Patients with degenerative diseases refer to feeling more pain during the night. However, it is unknown whether spinal nociception can be circadian and how is it controlled. We investigated whether the paw withdrawal threshold (PWT) could exhibit physiological circadian behavior as well as the contribution of the dopaminergic A11 nucleus and the spinal dopamine (DA) receptors (DRs) on the circadian PWT and the spinal clock gene transcription. Results revealed that control rats present a circadian PWT. Injecting 6-hydroxidopamine (6-OHDA) into the dopaminergic A11 nucleus reduced DA tissue content in the lumbar spinal cord, abolished the circadian PWT, induced allodynia, and reduced Period 1 and 2 (Per1 and 2), retinoid-related orphan receptor α (Rorα), Cryptochrome 1 (Cry1), and brain and muscle aryl-hydrocarbon receptor nuclear translocator-like protein (Bmal) mRNA. Likewise, administration of D1-like and D2-like DR antagonists blunted circadian PWT, producing allodynia, and altered the clock genes mRNA. In contrast, administration of D1-like or D2-like DR agonists blocked 6-OHDA-induced allodynia. This study shows that the spinal cord has physiological circadian PWT, which is modulated by the descending dopaminergic A11 through differential activation of the spinal DRs. Also, A11 nuclei and spinal DRs can regulate the clock gene transcription, which can likely modulate the circadian PWT.

This article presents a review of resources for batteries with a specific focus on sustainability aspects related to raw materials, electrode and electrolyte chemistries suitable for the automobile industry. We emphasize the importance of prioritizing sustainability in battery technology evolution, which is expected to shift towards earth-abundant materials. One notable advancement in this direction is the utilization of glass solid-state electrolytes, known for their high ionic conductivity, low electronic conductivity, and high dielectric constant. However, our review suggests that further research should explore synthesis methods to modify anisotropic properties, enhancing the performance of solid-state electrolytes, particularly in facilitating the flow of ions.

Recent studies have elucidated the diversity of genes encoding venom in sea anemones. However, most of those genes are yet to be explored in an evolutionary context. Insulin is a common peptide across metazoans and has been coopted into a predatory venom in many venomous lineages. In this study, we focus on the diversity of insulin-derived venoms in sea anemones and on elucidating their evolutionary history. We sourced data for 34 species of sea anemones and found sequences belonging to two venom families that have Insulin PFAM annotations (Cono-Insulin, VP302). Our findings show that both families have undergone duplication events. Members of each of the independently evolving clades have consistent predicted protein structures and distinct dN/dS values. Our work also shows that VP302 is part of a multidomain venom contig and has experienced a secondary gain into the venom system of cuticulate sea anemones.

Topic discovery is finding the main idea of large amounts of textual data. It indicates the recurring topics in the documents, allowing an overview of the texts. Current topic discovery models receive the texts, with or without pre-processing of Natural Language Processing. The processing consists of stopwords removal, text cleaning and normalization (lowercase conversion). A topic discovery model that receives texts with or without processing generates general topics since the input data is many uncategorized texts. The general topics do not offer a detailed overview of the input texts, and manual text categorization is a time-consuming and tedious task. Accordingly, it is necessary to integrate an automatic text classification task in the topic discovery process to obtain specific topics with their top words that contain relevant relationships based on belonging to a class. Text classification performs a word analysis that makes up a document to decide what class or category is being identified; then, integrating the text classification before a topic discovery process will provide latent topics depicted by top words with a high coherence in each topic based on the previously obtained classes. Therefore, this paper exposes a approach that integrates text classification into topic discovery from large amounts of English textual data, such as 20-Newsgroup and Reuters corpora. The text classification is accomplished with a Convolutional Neural Network(CNN) incorporating three embedding models based on semantic relationships. The topic discovery over categorized texts is realized with Latent Dirichlet Analysis(LDA), Probabilistic Latent Semantic Analysis(PLSA), and Latent Semantic Analysis(LSA) algorithms. An evaluation process was performed based on the normalized topic coherence metric. The 20-Newsgroup corpus was classified, and twenty topics with ten top words were discovered for each class, obtaining 0.1723 normalized topic coherence when applying LDA, 0.1622 with LSA, and 0.1716 with PLSA. The Reuters corpus was also classified, obtaining 0.1441 normalized topic coherence when applying the LDA algorithm to obtain 20 topics for each class.

Embedding representation models characterize each word as a vector of numbers with a fixed length. These models have been used in tasks involving text classification, such as recommen- dation and question-answer systems. Semantic relationships are words with a relationship between them providing a complete idea to a text. Therefore, it is hypothesized that an embedding model involving semantic relationships will provide better performance for tasks that use them. This paper presents three embedding models based on semantic relations extracted fromWikipedia to classify texts. The synonym, hyponym, and hyperonym semantic relationships were the ones considered in this work since previous experiments have shown that they are the ones that provide the most semantic knowledge. Lexical-syntactic patterns present in the literature were implemented and subsequently applied to the Wikipedia corpus to obtain the semantic relationships present in it. Several semantic relationships are used in different models: synonymy, hyponym-hyperonym, and a combination of the first two. A convolutional neural network was trained for text classification to evaluate the performance of each model. The results obtained were evaluated with the metrics of precision, accuracy, recall, and F1-measure. The best values obtained with the second model were accuracy of 0.79 for the 20-Newsgroup corpus. F1-measure and recall of 0.87 respectively for the Reuters corpus.

We assume that poetic devices have an implicit goal: producing an overall sound scheme that will induce the reader to associate intended and expressed meaning to the sound of the poem. Sounds may be organized into categories and assigned presumed meaning as suggested by traditional literary studies. In my work, I have extracted automatically the sound grids of all the sonnets by William Shakespeare and have combined them with the themes expressed by their contents. In a first experiment I have computed lexically and semantically based sentiment analysis obtaining an 80% of agreement. In a second experiment sentiment analysis has been substituted by Appraisal Theory thus obtaining a more fine-grained interpretation which in some cases contradicts the first one. The computation for the second poet - regarded by many critics the best of last century - includes both vowels and consonants. In addition, it combines automatic semantically and lexically based sentiment analysis with sound grids. The results produce visual maps that clearly separate poems into three clusters: negative harmony, positive harmony and disharmony where the latter instantiates the need by the poet to encompass the opposites in a desperate attempt to reconcile them.

This paper presents an alternative constraint handling approach within a specialized genetic algorithm (SGA) for the optimal reactive power dispatch (ORPD) problem. The ORPD is formulated as a nonlinear single-objective optimization problem aiming to minimize power losses while keeping network constraints. The proposed constraint handling approach is based on a product of sub-functions that represents permissible limits on system variables and that includes a specific goal on power loss reduction. The main advantage of this approach is the fact that it allows a straightforward verification of both feasibility and optimality. The SGA is examined and tested with the proposed constraint handling approach and the traditional penalization of deviations from feasible solutions. Several tests are run in the IEEE 30, 57, 118 and 300 bus test power systems. The results obtained with the proposed approach are compared to those offered by other metaheuristic techniques reported in the specialized literature. Simulation results indicate that the proposed genetic algorithm with the alternative constraint handling approach yields superior solutions when compared to other recently reported techniques.

Abstract: The increased penetration of renewables in distribution power systems has motivated researchers to take significant interest in local energy transactions. The major goal of Local Energy Markets (LEM) is to promote the participation of small consumers in energy transactions and providing an opportunity for transactive energy systems. Such energy transactions in LEM are considered as a bi-level optimization problem in which all agents at upper and lower levels try to maximize their profits. But typical bi-level problem is very complex as it is inherently nonlinear, discontinued and strongly NP-hard. So, this article proposes the application of hybridized Cross Entropy Covariance Matrix Adaptation Evolution Strategy (CE-CMAES) to tackle such a complex bi-level problem of LEM. The proposed CE-CMAES secured the 1st rank in Testbed-2 entitled, “Bi-level optimization of end-users’ bidding strategies in local energy markets (LM)” at international competitions on Smart Grid Problems, held at GECCO 2020 and WCCI 2020. CE method is used for global exploration of search space and CMAES is used for local exploitation as its adaptive step-size mechanism prevents its premature convergence. A practical distribution system with renewable energy penetration is considered for simulation. The comparative analysis shows that the overall cost, mean fitness and Ranking Index (R.I) obtained from CE-CMAES are superior to those obtained from the state-of-the-art participated algorithms. Wilcoxon Signed Rank Statistical test also proves that CE-CMAES is statistically different from the tested algorithms.

Androgens are key for pubertal development of the mammalian testis, a phenomenon tightly linked to Sertoli cell maturation. In this review, we discuss how androgen signalling affects Sertoli cell function and morphology by concomitantly inhibiting some processes and promoting others that contribute jointly to the completion of spermatogenesis. We focus on the molecular mechanisms that underlie AMH inhibition by androgens at puberty, as well as on the role androgens have on Sertoli cell tight junction formation and maintenance and, consequently, on its effect on proper germ cell differentiation and meiotic onset during spermatogenesis.

The effect of the addition of maleated polyethylene (MAPE) to compounds of natural rubber (NR) and Teline monspessulana flour (TMF) previously mercerized was investigated. Two factors were analyzed: A. concentration of MAPE with five levels 2; 4; 6; 8; 10 phr (parts per hundred rubber), B. concentration of TMF with two levels 25 and 40 phr. The effect of MAPE on compatibility between NR and HTM was evaluated by tensile testing the compounds. The mixing was performed in a laboratory scale mill. The test tubes were obtained by cutting or die-cutting crosslinked peroxide sheets, these were obtained during the compression molding process. Analysis indicate that the MAPE coupling agent improved the compatibility between HTM and NR, this effect was evidenced by the values of tensile strength and elongation at break. However, the gel content determination indicates that the addition of 10 phr of MAPE crosslinking decreases due to competition with coupling reaction MAPE - HTM.

Quantum Computation, in the gate array version, uses logical gates adopting convenient forms for computational algorithms based on those of classical computation. There, two-level quantum systems are the basic elements connecting the binary nature of classical computation with the settlement of quantum processing. Despite, their design depends on specific quantum systems and physical interactions involved, exacerbating the dynamics analysis. Predictable and controllable manipulation should be addressed to control the quantum states, but resources are restricted to limitations imposed by the physical settlement. This work presents a formalism to decompose the quantum information dynamics in SU(2^2d) for 2d-partite two-level systems into 2^2d-1 SU(2) quantum subsystems. Decomposition lets to set control procedures, to generate large entangled states and to design specialized dedicated quantum gates. There, easy and traditional operations proposed by quantum computation are recovered for more complex and large systems. Alternating the parameters of local and non-local interactions, the procedure states a universal exchange semantics on the generalized Bell states basis. It could be understood as a momentary splitting of the 2d information channels into 2^2d-1 pairs of 2 level quantum information subsystems and a settlement of the quantum information manipulation free of the imposed restrictions by the underlying physical system.

Forecasting streamflow accurately is essential to achieve an efficient integrated water resources management strategy and provide consistent support to water decision-makers. We present a simple, low-cost and robust approach for forecasting monthly and yearly streamflow during the hydrological year in course, applicable to headwater catchments. It combines the use of regression analysis techniques, the two-parameter Gamma continuous cumulative probability distribution function and the Monte Carlo method. It is based on a probabilistic comparison of the progression of the current hydrological year with the historic observed series. The methodology has been successfully applied to two headwater reservoirs within the Guadalquivir River Basin in southern Spain. The root-mean-square error and correlation coefficient were used to measure the accuracy of the model and the results showed good levels of reliability. The outputs are the probabilistic monthly and yearly streamflow and 80% confidence interval. Further reductions in prediction errors may be achieved from increasing the number of observed years. These risk-based predictions are of great value, especially, before the intensive irrigation campaign starts (usually in April), when Water Authorities are to take responsible management decisions about the best allocation of the available water volume between the different water users and environmental needs.

