Many species of Zingiber have great ornamental potential, due to durability and exotic appearance of the inflorescences. Despite its large phenotypic variability, they are scarcely exploited or not ...yet exploited regarding the ornamental potential. To conserve potential ornamental genotypes, and subsidize breeding program, the Agronomic Institute (IAC) maintain a Germoplasm Collection of Ornamental Zingiberales with promising accessions, including Zingiber. The aim was the morphophenological characterization of ten Zingiber accessions and the indication for landscape purposes. A large variation was observed to the evaluated characters: Clump height (CH); Inflorescence visualization (IV); Clump area (CA); Clump density (CD); Leaf stem Firmness (LSF); Number of leaf stems per clump (NLSC); Number of leaves per stem (NLS); Leaf color (LCol); Evergreen tendency (ET); Flower stem growth (FSG); Flower stem length (FSLe); Flower stem diameter (FSD); Flower stem per clump (FSC); Color sensorial perception (CSP); Flower stem weight (FSW); Inflorescence length (IL); Inflorescence diameter (ID); Bracts aspects (BAs); and Flowering season (FSe). The accessions very suitable and with the best performance to use for landscape purpose were Z. spectabile, IAC Anchieta (Z. spectabile), Z. newmanii.
School-based nutrition programs are crucial to reducing food insecurity. The COVID-19 pandemic adversely impacted students' school meal participation. This study seeks to understand parent views of ...school meals during COVID-19 to inform efforts to improve participation in school meal programs. Photovoice methodology was used to explore parental perception of school meals in San Joaquin Valley, California, a region of predominately Latino farmworker communities. Parents in seven school districts photographed school meals for a one-week period during the pandemic and then participated in focus group discussions and small group interviews. Focus group discussions and small group interviews were transcribed, and data were analyzed using a team-based, theme-analysis approach. Three primary domains emerged: benefits of school meal distribution, meal quality and appeal, and perceived healthfulness. Parents perceived school meals as beneficial to addressing food insecurity. However, they noted that meals were unappealing, high in added sugar, and unhealthy, which led to discarded meals and decreased participation in the school meal program. The transition to grab-and-go style meals was an effective strategy for providing food to families during pandemic school closures, and school meals remain an important resource for families experiencing food insecurity. However, negative parental perceptions of the appeal and nutritional content of school meals may have decreased school meal participation and increased food waste that could persist beyond the pandemic.
Predictive models based on empirical similarity are instrumental in biology and data science, where the premise is to measure the likeness of one observation with others in the same dataset. ...Biological datasets often encompass data that can be categorized. When using empirical similarity-based predictive models, two strategies for handling categorical covariates exist. The first strategy retains categorical covariates in their original form, applying distance measures and allocating weights to each covariate. In contrast, the second strategy creates binary variables, representing each variable level independently, and computes similarity measures solely through the Euclidean distance. This study performs a sensitivity analysis of these two strategies using computational simulations, and applies the results to a biological context. We use a linear regression model as a reference point, and consider two methods for estimating the model parameters, alongside exponential and fractional inverse similarity functions. The sensitivity is evaluated by determining the coefficient of variation of the parameter estimators across the three models as a measure of relative variability. Our results suggest that the first strategy excels over the second one in effectively dealing with categorical variables, and offers greater parsimony due to the use of fewer parameters.
In this study, an internet of things (IoT)-enabled fuzzy intelligent system is introduced for the remote monitoring, diagnosis, and prescription of treatment for patients with COVID-19. The main ...objective of the present study is to develop an integrated tool that combines IoT and fuzzy logic to provide timely healthcare and diagnosis within a smart framework. This system tracks patients' health by utilizing an Arduino microcontroller, a small and affordable computer that reads data from various sensors, to gather data. Once collected, the data are processed, analyzed, and transmitted to a web page for remote access via an IoT-compatible Wi-Fi module. In cases of emergencies, such as abnormal blood pressure, cardiac issues, glucose levels, or temperature, immediate action can be taken to monitor the health of critical COVID-19 patients in isolation. The system employs fuzzy logic to recommend medical treatments for patients. Sudden changes in these medical conditions are remotely reported through a web page to healthcare providers, relatives, or friends. This intelligent system assists healthcare professionals in making informed decisions based on the patient's condition.
Electrochemical immunosensors (EI) have been widely investigated in the last several years. Among them, immunosensors based on low-dimensional materials (LDM) stand out, as they could provide a ...substantial gain in fabricating point-of-care devices, paving the way for fast, precise, and sensitive diagnosis of numerous severe illnesses. The high surface area available in LDMs makes it possible to immobilize a high density of bioreceptors, improving the sensitivity in biorecognition events between antibodies and antigens. If on the one hand, many works present promising results in using LDMs as a sensing material in EIs, on the other hand, very few of them discuss the fundamental interactions involved at the interfaces. Understanding the fundamental Chemistry and Physics of the interactions between the surface of LDMs and the bioreceptors, and how the operating conditions and biorecognition events affect those interactions, is vital when proposing new devices. Here, we present a review of recent works on EIs, focusing on devices that use LDMs (1D and 2D) as the sensing substrate. To do so, we highlight both experimental and theoretical aspects, bringing to light the fundamental aspects of the main interactions occurring at the interfaces and the operating mechanisms in which the detections are based.
