Universidad de Las Américas

This chapter provides an explanation to the implementation gap in energy transition in two Latin American oil-exporting countries, Venezuela and Ecuador, under resource nationalist governments. A causal mechanism of the failed decarbonization is identified, which displays throughout three sets of activities: strategic planning, cross-sectorial coordination, and political interplays. Accordingly, the adoption of contradictory policy aims causes the sectorial policy mix being inconsistent, the government cannot accurately coordinate the decarbonization policy, thus allowing the persistence of oil interest groups’ hegemony, which eventually leads to the persistence of oil dependence and failed energy transition. Our conclusions are supported by the analysis of policy instruments of information, regulation, finances, and administrative organization. In order to turn their ideas into actual policy measures, both governments combine this instrument mix favoring oil rents to support state-driven policies and to finance energy subsidies, in order to secure political agreements and resolve social disputes.

Objectives Current evidence on the clinical effectiveness about the different types of exercises in the subacromial impingement syndrome (SIS) remains controversial. This study aims to compare the short-term (at 5 weeks) effects of a specific exercise programme with a general exercise programme on shoulder function in adults with SIS. Methods In total, 52 adults with SIS were randomly allocated to 5 weeks to perform specific exercises (experimental group, n=26) or general exercises (control group, n=26). The primary outcome was change in shoulder function, it was assessed using the Shoulder Pain and Disability Index (SPADI) from baseline to 5 weeks. Secondary end points included changes in upper limb function (Disabilities of the Arm, Shoulder, and Hand (DASH) Questionnaire), pain intensity (Visual Analog Scale (VAS)) and kinesiophobia (Tampa Scale of Kinesiophobia (TSK)). Results All participants completed the trial. The between-group differences at 5 weeks were: SPADI, 13.5 points (95% CI: 4.3 to 15.6; ƞ ² =0.22; p=0.001); DASH, 10.1 points (95% CI: 5.6 to 15.2; ƞ ² =0.27; p<0.001); VAS at rest, 0.2 cm (95% CI: 0.1 to 0.3; ƞ ² =0.07; p=0.553); VAS on movement, 1.7 cm (95% CI: 0.9 to 2.2; ƞ ² =0.24; p<0.001); and TSK, 16.3 points (95% CI: 13.2 to 15.3; ƞ ² =0.33; p<0.001). All differences favoured the experimental group and effect sizes were medium to large for most outcomes. Mediation analyses showed that the effect of the specific exercises on shoulder function was mediated by kinesiophobia (β=2.800; 95% CI: 1.063 to 4.907) and pain on movement (β= −0.690; 95% CI: −1.176 to −0.271). Conclusion In adults with SIS, specific exercises may have a larger effect than general exercises. However, most differences did not reach the minimum threshold to be considered clinically important and the evidence to support exercise as standard treatment warrant further study. Trial registration number Brazilian Registry of Clinical Trials UTN number U111-1245-7878. Registered on 17 January 2020 ( https://ensaiosclinicos.gov.br/rg/RBR-4d5zcg ).

(1) Background: Tropical Mountain forests (TMF) constitute a threatened major carbon sink due to deforestation. Carbon compensation projects could significantly aid in preserving these ecosystems. Consequently, we need a better understanding of the above-ground carbon (AGC) spatial distribution in TMFs to provide project developers with accurate estimations of their mitigation potential; (2) Methods: integrating field measurements and remote sensing data into a random forest (RF) modelling framework, we present the first high-resolution estimates of AGC density (Mg C ha−1) over the western Ecuadorian Andes to inform an ongoing carbon compensation mechanism; (3) Results: In 2021, the total landscape carbon storage was 13.65 Tg in 194,795 ha. We found a broad regional partitioning of AGC density mediated primarily by elevation. We report RF-estimated AGC density errors of 15% (RMSE = 23.8 Mg C ha−1) on any 10 m pixel along 3000 m of elevation gradient covering a wide range of ecological conditions; (4) Conclusions: Our approach showed that AGC high-resolution maps displaying carbon stocks on a per-pixel level with high accuracy (85%) could be obtained with a minimum of 14 ground-truth plots enriched with AGC density data from published regional studies. Likewise, our maps increased precision and reduced uncertainty concerning current methodologies used by international standards in the Voluntary Carbon Market.

