Hierarchical Classification Research Articles

Optimized wastewater management utilizing multivariate statistical analysis: a case study of the Mascara wastewater treatment plant, Algeria.

Effective wastewater management is crucial in regions experiencing water scarcity and environmental stressors, such as pollution and climate change. Optimizing treatment processes is essential for achieving environmental sustainability. This study aims to highlight the importance of effective wastewater management strategies, particularly in regions facing water scarcity. Our objective was to identify key factors influencing the treatment process. Therefore, we evaluated associations between physicochemical parameters using multivariate statistical methods, including Principal Component Analysis (PCA) and Hierarchical Ascendant Classification (HAC). Our findings categorize the monthly water samples into three distinct groups based on levels of organic pollution: the first group (July, August, and September) is characterized by high oxygenation levels and significantly low organic pollution, indicating optimal system operation. The second group (April, October, November, and December) exhibits low oxygenation and low organic pollution, promoting sludge settling and pollutant reduction. The third group (January, February, March, May, and June) shows significantly high organic pollution and low oxygenation, which corresponds to unfavorable environmental conditions. Our study demonstrates the effectiveness of multivariate statistical methods in optimizing wastewater treatment processes, providing crucial insights for environmental sustainability and water resource management.

Quadrilaterals hierarchical classification and properties of the diagonals: A study with pre-service mathematics teachers

The aim of this research is to investigate the main characteristics of the processes of constructing a hierarchical classification of quadrilaterals and identifying properties of their diagonals by pre-service middle and secondary school mathematics teachers (PMTs), when involved in whole-class discussions with the teacher educators, after having solved tasks focused on those topics. Data collection comprised video recordings of the sessions and PMTs’ written work, analyzed qualitatively. Findings indicate that constructing the hierarchical classification of quadrilaterals involved the PMTs in prototypical judgments, and dichotomous comparisons. In comparison the identification of the diagonals’ properties was influenced more by definitions and logical relationships, reflected in judgments and comparisons. It was concluded that participation in these processes, with whole-class discussions and development of schemes to illustrate inclusion relationships, may assist PMTs with prototypical phenomenon and dichotomous comparisons, benefiting their future teaching practice.

A novel methodology for offline English handwritten character recognition using ELBP-based sequential (CNN)

Handwritten character recognition falls under the domain of image classification, which has been under research for years. But still, specific gaps need to be highlighted as offline handwritten character recognition (OHCR) with the limitation of the unstructured hierarchy of character classification. However, the idea is to make the machine recognize handwritten human characters. The language focused on in this research paper is English, using offline handwritten character recognition for identifying English characters. There are many publicly available datasets, of which EMNIST is the most challenging. The key idea of this research paper is to recommend a deep learning-based ELBP-CNN method to help recognize English characters. This research paper proposes a deep learning CovNet with feature extraction and novel local binary pattern-based approaches, LBP (AND, OR), that is tested and compared with renowned pre-trained models using transfer learning. These parametric settings address multiple issues and are finalized after experimentation. The same hyperparametric settings were used for all the models under test and E-Character, with the same data augmentation settings. The proposed model, named the E-Character recognizer, produced 87.31% accuracy. It was better than most of the tested pre-trained models and other proposed methods by other researchers. This research paper further highlighted some problems, like misclassification due to the similar structure of characters.

Automatic mapping of winter wheat planting structure and phenological phases using time-series sentinel data