Selecting the right actuator for a portable exoskeleton involves a comprehensive evaluation of various design characteristics. In this study, we introduce a methodology for actuator selection based ...on specific tasks, enhancing the practical adoption of portable exoskeletons. By examining a range of candidate actuators designed for lower limb exoskeletons, our objective is to engineer a system that is both lightweight and power-efficient. These candidate actuators, developed by integrating diverse motors and transmission systems, were rigorously tested against defined tasks. Our methodology, applied to an assistive exoskeleton catered to the elderly, showed its potential in tailoring an efficient system with matching capabilities. The obtained results indicated that the ideal configuration achieved reductions in weight and power requirements by 35% and 80%, respectively. The present research delineates a strategic approach for actuator selection in portable exoskeletons, contributing to the evolution of high-performing assistive devices.
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Heavy metal pollutants are of great concern to environmental monitoring due to their potent toxicity. Electrochemical detection, one of the main techniques, is hindered by the mutual interferences of ...various heavy metal ions in practical use. In particular, the sensitivity of carbon electrodes to Cd
2+
ions (one of the most toxic heavy metals) is often overshadowed by some heavy metals (
e.g.
Pb
2+
and Cu
2+
). To mitigate interference, metallic particles/films (
e.g.
Hg, Au, Bi, and Sn) typically need to be embedded in the carbon electrodes. However, these additional metallic materials may face issues of secondary pollution and unsustainability. In this study, a metal-free and sustainable nanomaterial, namely cysteamine covalently functionalized graphene (GSH), was found to lead to a 6-fold boost in the Cd
2+
sensitivity of the screen-printed carbon electrode (SPCE), while the sensitivities to Pb
2+
and Cu
2+
were not influenced in simultaneous detection. The selective enhancement could be attributed to the grafted thiols on GSH sheets with good affinity to Cd
2+
ions based on Pearson's hard and soft acid and base principle. More intriguingly, the GSH-modified SPCE (GSH-SPCE) featured high reusability with extended cycling times (23 times), surpassing the state-of-art SPCEs modified by non-covalently functionalized graphene derivatives. Last, the GSH-SPCE was validated in tap water.
A metal-free thiol-modified graphene derivative introduces a reusable approach to alleviate mutual interference in electrochemical heavy metal detection.
Reggaeton lyrics accentuate differences based on stereotypes that are configured in society through symbolic violence, where perceptions, values and beliefs are naturalized from the collective ...imaginary imposed from a position of social roles. The article analyzes gender violence based on the symbolisms and meanings presented in reggaeton and incorporated into social coexistence. The results show that in the social sphere the attribution of stereotypes linked to power, recognition and acceptance are perpetuated, with emphasis on the binary distribution of being a woman and a man which is constantly legitimized in the interaction in their environments.
Las líricas del reggaetón acentúan diferencias basadas en estereotipos que se configuran en la sociedad a través de la violencia simbólica, donde las precepciones, valores y creencias se naturalizan a partir del imaginario colectivo impuesto desde una postura de roles sociales. El artículo analiza la violencia de género a partir de los simbolismos y significados presentados en el reggaetón, que son incorporados en la convivencia social. Los resultados muestran que en el ámbito social se perpetúan la atribución de estereotipos vinculados al poder, reconocimiento y aceptación; con énfasis en la distribución binaria del ser mujer y varón lo que se legitima constantemente en la interacción en sus entornos.
In the evolving landscape of psycholinguistic research, this study addresses the inherent complexities of data through advanced analytical methodologies, including permutation tests, bootstrap ...confidence intervals, and fractile or quantile regression. The methodology and philosophy of our approach deeply resonate with fractal and fractional concepts. Responding to the skewed distributions of data, which are observed in metrics such as reading times, time-to-response, and time-to-submit, our analysis highlights the nuanced interplay between time-to-response and variables like lists, conditions, and plausibility. A particular focus is placed on the implausible sentence response times, showcasing the precision of our chosen methods. The study underscores the profound influence of individual variability, advocating for meticulous analytical rigor in handling intricate and complex datasets. Drawing inspiration from fractal and fractional mathematics, our findings emphasize the broader potential of sophisticated mathematical tools in contemporary research, setting a benchmark for future investigations in psycholinguistics and related disciplines.
A fractile is a location on a probability density function with the associated surface being a proportion of such a density function. The present study introduces a novel methodological approach to ...modeling data within the continuous unit interval using fractile or quantile regression. This approach has a unique advantage as it allows for a direct interpretation of the response variable in relation to the explanatory variables. The new approach provides robustness against outliers and permits heteroscedasticity to be modeled, making it a tool for analyzing datasets with diverse characteristics. Importantly, our approach does not require assumptions about the distribution of the response variable, offering increased flexibility and applicability across a variety of scenarios. Furthermore, the approach addresses and mitigates criticisms and limitations inherent to existing methodologies, thereby giving an improved framework for data modeling in the unit interval. We validate the effectiveness of the introduced approach with two empirical applications, which highlight its practical utility and superior performance in real-world data settings.
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