Showing papers by "Open University of Catalonia published in 2021"

The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support

Abstract: Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of researchers and practitioners revisit the current status of SA, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems. Six areas are discussed that warrant further attention, including (1) structuring and standardizing SA as a discipline, (2) realizing the untapped potential of SA for systems modeling, (3) addressing the computational burden of SA, (4) progressing SA in the context of machine learning, (5) clarifying the relationship and role of SA to uncertainty quantification, and (6) evolving the use of SA in support of decision making. An outlook for the future of SA is provided that underlines how SA must underpin a wide variety of activities to better serve science and society.

BEPO: A novel binary emperor penguin optimizer for automatic feature selection

Abstract: Emperor Penguin Optimizer (EPO) is a metaheuristic algorithm which is recently developed and illustrates the emperor penguin’s huddling behaviour. However, the original version of the EPO will fix issues that are continuing in fact but not discrete. The eight separate EPO variants have been provided in this article. Four transfer features, s-shaped and v-shaped, that are used in order to map the search space into a separate research space are considered in the proposed algorithm. The output of the proposed algorithm is validated using 25 standard benchmark functions. It also analyses the statistical sense of the proposed algorithm. Experimental findings and comparisons suggest that the proposed algorithm performs better than other algorithms. The solution also applies to the issue of feature selection. The findings reveal the supremacy of the binary emperor penguin optimization algorithm.

Ant colony optimization with horizontal and vertical crossover search: Fundamental visions for multi-threshold image segmentation

Abstract: The ant colony optimization (ACO) is the most exceptionally fundamental swarm-based solver for realizing discrete problems. In order to make it also suitable for solving continuous problems, a variant of ACO (ACOR) has been proposed already. The deep-rooted ACO always stands out in the eyes of well-educated researchers as one of the best-designed metaheuristic ways for realizing the solutions to real-world problems. However, ACOR has some stochastic components that need to be further improved in terms of solution quality and convergence speed. Therefore, to effectively improve these aspects, this in-depth research introduced horizontal crossover search (HCS) and vertical crossover search (VCS) into the ACOR and improved the selection mechanism of the original ACOR to form an improved algorithm (CCACO) for the first time. In CCACO, the HCS is mainly intended to increase the convergence rate. Meanwhile, the VCS and the developed selection mechanism are mainly aimed at effectively improving the ability to avoid dwindling into local optimal (LO) and the convergence accuracy. To reach next-level strong results for image segmentation and better illustrate its effectiveness, we conducted a series of comparative experiments with 30 benchmark functions from IEEE CEC 2014. In the experiment, we compared the developed CCACO with well-known conventional algorithms and advanced ones. All experimental results also show that its convergence speed and solution quality are superior to other algorithms, and its ability to avoid dropping into local optimum (LO) is more reliable than that of its peers. Furthermore, to further illustrate its enhanced performance, we applied it to image segmentation based on multi-threshold image segmentation (MTIS) method with a non-local means 2D histogram and Kapur's entropy. In the experiment, it was compared with existing competitive algorithms at low and high threshold levels. The experimental results show that the proposed CCACO achieves excellent segmentation results at both low and high threshold levels. For any help and guidance regarding this research, readers, and industry activists can refer to the background info at http://aliasgharheidari.com/ .

A novel Black Widow Optimization algorithm for multilevel thresholding image segmentation

Abstract: Segmentation is a crucial step in image processing applications. This process separates pixels of the image into multiple classes that permits the analysis of the objects contained in the scene. Multilevel thresholding is a method that easily performs this task, the problem is to find the best set of thresholds that properly segment each image. Techniques as Otsu’s between class variance or Kapur’s entropy helps to find the best thresholds but they are computationally expensive for more than two thresholds. To overcome such problem this paper introduces the use of the novel meta-heuristic algorithm called Black Widow Optimization (BWO) to find the best threshold configuration using Otsu or Kapur as objective function. To evaluate the performance and effectiveness of the BWO-based method, it has been considered the use of a variety of benchmark images, and compared against six well-known meta-heuristic algorithms including; the Gray Wolf Optimization (GWO), Moth Flame Optimization (MFO), Whale Optimization Algorithm (WOA), Sine–Cosine Algorithm (SCA), Slap Swarm Algorithm (SSA), and Equilibrium Optimization (EO). The experimental results have revealed that the proposed BWO-based method outperform the competitor algorithms in terms of the fitness values as well as the others performance measures such as PSNR, SSIM and FSIM. The statistical analysis manifests that the BWO-based method achieves efficient and reliable results in comparison with the other methods. Therefore, BWO-based method was found to be most promising for multi-level image segmentation problem over other segmentation approaches that are currently used in the literature.

Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data.

Abstract: Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously published studies. We use a training dataset from 6322 MS patients to define MRI-based subtypes and an independent cohort of 3068 patients for validation. Based on the earliest abnormalities, we define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression (CDP) and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. Our findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials.

Balancing Technology, Pedagogy and the New Normal: Post-pandemic Challenges for Higher Education

Abstract: The Covid-19 pandemic has presented an opportunity for rethinking assumptions about education in general and higher education in particular. In the light of the general crisis the pandemic caused, especially when it comes to the so-called emergency remote teaching (ERT), educators from all grades and contexts experienced the necessity of rethinking their roles, the ways of supporting the students’ learning tasks and the image of students as self-organising learners, active citizens and autonomous social agents. In our first Postdigital Science and Education paper, we sought to distil and share some expert advice for campus-based university teachers to adapt to online teaching and learning. In this sequel paper, we ask ourselves: Now that campus-based university teachers have experienced the unplanned and forced version of Online Learning and Teaching (OLT), how can this experience help bridge the gap between online and in-person teaching in the following years? The four experts, also co-authors of this paper, interviewed aligning towards an emphasis on pedagogisation rather than digitalisation of higher education, with strategic decision-making being in the heart of post-pandemic practices. Our literature review of papers published in the last year and analysis of the expert answers reveal that the ‘forced’ experience of teaching with digital technologies as part of ERT can gradually give place to a harmonious integration of physical and digital tools and methods for the sake of more active, flexible and meaningful learning.

Gradient-Based Optimizer for Parameter Extraction in Photovoltaic Models

Abstract: Solar radiation is increasingly used as a clean energy source, and photovoltaic (PV) panels that contain solar cells (SCs) transform solar energy into electricity. The current-voltage characteristics for PV models is nonlinear. Due to a lack of data on the manufacturer’s datasheet for PV models, there are several unknown parameters. It is necessary to accurately design the PV systems by defining the intrinsic parameters of the SCs. Various methods have been proposed to estimate the unknown parameters of PV cells. However, their results are often inaccurate. In this article, a gradient-based optimizer (GBO) was applied as an efficient and accurate methodology to estimate the parameters of SCs and PV modules. Three common SC models, namely, single-diode models (SDMs), double-diode models (DDMs), and three-diode models (TDMs) were used to demonstrate the capacity of the GBO to estimate the parameters of SCs. The proposed GBO algorithm for estimating the optimal values of the parameters for various SCs models are applied on the real data of a 55 mm diameter commercial R.T.C-France SC. Comparison between the GBO and other algorithms are performed for the same data set. The smallest value of the error between the experimental and the simulated data is achieved by the proposed GBO. Also, high closeness between the simulated P-V and I-V curves is achieved by the proposed GBO compared with the experimental.

Does a Crisis Change News Habits? A Comparative Study of the Effects of COVID-19 on News Media Use in 17 European Countries

Abstract: Exogenous shocks like the COVID-19 pandemic unleashes multiple fundamental questions about society beyond public health. Based on the classical concept of ‘need for orientation’ and the literature ...

An innovative waste management system in a smart city under stochastic optimization using vehicle routing problem

Abstract: A smart city (SC) is a sustainable and efficient urban center that provides a high quality of life to its inhabitants through optimal management of its resources that nowadays have been wider and wider. In modern societies, municipal solid waste management (MSWM) is an important part of SCs, the main problem of MSWM is the cost that it generates and must be reduced. To solve this situation in this paper are considered two sub-models. The first sub-model uses vehicle routing problem (VRP) for routing fleet among generation waste to separation facilities. The second sub-model is designed to allocate resources from separation facilities to set of recovery plants or landfill centers. From the best of our knowledge, most of the past studies related to this topic have focused only on deterministic implementations. Also, recent studies usually focus on uncertain parameters in the area of waste generation. In addition, a few related studies have developed the uncertain parameter which has focused on facilitating separation. This study considers the uncertain parameters in the output rate of separation facilities as well as the importance of value recovery from each bin; the aim is to enhance the efficiency of operations. The purpose of this study is to minimize the total transportation cost and to maximize recycled revenue. Chance-constrained programming has been used to deal with stochastic optimization model. Four metaheuristic algorithms are employed to identify the best solution. Besides, the performance of the proposed algorithms is evaluated. Finally, sensitivity analyses along with number of scenarios have developed to measure the tightness of the proposed problem. The results of the study illustrate the optimized number of vehicles that can help the managers and decision-makers in various tightness conditions.

