Structural hydroxide groups in layered magnesium-aluminum double hydroxides were partially replaced by fluoride ions. Fluorinated and fluorine-free materials were used as hosts for two dyes, carminic acid and hydroxyl naphthol blue, resulting in a hybrid pigment color palette. Additionally, the pigments were protected with a magnesium hydroxide phase to prevent the color from fading over time. The color properties of the pigments were significantly influenced by the host of dye, since the presence of fluorine directly influences the acid-base properties of the layered double hydroxides. Finally, the pigments were successfully applied to prepare colored creams.

Cassava (Manihot esculenta Crantz), domesticated in the Amazonian region of South America, presents an important diversity in Ecuador, where it is a main staple food; however, only few Ecuadorian cassava accessions have been included in international molecular assessments. The purpose of this study was to apply suitable cassava mi-crosatellites to characterize the genetic variability of the Ecuadorian cassava collection composed mainly of local landraces from the Coast, Andes and Amazonia regions. The use of microsatellite markers allowed the determination of the genetic diversity of the collection. Seven selected SSR primers, permitted to identify homozygous and hetero-zygous materials within the cassava collection of 133 accessions. The loci presented an average genetic diversity value of 0.7 and an average PIC value of 0.67, which is con-sidered high. Low number of duplicates (8.8%) were identified in the Ecuadorian col-lection which is not fully duplicated at CIAT. Currently, a wide range of cassava diver-sity is still cultivated in multi-crop agro-ecosystem, mainly in the Coast and Amazo-nian regions. Especially in the Amazonian region, due to important cultural uses of cassava by local ethnic communities, more in depth studies in the region could unveil the genetic diversity present in situ today.

This study addresses the importance of conducting mass movement susceptibility mapping and hazard assessment using quantitative techniques, including machine learning, in the Northern Lima Commonwealth (NLC). Previous exploration of the topographic variables revealed high correlation and multicollinearity among some of them, which led to dimensionality reduction through principal component analysis (PCA). Six susceptibility models were generated using weights of evidence, logistic regression, multilayer perceptron, support vector machine, random forest, and naive Bayes methods to produce quantitative susceptibility maps and assess the hazard associated with two scenarios: the first being an El Niño phenomenon and the second being an earthquake exceeding 8.8Mw. The main findings indicate that machine learning models exhibit excellent predictive performance for the presence and absence of mass movement events, as all models surpassed an AUC value of >0.9, with the random forest model standing out. In terms of hazard levels, in the event of an El Niño phenomenon or an earthquake exceeding 8.8Mw, approximately 40% of the NLC area would be exposed to the highest hazard levels. It also highlights the importance of integrating methodologies to reduce model uncertainty, such as comprehensive analysis of variables supported by high quality data and organized workflows. The findings of this research are expected to serve as a supportive tool for land managers in formulating effective disaster prevention and risk reduction strategies.

Genetic parameters of overreach, term and acuteness in Peruvian Paso horses (PPH) have not been determined to date. It is important to estimate these parameters for application in PPH breeding, therefore, the aim of this study was to estimate the heritability, repeatability, and genetic correlations under field conditions of overreach, term, and acuteness of PPHs. The study included records of up to 137 stallions and mares. All measurements were recorded in MP4 video format with a resolution of 1920 x 1080 megapixels and at 60 frames per second. All traits were measured three times (once per stride), and each trait was analyzed. KINOVEA software version 0.9.5 was used to analyze the measurements. A multivariate repeated measures animal model with sex effect was used to estimate the variance components for each trait using WOMBAT software. The results showed heritability of 0.411, 0.476 and 0.405, for the traits of overreach, term, and acuteness, respectively. Repeatability was high in all traits (> 0.78). Genetic additive correlations ranged from -0.30 to 0.49. It can be concluded that overreach and term have high heritability values, which allows these traits to respond better in a selection process, unlike acuteness which has a moderate heritability value.

This work introduces a novel numerical method designed to address three-dimensional unsteady free surface flows incorporating integral viscoelastic constitutive equations, specifically, the K-BKZ-PSM (Kaye–Bernstein, Kearsley, Zapas - Papanastasiou, Scriven, Macosko) model. The new proposed methodology employs a second-order finite difference approach along with the deformation fields method to solve the integral constitutive equation, and the marker-particle method (known as \emph{Marker-and-Cell}) to accurately capture the evolution of the fluid's free surface. The newly developed numerical method has proven its effectiveness in handling complex fluid flow scenarios, including confined flows and extrudate swell simulations of Boger fluids. Furthermore, a new semi-analytical solution for velocity and stress fields is derived, considering fully developed flows of a K-BKZ-PSM fluid in a pipe.

Considering the various problems caused by infiltration in civil construction, this study aimed to identify the most appropriate waterproofing methods for different types of surfaces. A study was conducted on the mechanisms of water infiltration on surfaces and the waterproofing methods available on the market, focusing on asphalt blankets, in addition to a literature review highlighting state-of-the-art on this topic. A case study was also conducted in residence in Nova Lima, Brazil, analyzing different waterproofing techniques, including their characteristics and stages. Among the conclusions, it is highlighted that the implementation of adequate project, installation, inspection, and maintenance techniques can significantly reduce the waterproofing failure rate and repair costs and that the excellent choice of materials, along with the skill of the labor force in the application, is fundamental to guarantee the adequate performance of these materials in buildings.

Antisepsis of the hands of medical personnel is one of the most important steps in the process of patient care, since direct contact can cause the cross-transfer of potentially pathogenic microorganisms at surgical sites.This study aimed to analyze the prevalence of microorganisms on the hands of 131 surgeons in a university hospital before the surgical procedure. Swabs were collected from each clinician's hands before and after handwashing. The samples were placed in a transport medium and immediately delivered to a private Clinical Analysis Laboratory from São Luis-Maranhão. The microorganisms were identified by ionization source mass spectrometry and matrix-assisted laser desorption (MALDI-TOF), and antibiotic susceptibility tests (AST) were performed using the Vitek2 automated system. The results showed a high frequency (100%) of microorganisms before handwashing, but after surgical antisepsis, the rate dropped significantly (p<0.05) to 27.5%. Gram-positive cocci and rods were the most common microbes, followed by gram-negative bacilli species. The effectiveness of hand antisepsis was 72.5%. The ideal would be 100% efficacy, that is, 0% microorganisms in all surgeons. It is logical that the presence of any organism, no matter how infrequent it may be, will always represent a great risk of postoperative infection for any patient.

This research aims to introduce a model, SectraBank, designed to mitigate cyber fraud attacks, specifically targeting SIM Swapping and Fake App schemes in mobile banking users. A quantitative methodology with an experimental design was meticulously employed to examine the rise of cyber fraud within mobile banking. The study involved a sample of 100 professionals with advanced knowledge in Information and Communication Technologies (ICTs). Surveys were utilized to measure awareness of threats and perceptions of security before and after the implementation of the proposed model. SectraBank leverages unique user identifiers such as fingerprint authentication and the International Mobile Equipment Identity (IMEI) number, in addition to geolocation and One Time Passwords (OTP) for validating electronic transactions in mobile banking. The effectiveness of the proposed model was notable, achieving a 96.1% efficacy rate in mitigating Fake App attacks and a 90.2% success rate in countering SIM Swapping attacks. These findings underscore the potential of SectraBank as a robust solution to enhance cybersecurity measures in the realm of mobile banking.

This study aimed to conduct a cross-cultural adaptation of the development of the cat-owner/dog-owner relationship scales. The method encompassed several stages: conceptual, item, semantic, operational, measurement, and functional equivalence. Processes included translations, synthesis of translations, back-translations, consensus on the English versions, external evaluation by the original authors, expert committee evaluation, and pre-tests. The study involved surveying 342 pet owners throughout Brazil using a 20-item questionnaire. Data analyses utilized confirmatory factor analysis, covariance-based modeling, and multigroup analysis. The techniques confirmed that the scale preserved its psychometric properties and showed no variance between dog and cat owners, indicating that the measured constructs are universal and unaffected by specific cultural nuances. Hence, the cross-cultural adaptation and validation of the scale facilitate applying and testing concepts and measures in diverse contexts, enriching our understanding of the dynamics between owners and their pets (dogs and cats) in different living situations. In addition, this research holds particular significance in Brazil, given the country’s vast cultural diversity.

Cassava (Manihot esculenta Crantz) is a crop of nutritional and economic importance worldwide, cultivated in more than 100 tropical and subtropical countries including Ecuador, traditionally cultivated in its three continental regions: the Amazon, the Coast and in the valleys of the Sierra. The purpose of this study was to characterize 195 accessions from INIAP's Ecuadorian cassava collection through 1) morphological characterization with qualitative and quantitative descriptors; and 2) ecogeographic characterization to know the climatic, geophysical and edaphic conditions in which cassava grows and which environments are frequent or marginal for its cultivation. For the morphological characterization, 27 morphological descriptors were used (18 qualitative and nine quantitative), and for the ecogeographic characterization, 55 variables (41 climatic, two geophysical and 12 edaphic). As a result, four morphological groups and three ecogeographic groups were identified. In the research, morphological variability was evidenced, mainly in descriptors related to the leaf, stems and inflorescences. In addition, it was possible to identify accessions that can adapt to extreme conditions of drought and poor soils, which could be used for improvement.

In this research work, we examined the decomposition mechanisms of N-substituted diacetamides. We focused on the substituent effect on the nitrogen lone pair electron delocalization, withdrawing and electron donor groups. DFT functionals used included: B1LYP, B3PW91, CAMB3LYP, LC-BLYP, and X3LYP. Dispersion functions gd3bj were applied when necessary to improve non-covalent interactions in the transition state. Pople with higher angular moments and def2-TZVP basis sets were also applied and were crucial for obtaining consistent thermodynamic parameters. The proposed mechanism involves a six-membered transition state with the extraction of an α hydrogen. Several conformers of N-diacetamides were used to account for the decrease in entropy in the transition state in the rate-determining state. All calculations, including Natural Bond Orbital (NBO) analyses, were performed using the Gaussian16 computational package and its GaussView 6.0 visualizer, along with VMD and GNUPLOT software. The isosurfaces and IBSI indexes were calculated using MultiWFN and IGMPlot, respectively.

The notion that ‘Stock-G’ humpback whales off western South America are primarily deep oceanic migrators is poorly supported. We documented 66 land-based sightings from Pucusana, Lima Sur: 18 incidental (2002–2023) and 48 dedicated sightings during the northbound winter migration (June-July 2023), covering respectively 38 and 105 individual whales, despite a mean daily observer effort of only 58.7 min. Estimated distances from shore ranged 20–7,000 m (mean= 2,729 m) at depths 25–99 m, while 50% migrated at < 2,500 m (median) from shore. Several groups skirted the coastline from headland to headland. Of five southbound migrating groups, four included an adult with neonate; no northbound groups enclosed a neonate. Two cases of single juvenile/subadult whales moving multidirectionally and closely associated with large seabird feeding frenzies, were considered foraging also. In 2023, 94% of all northbound nearshore sightings occurred from 14 June-11 July, signalling the maximum threat period for interactions with coastal fisheries off metropolitan Lima. If migration is temporally and spatially predictable, effective mitigating measures should be feasible.

Coronavirus disease 2019 (COVID-19) fatal outcomes have been associated with multiple cardiovascular risk factors. In new epidemic areas, such as Latin America, there is a lack of studies about this. Here, we evaluated those factors in a retrospective cohort of patients in a national reference hospital of Lima, Peru. Design. A retrospective cohort observational study was done. For this study, information was obtained from clinical records of the hospital for the cases that were laboratory-diagnosed and related, during March 6^th and April 30^th, 2020. rRT-PCR was used for the detection of the RNA of SARS-CoV-2 following the protocol Charité, Berlin, Germany, from nasopharyngeal swabs at the National Institute of Health. Calculation of the odds ratio (OR) with the respective 95% confidence interval (95% CI) was done, also logistic regression for adjusted OR (multivariate) was done. Values of p < 0.05 were considered significant for all analyses. Results. One hundred six hospitalized patients were evaluated. The mean age of patients was 61.58 years (SD 16.81). Cardiovascular risk factors among them were hypertension (46.2%), diabetes (28.3%), and obesity (28.3%), among others. Fifty-six patients died (52.8%). Mortality associated factors at the multivariate analysis were arterial hypertension (OR=1.343, 95% 1.089-1.667), myocardial injury (OR=1.303, 95% 1.031-1.642), and mechanical ventilation (OR 1.262, 95% 1.034-1.665), as associated factors. Conclusion. As observed in other regions of the world, cardiovascular risk factors represent a significant and independent threat to be considered in patients with COVID-19. Further studies and interventions in Peru and Latin America are expected.

