Diversity Measures Research Articles

Methods for Evaluating the Effectiveness of Social Media Marketing Campaigns

This article examines the methodological aspects of evaluating the effectiveness of marketing campaigns on social media. It highlights the significance of such evaluations, emphasizing their role in optimizing processes, identifying areas for improvement, enhancing decision-making, ensuring alignment with objectives, and enabling iterative progress. The study identifies the unique characteristics of assessing social media marketing campaigns, contrasting them with traditional, non-digital methods commonly used in internal business marketing. The article reviews widely used methods for evaluating social media marketing performance and argues for the development of a customized approach that integrates multiple evaluation techniques with diverse origins, principles, and metrics. Particular attention is given to customer journey mapping as a tool for identifying and resolving potential "bottlenecks" in social media marketing efforts. The article describes the main stages of journey mapping and suggests key metrics for evaluation. It also discusses data sources and provides structured recommendations for creating a company-specific methodology for assessing social media marketing effectiveness. The study emphasizes the importance of setting clear goals, defining specific metrics and key performance indicators, incorporating diverse measures, and automating analytics for long-term use. Finally, it advocates for focusing on creative development as part of the optimization process. This research is intended for marketers working in social media, offering insights into comprehensive evaluation and strategies for improving campaign effectiveness.

MeStanG—Resource for High-Throughput Sequencing Standard Data Sets Generation for Bioinformatic Methods Evaluation and Validation

Metagenomics analysis has enabled the measurement of the microbiome diversity in environmental samples without prior targeted enrichment. Functional and phylogenetic studies based on microbial diversity retrieved using HTS platforms have advanced from detecting known organisms and discovering unknown species to applications in disease diagnostics. Robust validation processes are essential for test reliability, requiring standard samples and databases deriving from real samples and in silico generated artificial controls. We propose a MeStanG as a resource for generating HTS Nanopore data sets to evaluate present and emerging bioinformatics pipelines. MeStanG allows samples to be designed with user-defined organism abundances expressed as number of reads, reference sequences, and predetermined or custom errors by sequencing profiles. The simulator pipeline was evaluated by analyzing its output mock metagenomic samples containing known read abundances using read mapping, genome assembly, and taxonomic classification on three scenarios: a bacterial community composed of nine different organisms, samples resembling pathogen-infected wheat plants, and a viral pathogen serial dilution sampling. The evaluation was able to report consistently the same organisms, and their read abundances as provided in the mock metagenomic sample design. Based on this performance and its novel capacity of generating exact number of reads, MeStanG can be used by scientists to develop mock metagenomic samples (artificial HTS data sets) to assess the diagnostic performance metrics of bioinformatic pipelines, allowing the user to choose predetermined or customized models for research and training.

Harmonization of alcohol use data and mortality across a multi-national HIV cohort collaboration.

Alcohol use is measured in diverse ways across settings. Harmonization of measures is necessary to assess effects of alcohol use in multi-cohort collaborations, such as studies of people with HIV (PWH). Data were combined from 14 HIV cohort studies (nine European, five North American) participating in the Antiretroviral Therapy Cohort Collaboration. We analyzed data on adult PWH with measured alcohol use at any time from 6 months before starting antiretroviral therapy. Five cohorts measured alcohol use with AUDIT-C and others used cohort-specific measures. We harmonized alcohol use as grams/day, calculated using country-level definitions of a standard drink. For Alcohol Use Disorders Identification Test (AUDIT-C), we used Items 1 (frequency) and 2 (number of drinks on a typical day). Where alcohol was measured in categories, we used the mid-point to calculate grams/day. We used multivariable Cox models to estimate associations of alcohol use with mortality. Alcohol use data were available for 83,424 PWH, 22,447 (27%) had AUDIT-C measures and 60,977 (73%) recorded the number of drinks/units per week/day. Of the sample, 19,150 (23%) were female, 54,006 (65%) had White ethnicity, and median age was 42 years. Median alcohol use was 0.3 g/day (interquartile range [IQR] 0-4.8) and 0 g/day (IQR 0-20) for those with and without AUDIT-C. There was a J-shaped relationship between grams/day and mortality, with higher mortality for PWH reporting no alcohol use (adjusted hazard ratio [aHR] 1.46; 95% CI: 1.23-1.72) and heavier (>61.0 g/day) alcohol use (aHR 1.92; 1.41-2.59) compared with 0.1-5.5 g/day among those with AUDIT-C measures. Associations were similar among those with non-AUDIT-C measures. Grams/day is a useful metric to harmonize diverse measures of alcohol use. Magnitudes of associations of alcohol use with mortality may differ by setting and measurement method. Higher mortality among those with heavier alcohol use strengthens the case for interventions to reduce drinking.