Purpose of Review El Salvador, a small country with limited resources, was declared by the World Health Organization to have eliminated malaria on February 25, 2021. This study retrospectively investigated the evolution of strategies utilized to reduce malaria incidence and eliminate it in El Salvador. A retrospective systematic review of the malaria cases from 1960 until 2019 was carried out by analyzing the data from the MOH surveillance system, as well as a historical analysis of documents from El Salvador MOH, PAHO/WHO, and UN El Salvador Malaria Eradication Program since its origin in the 1950s. Recent Findings The peak of malaria cases in the country was observed in 1980 with 95,835 cases when the Civil War started with a subsequent decline reaching 0 indigenous cases in 2017, 2018, and 2019 with only 1 imported case in 2019. Although its neighboring countries Guatemala and Honduras maintain active malaria transmission, El Salvador interrupted transmission with 0 malaria indigenous cases reported from 2017 onwards leading to its certification of malaria elimination in the middle of the COVID-19 pandemic. Key strategies employed by El Salvador included the utilization of voluntary collaborators, optimizing medical treatment regimens, consistent domestic funding, environmental modifications targeting large mosquito breeding sites, decentralizing diagnostic laboratories, and establishing a national surveillance system with stratification of malaria-risk areas.

The identification of possible targets for a known compound by its sole molecular representation is one of the most important tasks for drug design and development. In this work, a methodology is proposed for target identification using supervised machine learning. To predict drug binding targets, classification models across targets were constructed using the k-NN algorithm by integrating multiple data types. Two different groups of descriptors are used: 1) Morgan’s fingerprint and 2) general molecular properties of interest. The findings demonstrate that the k-NN classification models achieved a higher f1-score with descriptors based on molecular properties of interest with 0.7 in comparison to the Morgan fingerprint descriptors that achieved a score of 0.57 or the fusion of both with a score of 0.58.

Objective: To evaluate the penetration of hydrogen peroxide (HP) into the pulp chamber and the color change of different bleaching varnishes in low concentrations used for at-home bleaching. Materials and methods: Ninety healthy premolars were used, randomly distributed into nine groups (n = 10) according to bleaching varnish (PL, PolaLuminate; VS, VivaStyle Paint On Plus; CA, Cavex Bite&White whitening pen and; AW AlignerWhite) and time (10 and 30 min), and a control group (no bleaching). The penetration of HP was evaluated by UV-Vis spectroscopy. To evaluate the color change (ΔEab , ΔE00 , ΔWID ) a digital spectrophotometer was used (α = 0.05). Results: The AW group in 10 min and the control group showed similar and lower HP penetration in the pulp chamber when compared to the other groups (p = 0.003). Increasing the application time to 30 minutes elevated the amount of HP inside the pulp chamber for all groups (p = 0.003), except for PL (p > 0.05). When applied for 30 min all bleaching varnishes showed higher color change (ΔWID ) when compared to 10 min (p = 0.04). Conclusions: For all bleaching varnishes evaluated, PolaLuminate applied for 30 min showed lower penetration into the pulp chamber and higher bleaching effects. Clinical significance: The use of bleaching varnishes seems promising for teeth bleaching, but it varies according to user product and protocol.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging virus that, since March 2020, has been responsible for a global and ongoing pandemic. Its rapid spread over the past nearly 3 years has caused novel variants to arise. To monitor the circulation and emergence of SARS-CoV-2 variants, surveillance systems based on nucleotide mutations are required. In this regard, we searched in the spike, ORF8, and nucleocapsid genes to detect variable sites among SARS-CoV-2 variants. We describe polymorphic genetic regions that enable us to differentiate between the Alpha, Beta, Gamma, Delta, and Omicron variants of concern (VoCs). We found 21 relevant mutations, 13 of which are unique for Omicron lineages BA.1/BA.1.1, BA.2, BA.3, BA.4, and BA.5. This genetic profile enables the discrimination between VoCs using only four reverse transcription PCR fragments and Sanger sequencing, offering a cheaper and faster alternative to whole-genome sequencing for SARS-CoV-2 surveillance. IMPORTANCE Our work describes a new (Sanger sequencing-based) screening methodology for SARS-CoV-2, performing PCR amplifications of a few target regions to detect diagnostic mutations between virus variants. Using the methodology developed in this work, we were able to discriminate between the following VoCs: Alpha, Beta, Gamma, Delta, and Omicron (BA.1/BA.1.1, BA.2, BA.3, BA.4, and BA.5). This becomes important, especially in low-income countries where current methodologies like next-generation sequencing have prohibitive costs. Furthermore, rapid detection would allow sanitary authorities to take rapid measures to limit the spread of the virus and therefore reduce the probability of new virus dispersion. With this methodological approach, 13 previously unreported diagnostic mutations among several Omicron lineages were found.