This paper presents a novel approach for the recognition of multiple sclerosis (MS) using wavelet entropy and a Particle Swarm Optimization (PSO)-based neural network. MS is a complex neurological disorder with diverse clinical manifestations, often challenging to diagnose accurately. In this study, we leverage wavelet entropy (WE) as a feature extraction method to capture intricate patterns within brain imaging data. These extracted features are then employed to train a neural network model optimized through PSO to enhance classification accuracy. Experimental results on a dataset comprising MS and healthy control subjects demonstrate the effectiveness of our proposed approach, viz., WE-PSONN, which achieves a sensitivity of 91.95±1.15, a specificity of 92.36±0.88, a precision of 92.28±0.88, an accuracy of 92.16±0.90. The combination of advanced signal processing techniques and machine learning optimization holds promise for improving the early diagnosis and management of MS, offering potential benefits to both patients and healthcare providers.

Leishmaniasis is a group of neglected infectious, non-contagious diseases caused by parasites of the Leishmania genus which affects millions of people worldwide. The drugs available for its treatment, especially in cases of visceral leishmaniasis, are old, outdated, have serious side effects and in many cases are ineffective due to the emergence of resistant strains. In this context, the search for new candidates for more effective and safer drugs which can become new therapeutic alternatives is a constant need. In this work, 10 new chalcones were synthesized in high yields through Claisen-Schmidt condensation and were evaluated in vitro against Leishmania infantum promastigotes and amastigotes. CP04 and CP06 compounds were the most promising, respectively showing IC50 values = 13.64 ± 0.25 and 11.19 ± 0.22 against promastigotes, and IC50 = 18.92 ± 0.05 and 22.42 ± 0.05 against amastigotes. The compounds did not show cytotoxicity in erythrocytes, showing selectivity indexes greater than 74 and 90. Molecular docking studies carried out under the sterol 14-alpha demethylase (CYP-51) (PDB: 3L4D) and trypanothione reductase (PDB: 5EBK) enzymes from L. infantum evidenced the great affinity of the CP04 molecule for these targets, showing Moldock score values of -94.0758 and -50.5692 KJ/mol-1, with these values being much lower than the co-crystallized ligand energies. Molecular dynamics simulations demonstrated great stability of CP04 in its targets, thus indicating a real interaction possibility. Our findings confirm the potential that chalcones have as candidates for anti-leishmania drugs, and suggest that the CP04 compound may become a promising drug candidate to design and develop novel chalcone-based analogs for leishmaniosis therapy.

Bats play important ecosystem roles. Anthropogenic activities cause the decrease and loss of biological diversity and, consequently, the loss of these ecosystem services. One way of measuring local habitat conditions and relating the landscape to biodiversity. Our objective is to investigate how the bat community is influenced by this change in the landscape. Collections were carried out at five points and 76 individuals of 12 species are sampled. Although the points present a high variation in relation to land use, we did not observe any correlation between species richness and guilds with land use. However, the difference in the composition of the guilds is related to the variation in land use, in which 74% of the variation in the abundance of guilds is related to the different patterns of land use. At SENAI, even though it was the place with the greatest anthropic impact, it was the one with the greatest abundance of species, while the points Module two and Sítio Jaburu had the greatest abundance of guilds. This result corroborates the idea that ecosystem services are dependent on habitat maintenance, since the greater the heterogeneity the greater the difference in the composition of the trophic guilds.

We present a general process for the halting problem, valid regardless of the time and space computational complexity of the decision problem. It can be interpreted as the maximization of entropy for the utility function of a given Shannon-Kolmogorov-Bernoulli process. Applications to non-polynomials problems are given. The new interpretation of information rate proposed in this work is a method that models the solution space boundaries of any decision problem (and non polynomial problems in general) as a communication channel by means of Information Theory. We described a sort method that order objects using the intrinsic information content distribution for the elements of a constrained solution space - modeled as messages transmitted through any communication systems. The limits of the search space are defined by the Kolmogorov-Chaitin complexity of the sequences encoded as Shannon-Bernoulli strings. We conclude with a discussion about the implications for general decision problems in Turing machines.

Weather-related risks in aviation are one of the main causes of accidents worldwide. This article aims to analyze aviation risks resulting from adverse weather conditions, using the case study of the Pucallpa accident that occurred on August 23, 2005, in eastern Peru. In this analysis, we have determined the relationship between meteorological information and aviation risks, presented a matrix of risks associated with the most predominant meteorological variables at Pucallpa airport, and drawn up three maps of meteorological risks indicating the month of the year in which their incidence is most relevant. Finally, we conclude that it is essential to emphasize the importance of considering the prevailing weather conditions at Pucallpa airport to help mitigate the potential risks that may occur during different phases of the flight.

The emergence of new plant diseases is an increasingly important concern. Climate change is likely to be among the factors causing most of the emerging diseases endangering forest and tree heritage around the world. Such diseases may be caused by latent pathogens or microorganisms cryptically associated with plants. The shift from a non-pathogenic to a pathogenic stage may depend on physiological alterations of the host, environmental changes and/or stress factors. In some woods of the Salento peninsula (Apulia region, Italy) sudden declines of holm oak plants (Quercus ilex L.) have been observed since 2016. The morphological and molecular characterization of representative fungal isolates associated with canker and necrosis in declining plants indicated that these isolates belong to the Botryosphaeriaceae family, and the most frequent species were Diplodia corticola and Diplodia quercivora, followed by Neofusicoccum vitifusiforme. In artificially inoculated young holm oak plants, both D. corticola and D. quercivora species produced intense and severe subcortical and leaf margin necrosis. N. vitifusiforme, although less aggressive, induced the same symptoms. Our research in addition to confirm the involvement of D. corticola in olm oak decline, represents the first report of D. quercivora as a new pathogen on Quercus ilex in Italy. Furthermore, to the best of our knowledge, we also found N. vitifusiforme as a new pathogen on Q. ilex for the first time.

In South Sudan, three case-control studies found a positive association between Nodding Syn-drome (NS) and a Mansonella perstans infection. To investigate whether M. perstans could play the primary and sole causal role in the development of NS, we examined blood films of persons with and without epilepsy in rural villages affected by NS in Mahenge, Tanzania, to identify M. perstans infections. Study participants were also examined for onchocercal nodules and skin lesions and tested for the presence of anti-O. volvulus antibodies (Ov16 ELISA). One hundred and thirteen epilepsy cases and 132 controls were enrolled, with a mean age of 28.3 years (interquartile range 22 – 34 years); 125 (51%) were females. Of the persons with epilepsy, 43 (38.1%) met the probable NS criteria. M. perstans infection was absent in all participants, but Ov16 prevalence and oncho-cerciasis-associated skin manifestations were positively associated with epilepsy, including probable NS. Onchocercal nodules in the mothers of cases were also positively associated with epilepsy. In conclusion, in contrast with O. volvulus, M. perstans is likely not endemic in Mahenge and therefore cannot be a co-factor for NS in the area. Hence, this filaria cannot be the primary and sole causal factor in the development of NS.

Mangroves are critical ecosystems providing essential environmental services, making their conservation crucial. High-resolution remote sensing, particularly through unmanned aerial vehicles (UAVs), offers unprecedented resolution in ecosystem mapping. This study utilizes a multispectral sensor mounted on a UAV to map mangroves at Cardoso Island on Brazil’s southeastern coast and Suape on the northeastern coast. Using Geographic Object-Based Image Analysis (GEOBIA) for segmentation and classification, we employed machine learning algorithms to classify the primary mangrove species: Laguncularia racemosa, Rhizophora mangle, and Avicennia schaueriana. Two flight altitudes were used: a higher altitude for broad-scale mapping and a lower altitude for classification training, supplemented by field measurements for calibration. This method enabled the precise delineation of tree crowns and provided insights into species distribution and zonation, influenced by the distinct estuarine conditions of tides and sediment input. The high-resolution mapping achieved in this study enhances detailed analyses of mangroves, improving ecological models and biophysical estimations such as biomass and blue carbon stocks. This work highlights the potential of UAV-based multispectral imaging in advancing mangrove research and conservation efforts.

Investments in public transit infrastructure in Latin America and the Caribbean often aim to reduce spatial and social inequalities by improving accessibility to jobs and other opportunities for vulnerable populations. The Metropolitano, Lima’s Bus Rapid Transit (BRT) project had as one of its central goals to connect low-income populations living in the peripheries to jobs in the city center. We examine the contribution of Lima’s BRT system to accessibility to employment in the city, particularly for low-income public transit users. Building on secondary datasets of employment, household socio-demographics and Origin-Destination surveys before and after the BRT began operations, we assess its effects on potential accessibility to employment, comparing impacts amongst lower versus higher income populations. Findings suggest that the BRT line reduced travel times to reach jobs, in comparison with traditional public transport in the city, amongst populations living within walking distance of the system. However, we also find that the coverage of the BRT declines in areas with high concentrations of poor and extreme poor populations, limiting the equitability of the accessibility improvements. We analyze the distributional effects of BRT infrastructure and services, discussing policy avenues that can improve the prospects for BRT system investments to include the poor in their mobility benefits.

Background: This study aimed to analyze the psychometric properties of the Brief-COPE Inventory and to determine its concurrent validity by exploring its association with perceived stress in Peruvian nurses. Methods: Psychometric study carried out in 434 Peruvian nurses to determine the psychometric properties by confirmatory factor analysis. Three stepwise variable selection regression models were implemented. Results: The three-factor of the Brief-COPE Inventory reported adequate fit (root mean square error of approximation = 0.052, standardized root mean square = 0.068, and both the comparative fit index and the Tucker–Lewis index = 0.95). Likewise, we found that its factors are correlated (p &lt; 0.001) and that there is adequate reliability (ω= 0.90). Moreover, nurses showed a medium level of perceived stress and the following associated factors: sex, having received stress management training, fear of COVID-19, and problem-focused coping strategies (p &lt; 0.05). Conclusion: This study proved that Brief-COPE Inventory is a valid tool for measuring coping strategies in Peruvian nurses because it has a good fit to the model, excellent reliability, and concurrent validity with perceived stress. However, further studies are required to evaluate its validity according to the nursing professionals’ perceived areas of performance.

Student-centered active learning (SCAL) shifts the focus from the teacher to the student. Implementing SCAL requires the development of new forms of assessment beyond memorization and comprehension. This paper aims to demonstrate the effectiveness of SCAL by analyzing student performance at different cognitive levels. In flipped classrooms, students completed tasks with varying cognitive demands. The tests measured knowledge (level 1), comprehension (level 2), application (level 3) and analysis (level 4). This approach to assessment assesses not only the acquisition of content, but also skills that are critical to a health science student's future career. The results of expository learning were compared with student-centered collaborative learning in the Anatomy and Embryology I course over three academic years. Student opinions were collected through 326 anonymous Wooclap® surveys. No differences in performance were found for low cognitive level questions. However, SCAL significantly improved performance on higher cognitive level questions that required problem solving and application of knowledge. Despite the benefits of SCAL, it requires more effort from both teachers and students. The current assessment system, which measures the quality of teaching, penalizes this effort and requires teachers to believe strongly in the effectiveness of SCAL.

A one-dimensional numerical overland flow model based on the cascade plane theory was de-veloped to estimate rainfall-induced runoff and soil erosion on converging and diverging plane surfaces. The model includes three components: (i) soil infiltration using Horton’s infiltration equation, (ii) overland flow using the kinematic wave approximation of the one-dimensional Saint-Venant shallow water equations for a cascade of planes, and (iii) soil erosion based on the sediment transport continuity equation. The model’s performance was evaluated by comparing numerical results with laboratory data from experiments using a rainfall simulator and a soil flume. Four independent experiments were conducted on converging and diverging surfaces under var-ying slope and rainfall conditions. Overall, the numerically simulated hydrographs and sediment graphs closely matched the laboratory results, showing the efficiency of the model for the tested controlled laboratory conditions. The model was then used to numerically explore the impact of different plane soil surface geometries on runoff and soil loss. Seven geometries were studied: one rectangular, three diverging, and three converging. Constant soil surface area, rainfall intensity, and slope gradient were maintained in all simulations. Results showed that increasing convergence angles led to higher peak and total soil loss, while decreasing divergence angles reduced them.

This research presents a novel algorithmic implementation to improve the analysis of visually controlled interception and accompanying motor action through the computational application of harmonic ratios and stimulation invariants. Unlike traditional models that focus mainly on psychological aspects, our approach integrates the relevant constructs into a practical mathematical framework. This allows for dynamic prediction of interception points with improved accuracy and real-time perception-action capabilities, essential for applications in neurorehabilitation and virtual reality. Our methodology uses stimulation invariants as key parameters within a mathematical model to quantitatively predict and improve interception outcomes. The results demonstrate the superior performance of our algorithms over conventional methods, confirming their potential for ad-vancing robotic vision systems and adaptive virtual environments. By translating complex theo-ries of visual perception into algorithmic solutions, this study provides innovative ways to im-prove motion perception and interactive systems. This study aims to articulate the complex inter-play of geometry, perception, and technology in understanding and utilizing cross-ratios at infin-ity, emphasizing their practical applications in virtual and augmented reality settings.