The province of Jaén (Andalusia, Spain), despite being declared the European territory with the largest number of defensive constructions (castles, fortifications, etc.), has few conservation plans, with many remains included on the Red List of Spanish Heritage ly-ing abandoned. This poses a challenge for landscape conservation and the optimal use of the province’s tourism potential. In this paper we present the early implementation of an ongoing methodological approach designed to alleviate this situation. It takes advantage of data from the Sentinel 2A and 2B satellites, HBIM, RPAS, GIS and fieldwork that will be applied across five archaeological case studies. It has been tested for the first time in the village of Magaña. Three main objectives and phases of this methodological approach have been designed: 1. Preparation of an Environmental Risk Chart with which to answer such ques-tions as “How have climate change, anthropic alterations and environmental characteris-tics affected the state of conservation of certain heritage sites?” 2. Preparation of HBIM models to foster monitorisation and conservation policies for the main archaeological remains and improving their protection based on digital and technological tools. 3. Building a Smart Tourism App for mobile devices aimed at promoting smart tourism by digitalising and virtualising tourist itineraries and archaeological remains. Finally, public administrations will be apprised of the need to implement a conservation policy for cultural assets and their surroundings in a simple, quick and cost-effective man-ner.

The province of Jaén (Andalusia, Spain), despite being declared the European territory with the largest number of defensive constructions (castles and fortifications), has few conservation plans, with many remains included on the Red List of Spanish Heritage lying abandoned. This presents a problem for the conservation of the landscape and the optimal use of the province’s tourism potential. Through the study case of Magaña hamlet two actions are proposed to alleviate this situation: The creation of an archaeological and environmental risk chart with which to answer such questions as “How have climate change, anthropic alterations and environmental charac-teristics affected the state of conservation of certain heritage sites?” and to put forward proposals for improving their protection using as a basis digital and technological tools, such as remote sensing (SAR), taking advantage of data from the Sentinel 2A and 2B satellites, HBIM, RPAS and GIS. To foster the promotion of smart tourism by digitalising and virtualising tourist routes and archaeological remains by building a Smart Tourism App for mobile devices. Finally, public administrations will be apprised of the need to implement a conservation policy for cultural as-sets and their surroundings in a simple, quick and cost-effective manner.

This study examines the feasibility of using event-related potentials (ERPs) obtained from electroencephalographic (EEG) recordings as biomarkers for long-term memory item classification. Previous studies have identified old/new effects in memory paradigms associated with explicit long-term memory and familiarity. Recent advancements in convolutional neural networks (CNNs) have enabled the classification of ERP trials under different conditions and identification of features related to neural processes at the single-trial level. We employed this approach to compare three CNN models with distinct architectures using experimental data. Participants (N = 25) performed an association memory task while recording ERPs that were used for training and validation of the CNN models. The EEGNET-based model achieved the most reliable performance in terms of precision, recall, and specificity compared with the shallow and deep convolutional approaches. The classification accuracy of this model reached 70% for known items and 62% for unknown items. Good overall accuracy requires a trade-off between recall and specificity and depends on the architecture of the model and the dataset size. These results suggest the possibility of integrating ERP and CNN into online learning tools and identifying the underlying processes related to long-term memorization.

By virtue of the knowledge of the beneficial effect of the warm and dry storage, for the growth of the chincuya seed embryo, defined as morphological dormancy. The effect of the same type of storage was studied for the seed viability and germination by soaking gibberellic acid. Also, fatty acids and their kinetics were identified during the warm and dry storage. The following treatments were evaluated: storage time (0, 3, 6, 9 and 12 months), seed soaking in gibberellic acid at 350 mg L-1, and for the control, imbibition in distilled water, both for 72 h. The seed viability was evaluated through the stain test with tetrazolium chloride. The embedded seeds were germinated on absorbent paper towels and incubated at different temperatures of 30 ° C during the day and 25 ° C at night and 12 hours of photoperiod. The lipid profile was developed by gas chromatography. Seeds were held at constant temperature of 25 ± 3 ° C, in darkness, during the storage time. Results demon-strated that the high initial viability was maintained until the ninth month, when it decreases and is statisti-cally different from the other treatments. The smaller germination percentage (26.1 %) was observed in the freshly harvested seeds and, the higher one corresponded to the six months (65.6 %), with these differences being statistically significant. Regarding the identified fatty acids and their average content, it was found that 33.41 % corresponded to palmitic acid, 7.72 % to stearic acid; the arachidic acid with 1.30 %, making a total of saturated fatty acids of 42.44 %. The detected unsaturated fatty acids were: palmitoleic acid 1.52 %, oleic acid 43.37 % and linoleic acid 7.24 %, in total, 52.15 %. No significative changes were observed in the fatty acids content that are the largest part of the total seed reserves; although the higher concentrations matched with the longest germination (three months), with an average of 92.84 % total fatty acids. Specified regressions indi-cated with statistical robustness that palmitoleic and palmitic acids increased while the oleic acid decreased during the dry storage. The arachidic acid showed a slightly downward trend. Also, the concentrations of fat-ty acids were significatively different between unstored and unincubated seeds (intact) and unstored and in-cubated, but without germinating seeds (dormant), in the latter, the total fatty acids content was significantly lower. Given that the application of gibberellic acid and warm dry storage favored germination, as well as the change in fatty acid content during warm dry storage, added to the results already reported on morphological changes and embryonic growth as an effect of the same type of storage, it is established that Annona purpurea seeds present morphophysiological dormancy, also this is the first identification report of arachidic and pal-mitoleic acids in chincuya seeds and the higher germination percentage reported (65.6 %).

Taxes on the consumption of certain products have played a key role in public revenue analysis in European countries. This work aims to assess the differences in tax planning on new non-combustion products across some European territories. Through theoretical analysis, the paper has three distinct aspects. Firstly, we show the taxpayer profile in different countries. Secondly, the study analyzes the risks for the health system and the individual. Thirdly, the design of the land tax is based on previous experience and planning in Europe. This paper concludes with a case study for different countries: 1) Spain “vapers” and “smokers”, where the analysis is based on micro-data from the European Health Interview Survey 2020. 2) Sweden with the analysis of snus, shows it as a practice to reach economic savings while decreasing economic costs thanks to the nudging of consumers towards better products. The results show taxation and, specially, land taxation, should consider differences in planning in the case of new products, such as the non-combustion products, given the user per capita income and the potential lower health risks involved.

Based on economic feasibility, renewable generators can use pumped hydro storage (PHS) to improve their profitability by performing energy arbitrage under real time pricing (RTP) schemes. In this paper, we present a new method to optimise the size and manage utility-scale wind-PV systems using PHS with energy arbitrage under RTP. The storage in the PHS is used to supply load consumption and/or energy arbitrage. Further, both load supply and power-generating systems are considered, and a genetic algorithm metaheuristic technique is used to perform the optimisation efficiently. Irradiance, wind speed, temperature, hourly electricity price, component characteristics, and financial data are used as data, and the system is simulated in 15 min time steps during the system lifetime for each combination of components and control variables. Uncertainty is considered for the meteorological data and electricity prices. The pump and turbine efficiencies and available head and penstock losses are considered as variables (not fixed values) to obtain accurate simulations. A sample application in Spain is demonstrated by performing a sensitivity analysis of different locations, electricity prices, and costs. The PHS is not worth considering with the present cost of components.

Microstructured optical fibres (MOFs) are a new type of optical fibres that possess a wide range of optical properties and many advantages over common optical fibres. Those are provided by unique structures defined by a pattern of periodic or quasi-periodic arrangement of air holes that run through the fibre length. In recent years, MOFs have opened up new possibilities in the field of optics and photonics, enabling the development of advanced devices and novel optical systems for different applications. The key application areas of PCFs vary from telecommunications and high-power energy transmission to quantum optics and sensing. The stack-and-draw method is a standard manufacturing technique for MOFs, where a preform is first manually created and then drawn in a high-tech furnace into a fibre with the required final dimensions and position of the air holes. Since in the manufacturing process experimenters can control only a few parameters, mathematical models and numerical simulations of the drawing process are highly requested. They not only allow to deepen the understanding of physical phenomena occurring during the drawing process, but they also accurately predict the final cross-section shape and size of the fibre. In this manuscript, we assume thermal equilibrium between the furnace and the fibre and propose a functional form of the fibre temperature distribution. We utilise it with asymptotic mass, momentum, and evolution equations for free surfaces already available in the literature to describe the process of fibre drawing. By doing so, the complex heat exchange problem between the fibre and the furnace need not be solved. The numerical results of the whole asymptotic model overall agree well with experimental data available in the literature, both for the case of annular capillaries and for the case of holey fibres.

Efficient alternatives in energy production and consumption are constantly investigated by increasingly strict policies. In this way, the pollutant emissions that contribute to the greenhouse effect reduce and sustainability of the electricity sector increase. With more than a third of the world's energy consumption, buildings have great potential to contribute these sustainability goals. Additionally, with growing incentives in the Distributed Generation (DG) and Electric Vehicle (EV) industry, it is believed that Smart Buildings (SBs) can be a key in the field of residential energy sustainability in the future. In this work, an energy management system in SBs are developed to reduce the power demanded of a residential building. In order to balance the demand and power provided by the grid, microgrids such as Battery Energy Storage System (BESS), EVs and Photovoltaic Generation panels (PV) are used. Here, a Mixed Binary Linear Programming formulation (MBLP) is proposed to optimize the charge and discharge scheduling of EVs and also BESS. In order to show the efficiency of the model, a case study involving three scenarios and an economic analysis is considered. The results point a 65% reduction in peak load consumption supplied by grid and a 28.4% reduction in electricity consumption costs.

A novel method based on a hybrid incremental modeling approach has been designed and applied to imbalance detection in ultra-high precision rotating machines. The model is obtained by a two-step iterative process that combines an overall model (least-squares fitting) with a local model (fuzzy k-nearest-neighbour) to take advantage of their complementary capacities. Three normalization strategies of evaluating the effect on accuracy are analyzed. A comparative study demonstrates that the hybrid incremental model provides better error-based performance indices for detecting imbalance than a nonlinear regression model and an adaptive neural-fuzzy inference system model. The suitability of Mahanolobis normalization for hybrid incremental modeling is also demonstrated in this case study. The proposed strategy for imbalance detection is simple, fast, and non-intrusive, reducing the deterioration in the performance of ultra-high precision rotating machines due to vibrations.

Monitoring complex electro-mechanical processes is not straightforward despite the arsenal of techniques nowadays availanle. This paper presents a method based on Adaptive-Network-based Fuzzy Inference System (ANFIS) to estimate eccentricity of its spinning axis. The method is experimentally tested on an ultra-precision rotating device commonly used for micro-scale turning. The developed model has three inputs, two obtained from a frequency domain analysis of a vibration signal and the third, which is the device rotation frequency. A comparative study demonstrates that an adaptive neural-fuzzy inference system model provides better error-based performance indices for detecting imbalance than a non-linear regression model. This simple, fast, and non-intrusive imbalance detection strategy is proposed to counteract eventual deterioration in the performance of ultra-high precision rotating machines due to vibrations.

Severe combined immunodeficiency (SCID) is a primary inherited immunodeficiency that could be fatal, usually due to opportunistic infections caused by bacteria, viruses, fungi, and protozoa if left untreated. Opportunistic infections are the most common presentation of SCID these include viral, fungal, or intracellular bacteria, and infection should prompt immunological investigation. This review aimed to provide a comprehensive approach to the microrganisms associated with severe combined immunodeficiency (SCID) and its management. The authors described SCID as a syndrome and summarized the different microorganisms that affect children and how they are managed.

The use of biolubricants as an alternative for petroleum-based products has played an important role in the last decade. Thus, due to the encouragement of global policies, which mainly support green chemistry and circular economy, there has been an increasing interest in bio-based products, including biolubricants, from scientific and industrial points of view. Due to the different applications of biolubricants for a wide range of practical uses, the raw materials, production, and characteristics might vary, making this field a continuously changing subject of study by researchers. The aim of this research work was to focus on biolubricant production from vegetable oil crops in a bio-refinery perspective, paying attention to the main raw materials used, the corresponding production methods (with special focus on double transesterification), the role of catalysts and techno-economic studies. Thus, the main factors affecting quality parameters such as viscosity or oxidative stability have been covered, including catalyst addition, reaction temperature or the use of raw materials, reagents or additives were also analyzed. The latest research trends were included, updating previous studies about this matter, considering current conclusions and future research.