The rapid expansion of artificial intelligence poses significant challenges in terms of data security and privacy. This article proposes a comprehensive approach to develop a framework to address these issues. First, previous research on security and privacy in artificial intelligence is reviewed, highlighting the advances and existing limitations. Likewise, open research areas and gaps that require attention to improve current frameworks are identified. Regarding the development of the framework, data protection in artificial intelligence is addressed, explaining the importance of safeguarding the data used in artificial intelligence models and describing policies and practices to guarantee their security, as well as approaches to preserve the integrity of said data. In addition, the security of artificial intelligence is examined, analyzing the vulnerabilities and risks present in artificial intelligence systems and presenting examples of potential attacks and malicious manipulations, together with security frameworks to mitigate these risks. Similarly, the ethical and regulatory framework relevant to security and privacy in artificial intelligence is considered, offering an overview of existing regulations and guidelines.

This review aims to characterize the current landscape of exoskeletons designed to promote medical care and occupational safety in industrial settings. Extensive exploration of scientific databases spanning industries, health, and medicine informs the classification of exoskeletons according to their distinctive attributes and specific footholds on the human physique. Within the scope of this review, a comprehensive analysis is presented, contextualizing the integration of exoskeletons based on different work activities. The reviewers extracted the most relevant articles published between 2008 and 2023 from IEEE, Proquest, PubMed, Science Direct, Scopus, Web of Science, and other databases. In this review, the PRISMA-ScR checklist was used, and a Cohen's kappa coefficient of 0.642 was applied, implying moderate agreement among the reviewers; 75 primary studies were extracted from a total of 344. The future of exoskeletons in contributing to occupational health and safety will depend on continued collaboration between researchers, designers, healthcare professionals , and industries. With the continued development of technologies and an increasing understanding of how these devices interact with the human body, exoskeletons will likely remain valuable for improving working conditions and safety in various work environments.

IoT devices have been deployed in different Industrial applications due to their feasible implementation thanks to the modern toolsets and many sensor brands to select the adequate sensor. However, even when their main task is collecting data, there are challenges, such as working in unpredictable environments, which can affect the communication channel and sensors' functionalities, which could cause the loss of information. On the other hand, statistical and deep learning-based forecasting methods can fill in missing data. In this work, we investigate those forecasting algorithms to select the best one for different data measurements in a real-world setting, such as temperature, humidity, and CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . Based on our results, the Holt-Winters methods were selected to forecast the temperature and CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , and Long-Short-Term Memory was used to forecast humidity. These models were then trained in the Edge-scale servers to define their computational requirements and feasibility to be exported to a microcontroller.