The number of physical devices connected to the Internet such as vehicles, household appliances and other "things" has been steadily increasing in recent years, thus forming the basis for the Internet of Things (IoT). IoT ecosystem extends beyond country borders and application domains, combining thousands of versatile devices that differ in terms of their structures, capabilities, and available resources. It is therefore not surprising that the landscape of wireless communication technologies and the degree of IoT devices available today is excessively broad and diverse. Interference between networks leads to frame collisions and consequent packet loss. Frame collisions occur when two or more packets overlap in time and frequency and use the same Long Range (LoRa) parameters, i.e. the same Spreading Factor (SF), Bandwidth (BW) and Carrier Frequency (CF). When most devices use the same configuration, collision probability is higher. The probability of frame collisions is also affected by traffic characteristics, particularly the periodicity of the transmission and the payload size. Larger payload sizes and more frequent transmissions accumulate with higher Time on Air (ToA) and channel occupancy [1]. Transmission powers and the location of the gateways also influences this situation. The aim of this experimental work is to verify the effects of collisions and interference in Long Range Wide Area Network (LoRaWAN), regarding the periodicity of transmissions and the payload sizes in a context of an inter-SF interference scenario. And to validate measuring tools like Received Signal Strength Indicator (RSSI), ToA and Packet Delivery Ratio (PDR) in the reliability of a network. We validate our conclusions by analyzing ToA according with RSSI and different data sizes, the RSSI for different SFs and different data sizes and finally the packet loss through the PDR for a better understanding of the reliability of packet transmission in our link. In the end we conclude that in an environment with inter-SF interference, the choice of SFs and packet lengths impacts ToA. When dealing with multiple devices near gateways, different SFs and varying packet lengths can lead to increased latency and longer ToA. Consequently, the risk of packet loss becomes more significant, especially if the signal-to-interference ratio (SIR) is low. This limitation will therefore affect scalability, particularly in Non-Line-Of-Sight (NLOS) scenarios.

In this article, it is demonstrated that if the zeta function does not have a sequence of zeros whose real part converges to 1, then it cannot have any zeros in the critical strip, showing that the Riemann Hypothesis is false.

Distributed Systems architectures are becoming the standard computational model for processing and transportation of information, especially for Cloud Computing environments. The increase in demand for application processing and data management from enterprise and end-user workloads continues to move from a single-node client-server architecture to a distributed multitier design where data processing and transmission are segregated. Software development must considerer the orchestration required to provision its core components in order to deploy the services efficiently in many independent, loosely coupled - physically and virtually interconnected - data centers spread geographically, across the globe. This network routing challenge can be modeled as a variation of the Travelling Salesman Problem (TSP). This paper proposes a new optimization algorithm for optimum route selection using Algorithmic Information Theory. The Kelly criterion for a Shannon-Bernoulli process is used to generate a reliable quantitative algorithm to find a near optimal solution tour. The algorithm is then verified by comparing the results with heuristic solutions in 3 test cases. A statistical analysis is designed to measure the significance of the results between the algorithms and the entropy function can be derived from the distribution. The tested results shown an improvement in the solution quality by producing routes with smaller length and time requirements. The quality of the results proves the flexibility of the proposed algorithm for problems with different complexities without relying in nature-inspired models such as Genetic Algorithms and Simulated Annealing. This algorithm can be used by orchestration applications to deploy services across large cluster of nodes by making better decision in the route design.

In recent years, news media has been greatly disrupted by the potential of technologically driven approaches in the creation, production, and distribution of news products and services. Artificial intelligence (AI) has emerged from the realm of science fiction and has become a very real tool that can aid society in addressing many issues, including the challenges faced by the news industry. The ubiquity of computing has become apparent and has demonstrated the different approaches that can be achieved using AI. We analyzed the news industry’s AI adoption based on the seven subfields of AI: (i) machine learning; (ii) computer vision (CV); (iii) speech recognition; (iv) natural language processing (NLP); (v) planning, scheduling, and optimization; (vi) expert systems; and (vii) robotics. Our findings suggest that three subfields are being developed more in the news media: machine learning, computer vision, as well as planning, scheduling, and optimization. Other areas have not been fully deployed in the journalistic field. Most AI news projects rely on funds from tech companies such as Google. This limits AI’s potential to a small number of players in the news industry. We make conclusions by providing examples of how these subfields are being developed in journalism and present an agenda for future research.

This study examines geothermal energy extraction potential from the basement rock within the Denver-Julesburg (DJ) Basin, focusing on flow performance and heat extraction efficiency of different geothermal well configurations. It specifically compares U-Shaped, V-Shaped, Inclined V-Shaped, and Pipe-in-Pipe configurations against Enhanced Geothermal Systems (EGS) setups. Through numerical modeling, we evaluated the thermal behavior of these systems under various operational scenarios and fracture conditions. Results suggest that while closed-loop systems offer moderate temperature increases, EGS configurations show substantial potential for high-temperature extraction. This underscores the importance of evaluating well configurations in complex geological settings like the DJ Basin. The insights from this study aid in strategic geothermal energy planning and development, marking significant advancements in geothermal technology and setting a foundation for future explorations and optimizations.

Individuals with Parkinson’s disease (PD) and freezing of gait (FOG) have a loss of presynaptic inhibition (PSI) during anticipatory postural adjustments (APAs) for step initiation. The mesencephalic locomotor region (MRL) has connections to the reticulospinal tract that mediates inhibitory interneurons responsible for modulating PSI and APAs. Here, we hypothesized that MLR activity during step initiation would explain the loss of PSI during APAs for step initiation in freezers. Thirty-four freezers were assessed in the ON-medication state. During a functional magnetic resonance imaging protocol of simulated APA task, we assessed beta of blood oxygenation level-dependent signal change of areas known to initiate and pace gait (e.g., MLR), in addition to PSI of the soleus muscle during APA for step initiation, and clinical (e.g., disease duration) and behavioral (e.g., FOG severity and APAs amplitude for step initiation) variables. A linear multiple regression model showed that MLR activity (R2=0.32, P=0.0006) and APA amplitude (R2=0.13, P=0.0097) explained together 45% of the loss of PSI during step initiation in freezers. Decreased MLR activity during a simulated APA task is related to a higher loss of PSI during APA for step initiation. Deficits in central and spinal inhibitions during APA may be related to FOG pathophysiology.

In this study, we investigated the relationship between age and microbial diversity in cattle, revealing significant changes in both protist diversity and fungal microbiota composition with age. Using fecal samples from 21 Simmental cattle, we analyzed microbial communities through 18S rRNA gene sequencing. Results indicated significant differences in alpha protist diversity among the three age groups, while fungal composition varied notably with age and was linked to hematological parameters. Despite the stability of fungal alpha diversity, compositional changes suggest the gut as a stable niche for microbial colonization influenced by diet, clinical parameters, and microbial interactions. All cattle were maintained on a consistent diet, tailored to meet specific nutritional needs of each age group. These findings emphasize the importance of understanding age-related microbial dynamics to enhance livestock management and animal health, contributing to broader ecological and biomedical research. However, the study was limited by the lack of comprehensive metabolic analyses correlating microbiota changes with specific age-related variations, indicating a need for further research in this area.

The present study concerns the application of electrochemical chloride extraction to concrete designed with partial replacements of Ordinary Portland Cement (OPC) from different binders, namely ground granulated blast furnace (GGBS), pulverized fly ash (PFA) and Silica Fume (SF), to attend the need of corrosion repair for modern constructions. Electrochemical Chloride Extraction (ECE) was applied to the specimens at a current density of 2A/m2 for 4 weeks. The chloride types and binding mechanisms appeared to have important implications on the different performance of treatment perceived for all binders. Considering total chlorides, removal was more significant for OPC and GGBS when compared to others. The main difference noted was that for GGBS the removal of bound chlorides was more significant due to a possible release of adsorbed chlorides from the silicate hydrates, present at higher intensities in this case. However, even though GGBS had greater rates of bound chloride removal it had still the same percentage as OPC for total efficiency due to the fact that in OPC the removal of free chlorides is more expressive.

Technology has transformed aeroponics, providing numerous advantages over traditional soil-based agriculture. However, there remains a need for comprehensive information on the types of technology employed in aeroponics and their impact on the growing process. This paper presents a literature review of 47 studies published between 2012 and 2023 in peer-reviewed journals and conference proceedings. As a result, it identifies the status and tendencies in the usage of technology in aeroponics as well as the main opportunities and challenges. Furthermore, this paper introduces the Technology Adoption and Integration in Sustainable Agriculture (TAISA) model. TAISA is a model that identifies the degree of technology integration in any sustainable agriculture system to determine how technology affects production and quality. Overall, the systematic review suggests that technology can play a vital role in improving the efficiency and sustainability of aeroponic farming. However, careful consideration must be given to the costs and technical requirements associated with using these technologies. Moreover, the TAISA model reveals that technology has primarily been employed in creating new processes that are not possible to implement without the help of technology. Therefore, we conclude that technology use has taken root in aeroponics and can be promoted to improve sustainable agriculture.

Background: Several environmental factors can strongly influence the accumulation of glucosinolates and plant pigments. This study aimed to evaluate the growth and bioactive compounds production of baby leaf kale on colored reflective growing benches with different wavelengths; Methods: In an greenhouse, five types of growing benches were assessed without and with bright reflective colored materials: control and reflective bright (white, yellow, red, and blue); Results: The growing bench with laminated white reflective material increased the photosynthetically active radiation the most. The use of growing benches with laminated reflective material increased the production of baby leaf kale, in which the white, yellow, red, and blue laminates increased the number of leaves by 35.5, 26, 25, and 20.8%, respectively, and the shoot fresh mass by 132, 112, 112, and 109%, respectively. Using the growing bench reflective bright blue laminate increased the bioactive compounds of baby leaf kale plants, increasing chlorophylls A, B, and total by 72, 67, and 70% respectively, and carotenoids by 55%; Conclusions: The use of reflective materials is an alternative method that provides plants with better conditions for assimilating photosynthetically active radiation, increasing the development, yield, and production of bioactive compounds that promote human health.

The spread of antimicrobial resistance (AMR) is a global challenge. Close and continuous surveillance for quick detection of AMR can be difficult, especially in remote places. This narrative review focuses on the contributions of pharmacovigilance (PV) as an auxiliary tool for identifying and monitoring the ineffectiveness of, resistance to, and inappropriate use of antibiotics (ABs). The terms “drug ineffective,” “therapeutic failure,” “drug resistance,” “pathogen resistance,” and “multidrug resistance” were found in PV databases and dictionnaires, and denote ineffectiveness. These terms cover a range of problems that should be better investigated because they are useful in warning about possible causes of AMR. “Medication errors,” especially those related to dose and indication, are highlighted in the literature, suggesting inappropriate use of ABs. Hence, the included studies show that the terms of interest related to AMR are not only present but frequent in PV surveillance programs. This review illustrates the feasibility of using PV as a complementary tool for antimicrobial stewardship activities, especially in scenarios where other resources are scarce.

Background: This study aimed to explore the association between the emotional impact of COVID-19 and emotional eating and the risk of alcohol use disorder among Peruvian health science students. Methods: We conducted a cross-sectional analytical study in which an online questionnaire was administered to 456 health science interns from four cities in Peru. We used the COVID-19 Emotional Impact Profile questionnaire, Mindful Eating Questionnaire, and Alcohol Use Disorders Identification Test. Spearman’s correlations were calculated and two multiple linear regression models were developed. Results: 68.4% of the participants were emotional eaters and 8.6% reported low-risk levels of alcohol use disorder. Based on the results of the first model, the overall emotional impact of COVID-19, being overweight or obese, depression and anxiety levels, and living with only one parent were factors associated with emotional eating. The results of the second model showed that the level of depression, living with just one parent, living alone, sex, and number of months as an intern were factors associated with the risk of alcohol use disorder. Conclusion: To reduce emotional eating and the risk of alcohol use disorder among interns, universities should implement interventions aimed at reducing the emotional impact of COVID-19 and provide nutritional counseling.