From smart to rebel city? Worlding, provincialising and the Barcelona model

Abstract: This article examines the evolution of the ‘Barcelona Model’ of urban transformation through the lenses of worlding and provincialising urbanism. We trace this evolution from an especially dogmatic...

Generic acquisition protocol for quantitative MRI of the spinal cord.

Abstract: Quantitative spinal cord (SC) magnetic resonance imaging (MRI) presents many challenges, including a lack of standardized imaging protocols. Here we present a prospectively harmonized quantitative MRI protocol, which we refer to as the spine generic protocol, for users of 3T MRI systems from the three main manufacturers: GE, Philips and Siemens. The protocol provides guidance for assessing SC macrostructural and microstructural integrity: T1-weighted and T2-weighted imaging for SC cross-sectional area computation, multi-echo gradient echo for gray matter cross-sectional area, and magnetization transfer and diffusion weighted imaging for assessing white matter microstructure. In a companion paper from the same authors, the spine generic protocol was used to acquire data across 42 centers in 260 healthy subjects. The key details of the spine generic protocol are also available in an open-access document that can be found at https://github.com/spine-generic/protocols . The protocol will serve as a starting point for researchers and clinicians implementing new SC imaging initiatives so that, in the future, inclusion of the SC in neuroimaging protocols will be more common. The protocol could be implemented by any trained MR technician or by a researcher/clinician familiar with MRI acquisition.

Participation and Co-creation in Citizen Science

Abstract: Citizen science practices have different frames to general scientific research – the adoption of participatory methods in research design has long been pursued in citizen science projects. The citizen science research design process should be inclusive, flexible, and adaptive in all its stages, from research question formulation to evidence-based collective results. Some citizen science initiatives adopt strategies that include co-creation techniques and methodologies from a wide variety of disciplines and practices. In this sense, the will to collaborate between researchers and other stakeholders is not new. It is traditionally found in public participation in science, including participatory action research (PAR) and the involvement of civil society organisations (CSOs) in research, as well as in mediatory structures, such as science shops. This chapter critically reviews methodologies, techniques, skills, and participation based on experiences of civic involvement and co-creation in research and discusses their limitations and potential improvements. Our focus is on the reflexivity approach and infrastructure needed to design citizen science projects, as well as associated key roles. Existing tools that can be used to enhance and improve citizen participation at each stage of the research process will also be explored. We conclude with a series of reflections on participatory practices.

A Channel Measurement Campaign for mmWave Communication in Industrial Settings

Abstract: Industry 4.0 relies heavily on wireless technologies. Energy efficiency and device cost have played a significant role in the initial design of such wireless systems for industry automation. However, high reliability, high throughput, and low latency are also key for certain sectors such as the manufacturing industry. In this sense, existing wireless solutions for industrial settings are limited. Emerging technologies such as millimeter-wave (mmWave) communication are highly promising to address this bottleneck. Still, the propagation characteristics at such high frequencies in harsh industrial settings are not well understood. Related work in this area is limited to isolated measurements in specific scenarios. In this work, we carry out an extensive measurement campaign in highly representative industrial environments. Most importantly, we derive the statistical link-level distributions of the channel parameters of widely accepted mmWave channel model of IEEE 802.11 ad that fit these environments. This model can be beneficial to understand the performance of mmWave systems in typical industrial settings. Beyond analyzing and discussing the insights, with this article we also share our extensive dataset with the community.

Recognizing Indigenous peoples' and local communities' rights and agency in the post-2020 Biodiversity Agenda.