Citation: Flor-Unda, O.; Simbaña, F.; Larriva-Novo, X.; Acuña, Á.; Tipán, R.; Acosta-Vargas, P. A Comprehensive Analysis of the Worst Cybersecurity Vulnerabilities in Latin America. Informatics 2023, 10, 71. https://doi.org/10.3390/ Abstract: Vulnerabilities in cyber defense in the countries of the Latin American region have favored the activities of cybercriminals from different parts of the world who have carried out a growing number of cyberattacks that affect public and private services and compromise the integrity of users and organizations. This article describes the most representative vulnerabilities related to cyberat-tacks that have affected different sectors of countries in the Latin American region. A systematic review of repositories and the scientific literature was conducted, considering journal articles, conference proceedings, and reports from official bodies and leading brands of cybersecurity systems. The cybersecurity vulnerabilities identified in the countries of the Latin American region are low cybersecurity awareness, lack of standards and regulations, use of outdated software, security gaps in critical infrastructure, and lack of training and professional specialization.

A new chiral amplification mechanism based on a stochastic approach is proposed. The mechanism includes five different chemical species, an achiral substrate (A), two chiral forms (L, D), and two intermediary species (LA, DA). The process occurs within a small, semipermeable compartment that can be diffusively coupled with the outside environment. The study considers two alternative primary sources for chiral species within the compartment, one chemical and the other diffusive. As a remarkable fact, the chiral amplification process occurs due to stochastic fluctuations of an intermediary catalytic species (LA, DA) produced in situ, given the interaction of the chiral species with the achiral substrate. The net process includes two different steps: the synthesis of the catalyst (LA and DA) and the catalytic production of new chiral species from the substrate. Stochastic simulations show that proper parameterization can induce a robust chiral state within the compartment regardless of whether the system is open or closed. We also show how an increase in the non-catalytic production of chiral species tends to negatively impact the homochirality degree of the system. By its conception, the proposed mechanism suggests a deeper connection with how most biochemical processes occur in living beings, a fact that could open new avenues for studying this fascinating phenomenon.

Circadian rhythms (CRs) are fundamental biological processes that significantly impact human well-being. Disruption of these rhythms can trigger insufficient neurocognitive development, insomnia, mental disorders, cardiovascular diseases, metabolic dysfunctions, and cancer. The field of chronobiology has increased our understanding of how rhythm disturbances contribute to cancer pathogenesis, and how circadian timing influences the efficacy of cancer treatments. As the circadian clock steadily gains recognition as an emerging factor in tumorigenesis, a thorough and comprehensive multi-omics analysis of CR genes/proteins has never been performed. To shed light on this, we performed, for the first time, an integrated data analysis encompassing genomic/transcriptomic alterations across 32 cancer types (n = 10,918 tumors) taken from the PanCancer Atlas, unfavorable prognostic protein analysis, protein–protein interactomics, and shortest distance score pathways to cancer hallmark phenotypes. This data mining strategy allowed us to unravel 31 essential CR-related proteins involved in the signaling crossroad between circadian rhythms and cancer. In the context of drugging the clock, we identified pharmacogenomic clinical annotations and drugs currently in late phase clinical trials that could be considered as potential cancer therapeutic strategies. These findings highlight the diverse roles of CR-related genes/proteins in the realm of cancer research and therapy.

The metabolome is the biochemical basis of plant form and function, but we know little about its macroecological variation across the plant kingdom. Here, we used the plant functional trait concept to interpret leaf metabolome variation among 457 tropical and 339 temperate plant species. Distilling metabolite chemistry into five metabolic functional traits reveals that plants vary on two major axes of leaf metabolic specialization-a leaf chemical defense spectrum and an expression of leaf longevity. Axes are similar for tropical and temperate species, with many trait combinations being viable. However, metabolic traits vary orthogonally to life-history strategies described by widely used functional traits. The metabolome thus expands the functional trait concept by providing additional axes of metabolic specialization for examining plant form and function.

Software development stands out as one of the most rapidly expanding markets due to its pivotal role in crafting applications across diverse sectors like healthcare, transportation, and finance. Nevertheless, the sphere of cybersecurity has also undergone substantial growth, underscoring the escalating significance of software security. Despite the existence of different secure development frameworks, the persistence of vulnerabilities or software errors remains, providing potential exploitation opportunities for malicious actors. One pivotal contributor to subpar security quality within software lies in the neglect of cybersecurity requirements during the initial phases of software development. In this context, the focal aim of this study is to analyze the importance of integrating security modeling by software developers into the elicitation processes facilitated through the utilization of abuse stories. To this end, the study endeavors to introduce a comprehensive and generic model for a secure software development process. This model inherently encompasses critical elements such as new technologies, human factors, and the management of security for the formulation of abuse stories and their integration within Agile methodological processes.