Glucose is a simple sugar molecule. The sugar molecule is chemically symbolised as C6H12O6. This means that the glucose molecule contains 6 carbon atoms (C), 12 hydrogen atoms (H) and 6 oxygen atoms (O). In human blood, the molecule glucose circulates as blood sugar. Normally after eating or drinking, our body breaks down the sugars in food and uses them to obtain energy for our cells. To do this, our pancreas produces insulin. Insulin "pulls" sugar from the blood and puts it into the cells for use. If someone has diabetes, their pancreas can't produce enough insulin. As a result, the level of glucose in the blood rises. This can lead one to many potential complications, including blindness, disease, nerve damage, amputation, stroke, heart attack and damage to blood vessels etc. In this study, a non-invasive method for monitoring blood glucose has been developed and is therefore easily usable. The device developed can measure the glucose level continuously and eliminates the disadvantages of the invasive system. Near-IR sensors (optical sensors) were used to estimate the concentration of glucose in the blood; these sensors have a wavelength of 940 nm. The sensor is placed on the tip of the finger and the output of the optical sensor is then connected to the microcontroller at the analogue input. Another sensor used, but only to provide more medical information was the heartbeat sensor. After processing and regression analysis, the glucose level is predicted, and data is sent via the Bluetooth network to a developed APP. The results of the implemented device were compared with available invasive methods. The study consists of a hardware platform inserted into an armband and then a small black parallelepiped-shaped box in which the optical sensors are inserted. The hardware consists of a microcontroller, a Near-IR optical sensor, a heartbeat sensor and a Bluetooth module.

Academic procrastination, more than merely postponing tasks, represents a significant failure in the self-regulation process of learning. Research on study skills highlights academic time management as one of the crucial elements of learning strategies and sustainable education. These abilities will help achieve the Sustainable Development Goals fourth goal. Therefore, this study aimed to understand how students plan time management in school activities and its influence on study procrastination, analyzing differences between genders and the hours students spend studying. The Time Management Planning Inventory, the Study Procrastination Questionnaire, and a personal and school data sheet were used. A sample of 506 students from elementary schools in northern Portugal was utilized. The results revealed that gender and study hours significantly influence how students plan time management for school tasks in the short and long term. This variable also showed a significant impact on the procrastination of study activities. The practical implications of this study are substantial, as they provide educators and researchers insights into the factors influencing academic procrastination and the role of time management planning and study hours. These insights can be applied to develop effective strategies to reduce academic procrastination and promote sustainable education.

Biosensors are analytical devices that utilize biological interactions to detect and quantify clinical biomarkers, contaminants, allergens, and microorganisms. They combine different disciplines including analytical chemistry, molecular biology, and electrical engineering. Biosensors operate by coupling a bioreceptor, such as nucleic acid or proteins, with a transducer that converts the biological interaction into an electrical signal. Electrochemical and optical transduction are commonly used approaches due to their high detection capability and compatibility with miniaturization. Biosensors provide both high specificity and sensitivity and can be integrated into low-cost microfluidic platforms for rapid and point-of-care applications. These attributes make these devices valuable tools in analytical chemistry, particularly for early diagnostic applications. However, conventional biosensors face challenges related to the immobilization of biorecognition elements on the transducer surface, leading to issues like lost of sensitivity and selectivity. To address these problems, the introduction of nanomaterials, particularly magnetic nanoparticles (MNPs) and magnetic beads (MBs), has been implemented. MNPs combine their magnetic properties with other interesting characteristics such as small size, high surface-to-volume ratio, and excellent biocompatibility. They can be tailored for specific applications and have been extensively used in various fields, including biosensing and clinical diagnosis. Furthermore, MNPs simplify sample preparation by isolating target analytes through magnetic separation, thus improving sensitivity, and reducing analysis time and interference phenomena. The synthesis and modification of MNPs play a crucial role in adjusting their properties for different applications. This review presents an overview of the synthesis and surface modifications of magnetic nanoparticles, their role in the development of biosensors and bioassays, and their applications across various scientific areas.

This paper is intended at highlighting the main mechanical parameters controlling the behavior of the so-called "acceleration-sensitive" Non-Structural Components (NSCs). In the first part a short review of the current state of knowledge and the critical issues related to the prediction of the seismic response of NSCs is reported. Then, the paper presents the results of a numerical parametric analysis intended to capture the key features of the dynamic response of a two-degree-of-freedom (2DOF) system which is supposed to be representative of both the main structure and the "non-structural" component (NSC). Particularly, it allows to simulate the coupled behaviour of both main structure and NSC and evaluating their response. The main parameters controlling the dynamic response of NSCs emerge from this study, which could pave the way towards formulating more mechanically consistent relationships for evaluating the maximum accelerations induced by seismic shakings on NSCs.

There is no simple method to measure glomerular filtration rate (GFR) in swine, an established model to study renal disease. We developed a protocol to measure GFR in conscious swine with the plasma clearance of iohexol. We used two groups: testing and validation, of 8 animals each. Ten milliliters of iohexol (6.47 g) were injected by the marginal auricular vein and blood samples (3 ml) were collected from the orbital sinus at different points after injection. GFR was determined considering two models: two-compartments (CL2: all samples) and one-compartment (CL1: the last six samples). In the testing group, CL1 overestimated CL2 by ~30%: CL2=245±93 and CL1=308±123 ml/mn. This error was corrected by a first order polynomial quadratic equation to CL1, which was considered the simplified method: SM=-47.909+(1.176xCL1)–(0.00063968xCL1²). SM showed narrow limits of agreement with CL2, and a concordance correlation of 0.97 and a total deviation index of 14.73%. Similar results were obtained for the validation group. This protocol is reliable, reproducible, can be performed in conscious animals, uses a single dose of the marker, and requires a reduced number of samples avoiding urine collection. Finally, it portends a significant improvement in animal-welfare conditions and handling necessities in experimental trials.

Opuntia ficus-indica has always interacted with many phytophagous insects at its center of origin; two of them are Dactylopius coccus and D. opuntiae. D. coccus, or true cochineal, is produced to extract carminic acid, and D. opuntiae or wild cochineal is a key invasive pest of O. ficus-indica in more than 20 countries around the world. Despite the economic and environmental relevance of this cactus and, D. opuntiae and D. coccus, there are few studies that have explored volatile organic compounds (VOCs) derived from the plant-insect interaction. The aim of this work was to determine VOCs of D. coccus and D. opuntiae feeding on cultivars suitable for their development and to identify different VOCs in cladodes infested by each Dactylopius species. Volatiles were identified through their essential oils, which were obtained by hydrodistillation. A total of 66 VOCs from both Dactylopius species were identified, and 125 from the Esmeralda and Rojo Pelón cultivars infested by D. coccus and D. opuntiae, respectively, were determined. Differential VOCs production due to infestation by each Dactylopius species was also found. Some changes in methyl salicylate, terpenes such as linalool or the alcohol p-vinylguaiacol were related to Dactylopius feeding on cladodes of their respective cultivars. Changes in these VOCs and their probable role in plant defense mechanisms should receive more attention because this knowledge could improve D. coccus rearing, or to its inclusion in breeding programs for D. opuntiae control in regions where it is a key pest of O. ficus-indica.

Centropomus undecimalis (common snook, sergeant fish or robalo) and Centropomus parallelus (fat snook, smallscale fat snook, little snook or chucumite) have tropical and subtropical wide distribution from southern Florida (Gulf of Mexico) to southern Brazil (Rio Grande do Sul) waters, and due the value of their flesh, these species have been explored with predatory fishing, generating a deficiency for their conservation. Adequate conservation of this species depends on population genetic parameters. Thus, in this study, we used microsatellite markers to investigate the genetic variability and the genetic characterization of C. undecimalis and C. parallelus populations, identifying if the citations species are geographically connected, using 10 loci of microsatellites, bringing strategies of management, sustainability, and conservation for species citations, and providing genetic subsidies needed to be used in breeding programs for snooks. Microsatellite molecular genetic markers were efficient in the study of the genetic variability of the samples of C. parallelus and C. undecimalis, resulting in data on the genetic structuring of these populations, thus aiding in their management and conservation. The data presented may provide subsidies for future use in programs aimed at animal breeding of sea bass, establishing tools for use in fish farming. In addition, despite being two species that are not on the endangered list, we can observe that there are genetic differences between the studied populations of the same species.

The fact that real dissipative (entropy producing) processes may be detected by non–comoving observers (tilted), in systems that appear to be isentropic for comoving observers, in general relativity, is explained in terms of the information theory, in analogy with the explanation of the Maxwell's demon paradox.

In this paper, we revisit a modified version of the classical Whitehead's theory of gravity in which all possible bilinear forms are considered to define the corresponding metric. Although, this is a linear theory that fails to give accurate results for the most sophisticated predictions of general relativity, such as gravity waves, it can still provide a convenient framework to analyze some new phenomena in the Solar System. In particular, recent development in the accurate tracking of spacecraft and the ephemerides of planetary positions have revealed certain anomalies in relation with our standard paradigm for celestial mechanics. Among them the so-called flyby anomaly and the anomalous increase of the astronomical unit play a prominent role. In the first case the total energy of the spacecraft changes during the flyby and a secular variation of the semi-major axis of the planetary orbits is found in the second anomaly. For this to happen it seems that a net energy and angular momentum transfer is taken place among the orbiting and the central body. We evaluate the total transfer per revolution for a planet orbiting the Sun in order to predict the astronomical unit anomaly in the context of Whitehead's theory. This could lead to a more deeply founded hypothesis for an extended gravity model.

The present study investigates the impact of electrode surface roughness on the electrofreezing of water. This research focuses on how the electrode roughness induced by a laser treatment affects the nucleation and growth of ice crystals under controlled electric fields. To achieve this, a series of experiments were conducted using deionized water samples placed between electrodes with varying surface roughness. The electrodes, fabricated with the Al6061 T6 alloy were structured via the Laser Shock Processing (LSP) method. For this purpose, the pulsed densities during the LSP process were varied from 900 to 2500 pulses/cm2. The increase of the pulse density was correlated to the surface structuring and to the microstructural features and average roughness of the LSP-treated Al6061 alloy. A wavy-like microstructure was induced upon the LSP treatment with average roughnesses between ~ 3.5 and 6 µm at the selected pulse densities. The results indicate that electrode roughness significantly influences the electrofreezing process. Rougher electrodes were found to lower the nucleation temperature compared to smoother ones, suggesting enhanced ice nucleation activity. The findings are attributed to the increased electric field concentration at the asperities of the rough surfaces, which may facilitate the alignment of water molecules and the formation of critical ice nuclei.

Unmanned Aerial Vehicles (UAVs) in particular multirotors are becoming the {\it de facto} tool for aerial sensing and remote inspection. In large industrial facilities, a UAV can transmit an online video stream to inspect difficult to access structures, such chimneys, deposits and towers. However, the communication range is limited, constraining the UAV operation range. This limitation can be overcome with relaying UAVs placed between the source UAV and the control station, creating a line of communication links. In this work we assume the use of a digital data packet network technology, namely WiFi, and tackle the problem of defining the exact placement for the relaying UAVs that creates an end-to-end channel with maximal delivery of data packets. We consider asymmetric communication links and we show an increase as large as $15$\% in end-to-end packet delivery ratio when compared to an equidistant placement. We also discuss the deployment of such a network and propose a fully distributed method that converges to the global optimal relay positions taking, on average, 1.4 the time taken by a centralized method.

Drilling centers are collaborative environments dedicated to facilitate decision-making in the well construction, where multidisciplinary teams work to support operations. The oil operators usually have drilling centers with different types of ergonomic features with considerable potential of integration, creating the opportunity to an Ergonomic Workplace Analysis. This paper aims to present the analysis of infrastructure requirements of one specific company in Brazil. The method was based on a survey with employees, which, coped with a statistical analysis, enabled understanding the impact of the layout requirements. The result is an approach to design collaborative environments, standardizing and defining models for the industry.

Defensive behavior is a function of specific survival circuits, the “aversive brain system”, that are thought to be conserved across vertebrates, and involve threat detection and the organization of defensive responses to reduce or eliminate threat. In mammals, these circuits involve amygdalar and hypothalamic subnuclei and midbrain circuits. The increased interest in teleost fishes as model organisms in neuroscience created a demand to understand which brain circuits are involved in defensive behavior. Telencephalic and habenular circuits represent a “forebrain circuit” for threat processing and organization of responses, being important to mounting appropriate coping responses. Specific hypothalamic circuits organize neuroendocrine and neurovegetative outputs, but are the less well-studied in fish. A “midbrain circuit” is represented by projections to interneurons in the optic tectum which mediate fast escape responses via projections to the central gray and/or the brainstem escape network. Threatening stimuli (especially visual stimuli) can bypass the “high road” and directly activate this system, initiating escape responses. Increased attention to these circuits in an evolutionary framework is still needed.