Prioritizing Context-Dependent Cancer Gene Signatures in Networks.

There are numerous ways of portraying cancer complexity based on combining multiple types of data. A common approach involves developing signatures from gene expression profiles to highlight a few key reproducible features that provide insight into cancer risk, progression, or recurrence. Normally, a selection of such features is made through relevance or significance, given a reference context. In the case of highly metastatic cancers, numerous gene signatures have been published with varying levels of validation. Then, integrating the signatures could potentially lead to a more comprehensive view of the connection between cancer and its phenotypes by covering annotations not fully explored in individual studies. This broader understanding of disease phenotypes would improve the predictive accuracy of statistical models used to identify meaningful associations. We present an example of this approach by reconciling a great number of published signatures into meta-signatures relevant to Osteosarcoma (OS) metastasis. We generate a well-annotated and interpretable interactome network from integrated OS gene expression signatures and identify key nodes that regulate essential aspects of metastasis. While the connected signatures link diverse prognostic measurements for OS, the proposed approach is applicable to any type of cancer.

On the infodynamics of ramifications in constructal design.

Infodynamics is the study of how information behaves and changes within a system during its development. This study investigates the insights that informational analysis can provide regarding the ramifications predicted by constructal design. First, infodynamic neologisms informature, defined as a measure of the amount of information in indeterminate physical systems, and infotropy - contextualized informature representing the degree of transformation of indeterminate physical systems - are introduced. Flow architectures can be designed using either symmetric or asymmetric branching. The infodynamic analysis of symmetric branching revealed diminishing returns in information content, demonstrating that informature serves as a measure of diversity. These findings align with the principle of "few large and many small, but not too many," which is consistent with higher thermofluid performance. The Performance Scaled Svelteness Ψ expresses the ability of the flow architecture to promote thermofluid performance. By contextualizing the informature with Ψ, a performance infotropy that quantifies the degree of transformation associated with the link between thermofluid performance and diversity in the ramified flow structure is obtained. A predicted growth and decay effect with increasing branching levels leads to a local maximum, highlighting that the evolutionary direction of the ramifications is inversely proportional to the scale of the environment in which the flow structure develops. Assuming an evolutionary trend toward maximum infodynamic complexity, a pattern of asymmetric ramifications emerges, similar to the sap distribution in leaves or branching of trees.

The bidirectional interaction between antidepressants and the gut microbiota: are there implications for treatment response?

This review synthesizes the evidence on associations between antidepressant use and gut microbiota composition and function, exploring the microbiota's possible role in modulating antidepressant treatment outcomes. Antidepressants exert an influence on measures of gut microbial diversity. The most consistently reported differences were in β-diversity between those exposed to antidepressants and those not exposed, with longitudinal studies supporting a potential causal association. Compositional alterations in antidepressant users include an increase in the Bacteroidetes phylum, Christensenellaceae family, and Bacteroides and Clostridium genera, while a decrease was found in the Firmicutes phylum, Ruminococcaceae family, and Ruminococcus genus. In addition, antidepressants attenuate gut microbial differences between depressed and healthy individuals, modulate microbial serotonin transport, and influence microbiota's metabolic functions. These include lyxose degradation, peptidoglycan maturation, membrane transport, and methylerythritol phosphate pathways, alongside gamma-aminobutyric acid metabolism. Importantly, baseline increased α-diversity and abundance of the Roseburia and Faecalibacterium genera, in the Firmicutes phylum, are associated with antidepressant response, emerging as promising biomarkers. This review highlights the potential for gut microbiota as a predictor of treatment response and emphasizes the need for further research to elucidate the mechanisms underlying antidepressant-microbiota interactions. More homogeneous studies and standardized techniques are required to confirm these initial findings.

Covariation Between Microbiome Composition and Host Transcriptome in the Gut of Wild Drosophila melanogaster: A Re-Analysis.