The precise extraction of winter wheat planting structure holds significant importance for food security risk assessment, agricultural resource management, and governmental decision-making. This study proposed a method for extracting the winter wheat planting structure by taking into account the growth phenology of winter wheat. Utilizing the fitting effect index, the optimal Savitzky–Golay (S–G) filtering parameter combination was determined automatically to achieve automated filtering and reconstruction of NDVI time series data. The phenological phases of winter wheat growth was identified automatically using a threshold method, and subsequently, a model for extracting the winter wheat planting structure was constructed based on three key phenological stages, including seeding, heading, and harvesting, with the combination of hierarchical classification principles. A priori sample library was constructed using historical data on winter wheat distribution to verify the accuracy of the extracted results. The validation of fitting effect on different surfaces demonstrated that the optimal filtering parameters for S–G filtering could be obtained automatically by using the fitting effect index. The extracted winter wheat phenological phases showed good consistency with ground-based observational results and MOD12Q2 phenological products. Validation against statistical yearbook data and the proposed priori knowledge base exhibited high statistical accuracy and spatial precision, with an extracting accuracy of 94.92%, a spatial positioning accuracy of 93.26%, and a kappa coefficient of 0.9228. The results indicated that the proposed method for winter wheat planting structure extracting can identify winter wheat areas rapidly and significantly. Furthermore, this method does not require training samples or manual experience, and exhibits strong transferability.

NHi-SEGA: n-Hierarchy SEmantic Guided Attention for few-shot learning

Humans excel at learning and recognizing objects, swiftly adapting to new concepts with just a few samples. However, current studies in computer vision on few-shot learning have not yet achieved human performance in integrating prior knowledge during the learning process. Humans utilize a hierarchical structure of object categories based on past experiences to facilitate learning and classification. Therefore, we propose a method named n-Hierarchy SEmantic Guided Attention (nHi-SEGA) that acquires abstract superclasses. This allows the model to associate with and pay attention to different levels of objects utilizing semantics and visual features embedded in the class hierarchy (e.g., house finch-bird-animal, goldfish-fish-animal, rose-flower-plant), resembling human cognition. We constructed an nHi-Tree using WordNet and Glove tools and devised two methods to extract hierarchical semantic features, which were then fused with visual features to improve sample feature prototypes.

The association between potential predictors and death of patients during the COVID-19 pandemic in Shiraz: a hierarchical multiple regression analysis

IntroductionIdentifying clinical factors that increase the risk of mortality in COVID-19 patients is crucial. This enables targeted screening, optimizing treatment, and prevention of severe complications, ultimately reducing death rates. This study aimed to develop prediction models for the death of patients (i.e., survival or death) during the COVID-19 pandemic in Shiraz, exploring the main influencing factors.MethodWe conducted a retrospective cohort study using hospital-based records of 1030 individuals diagnosed with COVID-19, who were hospitalized for treatment between March 21, 2021, and March 21, 2022, in Shiraz, Iran. Variables related to the final outcome were selected based on criteria and univariate logistic regression. Hierarchical multiple logistic regression and classification and regression tree (CART) models were utilized to explore the relationships between potential influencing factors and the final outcome. Additionally, methods were employed to identify the high-risk population for increased mortality rates during COVID-19. Finally, accuracy was evaluated the performance of the models, with the area under the receiver operator characteristic curve(AUC), sensitivity, and specificity metrics.ResultsIn this study, 558 (54.2%) individuals infected with COVID-19 died. The final model showed that the type of medicine antiviral (OR: 11.10, p = 0.038) than reference (antiviral and corticosteroid), and discharge oxygen saturation(O2) (OR: 1.10, p < 0.001) had a positive association with the chance of survival, but other variables were not considered as predictive variables. Predictive models for the final outcome(death) achieved accuracies ranging from 81 to 87% for hierarchical multiple logistic regression and from 87 to 94% for the CART model. Therefore, the CART model performed better than the hirerical multiple logistic regression model.ConclusionThese findings firstly elucidate the incidence and associated factors of the outcome (death) among patients in Shiraz, Iran. Furthermore, we demonstrated that antiviral medication alone (without corticosteroids) and high O2 increase the survival chances of COVID patients.

Bio-Based Materials as a Sustainable Solution for the Remediation of Contaminated Marine Sediments: An LCA Case Study.