Climate change is expected to affect the agricultural sector in ways that are often unclear to predict. If in the short- medium-terms farmers may adapt to climate change by adjusting agricultural practices, in the long-term these adjustments may become insufficient, forcing farmers to change their farming systems. The extent and direction in which these farming system transitions will occur is still a subject underexplored in the literature. We propose a new framework to explore the marginal effect of climate change on the choice of the farming system, while controlling the effect of other drivers known to also influence farming system choice. Using a spatial-explicit farming system choice-model developed by a previous study in an extensive agricultural region of southern Portugal, we applied a space-for-time approach to simulate the effect of climate change on the future dynamics of the farming systems in the study area. Results suggest that climate change will force many farmers to change the farming system in a foreseeable future. The extent of the projected changes in farming systems is likely to trigger significant social, economic, or environmental impacts, which should require early attention from policy makers.

There is an urgent need to develop new therapies for cancer treatment due to high rates of resistance and tumor relapse. Moreover, chemotherapy and radiation weaken the immune system and leave patients susceptible to sudden death from infections and organ failure since antineoplastic drugs lack specificity. Hence, scientists worldwide continue the search for natural alternatives with anticancer activities and fewer side effects. In this regard, traditional medicine offers strategies for preventing and treating numerous diseases and nowadays scientific studies worldwide highlight some of the mechanisms underlying their potential as an effective alternative for the management of cancer. In Mexico, Mayan healers have been prescribed mainly medicinal plants for more than 2,000 years. Furthermore, their ethnomedical knowledge has led to drug discovery or the development of herbal remedies. Medicinal plants from the genus of Cissus remain an important element of all indigenous medical systems in Mexico including the Mayan tribes in Yucatan, particularly for the management of gastrointestinal illnesses, sores, cutaneous diseases, and tumors. In this review, we present the reported bioactivities against cancer cells of the extract and compounds from the species of Cissus with a particular focus on recent studies on the phytochemistry of C. trifoliata which is widely distributed in Mexican territory and used for tumor management in traditional medicine.

Whole cell biosensors could serve as eco-friendly and cost-effective alternatives for detecting potentially toxic bioavailable heavy metals in aquatic environments. However, they often fail to meet practical requirements due to an insufficient limit of detection (LOD) and high background noise. In this study, we designed a synthetic genetic circuit specifically tailored for detecting ionic mercury, which we applied to environmental samples collected from artisanal gold mining sites in Peru. We developed two distinct versions of the biosensor, each utilizing a different reporter protein: a fluorescent biosensor (Mer-RFP) and a colorimetric biosensor (Mer-Blue). Mer-RFP enabled real-time monitoring of the culture’s response to mercury samples using a plate reader, whereas Mer-Blue was analysed for colour accumulation at the endpoint using a specially designed, low-cost camera set up for harvested cell pellets. Both biosensors exhibited negligible baseline expression of their respective reporter proteins and responded specifically to HgBr2 in pure water. Mer-RFP demonstrated a linear detection range from 1 nM to 1 μM, whereas Mer-Blue showed a linear range from 2 nM to 125 nM. Our biosensors successfully detected a high concentration of ionic mercury in the reaction bucket where artisanal miners produce a mercury-gold amalgam. However, they did not detect ionic mercury in the water from active mining ponds, indicating a concentration lower than 3.2 nM Hg2+ -a result consistent with chemical analysis quantitation. Furthermore, we discuss the potential of Mer-Blue as a practical and affordable monitoring tool, highlighting its stability, reliance on simple visual colorimetry, and the possibility of sensitivity expansion to organic mercury.

Climate and land-use changes have been contributing to increase the occurrence of extreme wild-fires, shifting fire regimes and driving desertification, particularly in Mediterranean-climate regions. However, few studies have researched the effects of land-use change on fire regimes and carbon storage at the broad national scale. To address this gap, we used spatially explicit data from annual burned areas in mainland Portugal to build a typology of fire regimes based on the accumulated burned area and its temporal concentration (Gini index) between 1984 and 2019, which was combined with 2018 carbon stock data (above- and below-ground), and a landscape typology re-sulting from cluster analysis over land-use composition, diversity and configuration, to explore relationships between landscape types and the two major ecosystem services at stake: wildfire reduction and carbon stock. Cross tabulations, logistic and linear regressions were performed on these data and results revealed a strong relationship between landscapes dominated by maritime pine and eucalypt forest plantations and high-hazardous fire regime but hold the highest carbon stock. Shrubland-mixed landscapes were associated with lower carbon stocks but less hazardous fire regimes. Specialized agricultural landscapes, as well as mixed native forests and mixed agro-forestry landscapes, were the least associated with wildfires. In the case of agricultural landscapes however, this good wildfire performance is achieved at the cost of the poorest carbon stock, de-noting land degradation, whereas native forests and agroforestry landscapes strike the best trade-off between carbon stock and fire regime. Our findings support how nature-based solutions promoting wildfire mitigation and carbon stock ecosystem services may prevent and revert land degradation harming Mediterranean regions.

Trehalose, a disaccharide made of two D-glucose molecules, is found in various organisms like bacteria, yeast, fungi, insects, and plants. It serves as an energy and carbon source in plants and has signaling functions in yeast and plants, affecting metabolic pathways and growth regulation. It also plays a role in protecting proteins and cell membranes from damage caused by stress. This study aims to determine the best level of trehalose supplementation in layer diets between 34 to 49 weeks of age, due to limited literature on its impact on laying hen productivity. Experimental diets were based on nutritional recommendations and included six trehalose levels. The study was conducted over five periods of 21 days each, evaluating various parameters such as feed intake, egg production, egg quality, and bone characteristics. The results showed that trehalose supplementation influenced the performance of laying hens, as well as some aspects of egg and bone quality.

In this work we present a new algorithm that achieves the perfect Shannon secret by means of the XOR function and a method that we call multiple key reuse. The algorithm has two execution modes: message authentication and data encryption. The XOR encryption scheme allows for batch encryption and exhibits Perfect Forward Secrecy (PFS). Furthermore, based on our fundamental algorithm, we have developed a new strategy for blockchain implementation that does not require Proof of Work (PoW), but defines a fair mechanism for miner selection and secure addition of blocks to the chain. Since our method is mainly based on the Boolean XOR function, the strength of the cryptosystem can be directly established thanks to its mathematical properties. Due to the risk that quantum computers represent for current cryptosystems based on prime factorization or discrete logarithm, we postulate that our method represents a promising alternative in the quantum era for the security of communications between Internet of Things devices as well as Blockchain technology.

In this study, we have designed a promising platform based on metal-organic frameworks (MOFs) to develop paper-based analytical devices (PADs) to immobilize biomolecules for application in biosensors. PADs have become increasingly popular in sensing applications in recent years due to their portability, low cost, simplicity, efficiency, fast detection capability, excellent sensitivity, and selectivity. In addition, MOFs are an excellent choice for developing highly sensitive and selective sensors. Their ability to reversibly and reproducibly adsorb and desorb specific molecules allows using these materials as sensors for various applications. They also offer the possibility to modify their structure and properties, making them highly versatile and adaptable to different environments and sensing needs. In this research, we synthesized and characterized three different amino-functionalized MOFs: UiO66-NH2 (Zr), MIL125-NH2 (Ti), and MIL101-NH2 (Fe). These MOFs were used to fabricate PADs capable of sensitive and portable monitoring of alkaline phosphatase (ALP) enzyme activity by laser-induced fluorescence (LIF). Overall, amino-derivated MOF platforms demonstrate significant potential for integration into biosensor PADs, offering key properties that enhance their performance and applicability in analytical chemistry and diagnostics.

The brain being arguably the most complex organ in the human body, its detailed functioning is yet to be fully deciphered, let alone accurately modeled. Nonetheless, the medical community has gathered quite a remarkable amount of studies about the consequences of illnesses and injuries on its development and functioning. This bibliographic review aims to cover mostly the findings related to the changes in the physical distribution of neurons and their connections - the connectome -, both structural and functional, as well as their modelling. It does not intend to offer an extensive medical description of all injuries and diseases affecting the brain, rather presenting the most common ones succinctly so the need for accurate brain modelling can be fully understood and pondered, offering propositions to this aim.

The aims of this research were to know the physicochemical parameters, texture properties and sensory profile of vegetarian sausages through the flash profile, overall liking and purchase intention. Additionally, protein, fat and carbohydrate contents, pH, water activity, color, instrumental texture and descriptive profile were quantified with the overall liking and purchase intention of four sausages: ovo-vegetarian (SO), classic vegan (SC), fine herb vegan (SH) and vegan quinoa (SQ). Regarding the physicochemical properties, significant differences (p &lt; 0.05) were obtained. Using the flash profile, consumers described SO with attributes of elasticity, tamale smell, strange smell, characteristic flavor, pastiness, softness, pastel color. SC described as soft, pale color, characteristic odor, characteristic flavor, porous and flexible; SH and SQ characterized by the presence of ingredients, stew flavor, spices, pale and soft color. The SO sample presented greater overall liking and purchase intention compared to the other samples, and had a better texture and higher protein content. Through the HMFA analysis, a positive correlation was obtained between the texture and descriptive sensory of the flash profile, and on the other hand, a correlation between the physicochemical characteristics pH, aw, color, overall liking and purchase intention.

In an analogous manner as occurred during the development of a connected metabolism that at some point reached characteristics associated with what is called ‘life’ ―due mainly to a catalytic closure phenomenon when chemicals started to autocatalyze themselves forming a closed web of chemical reactions― it is here proposed that cognition and consciousness (or features associated with them) arose as a consequence of another type of closure within the nervous system, the brain especially. Proper brain function requires an efficient web of connections and once certain complexity is attained due to the number and coordinated activities of the brain cell networks, the emergent properties of cognition and consciousness take place. Seeking to identify main features of the nervous system organization for optimal function, it is here proposed that while catalytic closure yielded life, neuroglial closure produced cognition/consciousness.

This study aims to investigate whether 16 weeks of High-Intensity Interval Training (HIIT) implemented on Physical Education classes (PEC), can improve physical fitness in high-school adolescents This study was a two-arm randomized controlled trial design with adolescents (15-17 years old). Twelve classes were randomized to either a 16 weeks of HIIT (HIIT-G, n = 106 students) implemented on PEC warm-up or a control group (CG, n = 123 students) of usual PEC warm-up. The HIIT sessions ranged from 14 to 20 all-out bouts intervals, adopting a 2:1 work-to-rest ratio. Post-intervention measures revealed a significant difference between groups in CRF and girls from HIIT-G increased their cardiorespiratory fitness (CRF) with significant difference between female groups and a medium to large effect size. The main findings from this study indicate that brief whole-body HIIT of an extremely low volume, over 16 weeks, can improve CRF in adolescent girls.

Organizations can play a significant role in the advancement of Sustainable Development, and companies with Quality, Environmental and Occupational Health and Safety (QEOHS) certified management systems address the three Sustainability Dimensions (economic, environmental and social). This research aims to map the present level of engagement of those companies in contributing and reporting to the 17 Sustainable Development Goals of The United Nations 2030 Agenda. The content of companies reports (available in web sites, by 31 December 2017) of a total of 235 Portuguese organizations with QEOHS certified management systems, was analyzed. The results show a moderate reporting of SDGs by those companies, with the top five being SDG 12 - Responsible consumption and production (23.8%), SDG 13 – Climate action (22.1%), SDG 09 - Industry, innovation, and infrastructure (21.3%), SDG 08 - Decent work and economic growth (20.0%) and SDG 17 - Partnerships for the goals (19.6%). The results of the statistical tests indicate that the communication of SDGs is more prominent in organizations (QEOHS) with higher business volume, that are members of the United Nations Global Compact Network Portugal, and that disclose their sustainability reports on their web site. This study can be useful for decision-makers that aim to support organizations to contribute to the Sustainable Development Goals.

The present research aimed to identify if consumption values influence the attitudes of Millennials who intend to purchase organic products. The research was quantitative, with a correlational scope and a cross-sectional design, and it was conducted on 509 Peruvian millennials. A questionnaire consisting of 23 questions was administered, and responses were quantified using a five-point Likert scale. The results were processed through an Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM) conducted using Statistical Package for Social Sciences 24 and AMOS 24. The study identified that environmental attitudes of Millennials do influence their intentions to purchase organic products, and the antecedents of these attitudes are functional and social values, whereas emotional, conditional, and epistemic values do not. The study confirmed that consumption values, either as a second-order variable or as a single construct, do not influence the attitudes of Millennials who intend to purchase organic products, thus confirming that these dimensions should be measured independently.

