 and 2019 for both the SSF and the DIP. Additionally, the main methods for the measurement of biomass in SSF were found, and are also indicated in the review, as DIP measurement processes have already been carried out using the same technology. In addition, the DIP has shown satisfactory results and could be an interesting alternative for biomass measurement in SSF, due to its ease and versatility.

The effect of mindfulness training on burnout syndrome in nursing: A systematic review and meta-analysis.

To analyse the effect of mindfulness training on levels of burnout among nurses. Burnout syndrome is a common occupational hazard for nursing staff. Mindfulness training has been proposed as a valid intervention for burnout. Systematic review and meta-analysis. The CINAHL, LILACS, Medline, ProQuest, PsycINFO, Scielo and Scopus databases were consulted, using the search equation 'Nurs * AND burnout AND mindfulness'. There was no restriction on the year of publication. Papers were selected for analysis in accordance with the PRISMA guidelines. The meta-analysis was carried out using Review Manager 5.3 software. The sample was of 17 articles including 632 nurses. Mindfulness training reduces levels of burnout, producing lower scores for emotional exhaustion and depersonalization and higher for personal accomplishment. The differences in the means were 1.32 (95% CI: -9.41-6.78), 1.91 (95% CI: -4.50-0.68) and 2.12 (95% CI: -9.91-14.14), respectively, between the intervention and control groups. Mindfulness training reduces the emotional burden and hence levels of burnout, among nurses. However, further randomized clinical trials are required.

Smartphones and Apps to Control Glycosylated Hemoglobin (HbA1c) Level in Diabetes: A Systematic Review and Meta-Analysis.

Introduction: Diabetes mellitus is a chronic endocrine-metabolic disease, the evolution of which is closely related to people’s self-control of glycemic levels through nutrition, exercise, and medicines. Aim: To determine whether smartphone apps can help persons with diabetes to improve their % levels of glycosylated hemoglobin. Method: A systematic review and meta-analysis were done. ProQuest, Pubmed/Medline, and Scopus databases were used. The search equation used was “(Prevention and Control) AND Diabetes Mellitus AND Smartphones”. The inclusion criteria applied were clinical trials, conducted in 2014–2019. Results: n = 18 studies were included in the review. The studies tried different applications to monitor glycemia and support patients to improve glycosylated hemoglobin (HbA1c) levels. More than half of the studies found statistically significant differences in HbA1c in the intervention group compared with the control group. Eleven studies were included in the meta-analysis and the study sample was n = 545 for the experimental group and n = 454 for the control group. The meta-analytic estimation of the HbA1c % level means differences between intervention and control group was statistically significant in favour of the intervention group with a mean difference of –0.37 (–0.58, –0.15. 95% confidence interval). Conclusion: Smartphone apps can help people with diabetes to improve their level of HbA1c, but the clinical impact is low.

A neighborhood search based cat swarm optimization algorithm for clustering problems

Clustering is an unsupervised technique that groups the similar data objects into a single subset using a distance function. It is also used to find the optimal set of clusters in a given dataset and each cluster consists of homogenous data objects. In present work, an algorithm based on cat swarm optimization (CSO) is adopted for finding the optimal set of cluster centers for allocating the data objects. Further, some improvements are also incorporated in CSO algorithm for improving clustering performance. These modifications are described as an improved solution search equation to improve convergence rate and an accelerated velocity equation for balancing exploration and exploitation processes of CSO algorithm. Moreover, a neighborhood-based search strategy is introduced to handle local optima problem. The performance of proposed algorithm is tested on eight real-life datasets and compared with well-known clustering algorithms. The simulation results showed that proposed algorithm provides quality results in comparison to existing clustering algorithms.

An improved artificial bee colony algorithm for balancing local and global search behaviors in continuous optimization

The artificial bee colony, ABC for short, algorithm is population-based iterative optimization algorithm proposed for solving the optimization problems with continuously-structured solution space. Although ABC has been equipped with powerful global search capability, this capability can cause poor intensification on found solutions and slow convergence problem. The occurrence of these issues is originated from the search equations proposed for employed and onlooker bees, which only updates one decision variable at each trial. In order to address these drawbacks of the basic ABC algorithm, we introduce six search equations for the algorithm and three of them are used by employed bees and the rest of equations are used by onlooker bees. Moreover, each onlooker agent can modify three dimensions or decision variables of a food source at each attempt, which represents a possible solution for the optimization problems. The proposed variant of ABC algorithm is applied to solve basic, CEC2005, CEC2014 and CEC2015 benchmark functions. The obtained results are compared with results of the state-of-art variants of the basic ABC algorithm, artificial algae algorithm, particle swarm optimization algorithm and its variants, gravitation search algorithm and its variants and etc. Comparisons are conducted for measurement of the solution quality, robustness and convergence characteristics of the algorithms. The obtained results and comparisons show the experimentally validation of the proposed ABC variant and success in solving the continuous optimization problems dealt with the study.