Contaminated sediments may induce long-term risks to humans and ecosystems due to the accumulation of priority and emerging inorganic and organic pollutants having toxic and bio-accumulation properties that could become a secondary pollution source. This study focused on the screening of novel bio-based materials to be used in the decontamination of marine sediments considering technical and environmental criteria. It aimed to compare the environmental impacts of cellulose-based adsorbents produced at lab scale by using different syntheses protocols that involved cellulose functionalization by oxidation and branching, followed by structuring of an aerogel-like material via Soxhlet extraction and freeze-drying or their combination. As model pollutants, we used 4-nitrobenzaldehyde, 4-nitrophenol, methylene blue, and two heavy metals, i.e., cadmium and chromium. When comparing the three materials obtained by only employing the Soxhlet extractor with different solvents (without freeze-dying), it was observed that the material obtained with methanol did not have a good structure and was rigid and more compact than the others. A Life Cycle Assessment (LCA) was conducted to evaluate the environmental performance of the novel materials. Apart from the hierarchical categorization of the materials based on their technical and environmental performance in eliminating organic pollutants and heavy metal ions, it was demonstrated that the cellulose-based material obtained via Soxhlet extraction with ethanol was a better choice, since it had lower environmental impacts and highest adsorption capacity for the model pollutants. LCA is a useful tool to optimize the sustainability of sorbent materials alongside lab-scale experiments and confirms that the right direction to produce new performant and sustainable adsorbent materials involves not only choosing wastes as starting materials, but also optimizing the consumption of electricity used for the production processes. The main results also highlight the need for precise data in LCA studies based on lab-scale processes and the potential for small-scale optimization to reduce the environmental impacts.

Disentangling disturbances with nested hierarchy classification of Mediterranean garrigue/maquis shrub community compositions through remote sensing and GIS

The paper presents an innovative nested hierarchical classification (NHC) method to disentangle landscape patterns between anthropogenic and natural drivers. This approach is motivated by the research need to differentiate between phytogeographic climate signals in vegetation communities based on these drivers. The method utilizes the non-conformity between spatial structures and granularity in remote sensing imagery with a twofold objective: (1) differentiate features within vegetation community compositions; and (2) aggregate these features with structural causal explanations. A sub-regional study in the southern Levant (Israel) is presented in which long-term ancient land use (LTA-LU) over 2300 years is juxtaposed with variable landscape patterns in modern semi-arid shrub communities. The modern land-cover is classified into three nestable classification levels using spaceborne, airborne, and unmanned aerial vehicle (UAV) imagery. NHC uses aggregation between the classification levels and the LTA-LU through a “bin sorting” technique based on the granularity and data type of the spatial structure. This technique enables the statistical feature extraction of vegetation community parts for association with their structural cause. The features identified indicate that areas of more intense LTA-LU are affected by plagioclimax attributed to industrial monoculture during the Hellenistic, Byzantine, and Ottoman periods. That form of land degradation, caused by long-term disturbance, results in substrates and vegetation compositions that cannot develop and recover to a climax state.

Trends in Patient Complexity, Practice Setting, and Surgeon Reimbursement for Urolithiasis: Do Rural Urologists Pay the Price?

ObjectivesTo evaluate the relationship between patient complexity, practice setting, and surgeon reimbursement for ureteroscopy and percutaneous nephrolithotomy (PCNL). MethodsThe “2021 Medicare Physician and Other Provider” file was used to collect Rural-Urban Commuting Area (RUCA) codes and hierarchical condition category (HCC) scores of urologists. Higher HCC score corresponds to higher medical complexity and higher RUCA code corresponds to a more rural area. Medicare reimbursement for ureteroscopy and PCNL were collected. Linear regressions were performed to predict change in reimbursement based on RUCA and HCC scores. ResultsIn 2021, 52,816 procedures under CPT code 52356 (ureteroscopy) and 1649 procedures under 50080 or 50081 (PCNL) were billed to Medicare. Mean reimbursement was $338.24 for ureteroscopy and $957.89 for PCNL. For ureteroscopy, higher HCC score predicted lower reimbursement (p<0.001). Higher HCC score predicted higher reimbursement for PCNL (p<0.01). Average RUCA for ureteroscopy was higher than for PCNL (p=0.02). Rural location predicted lower reimbursement for ureteroscopy (p<0.001), however there was no association for PCNL. ConclusionsFor ureteroscopy, higher-risk patients are associated with lower reimbursement while the opposite holds true for PCNL. Rural practices were associated with lower reimbursement for ureteroscopy, but there was no association between location and PCNL reimbursement. Together, these findings suggest practice pattern variation between ureteroscopy and PCNL and highlight gaps in reimbursement policy. Risk-adjusted reimbursement should be considered to incentivize urologists to treat complex patients within their practice scope.