The global issues of overweight and sedentary lifestyles demand comprehensive responses from health systems, yet health education still needs to be more cohesive and less sporadic. This study focuses on primary school children as a specific target for instilling healthy habits, employing ac-tive learning strategies to address healthy diet and physical activity. Through Problem-Based Learning projects, the study engaged fourth-grade primary students in acquiring healthy habits. The study was carried out over four consecutive years at a school with two lines, allowing the employment of an opportunity sample and evaluating intervention and control groups by com-paring their corresponding scores. Control groups integrated a total of 54 students, while the in-tervention group included a total of 159 students. A pre-test and post-test questionnaire were used for data collection. Evaluation differential scores revealed statistically significant higher scores in the intervention groups: 3.62 points in control groups and 6,94 in intervention groups, particu-larly in attitudinal competences. These strategies not only facilitated the development of crucial skills such as information search, synthesis, representation, analysis, decision-making, teamwork, and intrapersonal awareness but also significantly broadened the knowledge acquired in aspects related to healthy habits. This approach proved effective in helping children comprehend the im-portance of healthy choices and encouraged integrating such habits into their daily lives.

The main objective of this work is the design and implementation of self-adaptive capabilities in wireless sensors by applying control engineering and model-based design methodologies. It has been addressed the problem related to the changes in the flow of data packets through the network connection and the excess energy consumption that this causes in these devices. To design the solution, a systemic characterization of the scheduling and execution process of embedded tasks on the device has been carried out. This means defining cause-effect relationships in the system and its modelling theoretically and/or experimentally. In turn, these models facilitate the design of control strategies to improve the dynamic behavior of the system. As a solution, a self-adaptation strategy based on feedforward control algorithm has been designed and developed, which has been applied to improve the dynamic behavior and resource consumption. The developed solution has been satisfactorily evaluated experimentally.

This research examines the effects of the implementation of an educational programme of mindfulness training using Virtual Reality (VR) with students in adapted vocational training. The aim of this programme is to instruct and strengthen attention and self-regulation skills in relation to the learning process, with the objective of reducing anxiety and impulsivity in students, contributing to promoting sustainability in education, as well as reducing inequalities. The development and implementation of this project was carried out in IES Teguise in Lanzarote (Spain) with 11 students with intellectual disabilities who used the Virtual Reality Mindfulness application called TRIPP for three months. The use of Virtual Reality offers immersive experiences, while mindfulness enables concentration, which directly contributes to higher academic performance. After the implementation of the programme, it is observed that the use of this emerging technology provides students with a sense of calm and well-being, which translates into improvements in their academic grades, promoting equal and quality education in line with the Sustainable Development Goals (SDGs).

In this paper, we introduce Syntax-Enhanced Transformer Model (SET), a groundbreaking approach in the realm of Transformer-based language modeling that seeks to redefine the boundaries of linguistic analysis and comprehension. SET innovatively combine (i) the well-established high-level performance, scalability, and adaptability of traditional Transformers with (ii) a sophisticated analysis of syntactic structures. This synergy is enabled by a novel attention mechanism tailored to parse syntactic nuances and a deterministic process adept at transforming linearized parse trees into meaningful linguistic representations. Our comprehensive experiments reveal that SET significantly advance the field by surpassing existing benchmarks in sentence-level language modeling perplexity. They exhibit exceptional proficiency in tasks that require an acute awareness of syntax, setting new standards for language models in understanding complex linguistic structures. Furthermore, SET demonstrate an enhanced capability to grasp nuanced linguistic patterns that have traditionally been challenging for standard Transformer models. However, our studies also uncover a unique aspect of SET: while they excel in sentence-level tasks, their representation of sentences as singular vectors—owing to the syntactic composition constraints intrinsic to their design—introduces certain limitations in document-level language modeling. This observation points to an intriguing area for future exploration; it suggests the potential need for an alternative or complementary memory mechanism within Transformer models, one that functions independently from, yet in harmony with, syntactic structures. Such a mechanism could be pivotal in enhancing the model's ability to comprehend and process long-form texts effectively. In conclusion, SET mark a significant stride in the journey towards more sophisticated, syntax-aware language models. They offer promising insights into the integration of deep linguistic knowledge with cutting-edge machine learning techniques, potentially opening doors to a new era of natural language understanding and processing.

The paradigm of leveraging code as a dataset has recently gained traction, offering innovative solutions in domains such as automated commit message generation, pull request description automation, and program repair mechanisms. Consider the challenge in generating commit messages: traditional methods treat source code as a mere token sequence, applying neural machine translation models. This approach, however, overlooks the critical syntactic structures inherent in programming languages, which could offer deeper insights and improved accuracy. Building upon prior research, specifically the Code2Seq framework, which utilized Abstract Syntax Tree (AST) structural data for source code representation to automate method name generation, this paper extends and refines this concept. We introduce "CSR", a novel methodology adapted to represent code edits effectively. This paper investigates the impact of employing syntactic structure, focusing on the classification of code edits. Drawing inspiration from Code2Seq, "CSR" utilizes AST's structural properties, particularly the paths connecting leaf nodes, to enhance the task of code edit classification. This approach is rigorously evaluated on two distinct datasets, comprising fine-grained syntactic edits. Our comprehensive experiments reveal that incorporating syntactic structures does not significantly outperform simpler methodologies. While methods like Code2Seq and our proposed "CSR" show potential, our findings highlight that there is considerable scope for improvement and refinement before such techniques can be universally applied for learning representations of code edits. We anticipate that our findings will spark further research in this field, paving the way for more effective use of syntactic structures in code representation.

Contemporary advanced models for aspect-targeted opinion word extraction (ATOWE), which predominantly utilize BERT-based encoders at a word level, have shown limited advancements when integrated with graph convolutional networks (GCNs) for syntactic tree assimilation. Recognizing the prowess of BERT subwords in encapsulating rare or context-poor words, this study pivots from syntactic trees to BERT subwords, omitting GCNs from the structural framework. Our approach, named Aspect-Enhanced Wordpiece Extraction Model (AEWEM), focuses on refining aspect representation during encoding. We propose an input format of paired sentence-aspect, diverging from traditional single-sentence inputs. AEWEM demonstrates superior performance on benchmark datasets, establishing a robust foundation for future explorations in this domain.

The Transformer architecture, while adept at capturing context through self-attention, falls short in encapsulating complex syntactic structures effectively. Addressing this gap, we introduce the Linguistic Structure through Graphical Interpretation with BERT (LSGIB) approach in Machine Translation (MT) frameworks. Combining the strengths of Graph Attention Network (GAT) and BERT, LSGIB intricately captures syntactic dependencies as explicit knowledge from the source language. This enhances the source language representation and aids in more accurate target language generation. Our empirical analysis leverages gold-standard syntax-annotated sentences and employs a Quality Estimation (QE) model. This approach enables us to assess translation improvements in terms of syntactic accuracy, extending beyond traditional BLEU score metrics. The LSGIB model demonstrates superior translation quality across diverse MT tasks, maintaining robust BLEU scores. Our study delves into the optimal sentence lengths benefiting from LSGIB and identifies which syntactic dependencies are more precisely captured. We observe that GAT's ability to learn specific dependency relations directly influences the translation quality of sentences with those relations. Additionally, we discover that incorporating syntactic structure into BERT's intermediate and lower layers offers a novel approach to modeling linguistic structure in source sentences.

The field of program classification, a critical aspect of software engineering, facilitates understanding and categorization of code across various applications, including anomaly detection, and code quality assessment. The advancement of cross-language program classification opens up avenues for efficient code translation among different programming languages, significantly aiding developers in rapid coding and reducing development cycles. Existing research primarily focuses on semantic code analysis, with limited emphasis on cross-linguistic challenges. To address this, we introduce an innovative neural network model, CodeSemanticsNN, which leverages a refined Syntax Structure (SS) approach. This model consists of two integral mechanisms: first, it adopts a novel SS representation that combines sequential and graph-based SS structures, enhancing semantic feature capture. Second, it employs a 'unified vocabulary' strategy to bridge the gap between diverse programming languages, facilitating efficient cross-language classification. Additionally, we have compiled a comprehensive dataset of 20,000 files spanning five programming languages, serving as a benchmark for cross-language classification. Our experiments on this dataset demonstrate that CodeSemanticsNN surpasses existing models in four key metrics: Precision, Recall, F1-score, and Accuracy.

The evolution of sentence representation learning, especially with parse tree encoders, has shown remarkable progress. Traditional approaches predominantly rely on recursive encoding of tree structures, which impedes parallel processing capabilities. Additionally, these methods often overlook the significance of dependency tree arc labels. To overcome these limitations, we introduce the Syntax-Enhanced Transformer (SET), incorporating a novel dual-attention mechanism that integrates relation-focused attention alongside traditional self-attention. This design effectively encodes both dependency and spatial positional relationships within sentence dependency trees. Our approach innovatively incorporates syntactic information into the Transformer framework without compromising its inherent parallelizability. The SET demonstrates superior or comparable performance to contemporary methods across various sentence representation tasks, significantly enhancing computational efficiency.

This paper introduces a novel graph polynomial approach for differentiating tree structures in dependency grammar. Utilizing this polynomial representation, we develop a metric to assess the similarity in syntax. This approach offers a detailed and inclusive analysis of the dependency structures and relationships in sentence construction. We employ this polynomial method to examine sentence structures across various languages in the Parallel Universal Dependencies treebanks. Our analysis includes comparing the syntax of original sentences and their translated counterparts in diverse languages, alongside a comprehensive study of syntactic typologies within these treebanks. Additionally, we explore the application of our methodology in evaluating the syntactic diversity within language corpora.

Recent advancements in neural language models, particularly attention-based architectures like the Transformer, have substantially surpassed traditional methods in various natural language processing tasks. These models adeptly generate nuanced token representations by considering contextual relations within a sequence. However, augmenting these models with explicit syntactic knowledge, such as part of speech tags, has been found to remarkably bolster their effectiveness, especially under constrained data scenarios. This study introduces the Linguistic Enhanced Transformer (LET), which integrates multiple syntactic features, showing a notable increase in translation accuracy, evidenced by an improvement of up to 1.99 BLEU points on subsets of the WMT '14 English-German dataset. Furthermore, this paper demonstrates that enriching BERT models with syntax-aware embeddings enhances their performance on several GLUE benchmark tasks.

Conventional text style transfer (TST) methodologies primarily utilize style classifiers to segregate the content and stylistic elements of text for effective style transformation. Despite the pivotal role of these classifiers, their influence on TST techniques remains largely unexplored. This study embarks on a detailed exploration of the limitations inherent in style classifiers within current TST frameworks. We reveal that these classifiers often inadequately comprehend sentence syntax, leading to diminished performance in TST models. In response, we introduce the Syntax-Enhanced Style Transfer (SEST) model, a groundbreaking approach incorporating a syntax-sensitive style classifier. This classifier ensures that the extracted style representations robustly encapsulate syntax nuances, enhancing TST effectiveness. Rigorous evaluations across diverse TST benchmarks demonstrate that SEST significantly surpasses contemporary models in performance. Additionally, our case studies highlight SEST's proficiency in producing syntactically coherent sentences that aptly retain original content.

In this paper, experimental results about the performance of a Takagi-Sugeno Fuzzy Controller (TSFC) for an EV3 Ballbot Robotic System (EV3BRS) are reported. The physical configuration for the EV3BRS has the form of an inverted pendulum mounted on a ball. The EV3BRS is an underactuated robotic system with four outputs and two control torques. The Takagi-Sugeno Fuzzy Model (TSFM) design comes from linearization of the nonlinear model around two operation points near from the upright position of the EV3BRS's body. The Parallel Distributed Compensation (PDC) approach is used to the design of the TSFC. The Linear Matrix Inequalities (LMIs) approach is used to get the feedback gains for every local linear controller guaranteeing global asymptotically stability, via a more relaxed stability condition, of the overall fuzzy control system. Measurement and control data from and to the EV3BRS are transferred via telecontrol and telemetry.