Gut microbiota are fundamental for healthy animal function, but the evidence that host function can be predicted from microbiota taxonomy remains equivocal, and natural populations remain understudied compared to laboratory animals. Paired analyses of covariation in microbiota and host parameters are powerful approaches to characterise host-microbiome relationships mechanistically, especially in wild populations of animals that are also lab models, enabling insight into the ecological basis of host function at molecular and cellular levels. The fruitfly Drosophila melanogaster is a preeminent model organism, amenable to field investigation by 'omic analyses. Previous work in wild male D. melanogaster guts analysed paired measurements of (A) bacterial diversity and abundance, measured by 16S amplicon sequencing; and (B) the host gut transcriptome, but no signature of covariation was detected. Here, we re-analyse those data comprehensively. We find orthogonal axes of microbial genera, which correspond to differential expression of host genes. The differentially expressed gene sets were enriched in functions including protein translation, mitochondrial respiration, immunity and reproduction. Each gene set had a distinct functional signature, suggesting that wild flies exhibit a range of distinct axes of functional variation, which correspond to orthogonal axes of microbiome variation. These findings lay a foundation to better connect ecology and functional genetics of a leading host-microbiome model.

EXTENSION OF THE HYBRID SEIR MODEL WITH VITAL DYNAMICS FOR SOUTH AMERICA

This study builds on previous research conducted in the United States [1] and Europe [2] by extending the enhanced SEIR model with vital dynamics to South America, focusing on its most populous countries: Brazil, Colombia, Peru, Venezuela, and Argentina. The analysis aims to capture regional variations in Monkeypox (Mpox) outbreak dynamics by integrating vital population changes and a dynamic transmission rate (), while accounting for localized public health interventions.Argentina presents a particularly compelling case, as public health interventions during the Mpox outbreak were implemented, contrasting sharply with Brazil, where such measures were minimal. This contrast provides a unique opportunity to compare the dynamics of Monkeypox spread between the two most populous countries in South America.By examining these regional differences, this study investigates how diverse healthcare intervention measures influence epidemic trajectories. The findings underscore the critical role of public health measures in controlling outbreaks and demonstrate the adaptability of the enhanced SEIR model across different intervention scenarios. The study also highlights the robustness of the model in capturing both natural and intervention-driven epidemic dynamics.Through this investigation, the study offers actionable insights for tailoring epidemic responses to regions with differing healthcare infrastructure and intervention capacities. This approach underscores the importance of localized public health strategies and provides a valuable framework for modeling future outbreaks in diverse settings.

Synergistic effects of air pollution control policies: Evidence from China

To control severe air pollution, multiple policies have been adopted in China. However, the combined effect of these policies on reducing air pollution remains uncertain. By systematically evaluating the complementary and substitution effects of policy combinations across different regions, this study aims to identify the optimal combinations of multiple air pollution control policies and to explore the mechanisms of policy synergy, maximizing improvements in air quality. Using a panel data from 334 Chinese cities collected between 2015 and 2019, we assess the effectiveness of fifteen air quality policies across eight categories. Our findings indicate that combining diverse policy measures yields more significant improvements in air quality due to the complementary effects among policies. Conversely, combinations of similar policy types exhibit weaker effects because of substitution effects. A comprehensive policy package, integrating stringent industrial emissions standards, residential energy transition strategies, market-based trading mechanisms, and multi-level supervision, may represent the most effective approach for air quality enhancement.

High genetic variation, low differentiation, and Pleistocene expansions of the migratory and endangered long-nosed tequila bat, Leptonycteris nivalis, inferred using both maternal and paternal genetic markers.

Tequila bats (genus Leptonycteris) have gained attention for their critical role in pollinating different plant species, especially Agave spp. and columnar cacti. Leptonycteris nivalis is the largest nectar-feeding bat in the Americas, and the females exhibit migratory behavior during the breeding season. Due to its relatively small and seemingly declining population sizes, this species is protected by government agencies in the United States and Mexico. We conducted population genetics and phylogeographic analyses to elucidate the genetic structure and demographic history of the species using two mitochondrial markers and a Y chromosome-associated gene, to describe both maternal and paternal lineages. We estimated high haplotypic diversity measures for the different markers (Dloop-Hd = 0.775; Cyt-b-Hd = 0.937; DBY -Hd = 0.946). We found that geographic genetic differentiation is very low, and there is high connectivity among localities. The estimated divergence time between L. nivalis and L. yerbabuenae, the other species in the genus found in Mexico, aligns with previous estimates for the genus (6.91-9.43 mya). A demographic expansion was detected approximately at 600 ka-700 ka (thousands of years ago). The historical demographic changes observed in L. nivalis appear to be associated with environmental shifts during the Pleistocene, which likely impacted the distribution range of the plants that these bats feed on, such as Agave species.