Political Rituals and Ideological Competition: The Emergence of an “Islamic” May Day in Turkey

ABSTRACT This study focuses on the decoding of some of the many aspects of Islamism—labour relations in Turkey, based on the political rituals that historically operate as self-assertions and self-definitions of the working class. The epicentre of this article is May Day and seeks to analyse the process by which an organic part of the Islamist movement in the country, Hak-İş, dealt with it, through the effort for transformation of the symbolisms, and confrontation for the ideological meaning and social vision of this labour political ritual in Turkey. Through this framework, the study highlights three different stages in the political mobilization of Hak-İş regarding the 1st of May, within the broader framework of a process named ‘loss of proletarity’ and the Confederation’s vision about a classless society. The first stage is that of the absolute denial of the class content and hierarchies in society. The second stage is the process of the ‘reluctant adoption’ of May Day, combined with strategies to transform its material culture. Finally, the third stage is the attempt to ‘nationalise/Islamise’ this political ritual through the organization of separate May Day mobilizations in various cities in Anatolia.

The synergy of morphokinetic parameters and sHLA-G in cleavage embryo enhancing implantation rates.

Objective: This study aimed to assess the relationship between implantation and soluble HLA-G (sHLA-G) expression in cleavage embryo culture medium (ECM) in conjunction with early developmental kinetics determined by time-lapse imaging (TLI). Methods: A retrospective, single-center study was conducted involving 238 embryos from 165 patients who underwent Frozen-thawed embryo transfer (FET) using autologous oocytes, with either single or double embryo transfer. TLI morphokinetic parameters (t2, t3, t4, t5, t6, t7, t8, cc2, s2, cc3, s3) of embryos were analyzed, and sHLA-G levels in D3 ECM were measured using an enzyme-linked immunosorbent assay (ELISA). A hierarchical classification model was developed to categorize embryos into five groups (A, B, C, D, E). The correlation between sHLA-G levels, TLI classification of embryos, and embryo implantation was investigated to establish a non-invasive method for evaluating implantation potential. Multivariate logistic regression analysis was performed to identify potential influencing factors, and receiver operating characteristic (ROC) curves were used to evaluate the predictive value for implantation. Results: Multivariate unconditional logistic regression analysis indicated that TLI parameters t5 and s3 and sHLA-G level in ECM were independent risk factors affecting embryo implantation. The implantation rate decreased from TLI classification A to E. The proposed classification model effectively assessed the implantation potential of embryos. The implantation rate was higher in the sHLA-G positive group compared to the sHLA-G negative group (p < 0.001). The expression of sHLA-G in D3 ECM, combined with the TLI classification model, accurately evaluated the implantation potential of embryos with an AUC of 0.876. Conclusion: The integration of cleavage kinetics and embryonic sHLA-G expression could reliably identify embryos with a high likelihood of successful implantation.