Gymnophiona, the most poorly known group of extant amphibians, includes elongated limbless tetrapods, with compact ossified skulls and reduced eyes, mainly adapted to fossorial life (only the Typhlonectidae exhibits adaptations for an aquatic or semiaquatic behavior). Caecilians are poorly represented in the fossil record, and despite the low number of fossil specimens described until now (only four taxa, in addition to indeterminate fragmentary material), their fossils play a key role in the knowledge of Lissamphibia origin and evolution, as well as contribute directly to a better understanding of phylogeny, taxonomy and biogeography of extant gymnophionan taxa. These records are scattered throughout geological time (from the Jurassic to the Neogene) and space (they are represented only on North and South America and Africa). Here, we revisit the caecilian fossil record, providing a brief description of all known extinct taxa described so far, along with general remarks about their impact on systematics, time range and geographic distribution of the clade, as well as prospects for future research. Possible calibration constraints based on the caecilian fossil record are provided.

Background and Objectives: The techniques directed to the cranial field in osteopathy are the most questioned due to the lack of scientific evidence. In osteopathic practice, manual palpation is essential and, therefore, measuring reliability is fundamental. The objective of study is to assess the reliability and validity of osteopathic treatment depending on experience. Materials and Methods: A cross-sectional study of reliability and validity was conducted. For measurements, a strain gauge was placed on the sphenobasilar synchondrosis of the skull base, and three maneuvers (lateral compression, anteroposterior compression and compression maneuver of the mastoids) were repeated 25 times each by osteopaths with different time of experience (5-10 years, 1-5 years, <1 year). Measurement averages were computed for each of the three maneuvers to verify the average effect of each group in comparison to that of the Gold Standard (GS) (>10 years of experience). Data were analyzed to check for inter- and intra-observer reliability using intra-class correlation coefficients (ICC). Results: Reliability and validity in 5-10 experience of observer 1 and observer 2 in the tree maneuvers was excellent (p<0.001) against GS. Poor or enough reproducibility and concordance were observed in osteopaths with less experience. Conclusion: Experience of osteopaths determines the efficacy of cranial maneuvers in osteopathic treatment for patients’ rehabilitation.

I. Introduction: Adherence to ART reduces morbidity and mortality among PLHIV by suppression of viral replication, restoration and preservation of immune function. However, poor adherence may lead to treatment failure and death. PLHIV have high probability of treatment interruptions due to lockdowns related to COVID-19 protocol. Mortality rate is higher among COVID-19 patients with HIV than those with COVID-19 only. This led to determination of the barriers to antiretroviral medication adherence in PLHIV at the time of COVID-19 pandemic and seek significant association between identified HIV treatment barriers and socio-demographic characteristics. II. Methods: A cross-sectional study using online survey questionnaire was distributed via social media. Sample size was computed using Open Epi software. Data was analyzed using Stata software. Categorical variables and quantitative data were summarized using frequencies and percentages, and mean and standard deviation respectively. Chi-square test or Fisher’s exact test, whichever is more appropriate, was used to determine association between socio-demographic characteristics and HIV treatment barriers. III. Results: There is a total of 116 respondents, 115 were males, homosexual (59.5%), with mean age of 30.25 years old (SD = 6.22) and majority (53.4%) were from NCR. The most common HIV treatment barriers reported by PLHIV in accessing treatment and care were unavailability of transportation and cost of courier services for ARV delivery (62.1%), location of treatment hubs (52.6%) and financial assistance (37.9%). IV. Conclusion: There is significant association between location of treatment hubs and respondents who finished college/graduate studies; checkpoints and crossing borders and: 1. respondents from Northern Luzon, 2. unemployment; financial assistance and: 1. Respondents 18 to 25 years old, 2. unemployment, 3. respondents who finished elementary/high school; psychosocial support and: 1. Respondents from NCR, 2. Respondents 26 to 30 years old;

Antioxidants are molecules that neutralize free radicals. In general, the reaction mechanisms of antioxidants are well known. The main reaction mechanisms of antioxidants are electron transfer (ET), proton transfer (PT), H atom transfer (HAT) and radical adduction (RAF). The study of these mechanisms is helpful to understand how antioxidants control high free radical levels on the cell. There are many studies focused on determine the main mechanism of an antioxidant to neutralize a wide spectrum of radicals, mainly reactive oxygen species (ROS) type radicals. Most of these antioxidants are polyphenols type compounds. Some esters, amides and metal-antioxidants have shown antioxidant activity. There are few experimental and theoretical studies about the antioxidant reaction mechanism of the aforementioned compounds. In this work, we shown the reaction mechanism proposed of an amide and its metal-antioxidant counterpart. We show how the presence of the metal increase the electron transfer on polar media and the H transfer in non-polar media. Even though, esters and amides are non-polar compound, the scavenger activity is good for the metal-antioxidant compound in no-polar media

In the context of multiple repurposing of rural spaces, tourism represents a path for development, with the potential to revitalize these areas. The conservation and restoration of heritage, and its promotion through tourism, can become an opportunity for local development, in which a range of stakeholders fulfil different roles in the carrying out of the processes involved. The aim of the study was to analyse the process and results of channelling heritagisation through tourism in Mértola (Baixo Alentejo, Portugal). A series of interviews with the chief stakeholders in the process was conducted, from which the contexts and conceptualisations of development were determined. On the basis of secondary data (statistics), an analysis of the impacts of the process of heritagisation and the development of tourism was undertaken. The objectives of this study consisted in determining: a) the importance of the process of heritagisation in Mértola; b) the viability of the project, given the cost and lack of comprehensive conservation, in creating a unified whole; d) the performance of, and power relationships between, the various stakeholders; e) the limited participation of locals due to disaffection with the project; f) the correlation between heritage, rural tourism and local development.

The recovery and valorization of waste are some of the key aspects of sustainable production. The crustacean exoskeletons can be potentially used to obtain value-added products such as chitosan. A comprehensive analysis including both safety and sustainability aspects of chitosan production from shrimp shells is presented in this study. The inherent safety analysis and sustainability evaluation was performed using the Inherent Safety Index (ISI) methodology and the Sustainable Weighted Return on Investment Metric (SWROIM), respectively. The process was designed for a processing capacity of 57,000 t/y according to shrimp production in Colombia. The economic (%ROI), environmental (PEI output), energy (exergy efficiency), and safety (ITI) technical parameters were included in the sustainability evaluation. The three first were obtained from the previous analysis performed by the authors. The total inherent safety index was estimated at 25 indicating that the process is inherently unsafe. The main process risks were given by the dangerous substance, reactivity, and inventory subindices. The overall sustainability evaluation showed a SWROIM of 36.23% indicating that the case study showed higher weighted performance compared to the return on investment (ROI) metric of 18.08%.

Huge amounts of wastes are generated during shrimp processing, representing approximately 65% of the initial shrimp weight, which can become an environmental problem when accumulated. Residues such as shrimp shells can be processed to obtain value-added products such as chitin, chitosan, astaxanthin, and a nitrogenous extract under the biorefinery concept. In this work, the economic evaluation and the techno-economic sensibility analysis for a mass integrated biorefinery based on shrimp were developed to determine the economic feasibility of the project and to identify the critical techno-economic variables that affect the profitability of the process. The results showed that a biorefinery for the annual processing of 4,113.09 tons of fresh shrimp in Colombia is profitable, with a return on investment percentage (%ROI) equal to 65.88% and a net present value (NPV) of 10.40 MM USD. The process supports decreases of up to 28% in capacity of production and increases of 12% and 11% in the cost of raw materials and variable operating costs without incurring losses, respectively. However, the decrease over 500 USD/t in the shrimp meat selling price is not supported, thus it is mainly recommended to increase the selling price of this product.

The wave energy converter (WEC) technology constantly develops new designs and models. To encourage its development within the energy sector, research is required to improve current designs and create durable and effective devices. However, it is also crucial to investigate the suitability of a WEC to a site-specific sea condition as this can impact its optimal power absorption and constructability. This study evaluates and compares the efficiency of a heaving buoy with varied geometries in one of the most energetic places along the Mexican coast. A statistical analysis of the wave climate on the coast of Ensenada during the last 42 years is performed to define the conditions to which the device is subjected. The location of the WEC in shallow waters is chosen using a computational model which solves the modified mild slope equation in its elliptic shape. The heaving buoy floater is studied using three distinct geometries: a semi-sphere, a cylinder, and a proposed rounded semi-rectangle. The hydrodynamic response of the three geometries is then analysed in the frequency and time domain using ANSYS AQWA. The hydrodynamic study involves the assessment of the floating body dynamics, exerted forces, the power absorbed as well as the suitability of the proposed power take-off (PTO) system. Findings reveal that the proposed geometry absorbs the most energy, with an annual power of 135.11 MW, and that the transmission PTO design is appropriate for this type of technology.

As a form of protection, a nature park is often created to protect and valorise natural and cultural heritage in peripheral rural areas. However, in terms of multi-functionality, new nature parks are incorporated into traditional productive activities, such as recreational and tourist activities, which sometimes compromise sustainability. The research objective is to study the relationship between tourism and sustainability in the Nature Parks of “Sierra de Aracena y Picos de Aroche”, “Sierra Norte de Sevilla” and “Sierra de Hornachuelos” that makes up the Dehesas de Sierra Morena Biosphere Reserve in Andalusia, Spain. Therefore, selective interviews have been carried out with the stakeholders to establish their perception of sustainable tourism and the presence of dominant discourses. The main conclusions indicate (1) the presence of different dominant discourses on sustainability, namely the conservationist and mercantilist ones, with the prevalence of the economic dimension; (2) poor awareness and adaptation to the context of global change; and (3) the presence of competitive relationships that generate difficulties for the governance of sustainable tourism.

Helicobacter pylori cytotoxin-associated gene A protein (CagA) has been associated with the increase in virulence and risk of cancer. It has been demonstrated that CagA's translocation is dependent on its interaction with phosphatidylserine. We evaluated the variability of the N-terminal CagA in 127 sequences reported in NCBI, by referring to molecular interaction forces with the Phosphatidylserine and the docking of 3 mutations chosen from variations in specific positions. The major sites of conservation of the residues involved in CagA-Phosphatidylserine interaction were 617, 621 and 626 which had no amino acid variation. Position 636 had the lowest conservation score, so mutations in this position were evaluated to observe the differences in intermolecular forces of the CagA-Phosphatidylserine complex. We evaluated the docking of 3 mutations: K636A, K636R and K636N. The models of the crystal and mutations presented a ΔG of −8.919907, −8.665261, −8.701923, −8.515097 Kcal/mol, respectively, while mutations K636A, K636R, K636N and the crystal structure presented 0, 3, 4 and 1 H-bonds, respectively. Likewise, the bulk effect of the ΔG and amount of H-bonds was estimated in all of the docking models. The type of mutation affected both the ΔG (χ²(1) = 93.82, p-value < 2.2 × 10⁻¹⁶) and the H-bonds (χ²(1) = 91.93, p-value < 2.2 × 10⁻¹⁶). In all the data, 76.9% of the strains that exhibit the K636N mutation produced a severe pathology. The average H-bond count diminished when comparing the mutations with the crystal structure of all the docking models, which means that other molecular forces are involved in the CagA-Phosphatidylserine complex interaction.

Asthma is a significant public health concern. This study identified the provinces with the highest morbidity and mortality rates due to asthma among the working-age population (15-69 years) in the Republic of Ecuador. The secondary objective was to explain the possible differences attribut-able to occupational exposure. This nationwide ecological and exploratory study was conducted in 24 provinces between 2016 and 2019. Government databases were used as sources of infor-mation. Standardized rates were calculated for codes the J45 and J46. The hospitalization mor-bidity rate for asthma decreased from 6.51 to 5.76 cases per 100,000 working-age population, and the mortality rate has consistently been low and stable from 0.14 to 0.15 deaths per 100,000 working-age population. Geographic differences between the provinces were evident. The risk of hospitalization and death due to asthma was higher in the Pacific coast (five provinces) and the Andean region (two provinces). The high rates observed in provinces with greater agricultural and industrial development could be the main determinants of national heterogeneity and could act as occupational risk factors. The contribution of occupational hazards in each province should be examined in depth through ad hoc studies. These findings provide valuable information for creating public policies aimed at promoting and safeguarding the respiratory health of the popu-lation, particularly that of workers.