This study develops a set of measures to address the interrelationship among circular waste-based bioeconomy (CWBE) attributes, including those of government strategy, digital collaboration, supply chain integration, smart operations, and a green supply chain, to build a circular bioeconomy that feeds fish waste back into the economy. CWBE development is a potential solution to the problem of waste reuse in the fish supply chain; however, this potential remains untapped, and prior studies have failed to provide the criteria to guide its practices. Such an analytical framework requires qualitative assessment, which is subject to uncertainty due to the linguistic preferences of decision makers. Hence, this study adopts the fuzzy Delphi method to obtain a valid set of attributes. A fuzzy decision-making trial and evaluation was applied to address the attribute relationships and determine the driving criteria of CWBE development. The results showed that government strategies play a causal role in CWBE development and drive digital collaboration, smart operations, and supply chain integration. The findings also indicated that smart manufacturing technology, organizational policies, market enhancement, supply chain analytics, and operational innovation are drivers of waste integration from fisheries into the circular economy through waste-based bioeconomy processes.

Background: Burnout syndrome (BS) is composed of three interrelated dimensions (emotional exhaustion, depersonalization, and personal fulfillment), and it has been documented that it affects health professionals from an early age. Aims: Determine the prevalence of BS and associated factors in the integral clinic of the Dentistry Pilot School. Material and Methods. Two instruments were applied: (1) Maslach Burnout Inventory, which measures the degree of professional burnout through 22 items that describe the professional's attitudes and feelings toward work, as well as symptoms associated with this phenomenon; (2) the second questionnaire determines the possible symptoms of BS and consists of 14 questions that describe tiredness, sleep problems, digestive problems, respiratory problems and headaches, temporomandibular joint (TMJ), neck pain, back pain, and upper and lower extremity pain. The instruments were answered anonymously by a total of 300 students who participated in the study. Results: The emotional exhaustion of the participants was 48.3% at a higher level, the depersonalization was 46.7% at a higher level, and the low perception of personal fulfillment was 73%. In addition, it was shown that BS is significantly related to marital status (p < 0.001∗), with single people reporting being more exhausted, with the 6-month level (p = 0.011) and with the following symptoms: non-neck pain, head, TMJ, back, waist, upper and lower body pain. Conclusion: It was found that the BS had a prevalence of high levels of exhaustion and depersonalization correlated with the marital status and level of preparation (academic degree) of the person, finding a prevalence of symptoms such as pain in the neck, head, TMJ, and back.

This study reviews the relationship between customer perception factors and AI-enabled customer experience in the Ecuadorian banking industry. The study employs a self-designed online questionnaire with five factors for customer perception (convenience in use, personalization, trust, customer loyalty, and customer satisfaction) and two categories for AI-enabled customer experience (AI-hedonic customer experience and AI-recognition customer service). The final valid dataset consisted of 226 questionnaires. The data analysis and the hypotheses tests were conducted using SPSS 26 and structural equation modeling, respectively. The main findings displayed that all five customer perception factors (individual and joint effect) have a positive and significant effect (at least at the 5% level) on AI-enabled customer experience, AI-hedonic customer experience, and AI-recognition customer service in the Ecuadorian banking industry. Study results are aligned with previous findings from other countries, particularly the banking environment in the United Kingdom, Canada, Nigeria, and Vietnam. The AI techniques involved in the financial sector increase the valuation of customer experience due to AI algorithms recollecting, processing, and analyzing customer behavior. This study contributes a complete statistical and econometric model for determinants of AI-enabled customer experience. The main limitations of the study are that, in the analysis of the most demanded AI financial services, not all services and products are included and the inexistence of a customer perception index. For upcoming research, the authors recommend performing a longitudinal study using quantitative data to measure the effect of AI-enabled customer experience on the Ecuadorian banks’ performance.