Hierarchical multi-granularity classification based on bidirectional knowledge transfer

Hierarchical multi-granularity classification based on bidirectional knowledge transfer

A hierarchical urban power grid disaster response method based on automatic voice warning technology

This article proposes a hierarchical and graded urban power grid disaster response method based on automatic voice warning technology. This method combines automatic speech technology and hierarchical classification methods to achieve fast and accurate information transmission and response. This article first introduces the background and significance of this method, and then introduces the current research status in China. Next, this article elaborates in detail on the experimental dataset and preprocessing, CNN and RNN algorithm model design, experimental results and analysis, and the implementation of speech warning using TTS speech synthesis technology. Finally, this article summarizes the main work and contributions of this article, and points out future research directions.

Distortions in Judged Spatial Relations in Large Language Models

We present a benchmark for assessing the capability of large language models (LLMs) to discern intercardinal directions between geographic locations and apply it to three prominent LLMs: GPT-3.5, GPT-4, and Llama-2. This benchmark specifically evaluates whether LLMs exhibit a hierarchical spatial bias similar to humans, where judgments about individual locations’ spatial relationships are influenced by the perceived relationships of the larger groups that contain them. To investigate this, we formulated fourteen questions focusing on well-known U.S. cities. Seven questions were designed to challenge the LLMs with scenarios potentially influenced by the orientation of larger geographical units, such as states or countries, whereas the remaining seven targeted locations were less susceptible to such hierarchical categorization. Among the tested models, GPT-4 exhibited superior performance with 55 percent accuracy, followed by GPT-3.5 at 47 percent and Llama-2 at 45 percent. The models showed significantly reduced accuracy on tasks with suspected hierarchical bias. For example, GPT-4’s accuracy dropped to 33 percent on these tasks, compared to 86 percent on others. The models identified the nearest cardinal direction in most cases, however, reflecting their associative learning mechanism, thereby embodying human-like misconceptions. We discuss avenues for improving the spatial reasoning capabilities of LLMs.

Curriculum (dis)comfort: Silences and futurings for art education

In this article, two authors revisit qualitative research on art educators’ curriculum work in schools conducted a decade apart. By placing these studies into dialogue, they examine what is said and unsaid to reveal (dis)comforting trends in education. This narrative analysis reveals the permanence of dominant ideologies, the pull of the habitus, and the impact of teacher’s identity and positionality on curriculum and pedagogy. The authors conclude with a series of curricular (dis)comforts as invitations for envisioning and creating futurings that hold the enduring possibility for new paradigms in teaching and learning. Naming and critically reflecting on these (dis)comforts opens possibilities to move beyond the comforts of maintaining a dominant curriculum that upholds racial and class hierarchies.

Infproto-Powered Adaptive Classifier and Agnostic Feature Learning for Single Domain Generalization in Medical Images

AbstractDesigning a single domain generalization (DG) framework that generalizes from one source domain to arbitrary unseen domains is practical yet challenging in medical image segmentation, mainly due to the domain shift and limited source domain information. To tackle these issues, we reason that domain-adaptive classifier learning and domain-agnostic feature extraction are key components in single DG, and further propose an adaptive infinite prototypes (InfProto) scheme to facilitate the learning of the two components. InfProto harnesses high-order statistics and infinitely samples class-conditional instance-specific prototypes to form the classifier for discriminability enhancement. We then introduce probabilistic modeling and provide a theoretic upper bound to implicitly perform the infinite prototype sampling in the optimization of InfProto. Incorporating InfProto, we design a hierarchical domain-adaptive classifier to elasticize the model for varying domains. This classifier infinitely samples prototypes from the instance and mini-batch data distributions, forming the instance-level and mini-batch-level domain-adaptive classifiers, thereby generalizing to unseen domains. To extract domain-agnostic features, we assume each instance in the source domain is a micro source domain and then devise three complementary strategies, i.e., instance-level infinite prototype exchange, instance-batch infinite prototype interaction, and consistency regularization, to constrain outputs of the hierarchical domain-adaptive classifier. These three complementary strategies minimize distribution shifts among micro source domains, enabling the model to get rid of domain-specific characterizations and, in turn, concentrating on semantically discriminative features. Extensive comparison experiments demonstrate the superiority of our approach compared with state-of-the-art counterparts, and comprehensive ablation studies verify the effect of each proposed component. Notably, our method exhibits average improvements of 15.568% and 17.429% in dice on polyp and surgical instrument segmentation benchmarks.