Numerical and experimental investigation on vortex control and sedimentation suppression in a Y-shaped diversion channel with circular inlet tanks

A sudden angle change between the Y-shaped diversion channel and the inlet tank of the circular pump station often leads to boundary layer separation and large vortex formation. These flow patterns can cause significant sediment deposition in sediment-laden rivers, reducing pump efficiency. A mixture model is introduced to describe water-sediment two-phase flows. Simulation results analyze vortex characteristics and formation mechanisms, and evaluate potential sediment deposition areas. Without rectification, vortex areas cause significant sediment migration towards the sidewalls of the Y-shaped diversion channel and pump inlet. Diversion measures like sills and pressure plates are introduced to reduce flow vortices. Results show that sills generally optimize flow structures. Sills in the main Y-shaped diversion channel outperform those in other channels in terms of optimization. Pressure plates also optimize flow structures. Vortex structure and position are closely related to the distance between the pressure plate and the circular intake tank. Optimized measures with pressure plates significantly reduce the flow deviation angle and improve flow velocity uniformity. Numerical simulation accuracy is validated through experiments. Results reveal the formation mechanism of adverse vortices and their sedimentation effects, which are detrimental to pump stations. The proposed rectification scheme provides theoretical support for optimizing pump station operation.

Predicting College Students Lack of Longevity Promotion: From Family-of-origin chaos, through Current Unmet Interpersonal Needs, to Life-Diminishing Behavior

Identifying modifiable predictors of life-diminishing behaviors is paramount for health professionals seeking to promote the probability of longevity among young adults who can be at risk for suicide, overdose, and accidental injury and death. Family-of-origin experiences and unmet interpersonal needs are well-known risk factors for suicide; however, they have yet to be tested as predictors of a broad array of life-diminishing behaviors. College student participants (N=522, M=19.6 years) who were majority female (66.7%) and diverse (57.3% White) completed measures of family of-origin chaos, interpersonal needs, and engagement in four types of life-diminishing behaviors as measured via the Life-Attitudes Schedule: Short Form (LAS:SF). The LAS:SF assesses engagement in both potentially life-diminishing as well as potentially longevity-promoting behaviors; the overall construct assessed has been labelled lack of longevity promotion or suicide proneness. As expected, a history of greater family-of-origin chaos and current unmet interpersonal needs were associated with overall LAS:SF scores with medium effect sizes. Furthermore, the interaction between perceived burdensomeness and thwarted belongingness mediated the relationship between family-of-origin chaos and total LAS:SF scores, accounting for 20% of the variance in college students’ lack of longevity promotion. Importantly, the model held for each of the four lack of longevity proneness/suicide proneness domains (Lack of Health/Illness Promotion, Lack of Safety/Risk Promotion, Lack of Future Orientation/ Death or Suicide Promotion, and Lack of Self-Promotion) and across multiple demographic groups. Assessing and addressing interpersonal needs is an emerging intervention target for those seeking to improve at-risk young adults’ well-being while promoting longevity and preventing suicide and accidental death.

자원봉사 활동 참여가 노인의 자아통합감과 생활만족도에 미치는 영향

Objectives This study is meaningful in providing data for activating volunteer activities in the future by analyzing the relationship between self-integration and life satisfaction according to volunteer activities of the elderly and deriving the results that participation in volunteer activities has a positive effect on self-integration and life satisfaction. Methods To this end, 130 elderly people (73 women and 57 men) aged 60 or older living in area k were surveyed. SPSS 25.0 was used for analysis of the collected data, and frequency analysis, t-test, and one-way ANOVA were performed for analysis. Results The main results shown in the analysis were that first, the motivation to participate in volunteer activities was helpful, so the type of volunteer work was the most vulnerable group, the frequency of participation was 1-2 times a month, the participation time per session was 1-3 hours, and the total duration of activity was 1-5 years. Second, the sense of self-integration was higher in higher education, religion, spouse, and health. Third, life satisfaction was higher for women than men, higher education, religion, spouse, and health. Conclusions Participation in volunteer activities in old age has been shown to have a positive effect on the elderly's self-integration and life satisfaction, so active and diverse volunteer support measures should be taken at the government level for successful retirement.