Improved butterfly optimisation algorithm based on guiding weight and population restart

ABSTRACT Butterfly Optimisation Algorithm (BOA) is a kind of meta-heuristic swarm intelligence algorithm based on butterfly foraging strategy, but it still needs to be improved in the aspects of convergence speed and accuracy when solving with high-dimensional optimisation problems. In this paper, an improved butterfly optimisation algorithm is proposed, in which guiding weight and population restart strategy are applied to the original algorithm. By adding guiding weight to the global search equation, the convergence speed and accuracy of the algorithm are improved, and the possibility of jumping out of the local optimal solution is increased by the population restart strategy. In order to verify the performance of the proposed algorithm, 24 benchmark functions commonly used for optimisation algorithm experiments are applied in this paper, including 12 unimodal functions and 12 multimodal functions. Experimental results show that the proposed algorithm improves the convergence speed, accuracy and the ability to jump out of the local optimal solution.

A BIBLIOMETRIC ANALYSIS OF THE USE OF OPEN SOURCE SOFTWARE IN EDUCATIONAL CONTEXTS

Open source software has now become a significant alternative in meeting different needs in business, government and academic environments, such as needs related to economics, management, learning and innovation, among others. The purpose of this research was to examine the research trends and evolution of the field of open source software adoption between 2001 and 2019. The methodology used involved bibliometric analysis of 289 documents obtained through a Scopus extracted search equation, generating indicators of quantity and quality and analyzing the emerging themes in said field of knowledge. The findings of the research include the existence of a strong trend towards research and dissemination regarding open source software, particularly in countries such as the United States, whose institutions and authors demonstrate high levels of productivity and dissemination. There was also evidence of an interest in reducing barriers and encouraging the adoption and implementation of the software in other sectors where its use is still lagging behind. The main conclusion of the research is that the research of open source software adoption focuses on the following topics: innovation, Linux, FLOSS, engineering requirements, risk management, open innovation, the public sector, social network analysis and total cost of ownership. Keywords: bibliometric analysis, information technology, open source software, research trends, technology adoption.

Accessibility to the knowledge on anatomical variations from dentomaxillofacial CBCT

Objective: to investigate the accessibility of open access article on anatomical variations described on cone beam computed tomography (CBCT) using PubMed database. We wanted to investigate how many journals are sharing articles without pay-wall and how many are sharing articles without author publication charges.
 Material and methods: a search equation was designed with exclusion criteria limiting the search in PubMed to articles published in English and French. The search was performed by one observer. We had found 2228 articles; among them 709 were accessible as ‘full text’. After applying exclusion criteria and after full text reading only 50 articles remained for the review.
 Results: the 50 selected articles shared 306 annotated (visual marking, explanation like arrows) and 432 not annotated figures with the public. The 76% of articles were single studies on one specific topic. The main topic was endodontics with 22 articles. 28 journals from all continents participated in the effort of sharing of figures on anatomical variations from CBCT. However, only 2 journals were completely free of charges for authors and readers.
 Conclusions: we have found only 15 annotated and 3 not annotated figures in 2 articles published in 2 different open access journals (without reader pay-wall and without author publication charges). Sharing the knowledge on anatomical variations from dentomaxillofacial CBCT represents an exception in dental literature.

Anticoagulants, antiagrégants plaquettaires et gestes en rhumatologie

IntroductionInterventional rheumatology has a key role in the practitioner's activity. Many patients suffer from cardiovascular or neurologic diseases which are the reason for the introduction of an antiplatelet or anticoagulant treatment. Thus, they are exposed to a haemorrhagic risk, as well as a thrombosis risk if an interruption is needed. French recommendations were published in 2008 and 2013, and mainly rely on expert's opinions more than evidence-based medicine. MethodsWe proceeded in a literature review of all datas published since 2013 about those two risks in interventional rheumatology, using the search equation available in the 2013 HAS document. ResultsWe propose a summary of the international recommendations pertaining to those interventions, detailing for each antiplatelet or anticoagulant treatment how to proceed, in accordance to the various infiltrations realized in common practice. DiscussionThe existing recommendations can be conflicting with the most up-to-date literature. This urges to elaborate new recommendations focusing on the rheumatologic practice.