Abstract: The Convention on Biological Diversity is defining the goals that will frame future global biodiversity policy in a context of rapid biodiversity decline and under pressure to make transformative change. Drawing on the work of Indigenous and non-Indigenous scholars, we argue that transformative change requires the foregrounding of Indigenous peoples' and local communities' rights and agency in biodiversity policy. We support this argument with four key points. First, Indigenous peoples and local communities hold knowledge essential for setting realistic and effective biodiversity targets that simultaneously improve local livelihoods. Second, Indigenous peoples' conceptualizations of nature sustain and manifest CBD's 2050 vision of "Living in harmony with nature." Third, Indigenous peoples' and local communities' participation in biodiversity policy contributes to the recognition of human and Indigenous peoples' rights. And fourth, engagement in biodiversity policy is essential for Indigenous peoples and local communities to be able to exercise their recognized rights to territories and resources.

Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer’s disease dementia: a systematic review

Abstract: BACKGROUND An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer's disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer's disease dementia. METHODS We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer's disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis. RESULTS Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer's disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance. CONCLUSIONS Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.

Safety and efficacy of bexarotene in patients with relapsing-remitting multiple sclerosis (CCMR One): a randomised, double-blind, placebo-controlled, parallel-group, phase 2a study

Abstract: Summary Background Progressive disability in multiple sclerosis occurs because CNS axons degenerate as a late consequence of demyelination. In animals, retinoic acid receptor RXR-gamma agonists promote remyelination. We aimed to assess the safety and efficacy of a non-selective retinoid X receptor agonist in promoting remyelination in people with multiple sclerosis. Methods This randomised, double-blind, placebo-controlled, parallel-group, phase 2a trial (CCMR One) recruited patients with relapsing-remitting multiple sclerosis from two centres in the UK. Eligible participants were aged 18–50 years and had been receiving dimethyl fumarate for at least 6 months. Via a web-based system run by an independent statistician, participants were randomly assigned (1:1), by probability-weighted minimisation using four binary factors, to receive 300 mg/m2 of body surface area per day of oral bexarotene or oral placebo for 6 months. Participants, investigators, and outcome assessors were masked to treatment allocation. MRI scans were done at baseline and at 6 months. The primary safety outcome was the number of adverse events and withdrawals attributable to bexarotene. The primary efficacy outcome was the patient-level change in mean lesional magnetisation transfer ratio between baseline and month 6 for lesions that had a baseline magnetisation transfer ratio less than the within-patient median. We analysed the primary safety outcome in the safety population, which comprised participants who received at least one dose of their allocated treatment. We analysed the primary efficacy outcome in the intention-to-treat population, which comprised all patients who completed the study. This study is registered in the ISRCTN Registry, 14265371, and has been completed. Findings Between Jan 17, 2017, and May 17, 2019, 52 participants were randomly assigned to receive either bexarotene (n=26) or placebo (n=26). Participants who received bexarotene had a higher mean number of adverse events (6·12 [SD 3·09]; 159 events in total) than did participants who received placebo (1·63 [SD 1·50]; 39 events in total). All bexarotene-treated participants had at least one adverse event, which included central hypothyroidism (n=26 vs none on placebo), hypertriglyceridaemia (n=24 vs none on placebo), rash (n=13 vs one on placebo), and neutropenia (n=10 vs none on placebo). Five (19%) participants on bexarotene and two (8%) on placebo discontinued the study drug due to adverse events. One episode of cholecystitis in a placebo-treated participant was the only serious adverse event. The change in mean lesional magnetisation transfer ratio was not different between the bexarotene group (0·25 percentage units [pu; SD 0·98]) and the placebo group (0·09 pu [0·84]; adjusted bexarotene–placebo difference 0·16 pu, 95% CI –0·39 to 0·71; p=0·55). Interpretation We do not recommend the use of bexarotene to treat patients with multiple sclerosis because of its poor tolerability and negative primary efficacy outcome. However, statistically significant effects were seen in some exploratory MRI and electrophysiological analyses, suggesting that other retinoid X receptor agonists might have small biological effects that could be investigated in further studies. Funding Multiple Sclerosis Society of the United Kingdom.