Pratiloma Paranoia: Class Hierarchy, Conservatism, and Ethics in Classical Hindu Law

The Hindu law tradition grounds its social ethics on an ideological hierarchy of class or caste known as varṇa. The positive inculcation of this hierarchy is bolstered by a fear of social inversion, known as pratiloma, in every area of law and society. Through an examination of the concept of pratiloma, this article contends first that the central Hindu law principle of dharma, religious and legal duty, depends upon knowing and abiding by one’s place in society. From this Hindu articulation of social rank as the foundation of ethics, the article then draws a comparison between classical Anglo-American conservatism and Hindu law to suggest that conservative traditions in general base moral action on social station and the fear of breaking social rank. Ethics in Hindu law, therefore, are derived from an acceptance of social station within the varṇa hierarchy and the constant cultivation of local expectations of proper behavior according to social position.

Cash Cows and Dogs with Dentures: Prostheses for Animals in Nineteenth-Century British Culture

Abstract This article shows how the history of prostheticized animals is much deeper and richer than one might think. Investigating the interest in fitting animals with prostheses throughout the nineteenth century in Britain, this article explores the emergence of animal prostheses in bovine agriculture through to their use in late Victorian pet keeping. The key concern of this piece is the varied responses to prostheticized animals in print culture. As this article shows through an analysis of magazine and newspaper contributions, reactions to animal prostheses varied from admiration and amazement to annoyance and disgust. By analysing this coverage, the article reveals how representations of animal prostheses brought into collision a surprising variety of important social discourses, including those related to economic productivity, prosthesis access, food adulteration, professional recognition, social norms, class hierarchies, and gender relations. Animal prostheses proved a novel yet apt image that distilled a wide variety of societal concerns. Redirecting the critical momentum established by recent scholarship that unites disability studies and critical animal studies, this article uses nineteenth-century animal prostheses as a case study to demonstrate how the concepts of disability, ableism, classism, and misogyny have been extended by humans across species boundaries and how our approaches to nonhuman care are impacted by them. It therefore establishes a new field of inquiry within Victorian studies for scholarship on the interface between discourses of disability and nonhuman animals.

P-769 Congenital heart defects in children born after assisted reproductive technology: A large cohort study from the Committee of Nordic Assisted Reproductive Technology and Safety (CoNARTaS)