Colombia is noteworthy as a biodiversity hotspot, featuring an extraordinary number of en-demic orchids, birds, and butterflies. This exploratory study examines the perceptions of desti-nation image considering the cognitive and affective image in predicting behavioral intentions of ecotourists through symmetric data analysis. Using Partial Least Squares (PLS), the author(s) analyzed 64 survey responses collected of rural areas, including a new 15 statement scale spe-cialized on birdwatching. The findings support the reliability of the model, symmetric analysis presents the higher influence of emotions and affections in increasing intentions of recommen-dation, considering birdwatching as based on personal relationships. Additionally, the cognitive image for the birders despite representing destination attributes or sets of destination resources of a mental picture does not have the same impact on behavioral intentions. Therefore, manag-ers should develop positioning strategies based on the generation of emotions, because bird-watching tourists seek to have more emotional experiences.

Ants have a very sophisticated olfactory system; their communication is based on the interpretation of chemical trails known as semiochemicals. The escamolera ant (Liometopum apiculatum), has behaviors and physiological responses is induced by semiochemicals, without however, these have not been studied. Using an electronic nose analysis (e-nose) and chromatographic techniques, semiochemicals of L. apiculatum in nests, foraging trails and dissected the gasters were evaluated. Data were analyzed with multivariate statistics and a logistic regression model based on predictors (ant counts) and the semiochemicals identified in pre-season (PRE), season (TEM) and post-season (POS) of larval collecting. From the general pattern of volatiles with a natural separation in POS and a partial distribution between PRE and TEM 32 semiochemicals were identified inside the nests, those who can induce the behavioral responses of trail-following, alarm, aggression, and nestmate recognition. Trail counts indicated that ant traffic was higher in POS and that the presence of certain semiochemicals (response variable) have good fit in the model regression. These findings are intended to provide useful information and support decision-making for the conservation and sustainable use of L. apiculatum in central Mexico.

Franklinite-Zincochromite-Gahnite solid solutions have been prepared by ceramic or coprecipitation method and its pigmenting capacity as cool ceramic pigments in different glazes (double and single firing frits and porcelain frit) was studied. XRD, UV-Vis-NIR diffuse reflectance, CIEL*a*b* colour analysis, bandgap measurement and the photocatalytic activity over the Orange II azoic dye were carried out for the characterization of samples. Following criteria of high red colouring capacity and high NIR reflectance at minimum Cr amount, Zn(Fe1.8Cr0.2), Zn(Al1.5Cr0.5) and Zn(Al1.3Cr0.5Fe0.2)O4 turn out to be the optimal compositions for an intense reddish cool pigment. All powders show a direct semiconductor behaviour with bandgap around 2 eV, falling in the visible range (620 nm), and according to literature, the associated photocatalytic activity over Orange II with visible light irradiation is moderate in all cases, but the Franklinite-Zincochromite Zn(Fe1.8Cr0.2) stands out.

The art of representing source code is pivotal in numerous programming analysis applications. Recent strides in neural networks have marked notable successes in this realm. However, the peculiar structural characteristics inherent in programming languages have not been fully exploited in existing models. While neural models based on abstract syntax trees (ASTs) adeptly manage the tree-like nature of source codes, they fall short in discerning the diverse substructural nuances within programs. This paper introduces the Dynamic Syntax Tree Model (DSTM), an innovative approach that fuses various neural network modules into tree architectures tailored to the specific AST of the input. Distinct from preceding tree-based neural models, DSTM adeptly discerns the semantic variances across different AST substructures. We validate DSTM through rigorous testing in program classification and code clone detection, outperforming contemporary methods and demonstrating the benefits of harnessing intricate source code structures.

The theory and working principle of fabric phase sorptive extraction (FPSE) is presented that eloquently explains the mystery behind this new and powerful sample preparation technique. FPSE innovatively integrates the benefits of sol-gel coating technology and the rich surface chemistry of cellulose/polyester/fiberglass fabric, resulting in a microextraction device with very high sorbent loading in the form of an ultra-thin coating. This porous sorbent coating and the permeable substrate synergistically facilitate very fast extraction equilibrium. The flexibility of the FPSE device allows for direct insertion into original, unmodified samples of different origin. Strong chemical bonding between the sol-gel sorbent and the fabric substrate permits the exposure of FPSE devices to any organic solvent for analyte back-extraction/elution and to highly acidic or basic environments (pH 1-12) if required. A sol-gel derived sorbent, highly polar sol-gel poly(ethylene glycol) coating, was generated on cellulose substrates. Five cm2 segments of these coated fabrics were used as the FPSE devices for sample preparation using direct immersion. An important class of environmental pollutants, substituted phenols, was used as model compounds to evaluate the extraction performance of FPSE. The high primary contact surface area (PCSA) of the FPSE device and porous structure of the sol-gel coatings resulted in very high sample capacities and incredible extraction sensitivities for both the compound classes in a relatively short period of time. Different extraction parameters were evaluated and optimized. The new extraction devices demonstrated part per trillion level detection limits for substituted phenols, a wide range of detection linearity, and good performance reproducibility.

Adipose tissue has acquired considerable importance due to its role in maintaining body homeostasis. Beyond storing energy, adipose tissue is essential in numerous processes of energy metabolism, food intake, endocrine and immunological functions as well as neromodulatory functions by secreting hormones that participate in the regulation of homeostasis and being responsive to soluble proteins secreted by other tissues. An imbalance of these functions will generate structural and functional changes in the adipose tissue, favouring the secretion of deleterious adipocytokines that benefit a pro-inflammatory state, allowing the development of metabolic and cardiovascular diseases. Obesity is a disease that has increased rapidly in recent decades and is caused by environmental, hormonal, and genetic factors, which probably work in combination. Environmental factors that contribute to the increase in obesity include decreased physical activity, associated with increased screen time, increased accessibility and consumption of foods rich in fats and sugars. Weight loss should be recommended for all obese patients and overweight patients with comorbidities. For many patients who need to lose weight for medical reasons, the initial goal is to lose 5-10% of their body weight in the first 6 months. A common theme worldwide has been the development of professional guidelines for the control and treatment of obesity and in Chile, the Chilean Society of Metabolic and Bariatric Surgery led the process of adopting the adult obesity clinical practice guideline, which establishes changes in the approach to managing obesity as a chronic disease, with an emphasis on multifactorial lifestyle interventions that include dietary changes, increased physical activity, and behavioural modifications. Pharmacotherapy, medical devices, and bariatric surgery are other options for patients who need additional interventions, so the objective of this review is to know the pathophysiological mechanisms of obesity from its origin to the current treatments available for treatment.

Introduction: The purposes of this study were to seek for overall ratio (OR) and anterior ratio (AR) patients data in normal occlusion and Angle’s malocclusion studies, and to assess if such results support Bolton’s standards as general references. Methods: Pubmed, Medline, CENTRAL and Scholar databases were searched up to February 2018 (CRD42018088438). Gray literature was explored through OpenGray. Non-randomized clinical studies, published in English and assessing Bolton’s OR and AR in normal occlusion and Angle’s malocclusion groups (Class I, Class II, Class II division 1, Class 2 division 2, Class III) patients were included. OR and AR means and standard deviations (SD) were collected. Potential covariates (study design, publication year, country where the study was conducted, number of cases, gender, mesiodistal measurement method, and calibration method) were also extracted. The National Health Heart Lung, and Blood Institute’s Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies was used to assess each included studies quality. Pairwise Random-Effects and Multilevel Bayesian Network Meta-Analyses were used to synthesize available data. Results: Fifty-two observational studies were included (8872 participants; male/females 2674/3272; 16 studies lacked gender information). For normal occlusion, global pooled estimates for OR and AR means were 91.74% (95% CI: 91.37-92.10) and 78.24% (95% CI: 77.85-78.63), respectively. We could identify on Angle’s Class III patients meaningful OR and AR mean deviations from normal occlusion (0.89, 95% credible interval [CrI], 0.66-1.12, and 0.66, 95% CrI, 0.38-0.94, respectively), while on Class I patients we found a meaningful mean deviation from normal occlusion only for OR (0.25, 95% CrI, 0.03-0.47). Concerning gender impact, male patients presented higher OR (0.30, 95% CI 0.00-0.59) and AR (0.41, 95% CI 0.00-0.83) mean values than females in Class I. Conclusions: The results show that global pooled OR and AR mean values for normal occlusion patients are slightly above Bolton’s original values. Class I, for OR mean values, and Class III, for both OR and AR, are proportionally larger than normal occlusion patients. Gender had almost no impact on teeth mesiodistal proportion.

The Sustainable Development Goals (SDGs) is a global strategy that aims to obtain a more equitable and just world. These objectives are organized in 17 SDGs, which are detailed in 169 targets. Different international institutions have emphasized education's relevance to developing citizens who contribute to the SDGs' achievement for 2030. However, a review focused on Physical Education exclusively has not been performed yet. Therefore, the objective of this work is double. First, to analyze and select the specific goals of the SDGs that can be implemented in the subject of Physical Education. And second, to relate these specific goals to the different models based on Physical Education practices. This review showed how three institutional documents have previously related sport, physical exercise and physical education to the specific goals of the SDGs. Based on the search done, this document selects those goals that could be integrated into the educational context through Physical Education. The bibliographic and narrative analysis carried out in this research shows that of the 169 specific goals proposed in the SDGs, only 24 could be worked on in Physical Education. In addition, after this previous analysis, a proposal for the relationship between the practice-based models and these 24 goals is presented. The contributions made in this paper will allow teachers to establish links between PE sessions and SDGs while raising awareness to develop students who contribute to a more sustainable world.

The COVID-19 pandemic has led to the growth of hybrid and online learning environments and the trend to introduce more technology into the classroom. One such change would be the use of smart synchronous hybrid learning environments (SSHLE), which are settings with both in-person and online students concurrently, and in which technology plays a key role in sensing, analyzing, and reacting throughout the teaching and learning process. These changing environments and the incorporation of new technologies can place a greater orchestration load on participants and a reduction in teacher agency. In this context, the aim of this paper is to analyse the orchestration load and teacher agency in different SSHLEs. The NASA-TLX model was used to measure the orchestration load in several scenarios. Questionnaires and interviews were used to measure teacher agency. The results obtained indicate that the orchestration load of the teacher tends to be high (between 60 and 70 points out of 100 of the NASA-TLX workload), especially when they lack experience in synchronous hybrid learning environments, and the orchestration load of the students tends to have average values (between 50 and 60) in the SSHLEs analysed. Meanwhile, the teacher agency does not appear to be altered but shows potential for improvement.

Chronic granulomatous disease (CGD) is a primary immunodeficiency caused by a defect in the phagocytic function of the innate immune system owing to mutations in genes encoding the five subunits of the nicotinamide adenine dinucleotide (NADPH) oxidase enzyme complex. This review aimed to provide a comprehensive approach to the pathogens associated with chronic granulomatous disease (CGD) and its management. The topics covered included common pathogens infecting CGD patients, granulomas, and CGD-related infectious diseases, including invasive aspergillosis, Mulch pneumonitis, and liver abscesses. In addition, we also covered the differential diagnosis of CGD and its management. Patients with CGD, often children, have recurrent life-threatening infections and may develop infectious or inflammatory complications. The most common microorganisms observed in the patients with CGD are Staphylococcus aureus, Aspergillus spp, Candida spp, Nocardia spp, Burkholderia spp, Serratia spp, and Salmonella spp. Triple-dose antimicrobial prophylaxis with empiric regimens is recommended. Therefore, therapeutics against this disease must be optimised, considering the lack of curative therapies and the long-term side effects of prophylactic regimens. Consequently, allogeneic haematopoietic stem cell transplantation from a human leukocyte antigen-identical donor has been proven to cure CGD.