Space observation for the sustainable utilization of world heritage sites along the silk roads: the routes network of Chang’an-Tianshan corridor

ABSTRACT Urban development under a changing climate pose threats to heritage conservation, necessitating vigilant monitoring of human activities and natural disasters. This study proposes a dynamic monitoring and risk assessment technology aimed at identifying interferences to heritage sites along the Silk Road. The characterization of land use was investigated, and changes within the protected areas were extracted using an object-based deep learning method to reveal instances of human interference. Nighttime light changes were analyzed to assess socio-economic development in heritage regions and cross-referenced with results indicating human interference to examine the relationship between urban development and heritage conservation. Additionally, we extracted various environmental and climate variables to evaluate the risk of flood disasters for these heritage sites. The findings suggest that heritage sites along the Silk Road are generally in good condition. Nevertheless, the substantial increase in construction land highlights challenges in heritage conservation and economic development. This study highlights the importance of vegetation coverage and water diversion measures to prevent floods, emphasizing the effectiveness of space observation in identifying risks. Such observation can help the government manage and protect valuable sites, advancing heritage protection within the framework of sustainable development goals (SDGs).

Integrated analysis of rumen metabolomics and metataxonomics to understand changes in metabolic and microbial community in Korean native goats under heat stress

Heat stress (HS) is an impactful condition in ruminants that negatively affects their physiological and rumen microbial composition. However, a fundamental understanding of metabolomic and metataxonomic mechanisms in goats under HS conditions is lacking. Here, we analyzed the rumen metabolomics, metataxonomics, and serum metabolomics of goats (n = 10, body weight: 41.08 ± 1.83 kg) under optimum temperature period (OTP) (HS-free, temperature humidity index (THI): 57.13 ± 3.98) and high temperature period (HTP) (HS-exposed, THI: 80.27 ± 1.22) conditions, to identify changes in key metabolites and the rumen microbiome induced by HS. Compared to the OTP and HTP conditions, metabolomic analysis revealed significant changes in rumen metabolites related to energy and amino acid metabolism, with HTP goats showing potential rumen metabolic biomarkers, such as butyrate, isopropanol, phenylacetate, and 2-oxoisocaproate (P < 0.001). Serum analysis revealed significant changes in energy metabolism and immune response, with HTP goats showing potential metabolic biomarkers, including acetate, betaine, glucuronate, and kynurenine (P < 0.05). Metataxonomic analysis revealed that HS affected the alpha diversity measurements, including the Chao1 estimate (P < 0.05) and evenness (P < 0.05) between OTP and HTP groups. Through the metabolic association of the rumen microbiome with the metabolome, we found that Fibrobacter and Ruminococcus were enriched in HTP and positively correlated with ruminal microbial metabolites, such as acetate. In addition, Prevotellaceae UCG-003, which was denoted as the keynote genus in the HTP, co-occurred with acetate-producing bacteria such as Quinella and Ruminococcus. Furthermore, we identified that Oscillospiraceae UCG-002, an enriched bacterial genus in HTP, showed a positive correlation with functional features, such as biotin and sulfur metabolism. Our study provided fundamental insights into how HS affected the physiology and rumen microbial compositions of goats and how both microbiome and host-dependent mechanisms contributed to these changes. These findings could potentially suggest strategies for mitigating the adverse effects of HS, including changes in the microbial population and energy metabolism in goats.

Takeaways From the COVID-19 Epidemic Progression and Combating Strategies in Southeast Asia

Southeast Asia grappled with an early wave of COVID-19 cases, witnessing a spike in 2021 and early 2022 before vaccination efforts led to a decline. Governments in the region adopted diverse strategies, including lockdowns and public health measures, tailored to each country's cultural and socioeconomic landscape. This article delves into the COVID-19 outbreak across the 11 member countries, encompassing all ASEAN nations and Timor-Leste. It highlights the region's progression from low infection rates in 2020 to heightened mortality in 2021, followed by a drop in cases as vaccination coverage increased. Despite shared challenges, disparities in culture, wealth, and political context influenced the implementation and effectiveness of containment measures.

The Power of Words from the 2024 United States Presidential Debates: A Natural Language Processing Approach

This study analyzes the linguistic patterns and rhetorical strategies employed in the 2024 U.S. presidential debates from the exchanges between Donald Trump, Joe Biden, and Kamala Harris. This paper examines debate transcripts to find underlying themes and communication styles using Natural Language Processing (NLP) advanced techniques, including an n-gram analysis, sentiment analysis, and lexical diversity measurements. The methodology combines a quantitative text analysis with qualitative interpretation through the Jaccard similarity coefficient, the Type–Token Ratio, and the Measure of Textual Lexical Diversity. The empirical results reveal distinct linguistic profiles for each candidate: Trump consistently employed emotionally charged language with high sentiment volatility, while Biden and Harris demonstrated more measured approaches with higher lexical diversity. Finally, this research contributes to the understanding of political discourse in high-stakes debates through NLP and can offer information on the evolution of the communication strategies of the presidential candidates of any country with this regime.