Abstract Study question Are children conceived after assisted reproductive technology (ART) more likely to be born with congenital heart defects (CHDs) than those born after spontaneous conception (SC)? Summary answer Major CHDs and severe CHDs are associated with ART, with a higher risk in both ART-conceived singletons and multiples compared to singletons from SC. What is known already Children born after ART have higher risk of birth defects compared to children born after SC. Among these defects, CHDs hold the highest prevalence, accounting for about 50% of all major birth defects and affecting approximately 1-2% of children in the general population. Several cohort studies and systematic reviews, among these a recent review including 41 studies and in total 25,856 ART children, shows increased risk of CHDs in children born after ART. However, conflicting findings have been reported for specific CHDs. Study design, size, duration This Nordic registry-based cohort study included 7,747,637 liveborn children, with 171,735 conceived after ART. All liveborn children (singletons and multiples) in Denmark (1994–2014), Finland (1990–2014), Norway (1984–2015), and Sweden (1987–2015) were included. National data from ART registries, medical birth registries, patient registries, cause of death registries, and population registries were cross-linked. Participants/materials, setting, methods The International Classification of Diseases (ICD) versions 8, 9, 10 were used for selection of CHDs. Major and severe CHDs were defined according to European Concerted Action on Congenital Anomalies and Twins (EUROCAT). Outcomes were major CHDs, severe CHDs, six hierarchical CHD lesion groups, and ten selected major CHDs. CHD diagnoses were considered prenatally or within the first year of life in Denmark, Finland, Sweden, and prenatally or at birth in Norway. Main results and the role of chance Major CHDs occurred among 1.84% (n = 3159) children born after ART and among 1.15% (n = 86,824) children born after SC, with adjusted odds ratio (AOR) 1.36; 95% CI 1.31–1.41. Risk was highest in multiples, regardless of conception method. Severe CHDs were detected in 0.35% (n = 594) children born after ART and in 0.26% (n = 19,375) children born after SC; AOR 1.30; 95% CI 1.20–1.42. When analysing the risk of CHD for the specific groups of CHDs by the hierarchical classification, children born after ART had higher risk of CHDs than children born after SC for five of the six lesion groups: conotruncal defects, non-conotruncal defects, ventricular septal defect, atrial septal defects, and other CHDs. ART was further associated with an elevated risk in four of ten selected major CHDs. Major CHDs occurred among 1.62% (n = 2059) singletons born after ART and among 1.11% (n = 18,539) singletons born after SC; AOR 1.19; 1.14–1.24. The prevalence of severe CHDs was 0.31% (n = 399) among singletons born after ART and 0.25% (n = 18,539) among singletons born after SC; AOR 1.20; 95% CI 1.09–1.33. In singletons born after ART, no difference was seen between ICSI and IVF or between frozen and fresh embryo transfer for major CHDs or severe CHDs. Limitations, reasons for caution A main limitation in this study is the lack of information on CHDs in miscarriages, pregnancy terminations, and stillbirths. Another limitation is that information on specific techniques used in assisted reproduction was not available from Finland. Lastly, we did not have information on causes of infertility. Wider implications of the findings CHDs, although rare, imply severe risks. Our study reveals an association between ART and major CHDs. While most CHD-affected children survive to adulthood, high morbidity and mortality rates persist in childhood and adulthood. The potential benefit of fetal echocardiography screening in ART pregnancies, beyond routine obstetric ultrasound, remains uncertain. Trial registration number ISRCTN 11780826

How the Ecology of Calcified Red Macroalgae is Investigated under a Chemical Approach? A Systematic Review and Bibliometric Study.

Characteristics such as calcareous morphology and life cycle are used to understand the ecology of calcified rhodophytes. However, there is limited information regarding their chemical profiles and biological activities. Therefore, a systematic review (PRISMA) was conducted to assess the influence of the chemistry of calcareous rhodophytes on ecological interactions in the marine environment. The keywords used were: ["Chemical AND [Ecology OR Interaction OR Response OR Defense OR Effect OR Cue OR Mediated OR Induce]"] AND ["Red Seaweed" OR "Red Macroalgae" OR Rhodophy?] AND [Calcified OR Calcareous] in Science Direct, Scielo, PUBMED, Springer, Web of Science, and Scopus. Only English articles within the proposed theme were considered. Due to the low number of articles, another search was conducted with three classes and 16 genera. Finally, 67 articles were considered valid. Their titles, abstracts, and keywords were analyzed using IRaMuTeQ through factorial, hierarchical and similarity classification. Most of the studies used macroalgae thallus to evaluate chemical mediation while few tested crude extracts. Some substances were noted as sesquiterpene (6-hydroxy-isololiolide), fatty acid (heptadeca5,8,11-triene) and dibromomethane. The articles were divided into four classes: Herbivory, Competition, Settlement/Metamorphosis, and Epiphytism. Crustose calcareous algae were associated with studies of Settlement/Metamorphosis, while calcified algae were linked to herbivory. Thus, the importance of chemistry in the ecology of these algae is evident,and additional studies are needed to identify the substances responsible for ecological interactions. This study collected essential information on calcified red algae, whose diversity appears to be highly vulnerable to the harmful impacts of ongoing climate change.