Adaptive mechanisms, such as channel hopping and packet replication, are used in low-power wireless networks to deal with the spatial and temporal variations in the link quality, and meet the reliability requirements of industrial applications (i.e., PDR>99%). However, the benefits of such mechanisms are limited and may have a large impact on end-to-end latency and energy consumption. Hence, in this paper we propose using adaptive modulation diversity, which allows to dynamically select different modulations, to improve link reliability. We present three adaptive modulation diversity selection strategies and validate them using the data derived from a real-world deployment using the IEEE 802.15.4g SUN modulations (i.e., SUN-FSK, SUN-OQPSK and SUN-ODFM) in an industrial environment. The results show that by using adaptive modulation diversity it is possible to improve link reliability regardless of node conditions.

Opossums, native to the Americas, are unique marsupials with over 100 species. The Common Opossum and Virginia Opossum are notable species. They are nocturnal, omnivorous, and adapt to various habitats. Their reproduction involves a short gestation and marsupial pouch development. Despite their ecological benefits like seed dispersal and insect control, their proximity to human habitats exposes them to pathogens, posing public health risks. These include protozoa, helminths, and arthropods that can spread zoonotic parasites and vector-borne diseases. The marsupial immune system, including opossums, is complex, rivaling eutherian mammals. Key immune genes were identified in the grey short-tailed opossum genome. CD1 protein and VpreB surrogate light chain were also found. Marsupials are proposed as ideal models for studying developmental immunology. A West Indian opossum (Didelphis marsupialis) was found and examined. Blood tests revealed abnormalities, including a low white blood cell count and signs of anemia. The results contribute to understanding the species’ health and adaptability in the wild.

Objective: Despite increasing interest in understanding factors influencing awareness, knowledge, and acceptability of the human papilloma virus (HPV) vaccine among Latino parents to date, limited information is available specific to Latino fathers living in the United States. Methods: This descriptive qualitative study explored Latino fathers’ awareness, knowledge, and acceptability of the HPV vaccine for their adolescent children. Data were collected through individual, semi-structured interviews and analyzed using a hybrid method of thematic analysis that incorporated deductive and inductive approaches. Results: Nineteen, majority foreign-born Latino fathers (63.2%; n = 12) fathers of male and female adolescents participated in the study. Four main themes and two subthemes emerged from the analyses. Results found fathers’ low awareness and knowledge of HPV and the HPV vaccine. Results also identified fathers’ positive attitude toward vaccines in general. Moreover, results revealed fathers’ trust in healthcare providers. This trust translated into an increased willingness to vaccinate their children against HPV if recommended by their child’s primary healthcare provider. Conclusion: Findings indicate the need for increased efforts to raise awareness and knowledge among Latino fathers of HPV and the HPV vaccine. In addition, findings underscore the critical role of healthcare providers’ recommendation of the HPV vaccine. Given the limited research focused on Latino fathers, this study’s findings are valuable in building a knowledge foundation needed for developing future studies and interventions to promote the HPV vaccine by targeting Latino fathers living in the United States. Future research should quantify Latino fathers' awareness, knowledge, and acceptability of the HPV vaccine for their children, and preferences for educational interventions to promote HPV vaccination.

Nowadays, the European Union and the governments of the different countries have focused on the development of the Sustainable Development Goals (SDG) and the 2030 agenda, something that has been translated into education itself. Video Games, Gamification, and Game Based Learning have become different strategies and tools to enhance the learning process and some of the growing approaches used by teachers to develop sustainable education in the classrooms. This research aims to analyze the characteristics to promote sustainability in education using games and technology, specifically its learning benefits for Higher Education. A systematic review of the literature was conducted following the PRISMA methodology. At first, 2025 documents were found which, after the filtering phases, the number of articles has been reduced to nine, which subsequently were analyzed in depth. The results indicated that among the benefits of the use of games mediated by technologies are the following: it favors education for sustainability and it promotes the educational inclusion and the work of various social skills such as collaborative and cooperative work. Also, showed an increase of the number of publications between 2019 and 2023, reflecting the growing interest in the topic. However, there are some research gap in this field.

The crisis of mining-industrial activities in Western Europe since the middle of the 20th century caused the cessation of mining, triggering a structural crisis. It is necessary to look for alternatives, among which tourist activities based on mining heritage stand out. However, there are unattractive peripheral spaces in which new activities encounter obstacles to their development, facing the post-industrial and rural crises. The Riotinto Mining Basin (Huelva, Andalusia) represents an example of such spaces with an enormous cultural heritage. A central agent, the Río Tinto Foundation, has opted to enhance tourism value, while the mine has recently been reactivated. The objective of this research is to analyze the contribution of mining tourism to sustainable rural development. The methodology applied has been mixed based on conducting interviews and analyzing secondary data. The results are (a) achievement of the tourism value of the mining heritage; (b) difficulties for tourism to overcome its peripheral condition; (c) relative contribution of tourism to the improvement of the territorial image; (d) lack of coordination among stakeholders; (e) limited contribution of tourism to sustainable rural development, moving to a secondary economic role after the reopening of the mine.

This research aims to contribute to the growing body of knowledge of Collaborative Consumption (CC) by exploring the motivations that influence the use of Peer-to-Peer (P2P) accommodation platforms from the demand side. There has been a significant increase in interest in sustainable choices regarding P2P accommodation in recent years. To address this gap and contribute to the collaborative consumption and P2P accommodation theory a quantitative study was conducted through a survey yielding 235 responses from both users and non-users of P2P platforms. The findings of this study suggest that P2P accommodation platform usage is influenced by multiple motivations, including sustainability, belonging, economic benefits, and convenience. Interestingly, the study also found that individuals with higher levels of academic qualifications are more likely to adopt this mode of consumption. Moreover, the study is the first of its kind to quantitatively address this topic and, therefore, contributes significantly to the field of collaborative consumption and P2P accommodation theory. By understanding the motivations of P2P platform users, policymakers and practitioners can develop targeted strategies to promote sustainable choices in this sector. Overall, this study provides a useful framework for future research on collaborative consumption and sustainable P2P accommodation.

In rural spaces, new activities are developed to add to the traditional ones in the context of multifunctionality, considering the protection and enhancement of natural and cultural heritage, as in Nature Parks. They incorporate new recreational and tourist functions, understood as an instrument for local development. This research aims to study the relationships between tourism and local development in three Natural Parks (Sierra de Aracena and Picos de Aroche, Sierra Norte de Sevilla and Sierra de Hornachuelos) in Andalusia (Spain). The research uses a mixed methodology consisting of, on the one hand, selective interviews with stakeholders focused on tourism and local development processes and, on the other, secondary data to analyze the repercussions on local development. The results show that (1) prevalence among the informants of the economic dimension in local development above the environmental and socio-cultural dimensions; (2) influence of declarations of protection and implementation of development policies on the growth of the tourist offer; (3) uneven tourism development in the different municipalities and Natural Parks; and (4) limited effects of tourism on local development.

Local Productive Systems (hereinafter LPS) based on agro-food industries constitute alternative models of development in peripheral rural areas that are subject to internal and external dynamics and processes. The main objective of the research is to investigate the processes and their consequences on four SPLs based on the Iberian Pig Transformation Industry (hereinafter LPS-IPTI) in SW Spain: Fregenal de la Sierra, Higuera la Real, Cumbres Mayores and Jabugo. Using secondary data, a comparison is made between 2002 and 2020 to establish the changes, causes and consequences on the LPS-IPTI studied. The results obtained indicate (1) business and territorial concentration of LPS-IPTI; (2) productive and territorial specialisation in standardised products and quality products; (3) simplification of industrial processes; (4) loss of employment, especially female; (5) external control of companies in the sector which, accordingly, results in the loss of prominence of local actors in favour of foreign companies, reduced social capital and the progressive loss of ownership of the LPS.

The objective of this work is to present the results of an investigation on the technological, pedagogical and content knowledge evidenced by mathematics teachers in the second-level of initial training (preservice) at the Universidad Nacional, from the TPACK model perspective, on the subject of functions. The research has a qualitative approach with an interpretive hermeneutical stance. A sample of 27 teachers in training who were enrolled in courses related to the three base domains of the TPACK model (pedagogical, technological and content of quadratic function) during the first semester (I cycle) of 2020 was used. A theoretical inquiry was carried out that allowed the creation of an instrument that made possible the description of the participants’ knowledge based on this model focused on the topic of quadratic function. The results show that participants possess instrumental dominance over the basic forms of knowledge underlying the model. It is concluded that, although participants have already experimented in courses related to these forms of knowledge, there is insufficient evidence to ascertain that their current knowledge would allow them to integrate technologies as a didactic resource within the teaching of the subject of quadratic function.

The rapidly evolving domain of multimodal sentiment analysis is crucial for unraveling the intricate layers of emotional expression in social media content, customer service interactions, and personal vlogs. This research introduces a cutting-edge Advanced Cross-Modal Gating (ACMG) framework that significantly enhances the precision of sentiment analysis by refining the interplay among textual, auditory, and visual modalities. Our approach addresses three foundational aspects of sentiment analysis: (1) Advanced learning of cross-modal interactions, which focuses on extracting and synthesizing sentiment from varied modal inputs, thus providing a holistic view of expressed emotions; (2) Mastery over the temporal dynamics of multimodal data, enabling the model to maintain context and sentiment continuity over extended interactions; and (3) Deployment of a novel fusion strategy that not only integrates unimodal and cross-modal cues but also dynamically adjusts the influence of each modality based on its contextual relevance to the sentiment being expressed. The exploration of these dimensions reveals that the nuanced modeling of cross-modal interactions is crucial for enhancing model responsiveness and accuracy. By applying the ACMG model to two highly regarded datasets—CMU Multimodal Opinion Level Sentiment Intensity (CMU-MOSI) and CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI)—we achieve groundbreaking accuracies of 83.9\% and 81.1\%, respectively. These results represent significant improvements of 1.6\% and 1.34\% over the current state-of-the-art, showcasing the superior performance and potential of our approach in navigating the complexities of multimodal sentiment analysis.

Nowadays, the research and development of on-chip LiDAR sensors for vehicle collision avoidance is growing very fast. Therefore, the assessment of the reliability in obstacle detection using the information provided by LiDAR sensors has become a key issue to be explored by the scientific community. This paper presents the design and implementation of a self-tuning method in order to maximize the reliability of an Internet-of-Things sensors network and to minimize the number of sensors to localize with the required accuracy obstacles by a detection threshold. In order to achieve this goal, models that predict accuracy (i.e., prediction error) for object localization using data collected by LIDAR sensors are designed and implemented in Webots Automobile 3D simulation tool. The approach is based on combining different techniques. Firstly, point-cloud clustering technique and an error prediction model library composed by a multilayer perceptron neural network with backpropagation, k-nearest neighbors and linear regression are explored. Secondly the above-mentioned techniques for modeling are also combined with a supervised and reinforcement machine learning technique, Q-learning in order to minimize the detection threshold. In addition, a IoT driving assistance simulated scenario with a LiDAR sensor network is designed in order to validate the prediction model and the optimal configuration of the sensor network to guarantee reliability in obstacle localization. The results demonstrate that the self-tuning method is appropriate to increase the reliability of the sensor network whereas minimizing the detection threshold

Responsible innovation combines philanthropic and economic aspects and it is common to refer to entrepreneurs who lead it as "social entrepreneurs". The present study of 100 Mexican SMEs, provides knowledge of exploratory nature about what the models of organization are conducive to SMEs in the generation and development of responsible innovations. Through the statistical technique of cluster analysis, this study identified and characterized four models of organization according to the level of social entrepreneurship reached: (1) “The techno-scientific organization”, (2) “The techno-social organization”, (3) “The capitalist-social organization” and (4) “The capitalist organization”. While in Europe the dominant discourse about responsible innovation focuses on the control of the risk of social rejection of the advance of science and technology; in contexts such as the Mexican, the phenomenon is configured as the mechanism through which entrepreneurs articulate its technological and scientific capabilities to solve priority and specific problems of the society, however, the social impact does not crucially affect their business initiatives. The techno-scientific organization (50% of studied SMEs) is proposed as the model of organization with greater viability for Mexican entrepreneurs.