The health benefits of extra virgin olive oil (EVOO) are related with its chemical composition and the presence of bioactive compounds with antioxidant properties. The aim of this study was evaluate antioxidant compounds (pigments, CoQ10 and phenolic compounds) and antioxidant properties of EVOO from the same region comparing different cultivars (Hojiblanca and Arbequina), harvest year and crop stage. Antioxidant properties of oils were studied before and after a gastrointestinal digestion process, by in vitro assays (DPPH, ABTS and FRAP) and antioxidant markers in Caco-2 cells (reactive oxygen species production). The content of bioactive compounds measured was significantly affected by cultivar and harvest year (except for carotenoids) and by the crop stage (except for coenzyme Q10). Higher amount of coenzyme Q10 was observed in Hojiblanca than in Arbequina EVOO. Total phenol content and antioxidant properties were also different depending on cultivar and harvest year and the in vitro digestion process strongly improved antioxidant marker values. Antioxidant potential in bioaccessible fractions was mainly related with content of coenzyme Q10 and phenolic compounds in EVOO. Chemometric analysis showed that the oils were clearly classified by cultivars, harvest and crop stage, according with chemical composition and antioxidant activity analyzed in the present study

Climate change in the coming decades could intensify extreme events such as severe droughts. Combined with the possible increase of water demands, these changes exert a large pressure on the water systems. In order to confirm this assumption, a set of scenarios was proposed in this study to consider the combined impact of climate changes and the increase in water demand on the main multiple-use reservoirs of São Francisco River, Brazil. For this purpose, five CMIP6 climate models were used considering two greenhouse gas emissions scenarios: the SSP2-4.5 and SSP5-8.5. The naturalized and withdraw streamflows were estimated to the adopted reser-voirs considering all existing, new and projected demands. The conjunction of scenarios indicat-ed an increase in Potential Evapotranspiration, a possible significant reduction in water availa-bility, a growth in water demand mainly for irrigation and a substantial reduction in the perfor-mance of the evaluated reservoirs.

The phosphogypsum (PG) pile of solid waste is of immense proportions and poses a grave threat to the ecological environment, with its reserves steadily increasing year after year. The urgent need for large-scale consumption and high-quality utilization of PG in industrial production has become apparent. The present study provides a comprehensive analysis of the correlation be-tween the macroscopic mechanical properties and microstructure of PG, elucidating the crystal morphology characteristics in different phases and outlining the crystallization preparation pro-cess. By integrating the physical and chemical properties of diverse PG phases with the intricacies associated with its preparation process, it is imperative to explore avenues for its high-value utili-zation, investigate possibilities for industrial-scale production, and analyze and predict the appli-cation trends and developmental directions in comprehensive PG utilization. These endeavors hold immense significance in guiding the realization of resource utilization potential inherent in PG.

We aimed to assess whether play behavior can be used as a welfare indicator for farmed spotted pacas (Cuniculus paca) by examining its association with other positive welfare markers including affiliative behavior and low-amplitude vocalizations. We submitted six groups of spotted pacas (one male/two females per group) (N=18) to an ABA experimental design (A1/A2: without ball; B: with three boomer balls). Play behavior occurred only during phase B (mean=35.5s, SE=6.4). Spotted pacas spent more time in affiliative and exploratory behaviors, while they spent less time engaging in agonistic interactions during phase B than in both control phases (A1 and A2) (P<0.05). Moreover, spotted pacas emitted more low-amplitude bark vocalizations during phase B than during either control phase (P<0.05), which have previously been shown to indicate a positive affective state and low arousal level. Because the expression of play was associated with a decrease in aggression, an increase in affiliative behavior, and an increase in low-amplitude barking, indicating thus a positive state, we suggest that play behavior can be used as a non-invasive indicator of positive welfare in this species.

This research explores the application of ARIMA (Autoregressive Integrated Moving Average) models and parallel computing techniques to analyze and forecast anemia diagnoses in children under 36 months in the Junín region of Peru. Using health data from 2023-2024, including insurance type, patient information, diagnosis dates, hemoglobin levels, and treatment details, we develop predictive models to understand trends and patterns in childhood anemia. The study aims to demonstrate the effectiveness of time series analysis and high-performance computing in addressing this critical public health issue. Results indicate improved forecasting accuracy and computational efficiency, potentially aiding in resource allocation and policy development for anemia prevention and treatment programs.

Cellulose nanofibrils (CNFs) are materials with high aspect ratio. Typically, TEMPO-oxidised, or cationic CNFs consist of long fibrils (up to 2 μm) with very low thickness (less than 10 nm). Derived from their high aspect ratio, CNFs form strong hydrogels with high elasticity at low concentrations. Thus, CNF suspensions appear as an interesting rheology modifier to be applied in cosmetics, paints, foods, as a mineral suspending agent, among other applications. The high viscosity results from the strong 3D fibril network, which is related to good fibrillation of the material, allowing the nanofibrils to overlap. The overlap concentration (c*) was found to vary from ca. 0.13 to ca. 0.60 wt.% depending on the type and intensity of the pre-treatment ap-plied during the preparation of the CNFs. The results confirm the higher tendency for the fibres treated with CHPTAC (cationic) and TEMPO to form a 3D network, resulting in the lowest c*. For the TEMPO-oxidised CNF suspensions, it was also found that aggregation is improved at acidic pH conditions due to lower charge repulsion among fibrils, leading to an increase in the suspension viscosity, as well as higher apparent yield stresses. TEMPO CNF suspensions with low content of carboxylic groups tend to precipitate at moderate acidic pH values.

Supply Chain Management (SCM) encompasses a wide variety of decision-making problems that affect business and supply chain performance as a whole. Since most of these problems involve uncertainty and hesitation on the part of Decision Makers (DMs), various studies have emerged recently that present SCM applications of techniques based on Hesitant Fuzzy Linguistic Term Sets (HFLTSs) and HFLTS extensions. Given the relevance of this subject and the lack of literature review studies, this study presents a systematic review of HFLTS and HFLTS extension applications to SCM decision-making problems. In order to answer a set of research questions, the selected papers have been classified in accordance with a group of factors that are pertinent to the origins of these studies, SCM, HFLTSs, and decision-making. The results demonstrate that the Source and Enable processes have been studied with greater frequency, while the most common problems have to do with supplier selection, failure evaluation, and performance evaluation. The companies of the automotive sector and Sustainable SCM and Green SCM strategies predominate in the analyzed studies. Even though most of the studies use techniques based on HFLTSs, we have identified applications of seven distinct HFLTS extensions, with Double Hierarchy Hesitant Fuzzy Linguistic Term Sets and Probabilistic Linguistic Term Sets being the most utilized. The identification of gaps in the literature presents avenues for future studies focused on innovative applications, integrations of techniques, comparisons of techniques, group decision-making, and validation procedures for new models. The results of this study offer a panorama of the state of the art in regard to this subject and can help researchers and practitioners develop new studies which involve the use of methods that employ HFLTSs and HFLTS extensions in SCM decision-making problems.

The vegetation in constructed wetlands (CWs) plays an important role in wastewater treatment. Popularly, the common emergent plants in CWs have been vegetation of natural wetlands. However, there are ornamental flowering plants that have some physiological characteristics similar to the plants of natural wetlands that can stimulate the removal of pollutants in wastewater treatments; such importance in CWs is described here. A literature survey of 87 CWs from 21 countries showed that the four most commonly used flowering ornamental vegetation genera were Canna, Iris, Heliconia and Zantedeschia. In terms of geographical location, Canna spp. is commonly found in Asia, Zantedeschia spp. is frequent in Mexico (a country in North America), Iris is most commonly used in Asia, Europe and North America, and species of the Heliconia genus are commonly used in Asia and parts of the Americas (Mexico, Central and South America). This review also compares the use of ornamental plants versus natural wetland plants and systems without plants for removing pollutants (COD, BOD, nitrogen and phosphorous compounds). The removal efficiency was similar between flowering ornamental and natural wetland plants. However, pollutant removal was better when using ornamental plants than in unplanted CWs. The use of ornamental flowering plants in CWs is an excellent option, and efforts should be made to increase the adoption of these system types and use them in domiciliary, rural and urban areas.

When computers start to become a dominant part of technology around the 1950s, fundamental questions about reliable designs and robustness were of great relevance. Their development gave rise to the exploration of new questions such as what made brains reliable (since neurons can die) and how computers could get inspiration from neural systems. In parallel, the first Artificial Neural Networks came to life. Since then, the comparative view between brains and computers has been developed in new, sometimes unsuspected directions. With the rise of deep learning and the development of connectomics, an evolutionary look at how both hardware and neural complexity have evolved or designed is required. In this paper, we argue that important similarities have resulted both from convergent evolution (the inevitable outcome of architectural constraints) and inspiration of hardware and software principles guided by toy pictures of neurobiology. Moreover, dissimilarities and gaps originate from the lack of major innovations that have paved the way to biological computing (including brains) that are completely absent within the artificial domain. As it occurs within synthetic biocomputation, we can also ask whether alternative minds can emerge from A.I.\ designs. Here we take an evolutionary view of the problem and discuss the remarkable convergences between living and artificial designs and what are the pre-conditions to achieve artificial intelligence.

Recently, we demonstrated the validity of anomalous numbers law, known as Newcomb-Benford´s law, at the mammalian neuromuscular transmission, considering different extracellular calcium concentrations . The present work continues to examine how alterations in extracellular solution modulate the first digit law in the context of the spontaneous release of acetylcholine from the neuromuscular junction. We investigated if the intervals of miniature potentials collected at the neuromuscular junction obey the law in a hyperkalemic environment. The analysis showed that the interval between the miniature potentials at high potassium concentrations follows Newcomb-Benford´s law. Also, our data allowed us to uncover a conformity fluctuation as a function of the number of intervals of the miniature potentials. Finally, we discuss the biophysical implications of the present findings.

The food system, the most important driver of planetary transformation, is in a deep crisis. Therefore, seeking a sustainable and socially-fair transition pathway becomes an issue of utmost priority for our own survival. The consideration of food as a commodity, a social construct that played a central role in driving this crisis, remains the uncontested narrative to lead the different transition pathways what seems rather contradictory. By exploring the normative values in the transition landscape, this paper seeks to understand how relevant is the hegemonic narrative of “food as commodity” and its alternative of “food as commons” to determine transition trajectories and food policy beliefs. Applying the Multi-level Perspective framework and developing the ill-studied “agency in transition”, this research enquired food-related professionals that belong to an online community of practice (N=95) on valuation of food dimensions and agency in food transitions to check whether the valuation of food is relevant to explain personal stances in transition. Results suggest the socially-constructed view of food as commodity is positively correlated to the gradual reforming attitude, whereas food as commons is positively correlated to the counter-hegemonic transformers regardless the self-defined position in the transition landscape (regime or niches). At personal level, there are multiple loci of resistance with counter-hegemonic attitudes in varied institutions of the regime and the innovative niches, many of them holding this discourse of food as commons. Conversely, alter-hegemonic attitudes are not positively correlated to this alternative discourse and they may inadvertently or purportedly reinforce the ‘‘neoliberal narrative’’. Food as commons, a different narrative whose rationale is explained in the paper, seems to be a relevant framework that could enrich the multiple transformative constituencies that challenge the industrial food system and therefore facilitate the convergence of movements that reject the commodification of food.

The in vitro cultivation of M. leprae has not been possible since it was described as causing leprosy, and the limitation of animal models for clinical aspects makes studies on leprosy and bacteria-human host interaction a challenge. Our aim was to standardize the ex vivo skin model (hOSEC) to maintenance and study of M. leprae as an alternative animal model. Bacillary suspensions were inoculated into human skin explants and sustained in DMEM medium until 60 days. Explants were evaluated by RT-PCR-16SrRNA, and cytokines gene expression. Viability and infectivity of bacilli recovered from explant (D28 and D60) were evaluated using the Shepard’s model. All explants were RT-PCR-16srRNA positive. Viability and infectivity of recovered bacilli from explants, analyzed after 5 months of inoculation in mice showed an average positivity of 31%, being the highest positivity in the D28 groups (80%). Furthermore, our work showed different pattern in cytokines gene expression (TGF-β, IL-10, IL-8 and TNF-α) in the presence of alive or dead bacilli. Although changes can be made for improve future experiments, our results have demonstrated that it is possible to use the hOSEC to maintain M. leprae for 60 days, interacting with the host system, an important step in the development of experimental models for studies on the biology of the bacillus, its interactions, and drug susceptibility.