S-shaped and V-shaped gaining-sharing knowledge-based algorithm for feature selection

Abstract: In machine learning, searching for the optimal feature subset from the original datasets is a very challenging and prominent task. The metaheuristic algorithms are used in finding out the relevant, important features, that enhance the classification accuracy and save the resource time. Most of the algorithms have shown excellent performance in solving feature selection problems. A recently developed metaheuristic algorithm, gaining-sharing knowledge-based optimization algorithm (GSK), is considered for finding out the optimal feature subset. GSK algorithm was proposed over continuous search space; therefore, a total of eight S-shaped and V-shaped transfer functions are employed to solve the problems into binary search space. Additionally, a population reduction scheme is also employed with the transfer functions to enhance the performance of proposed approaches. It explores the search space efficiently and deletes the worst solutions from the search space, due to the updation of population size in every iteration. The proposed approaches are tested over twenty-one benchmark datasets from UCI repository. The obtained results are compared with state-of-the-art metaheuristic algorithms including binary differential evolution algorithm, binary particle swarm optimization, binary bat algorithm, binary grey wolf optimizer, binary ant lion optimizer, binary dragonfly algorithm, binary salp swarm algorithm. Among eight transfer functions, V4 transfer function with population reduction on binary GSK algorithm outperforms other optimizers in terms of accuracy, fitness values and the minimal number of features. To investigate the results statistically, two non-parametric statistical tests are conducted that concludes the superiority of the proposed approach.

Exploring the Relationship between Despotic Leadership and Job Satisfaction: The Role of Self Efficacy and Leader–Member Exchange

Abstract: This study explores the effects of despotic leadership (DL) on employee job satisfaction (JS) using self-efficacy (SE) as a mediating variable and leader-member exchange (LMX) as a moderated variable. Building on the social learning and social exchange theory, our research proposes a research model. In this model, despotic leadership affects employee job satisfaction both directly and indirectly through self-efficacy and leader-member exchange. We used a questionnaire survey analysis approach to collect data. Data were collected from the employees of small- and medium-sized enterprises (SMEs) located in Guangdong Province, P.R. China. A pilot test of 20 participants with similar demographics as the final sample was performed to test the usability of the questionnaire. We distributed 500 questionnaires among the target population. In total, 230 usable questionnaires were returned, resulting in a response rate of 53%. To estimate the proposed relationships in the theoretical framework, we used SPSS and AMOS. The results of this study confirmed that despotic leadership has a negative impact on employee job satisfaction. Moreover, the outcomes of this study indicate that self-efficacy has a mediating effect between despotic leadership and employee job satisfaction. Similarly, the results also confirm that LMX has a moderating effect between despotic leadership and employee self-efficacy. Therefore, we conclude that the community is understanding of the mechanism of despotic leadership, identify the mechanism to effectively deal with its negative effects, broaden the relevant research on the antecedent variable of self-efficacy, and provide practical enlightenment enterprises to retain and employ people.

Does the platform matter? Social media and COVID-19 conspiracy theory beliefs in 17 countries:

Abstract: While the role of social media in the spread of conspiracy theories has received much attention, a key deficit in previous research is the lack of distinction between different types of platforms. ...

An NLP-Based Architecture for the Autocompletion of Partial Domain Models

Abstract: Domain models capture the key concepts and relationships of a business domain. Typically, domain models are manually defined by software designers in the initial phases of a software development cycle, based on their interactions with the client and their own domain expertise. Given the key role of domain models in the quality of the final system, it is important that they properly reflect the reality of the business.

Echo Chambers on Social Media: A Systematic Review of the Literature

Abstract: There have been growing concerns regarding the potential impact of social media on democracy and public debate. While some theorists have claimed that ICTs and social media would bring about a new independent public sphere and increase exposure to political divergence, others have warned that they would lead to polarization through the formation of echo chambers. The issue of social media echo chambers is both crucial and widely debated. This article attempts to provide a comprehensive account of the scientific literature on this issue, shedding light on the different approaches, their similarities, differences, benefits, and drawbacks, and offering a consolidated and critical perspective that can hopefully support future research in this area. Concretely, it presents the results of a systematic review of 55 studies investigating the existence of echo chambers on social media, providing a first classification of the literature and identifying patterns across the studies’ foci, methods and findings. We found that conceptual and methodological choices influence the results of research on this issue. Most importantly, articles that found clear evidence of echo chambers on social media were all based on digital trace data. In contrast, those that found no evidence were all based on self-reported data. Future studies should take into account the possible biases of the different approaches and the significant potential of combining self-reported data with digital trace data.