Revaluation of the genetic diversity–area relationship by integrating nucleotide and haplotype diversity

Abstract Understanding the genetic diversity–area relationship (GAR) is essential for comprehending how species adapt to environmental changes, as genetic diversity is an indicator of a species' adaptive potential. Variation in environmental adaptation capacity exists among species and animal taxa with different distribution areas, highlighting the importance of understanding the GAR. To obtain a more comprehensive understanding of the GAR in terrestrial vertebrates, we assessed both haplotype diversity–area and nucleotide diversity–area relationships using 25,453 cytochrome c oxidase subunit I (COI) sequences from 142 amphibian species, 574 bird species, and 342 mammal species. We found that both measures of genetic diversity increased with species range size across major animal groups. Nevertheless, the GAR did not differ among animal groups, while haplotype diversity performed better than nucleotide diversity in profiling the GAR, as indicated by higher R2 values. The difference in the modelling fit may stem from the distinct biological and mathematical significance of nucleotide diversity and haplotype diversity. These results suggest that the GAR follows similar rules among different animal taxa. Furthermore, haplotype diversity may serve as a more reliable indicator for assessing the potential effects of area size changes on animal populations and provide better guidance for conserving genetic diversity.

Transferable deep learning with coati optimization algorithm based mitotic nuclei segmentation and classification model

Image processing and pattern recognition methods have recently been extensively implemented in histopathological images (HIs). These computer-aided techniques are aimed at detecting the attentive biological markers for assisting the final cancer grading. Mitotic count (MC) is a significant cancer detection and grading parameter. Conventionally, a pathologist examines the biopsy image physically by employing higher-power microscopy. The MC cells have been marked physically at every analysis, and total MC must be utilized as a major aspect for the cancer ranking and considered as the initiative of cancers. Numerous pattern recognition algorithms for cell-sized objects in HIs depend upon segmentation to assess features. The correct description of the segmentation has been difficult, and feature outcomes can be highly complex to the segmentation. The MC cells are an essential element in many cancer grading methods. Extraction of the MC cell from the HI is a highly challenging assignment. This manuscript proposes the Coati Optimization Algorithm with Deep Learning-Driven Mitotic Nuclei Segmentation and Classification (COADL-MNSC) technique. The major aim of the COADL-MNSC technique is to utilize the DL model to segment and classify the mitotic nuclei (MN). In the preliminary stage, the COADL-MNSC approach implements median filtering (MF) for pre-processing. Besides, the COADL-MNSC approach utilizes the Hybrid Attention Fusion U-Net (HAU-UNet) model to segment the MN. Moreover, the capsule network (CapsNet) model is employed for the feature extraction method, and its hyperparameters are adjusted by utilizing the COA model. At last, the classification procedure is performed using the bidirectional long short-term memory (BiLSTM) model. Extensive simulations are performed under the MN image dataset to exhibit the excellent performance of the COADL-MNSC methodology. The experimental validation of the COADL-MNSC methodology portrayed a superior accuracy value of 98.89% over existing techniques under diverse measures.

Task sequencing on oral performance in Portuguese as a foreign language

This empirical study examined Robinson’s (2010, 2015, 2022) Simplify, Stabilise/ Automatise/ Restructure, Complexify (SSARC) instructional design model of task sequencing. Thirty-four university learners of Portuguese as a foreign language participated in the research. The analysis employed a monologic narrative task and two independent variables: “± here-and-now” and “± planning time”. Measures of syntactic complexity, lexical diversity, accuracy, and fluency were selected to analyse the learners’ output. A between-subjects design was adopted. Participants were divided into two groups: (i) sequencing group, in which the students (n=15) performed three tasks with different levels of cognitive complexity (simple task – complex task – more complex task); (ii) individual performance group (n=19), in which the students performed only one task (simple task, complex task, or more complex task). The inferential statistical tests showed gains in fluency and syntactic complexity (coordination index) regarding the complex task in the sequencing group, and increased accuracy and fluency for the more complex task, as compared to individual group performance. The SSARC model was considered to have pedagogical merit, which can help teachers design task-based syllabuses. To the best of our knowledge, this is the first study to fully test the SSARC model applied to oral task sequencing in Portuguese as a foreign language.