Cancer is a prevalent disease worldwide and treatments such as radiotherapy and chemotherapy sometimes lead to adverse events. Oral mucositis is one of the most disabling and clinical guidelines do not take into account nutritional interventions. The primary endpoint was to gather the evidence about the efficacy of nutritional interventions in the prevention and/or treatment of antineoplastic induced oral mucositis in oncological patients. It was carried out a bibliographic review in PubMed data base by combining MesH terms with boolean operators. Articles were selected based on inclusion and exclusion criteria; 50 final articles were found. Although further evidence is needed, glutamine, honey and vitamins appear to be a good therapeutic option. The rest of the compounds presented controversial or insufficient results to draw conclusions over their utilization as prevention or treatment options. Low evidence is reported about oral mucositis nutritional interventions in spite of being attainable and affordable compounds. Scarce evidence is shown in paediatric patients compared to adults. Developing higher quality studies and combinations with the compounds researched is necessary to create stronger evidence.

Fluoride (iF) is important inorganic pollutant in groundwater in central México. The aim of this study was estimated environmental risk exposure of iF in residents of Jerez Zacatecas México. Fifty-five tap water samples were collected from households to determine iF concentrations using the ion-selective electrode method. Based on these environmental values, the Hazard Quotient (HQ) was estimated following the methodology of the Pan American Health Organization (PAHO). Subsequently, risk maps were generated from these values to visualize the spatial distribution of high-risk areas within the city. iF in tap water ranger in concentration from 1.3 – 7.3 mg/L (ppm). We estimated HQ in a range of 0.63 to 3.73. iF ex-posure in tap water is a health problem that must be addressed, carrying out risk communication actions that allow the population to take care of their health through simple actions, avoiding future health costs.

Larrea tridentata is an endemic plant from the Mexican highlands used in traditional medicine to treat hypertension and kidney stones, among many ailments. This study evaluated a vasorelaxant compound: 5- Hydroxy-2-(4-hydroxy-3-methoxyphenyl)-6,7,8-trimethoxy-4H-1-benzopyran-4-one (also known as 8-Methoxycirsilineol: 8-M) which we purified from an extract of this plant. Mesenteric artery rings were isolated from male guinea pigs for isometric force measurements. Freshly isolated smooth muscle cells from mesenteric artery were used for ionic current measurement for calcium and potassium. 10 μM 8-M completely relaxed vascular rings contracted with phenylephrine in a dose-dependent fashion with an EC50 = 4.26 ± 1.16 10-6 M and 4.56 ± 1.64 10-6 M for endothelium intact and endothelium-free tissue, respectively. The relaxant effect of 8-M was not affected by maximal doses of nifedipine, verapamil or iberiotoxin. Nevertheless, the relaxant effect was partially abolished by addition of 10 mM Tetraethylammonium (TEA). In accordance to this, calcium currents evoked by depolarization were not affected by 8-M, whereas TEA-sensitive potassium currents, were increased upon its addition. Vasorelaxation due to 8-M is partly due to activation of KV currents in an endothelium independent mode.

A large amount of data is produced every day by stakeholders of the Air Traffic Management (ATM) system, in particular airline operators, airports, and air navigation service provider (ANSP). Most data is kept private for many reasons, including commercial and security concerns. More than data, shared information is precious, as it leverages intelligent decision-making support tools designed to smooth daily operations. We present a framework to detect, identify and characterise anomalies in past aircraft trajectory data. It is based on an open source of ADS-B based aircraft trajectories, and extracted information can benefit a wide range of stakeholders: Air Traffic Control (ATC) training centres could play more realistic simulations; ANSP may improve capacity indicators; academics improve safety models and risk estimations; and commercial stakeholders, like airlines and airports, may use such information to improve short-term predictions and optimise their operations. The technique is based on autoencoding artificial neural networks applied on flows of trajectories, which provide a useful reading grid associating cluster analysis with quantified level of abnormality. In particular, we find that the highest anomaly scores correspond to poor weather conditions, whereas anomalies with a lower score relate to ATC tactical actions.

The capacitated lot-sizing problem with product recovery (CLSP-RM) holds significant importance in reverse logistics but is notoriously complex (NP-hard). In this study, two techniques are introduced to confront this challenge. The first technique entails devising a linear optimization task that eliminates capacity limitations across a wide problem spectrum, yielding a remarkably accurate approximation of the optimal solution. This adaptable approach presents a potent alternative and holds potential for extension to diverse problem categories owing to its versatile nature. The second technique employs a simulation methodology utilizing Halton’s uniform random numbers to address the issue. This randomized production search method sidesteps considerations of production costs, inventory expenditures, and production order when determining production batches. The research’s novelty lies in its application of these techniques to the problem. The suggested methods undergo evaluation via a benchmark dataset of approximately 4200 instances, with comparison against solutions derived through the Gurobi solver. The results underscore the efficacy and resilience of the introduced methodology in tackling the CLSP-RM predicament.

The number of physical devices connected to the Internet such as vehicles, household appliances and other "things" has been steadily increasing in recent years, thus forming the basis for the Internet of Things (IoT). IoT ecosystem extends beyond country borders and application domains, combining thousands of versatile devices that differ in terms of their structures, capabilities, and available resources. It is therefore not surprising that the landscape of wireless communication technologies and the degree of IoT devices available today is excessively broad and diverse. Interference between networks leads to frame collisions and consequent packet loss. Frame collisions occur when two or more packets overlap in time and frequency and use the same LoRa parameters, i.e. the same spreading factor (SF), bandwidth (BW) and carrier frequency (CF). When most devices use the same configuration, collision probability is higher. The probability of frame collisions is also affected by traffic characteristics, particularly the periodicity of the transmission and the payload size. Larger payload sizes and more frequent transmissions accumulate with higher time on air (ToA) and channel occupancy [1]. Transmission powers and the location of the gateways also influences this situation. The aim of this experimental work is to verify the effects of collisions and interference in LoRaWAN, regarding the periodicity of transmissions and the payload sizes. The hardware used is an Arduino board programmed in C/C++, a LoRa Bee module operating at a frequency of 868 MHz and a LoRaWAN LG02 gateway. Different scenarios were tested in an outdoor environment, with Line of Sight and Non-Line of Sight, and different variables such as: SFs equal to 7, 9 and 12; distances of 20, 40 and 60 meters and different data sizes of 14, 32 and 51 bytes, over the same channel. The packets with different SF’s and different data sizes were transmitted in a (pseudo) random way, one after the other, overlapping different SFs in the same channel and testing this way (theoretical) orthogonality among them. By fixing the bandwidth in 125 kHz, and testing different SFs we intend to increase the probability of cause packet loss by applying to our connection a combination of emitted power/short distance that leaves an interference power received, strong enough [2] for the effect. We validate our conclusions by analyzing ToA according with RSSI and different data sizes, the RSSI for different SFs and different data sizes and finally the packet loss through the PDR-Packet Delivery Ratio for a better understanding of the reliability of packet transmission in our link.

This paper presents high-resolution digital measurements at low cost, combining oversample and averaging. Experiments of processing data between samples and between multiple temperature sensors in the same environment are conducted. They provide results with effective increase in the analog to digital converter bits, in correspondence with the minimization on the effects of noise. It is shown that averaging a set of small, low-cost sensors at a small microcontroller can give flexible access to improved measurement accuracy.

Background: Sarcopenia, a subject of extensive research, has led to numerous clinical trials and systematic reviews (SR). These reviews aid healthcare professionals by summarizing results and conducting meta-analyses, enhancing reliability. However, the abundance of reviews complicates decision-making on sarcopenia management. To address this, SR of SR have emerged, consolidating data from various sources into comprehensive documents. Objective: to assess the isolated impact of dietary interventions on sarcopenia's diagnostic criteria for older individuals. Methods: a study protocol for a SR of SR, following Cochrane and PRISMA recommendations. The search strategy includes the MeSH ‘sarcopenia’ and its subheadings; ‘aged’ and its subheadings; ‘nutrition’ and its subheadings; and filter ‘systematic review’, performed at main databases. Selected studies must include older adults, submitted to nutritional interventions compared to control groups. Quantitative analyses will use inverse variance statistic method (random effects); the effect measures mean difference. Heterogeneity measured with Q-Test. Results: The results of this SR of SR will may provide valuable information about the sarcopenia treatment, deepening the knowledge about. Conclusion: this protocol is reproducible, requires low cost and personnel, and may allow a higher understanding on sarcopenia treatment and management on older people.

Metformin is an oral antidiabetic agent with antimicrobial properties, excreted in an active and unmetabolized form. Like some non-antibiotic drugs, subinhibitory concentrations of metformin may contribute to the expression of microbial resistance in wild-type strains. This work aimed to evaluate the biofilm formation of Pseudomonas aeruginosa TGC04 exposed to 100 and 200 µg/mL of metformin, varying the Chemical Oxygen Demand (COD) and flow regime. Biofilms were quantified after 2 and 24 hours, observing reductions and increases in biofilm growth around 13 and 64%, respectively. The greatest biofilm growth occurred in cells exposed to 200 µg/mL of metformin. Both the flow regime and COD concentration were important for the development of the biofilm, whose cells were mostly weakly to moderately adhered. The study provides evidence that in polluted aquatic systems with turbulent regimes, the inhibitory concentration of metformin promoted the least disturbances to the biofilm.

Accurate channel estimation is of utmost importance for massive MIMO systems that allow providing significant improvements in spectral and energy efficiency. In this work, we investigate the spectral efficiency performance and present a channel estimator for multi-cell massive MIMO systems subjected to pilot-contamination. The proposed channel estimator performs well under moderate to aggressive pilot contamination scenarios without prior knowledge of the inter-cell large-scale channel coefficients and noise power. The estimator approximates the performance of a linear Minimum Mean Square Error (MMSE) as the number of antennas increases. Following, we derive a lower bound closed-form spectral efficiency of the Maximum Ratio Combining (MRC) detector in the proposed channel estimator. The simulation results highlight that the proposed estimator performance approaches the linear minimum mean square error (LMMSE) channel estimator asymptotically.

Pumpo is a Simmental breed and an essential livestock resource in the nucleus genetic cattle of Peru. This study provides a draft genome sequence of a top bull using a de novo assembly approach on the Illumina Novaseq X platform, yielding 208 GB of raw sequencing data with 150 bp paired-end reads. The final genome assembly resulted in a size of 2.06 Gb with an N50 contig length of 108 Mb and a completeness of 95.7% according to BUSCO analysis. A total of 973,925 simple sequence repeats (SSRs) were identified, with a predominance of mononucleotide repeats. The genome showed low heterozygosity (0.568%) and moderate repeatability (11.5%), aligning with other Bos taurus genomes. Reference-guided scaffolding improved the assembly quality significantly, producing an N50 scaffold value of 108 Mb. The SSR analysis of the Pumpo genome identified 973,925 SSRs with a frequency of 2,808 SSRs per kilobase, predominantly mononucleotide repeats, and 85,453 found in compound formations. Obtaining knowledge of the genome of a breeding Simmental bull is essential to optimize breeding programs and improve productivity.

This paper presents a dynamic homotopy technique that can be used to calculate a preliminary result for a power flow problem (PFP). This result can then be used as an initial estimate to efficiently solve the PFP using either the classical Newton-Raphson (NR) method or its fast decoupled version (FDXB) while still maintaining high accuracy. The preliminary stage for the dynamic homotopy problem is formulated and solved by employing integration techniques, where implicit and explicit schemes are studied. The dynamic problem assumes an initial condition that coincides with the initial estimate for a traditional iterative method such as NR. In this sense, the initial guess for the FPF is adequately set as a flat start, which is a starting for the case when this initialization is of difficult assignment for convergence. The static homotopy method requires a complete solution of a PFP per homotopy pathway point, while the dynamic homotopy is based on numerical integration methods. This approach can require only one LU factorization at each point of the pathway. Allocating these points properly helps avoid several PFP resolutions to build the pathway. The hybrid technique was evaluated for large-scale systems with poor conditioning, such as a 109272-bus model and other test systems under stressed conditions. A scheme based on the implicit backward Euler scheme demonstrated the best performance among other numerical solvers studied. It provided reliable partial results for the dynamic homotopy problem, which proved to be suitable for achieving fast and highly accurate solutions using both the NR and FDXB solvers.

The spin-1/2 quantum transverse Ising model, defined on a ladder structure, with nearest-neighbor and four-spin interaction on a plaquette, is studied by using exact diagonalization and finite-size-scaling procedures. The quantum phase transition between the ferromagnetic and paramagnetic phases has been obtained in the thermodynamic limit. The critical transverse field decreases as the antiferromagnetic four-spin interaction increases, and reaches a multicritical point. However, the exact diagonalization approach was not able to capture the essence of the dimer phase beyond the multicritical transition.

Generative adversarial networks (GANs) have become a recent and rapidly developing research topic in Machine Learning. Since their inception in 2014, a significant number of variants have been proposed to address various topics across many fields, and has particularly excelled not only in image and language processing, but also in the medical and data science domains. In this paper, we aim to highlight the significance and advance that these GAN models can introduce in the field of Business Economics, where they have yet to be fully developed. To this end, a review of the literature of GANs is presented in general together with a more specific review in the field of Business Economics wherein only a few papers can be found. Furthermore, the most relevant papers are analysed in order to provide an approach the opportunity to research into GANs in the field of Business Economics.