Zidovudine (AZT) has significantly reduced the mortality and morbidity rates among AIDS patients, but it has been associated with hepatotoxicity. AZT treatment has been linked to disrupted lipid and glucose metabolism, and the enhancement of pro-inflammatory chemokines and other mediators, which can lead to liver steatosis. In contrast, the administration of adenosine (ADO) has proven to be an effective hepatoprotector against both acute and chronic liver damage. Therefore, present study was aimed to investigate the harmful effects of chronic AZT administration and the beneficial effects of the co-administered ADO. Chronic oral AZT administration did not induce hyperglycemia or dyslipidemia, but serum liver marker enzyme activities were increased, as well as evidence of liver steatosis and inflammation. These findings were associated with low insulin and glucagon levels, elevated serum levels of pro-inflammatory cytokines and disruptions in cell redox states in these animals. ADO co-administration attenuated the deleterious effects induced by AZT: blocking the production of inflammatory molecules, increasing serum insulin and glucagon levels, and restoring liver cellular redox state. Then, blood insulin levels seemed to be greatly influenced by the cellular redox state, which was altered by AZT administration. This alteration is likely to be connected to mitochondrial integrity and metabolism.

In the present work, a study has been carried out on the contamination of heavy metals in urban dust deposited on the roads of the city of Cartagena (Spain), in order to know the content of metals such as Ni, Zn, Pb, Cd, Cr and Cu. Likewise, the possible relationship of the concentration of heavy metals with the color of the sample, level of magnetism and traffic density was studied. Contamination was evaluated using several indices such as contamination factor (CF), enrichment factor (EF), geoacumulation index (Igeo), pollutant load index (PLI) and ecological risk index (RI). A total of 88 samples were taken in the urban area of Cartagena, and the metals were determined by acid digestion and measured by inductively coupled plasma mass spectrometry. The concentration of heavy metals in urban dust from Cartagena was Zn (672 mg/kg) &gt; Cu (248.9 mg/kg) &gt; Pb (227 mg/kg) &gt; Cr (82.7 mg/kg) &gt; Ni (47.7 mg/kg) &gt; Cd (4.1 mg/kg). Contamination levels were high in Pb, Zn, Cd and Cu, according to environmental indices. A correlation was found between magnetism and metal concentration, which was repeated for all metals except Cd. Dark-colored samples contained higher metal concentrations than light-colored samples. Meanwhile, streets with medium and low traffic intensity were found to have higher concentrations of heavy metals. This study's objective was to identify pollution hotspots caused by heavy metals in dust in the urban ecosystem of Cartagena city.

The current research investigated the environmental risk of the polycyclic musk compounds, Galaxolide® (HHCB) and Tonalide® (AHTN), in the marine environments. These substances are lipophilic, bioaccumulated and potentially biomagnified in aquatic organisms. To understand the toxicity of HHCB and AHTN, we performed acute toxicity tests by exposing marine microalgae (Phaeodactylum tricornutum, Tretraselmis chuii and Isochrysis galbana), crustaceans (Artemia franciscana), echinoderms (Paracentrotus lividus), bivalves (Mytilus galloprovincialis), fish (Sparus aurata) and a candidate freshwater microalga (Raphidocelis subcapitata) to environmentally relevant concentrations (0.005 - 5 µg/L) following standardized protocols. The effects of both substances on microalgae growth were incipient and only I. galbana was sensitive to HHCB and AHTN, with IC10 values of 5.22 µg/L and 0.328 µg/L, respectively. Significant (p < 0.01) concentration dependent responses were measured in P. lividus and M. galloprovincialis larvae developments as well as S. aurata mortality tested with HHCB. The effect of HHCB on P. lividus larvae development was the most sensitive endpoint recorded, producing an EC50 value of 4.07 µg/L. Our results show that HHCB represents a high risk to P. lividus larvae development for early life stages in marine environments.

Frontotemporal lobar degeneration (FTLD) belongs to a heterogeneous group of highly complex neurodegenerative diseases and represents the second cause of presenile dementia in individuals under 65. Frontotemporal-TDP is a subgroup of frontotemporal dementia characterized by the aggregation of abnormal protein deposits, predominantly transactive response DNA-binding protein 43 (TDP-43), in the frontal and temporal brain regions. These deposits lead to progressive degeneration of neurons resulting in cognitive and behavioral impairments. Limbic age-related encephalopathy (LATE) pertains to age-related cognitive decline primarily affecting the limbic system which is crucial for memory, emotions, and learning. Though distinct, emerging research suggests a potential overlap in pathogenic processes, with some cases of limbic encephalopathy displaying TDP-43 pathology. Genetic factors play a pivotal role in both disorders. Mutations in various genes, such as progranulin (GRN) and chromosome 9 open reading frame 72 (C9orf72), have been identified as causative in frontotemporal-TDP. Similarly, specific genetic variants have been associated with an increased risk of developing LATE. Understanding these genetic links provides crucial insights into disease mechanisms and the potential for targeted therapies.

Pancreatic cancer leads the most common lethal tumor in America. This lethality is related to limited treatment options. Conventional treatments involve a non-specific use of chemotherapeutical agents like 5-FU, capecitabine, gemcitabine, cisplatine, oxaliplatine, or irinotecan, that produce several side effects. This review we focus on the use of targeted nanoparticles as an alternative to the standard treatment for the pancreatic cancer. The principal objective of the use of nanoparticles is the reduction in side effects that conventional treatments produce, mostly because of their nonspecificity. Currently, several molecular markets of pancreatic cancer cells have been studied to target nanoparticles and improve the actual treatment. Therefore, properly functionalizated nanoparticles with specific aptamers or antibodies can be used to recognize pancreatic cancer cells and once cancer is recognized, these nanoparticles can attack the tumor by drug delivery, hyperthermia, or gene therapy.

The broad bean plant contains L-DOPA, a compound that is essential for patients with Parkin-son’s disease. However, little has been reported on other broad bean compounds that have bene-ficial effects on health. The objective was to evaluate plants of four Mexican broad bean varieties to determine the content and yield of total phenolic compounds (TPC), total flavonoids (TF), and L-DOPA, as well as to analyze the flavonoid profile and antioxidant (AA) and anti-inflammatory (AANTI) activity in vitro. Broad bean seeds were sown in the field and plants were harvested 20 days after emergence. The analyses were performed with visible UV spectrophotometry and HPLC. The variety José María produced the highest yield of TPC (9.30 g m-2), TF (8.08 g m-2) and L-DOPA (5.64 g m-2) per unit of area. The highest yields per plant were obtained with the Rojita variety: TPC (0.25 g plant-1), TF (0.21 g plant-1) and L-DOPA (0.17 g plant-1). This variety also had the highest antioxidant (CI50= 87.68 µg mL-¹) and anti-inflammatory (CI50= 74.40 mg mL-1) activi-ty, which was attributed to the L-DOPA compounds and to rutin and isoorientins, respectively. The flavonoid profile revealed the presence of rutin and isoorientins, which had not been previ-ously detected in the broad bean plant.

Objectives: To analyse and detail surgical process improvement activities that achieve the highest economic impact. Methods: Over 4 years, a team of technicians and healthcare professionals implemented a set of Lean surgical process improvement projects at Vall d’Hebron University Hospital (VHUH), Barcelona, Spain. These projects now form part of the daily routine in the surgical process. The economic impact on the hospital's surgical activity budget was analysed. Results: Process improvements have led to annual operational savings of over € 8.5 million. These improvements include better patient flow, better management of information between healthcare professionals, and improved logistic circuits. Conclusions: The current cultural shift towards process management in large hospitals implies shifting towards results-based healthcare, patient-perceived value (VBHC) and value-added payment. A Lean project implementation process requires long-term stability. The reason a considerable number of projects fail to complete process improvement projects is the difficulty involved in establishing the project and improving management routines. Few studies in the literature have investigated the economic impact of implementing Lean management a posteriori, and even fewer have examined actual cases. In this real case study, changes to surgical block management were initiated from zero. After being carefully thought through and designed, changes were carried out and subsequently analysed.

Collision avoidance is an important feature in advanced driver-assistance systems, aiming at providing correct, timely and reliable warnings before an imminent collision (objects, vehicles, pedestrians, etc.). A co-simulation framework is proposed in this paper to address the design and evaluation of collision avoidances in a cyber-physical system. The co-simulation framework is supported on the interaction between SCANeR and Matlab/Simulink. From the best of authors’ knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip LIDAR sensors in a cyber-physical system (CPS) considering traffic scenarios is presented. The CPS is designed and implemented in SCANeR. Secondly, an obstacle recognition library with three specific Artificial Intelligence-based methods is also designed based on sensory information database provided by SCANeR. Three methods for collision avoidance detection are considered, i.e.; a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods for detecting obstacles before different weather conditions is done with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and fog conditions, the support vector machine in rainy and self-organized map in snowy conditions.

Structural deformations are one of the most significant factor that affects machine tool (MT) positioning accuracy. These induced errors are complex to be represented by a model, nevertheless they need to be evaluated and predicted in order to increase the machining performance. This paper presents a novel approach to calibrate a machine tool in real-time, analyzing the thermo-mechanical errors through Fibre Bragg Grating (FBG) sensors embedded in the MT frame. The proposed configuration consists of an adaptronic structure of passive materials, Carbon Fibre Reinforced Polymers (CFRP), equipped by FBG sensors that are able to measure in real-time the deformed conditions of the frame. By using a proper thermo-mechanical kinematic model, the displacement of the end effector may be predicted and corrected when it is subjected to external undesired factors. By starting from a set of FE simulations to develop a model able to describe the MT structure stresses, a prototype has been fabricated and tested. The scope was to compare the numerical model with the experimental tests using FBG sensors. The experimental campaign has been performed varying the structure temperature over time and measuring the tool tip point (TTP) positions. The obtained results showed a substantial matching between the real and the predicted position of TTP confirming the effectiveness of the proposed calibration system.

This study determines the equivalent stress intensity factor (SIF) model that best fits the experimental behavior of low-carbon steel under mixed mode (I and II). The study assessed Tanaka, Richard, and Pook's equivalent SIF models. The theoretical values used for comparison correspond to experimental results in a modified C(T) geometry by machining a hole ahead of the crack tip subjected to fatigue loads with a load ratio of R = 0.1. The comparison involved the SIF for six experimental points and the values computed through the numerical simulation. The Paris, Klesnil, and modified Forman-Newman crack growth models were used with each equivalent SIF to analyze the prediction in the estimated number of cycles. The Klesnil model showed the closest prediction since the error between the calculated and experimentally recorded number of cycles is the lowest. However, the material behavior reflects a reduced crack propagation rate attributed to plasticity in the crack tip. Results suggest that Asaro equivalent SIF conservatively estimates the element lifespan with increasing errors from 2.3% at the start of growth to 27 % at the end of the calculation.

In recent years, the use of long-term seismic monitoring systems for the protection of structures is becoming increasingly widespread. This is because more and more awareness is gradually developing regarding the importance of keeping structures under observation so as to prevent any damage scenarios. Within the field of Structural Health Monitoring, data driven techniques allow to process and analyze data collected on site. This paper presents a Mode Tracking procedure to obtain the time-histories of the natural frequencies of a monumental structure. The procedure is applied on the Sanctuary of Vicoforte, an important monumental structure site in Piedmont known for its imposing oval dome and characterized by a permanent structural monitoring system. Through the procedure presented in the paper it has been possible to obtain and observe the time series of the first natural frequencies of the structure considering various aspects, including the temperature variation.