Abstract: Objective: The aim of this study is to evaluate the effectiveness of an affective-sexual education programme among 14-15 year-old adolescents and its impact on their perception of their health. Design: This is a quantitative, descriptive and prospective quasi-experimental study, employing various cross-sections throughout the school year. It constitutes an educational promotion intervention where a health blog serves as a supplementary component to the "Health and School" programme. This phase is assessed using specific tools designed to evaluate scales. The study aims to assess the programme's effectiveness in the areas of knowledge and attitudes related to sexuality, sexually transmitted diseases, and affectivity. Additionally, it aims to evaluate the intervention's impact on 292 secondary school students, divided into a control group (N=144) and an intervention group (N=148). Both groups belong to the same population and have undergone affective-sexual education interventions focusing on gender and sexual diversity perspectives. The assessment considers perceptions and behaviours related to knowledge about HIV and other STDs/STIs, attitudes towards HIV, health anxiety, and AIDS phobia. Cross-sectional comparisons between the control and intervention groups are conducted at three points during the school year: longitudinal study, baseline, at month 4, and at month 9 post-intervention. Methods: Median and interquartile range, Wilcoxon test for numerical variables and Fisher's test for categorical variables were calculated according to intervention group. Results: 53% of the students reported a very good perception of health, compared to 45% with a good perception. Boys reported a good perception of health (85%) and had perceptions of their physical appearance and health (p=0.023) and (p<0.001) in the intervention group. Differences were found between perceptions of physical appearance in the control group, which was close to normal (p=0.429). In terms of level of knowledge about HIV and other STDs/STIs, the most significant difference was found in knowledge about HIV transmission, which varied between groups. Conclusions: Early affective-sex education sessions have an impact on health perception and behaviours related to knowledge about HIV and other STDs/STIs, leading to reduced anxiety in adolescents. School nurses should focus on health promotion, prevention of sex-related diseases, healthy attitudes from a gender perspective and integrated affective relationships in all their dimensions.

Mothers of children with a specific clinical condition such as type 1 diabetes mellitus (T1DM) may experience higher levels of stress predisposing them to a poorer perception of quality of life and alterations in nutritional status. The aim of this research was to evaluate the relationship between nutritional status and quality of life perception of mothers caring for children with Type 1 Diabetes Mellitus. This research was conducted from July to September 2022, with mothers of children aged 5 to 10 years with diabetes. Data were collected through an online questionnaire including soci-oeconomic information, anthropometric data, clinical data of the children, and the WHO-QOL-abbreviated quality of life questionnaire. For statistical analysis, Statistical Package for Social Science 24 was used. Fifty-five mothers of children with diabetes were evaluated. Over 33% of the mothers were overweight and reported a regular perception of quality of life. Higher BMI was associated with poorer perception of quality of life in the psychological and self-assessment domains, and longer diagnosis time was associated with better perception of quality of life in the physical, psychological, and environmental domains. Therefore, it is understood that actions aimed at improving the quality of life of these mothers are necessary.

The scientific community has explored new packaging materials due to environmental challenges and pollution from plastic waste. Bacterial cellulose (BC), produced by bacteria like Gluconaceto-bacter xylinus, shows high potential for food preservation due to its exceptional mechanical strength, high crystallinity, and effective barrier properties against gases and moisture, making it a promising alternative to conventional plastics. This review highlights recent advances in BC production, particularly agro-industrial residues, which reduce costs and enhance environmental sustainability. Incorporating antimicrobial agents into BC matrices has also led to active packaging solutions that extend food shelf life and improve safety. A bibliometric analysis reveals a significant increase in research on BC over the last decade, reflecting growing global interest. Key research themes include the development of BC-based composites and the exploration of their antimicrobial properties. Critical areas for future research include improving BC production's scalability and economic via-bility and its integration with other biopolymers. These developments emphasize BC's potential as a sustainable packaging material and its role in the circular economy through waste valorization.

Introduction: Sarcopenia, muscle loss, often coexists with MASLD, a common liver disorder, which is more prevalent in those with sarcopenia. Sarcopenia also increases the risk of liver conditions like steatosis and fibrosis. Studies on sarcopenia in MASLD are common in developed countries, but fewer follow the EWGSOP2 guidelines in Brazil. Aim: to assess sarcopenia prevalence in MASLD patients. Methods: Cross-sectional study conducted at the Gastroen-terology/Hepatology Service of ISCMPA with patients diagnosed with MASLD. The EWGSOP2 criteria were used to evaluate sarcopenia. Categorical data presented as absolute and relative frequency; parametric continuous data ex-pressed as mean±standard deviation; non-parametric continuous data as me-dian and IQR. Gender differences in were analyzed using Fisher's Exact Test or Chi-squared tests, and for continuous variables, T Student tests (parametric) and Mann-Whitney U tests for independent samples (non-parametric). The significance level was set at 5% (p<0.05). Results: The study involved 103 MASLD patients with an average age of 60.39 years, comprising 48 (46.60%) adults and 55 (53.40%) older individuals. Concerning sarcopenia diagnosis, four individuals exhibited decreased muscle strength; two had reduced MME (sarcopenia); and one showed decreased walking speed (severe sarcopenia). Among the participants, 63 (60.6%) were physically active. 35 (62.5%) had mild to moderate steatosis, while 21 (37.5%) had severe steatosis. In terms of EHNA, 13 subjects (24.08%) had moderate to severe EHNA. Regarding fibrosis classi-fication, 68 (72.34%) individuals had undetermined or high probability based on the NAFLD score, with higher prevalence in males (n=23; 88.5%). Fibrosis assessment via liver biopsy revealed 27 (28.72%) in F1 and F2 and 15 (16.96%) in F3 and F4. Stratification of fibrosis into F3 and F4 was more com-mon among men (n=9; 47.4%). Conclusion: Most of the population was physi-cally active. The parameters indicating sarcopenia exceeded the thresholds recommended by EWGSOP2. The prevalence of sarcopenia was low in individuals with MASLD.

This paper presents a methodology to calculate day-ahead wind speed predictions based on historical measurements done by weather stations. The methodology was tested for three locations: Colombia, Ecuador, and Spain. The data is input into the process in two ways: 1) as a single time series containing all measurements, and 2) as twenty-four separate parallel sequences, corresponding to the values of wind speed at each of the 24 hours in the day over several months. The methodology relies on the use of three non-parametric techniques: Least-Squares Support Vector Machines, Empirical Mode Decomposition, and the Wavelet Transform. Also, the traditional and simple Auto-Regressive model is applied. The combination of the aforementioned techniques results in nine methods for performing wind prediction. Experiments using a MATLAB implementation showed that the Least-squares Support Vector Machine using data as a single time series outperformed the other combinations, obtaining the least mean square error.

The smoke house works as a tourism model that will directly impact women living across Quintana Roo communities and small towns, with the idea that this project will empower them, and provide them with a more dignified income, with the end goal to reduce the poverty rates in the state Likewise, to introduce an adequate formula of cuisine that promotes the regional gastronomic identity, since this typology of cultural heritage is linked to the experience of enjoying the state's native food. (Carrillo, J. and Vazquez, L., 2018) It should be noted that the main representatives and transmitters of gastronomy are women, usually housewives. Therefore, a methodology based on the qualitative approach was designed, taking as a basis the ethnographic method, which allows understanding the behavioral patterns of a society. In the first instance, a gastronomic laboratory is proposed for the university, which will later be used as a business model within the tourism industry, directed at people who seek to enjoy cultural and ex-periential tourism. And at the same time, it will benefit communities across the state by generating more income for them. In addition, the project of model smoke kitchen is oriented to go in accordance with the 2030 agenda. Which includes 17 objectives and 169 goals; six of those objectives are directly aligned with this project, and the rest can be observed to relate it in a more indirectly manner. In the same way, a summary of the results obtained by the five-year groundwork is presented, as well as the division of the gastronomy in the state according to the characteristics that conform the gastronomic region.

In the last decade, the integration of robots into agricultural tasks has significantly transformed food production from planting to harvest. The precision and efficiency of a robot allow for specialized crop management in cases such as; plant disease identification, optimization of water and fertilizer use, monitoring of environmental and soil conditions, among others. That is, the adoption of robots in agricultural practices through intelligent automation increases crop yields and decreases environmental impact. Intelligent automation faces contemporary challenges such as climate change and population growth, promoting sustainable food security. Therefore, this article presents a review of the state of the art of modular robots and their applications in agriculture. Modular robots have the ability to reconfigure and adapt to different tasks, promoting versatile solutions for the agricultural sector. These solutions are linked to the robot's control systems, which can be centralized, decentralized, and hybrid. Firstly, centralized control allows for unified management and coordination in high-precision tasks. Secondly, the decentralized approach offers flexibility and robustness in changing environments where adaptability is required. Thirdly, hybrids incorporate features of both control types balancing control and autonomy to increase efficiency and effectiveness in practical applications. In some cases, control systems incorporate bio-inspired motion control techniques, where the robot mimics natural movements and behaviors to improve its adaptability in performing a task. For example, the chemosynthesis model, which is a biological process where bacteria convert an inorganic compound into energy, has been adapted for individual robots to explore and navigate their environment. Therefore, this article presents an overview of modular robots, the incorporation of bio-inspired motion control techniques, and their convergence towards the sustainable solution of contemporary problems in the agro-industry.

Despite much published literature on the impacts of agriculture on water quality, knowledge gaps persist regarding which farming systems are of most concern for these relationships, which could help water resource planners better target water management efforts. This study addresses these subjects, seeking to understand how this relationship varies across different farming systems. We used data on water quality status in watersheds of an agricultural region in southern Portugal and crossed it with a map of farming systems for the same region provided by a previous study. By overlaying both data layers, we characterized the areal shares of the farming systems in the watersheds and inspected how these shares relate with water quality status through logistic regression. Results support that the impact of agriculture on water quality is mostly related with specific farming systems. We believe this type of information can be of high interest for agricultural planners and policymakers interested in meeting water quality standards, and we conclude by suggesting innovative policy options based on payments to farmers operating selected farming systems, as a cost-effective way to reconcile agricultural and environmental policy objectives.

Precision medicine approaches often relies on complex and integrative analysis of multiple biomarkers from “omics” data to generate insights that can help either diagnostics, prognostics or therapeutical decisions. Such insights are often made using Machine learning (ML) models that make sample classification for a particular phenotype (yes/no). Building such models is a challenge and time-consuming, requiring advanced coding skills and mathematical modelling expertise. Artificial intelligence (AI) is a methodological solution that has the potential to facilitate, optimize and scale model development. In this work, we developed an AI-based, user-friendly and code-free platform (https://digitalphenomics.com) that fully automates the development of predictive models from quantitative “omics” data. Here, we show the application of this tool with the development of cancer survival prognostics models using real-life data from breast, lung and renal cancer transcriptomes. We report and compare their sensitivity, specificity, accuracy and Receiver Operating Characteristic (ROC) curve Area Under the Curve (AUC). Further, we report the associated sets of genes (biomarkers) and their expression pattern that are predictive of cancer survival. Moreover, we made our models available as online tools to generate prognostic predictions based on the gene expression of the biomarkers. In conclusion, we demonstrated that our tool is a robust user-friendly solution to develop bespoke predictive tools from “omics” data which facilitate precision medicine introduction to the point-of-care.

Scientific literature shows increased interest in the aged and the aging phenomenon. The Aging Attitudes Questionnaire - AAQ was validated for the Portuguese population to understand the importance of attitudes towards old age and their impact on the subjective well-being of the elderly. A sample of 400 subjects (from 18 to 93 years) answered a socio-demographic questionnaire, and the AAQ was composed of three subscales (psychosocial losses, physical change, and psychological growth). The CFA confirmed the tri-factorial structure with very good adjustment of the model to the data with the Cronbach alpha of the total scale scoring .84 and ranging from .65 to .77 for each factor. A total of 9 items were omitted both for poor factor loadings (<0.50. Notwithstanding, 3 items below the criteria were maintained, as they conceptually fit into the factor. Of the final 15 AAQp items, 5 belong to the Psychosocial Loss factor, 6 to Physical Change, and 4 to Psychosocial Growth. This tree factor model explained 50.1 % of the total variance. In conclusion, this study supports that AAQ has acceptable validity, confirming the composite reliability and the discriminant validity, but not the convergent validity. Through multi-group analysis, the invariance of the scale was confirmed. This validation is of pivotal importance once it allows measuring the attitudes towards aging, thus facilitating the promotion of wellbeing across the lifespan.