Among the plant resources in pre-Columbian Mexico and Central America, the Chia (Salvia hispanica L.) is unique in being the only member of the Lamiaceae cultivated for the mericarps. Recently the crop has gained considerable attention due to its high of content omega-3 polyunsaturated fatty acid, antioxidants, fibre and protein classifying it as a nutraceutical.

The prevalence of diagnosed cases of autism has increased rapidly, which raise interest in studying the variables related to the well-being of these families. The purpose of this paper is to review recent literature on other variables related to family well-being such a parenting styles. We conducted a systematic review using PRISMA check list and bias assessment ESTROBE with the aim of analyzing if the concepts of autism, well-being and parenting style are related. We screened 753 references, 16 full text articles and abstracts were read. It was identified that authoritative parenting style, those based on warmth, establishing relationships and emotional bonding, and low expressed emotion were positively related to family well-being. On the other hand, authoritarian, permissive and overprotective styles, as well as critical, punishing and training-based were negatively associated with well-being and quality of family life.

There is sufficient knowledge of the beneficial effect of warm-dry storage on breaking the morpho-logical dormancy of the chincuya (Annona purpurea Moc. & Sessé ex Dunal) seed embryo. Chincuya is an underutilized, tropical wild species native to Mexico, with potential as a biopesticide. The impact of warm-dry storage and gibberellic acid treatment on seed viability and germination was determined to improve that knowledge further. Also, the content and kinetics of fatty acids in chincuya seeds were profiled during warm-dry storage. Seed treatments included storage time (0, 3, 6, 9, and 12 months), seed imbibition for 72 h in gibberellic acid at 350 mg L-1, and distilled water im-bibition for 72 h for the control treatment. Seed viability was determined using the TTZ classic test. Imbibed seeds were germinated on paper towels and incubated at 30 °C during the day and 25 °C at night in a 12:12 h dark:light photoperiod. The lipid profile was obtained by gas chromatography. During the storage treatment, seeds stayed in darkness at 25 ± 3 °C. Results showed that high viability remained up to month 9 in storage when it significantly decreased. Freshly harvested seeds germi-nated at a statistically significantly lower rate (26.1 %) than six-month-old seeds (65.6 %). The profile of fatty acids breaks down as palmitic acid (33.41 %), stearic acid (7.72 %), and arachidic acid (1.30 %), for a total of 42.44 % of saturated fatty acids. The detected unsaturated fatty acids (52.15 %) were palmitoleic acid (1.52 %), oleic acid (43.37 %), and linoleic acid (7.24 %). No significant changes were detected in fatty acid content; however, the highest fatty acid content (92.84 %) occurred with the highest germination rate. Regression analysis showed significant content changes: palmitoleic and palmitic acid increased while oleic acid decreased during dry storage. Arachidic acid showed a slight downward trend. Also, fatty acid content significantly differed between intact seeds (unstored and unincubated) and latent seeds (unstored, incubated, and ungerminated). The total fatty acids content for latent seeds was significantly lower. These results show that Annona purpurea seeds present morphophysiological dormancy. Also, we document the highest germination rate reported (65.6 %) and report the presence of arachidic and palmitoleic acids in chincuya seeds.

Background: The maximal one-repetition test (1-RM) is widely used in scientific research; however, there are conflict results regarding its reproducibility in elderly population. The present study aimed to analyze the reproducibility the test both before and after a 12 week training period by using the bench press and leg press 45° 1-RM tests in the elderly taking into consideration the training experience and strength level of the women. Methods: Elderly women (n = 376; age, 68.5 ± 14.1 years; height, 162.7 ± 5.5 cm; body mass, 71.2 ± 16.0 kg) who underwent ≥3 months of resistance training performed an initial week of familiarization and a second week of testing and retest, with a 48–72 hour interval. Results: The results showed maximal and relative load strength of 39.3 kg and 0.59 kg/body mass for lower limbs and 20.9 kg and 0.31 kg/body mass for upper limbs. The Kappa indices were 0.93 and 0.95, and the intraclass correlation coefficients were 0.99 and 0.99 for the lower and upper limbs, respectively. Conclusion: Therefore, the present study confirms that the 1-RM test has high reliability and reproducibility in the elderly, for both upper and lower limbs.

This review delves into the critical role of automation and sensor technologies in optimizing parameters for thermal treatments within electricity power generation. The demand for efficient and sustainable power generation has led to a significant reliance on thermal treatments in power plants. However, ensuring precise control over these treatments remains challenging, necessitating the integration of advanced automation and sensor systems. This paper evaluates the pivotal aspects of automation, emphasizing its capacity to streamline operations, enhance safety, and optimize energy efficiency in thermal treatment processes. Additionally, it highlights the indispensable role of sensors in monitoring and regulating crucial parameters such as temperature, pressure, and flow rates. These sensors enable real-time data acquisition, facilitating immediate adjustments to maintain optimal operating conditions and prevent system failures. It explores the recent technological advancements, including machine learning algorithms and IoT integration, which have revolutionized automation and sensor capabilities in thermal treatment control. Incorporating these innovations has significantly improved the precision and adaptability of control systems, resulting in heightened performance and reduced environmental impact. This review underscores the imperative nature of automation and sensor technologies in thermal treatments for electricity power generation, emphasizing their pivotal role in enhancing operational efficiency, ensuring reliability, and advancing sustainability in power generation processes.

Steel is one of the essential materials in the world's civilization. It is essential to produce many products such as pipelines, mechanical elements in machines, vehicles, profiles, and beam sections for buildings in many industries. Until the '50s of the 20th century, steel products required a complex process known as ingot casting; for years, steelmakers focused on developing and simplifying this process. The result was the con-tinuous casting process (CCP); it is the most productive method to produce steel. The CCP allows producing significant volumes of steel sections without interruption and is more productive than the formal ingot casting process. The CCP begins by transferring the liquid steel from the steel-ladle to a tundish. This tundish or vessel distributes the liquid steel, by flowing through its volume, to one or more strands having wa-ter-cooled copper molds. The mold is the primary cooling system, PCS, solidifying a steel shell to withstand a liquid core and its friction forces with the mold wall. Further down the mold, the rolls drive the steel section in the SCS. Here the steel section is cooled, solidifying the remaining liquid core, by sprays placed in every cooling segment all around the billet and along the curved section of the machine. Finally, the steel strand goes towards a horizontal-straight free-spray zone, losing heat by radiation mechanism, where the billet cools down further to total solidification. A moving torch cutting-scissor splits the billet to the desired length at the end of this heat-radiant zone.

The IEEE 802.15.4-2015 standard includes the SUN (Smart Utility Networks) modulations, i.e., SUN-FSK, SUN-OQPSK and SUN-OFDM, which provide long range communications and allow to trade data rate, occupied bandwidth and reliability. However, given the constraints of low-power devices and the challenges of the wireless channel, communication reliability cannot still meet the PDR (Packet Delivery Ratio) requirements of industrial applications, i.e., PDR>99%. Hence, in this paper we evaluate the benefits of improving communication reliability by combining packet transmissions with modulation diversity using multiple IEEE 802.15.4g SUN modulations. The results derived from a real-world deployment show that going from 1 to 3 packet transmissions with the same SUN modulation can increase PDR from 85.0/84.6/71.3% to 94.2/94.1/86.0% using SUN-FSK, SUN-OQPSK and SUN-OFDM, respectively. Combining the same number of packet transmissions with modulation diversity allows to further increase the average PDR to 97.1%, indicating its potential as a tool to help meeting the reliability requirements of industrial applications.

In this article we present a deployment of 11 nodes using the three different SUN (Smart Utility Network) modulation schemes, as defined in the IEEE 802.15.4-2015 standard. The nodes were deployed in a 110.044 m2 warehouse for 99 days, and the resulting dataset contains a total of 10.710.868 measurements with RSSI (Received Signal Strength Indicator), CCA (Clear Channel Assessment) and PDR (Packet Delivery Ratio) values. The analyzed results show a high variability in average RSSI (i.e., between -82.1 dBm and -101.7 dBm) and CCA (i.e., between -111.2 dBm and -119.9 dBm) values, which are caused by the effects of multi-path propagation and external interference. Despite being above the sensitivity limit for each modulation, this values result in poor average PDR values (i.e., from 65.9% to 87.4%), indicating that additional schemes are required for low-power wireless communications to meet the dependability requirements of industrial applications. For that purpose, we also introduce the concept of modulation diversity, which can be combined with packet repetition to meet such requirements (i.e., PDR>99%) while minimizing the energy expenditure of nodes and meeting regulatory constraints.

Medical records contain many terms which are difficult to process. Our aim in this study is to allow the visual exploration of the information in medical databases where the texts presents a large number of syntactic variations and abbreviations, through an interface which facilitates content identification, navigation and information retrieval. We propose the use of multi-term tag clouds as content representation tools and as assistants for the browsing and querying tasks. The tag cloud generation is achieved through a novelty mathematical method that allows related terms to remain grouped together within the tags To evaluate this proposal, we have used a database with 24,481 records. 23 expert users in the medical field were tasked to complete a survey to evaluate the generated tag clouds properties and we obtained a precision of 0.990, a recall of 0.870 and a F1score of 0.904 in the evaluation of the tag cloud as an information retrieval tool. The main contribution of this approach is that we automatically generate a visual interface over the text capable of capturing the semantics of the information and facilitating access to medical records.

Many studies in the field of robot navigation have focused on environment representation and localization. The goal of map representation is to summarize spatial information in topological and geometrical abstracts. By providing strong priors, maps improve the performance and reliability of automated robots. Due to the transition to fully automated driving in recent years, there has been a constant effort to design methods and technologies to improve the precision of road participants and the environment's information. Among these efforts is the High Definition (HD) Map concept. Making HD maps requires accuracy, completeness, verifiability, and extensibility. Because of the complexity of HD mapping, it is currently expensive and difficult to implement, particularly in an urban environment. In an urban traffic system, the road model is at least a map with sets of roads, lanes, and lane markers. While more research is being dedicated to mapping and localization, a comprehensive review of the various types of map representation is still required. This paper presents a brief overview of map representation, followed by a detailed literature review of HD Map for automated vehicles. The current state of AV mapping is encouraging, the field has matured to a point where detailed maps of complex environments are built in real-time and have been proved useful. Many existing techniques are robust to noise and can cope with a large range of environments. Nevertheless, there are still open problems for future research. AV mapping will continue to be a highly active research area essential to the goal of achieving full autonomy.

Currently, the most important challenge in any assessment of state-of-the-art sensor technology and its reliability is to achieve road traffic safety targets. The research reported in this paper is focused on the design of a procedure for evaluating the reliability of Internet-of-Things (IoT) sensors and the use of a Cyber-Physical System (CPS) for the implementation of that evaluation procedure to gauge reliability. An important requirement for the generation of real critical situations under safety conditions is the capability of managing a co-simulation environment, in which both real and virtual data sensory information can be processed. An IoT case study that consists of a LiDAR-based collaborative map is then proposed, in which both real and virtual computing nodes with their corresponding sensors exchange information. Specifically, the sensor chosen for this study is a Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. Implementation is through an artificial-intelligence-based modeling library for sensor data-prediction error, at a local level, and a self-learning-based decision-making model supported on a Q-learning method, at a global level. Its aim is to determine the best model behavior and to trigger the updating procedure, if required. Finally, an experimental evaluation of this framework is also performed using simulated and real data

This study examines the relationship between semantic memory and social cognition in schizophrenia, addressing how these cognitive domains intersect. Semantic memory, which includes general world knowledge and word meanings, was evaluated using verbal fluency tasks and the Camels and Cactus Test. Social cognition, essential for social interaction, was assessed through emotion recognition (Faces Test) and Theory of Mind (Hinting Task). Participants included 50 individuals with schizophrenia and 30 controls. The schizophrenia group showed significantly lower performance on both semantic memory and social cognition tasks. Notably, strong correlations were found between the Camels and Cactus Test and social cognition measures, suggesting that social cognition deficits in schizophrenia may be linked to semantic memory impairments. Regression analyses highlighted that the Camels and Cactus Test significantly predicted social cognition performance, independent of symptomatology. These findings underscore the interconnectedness of semantic memory and social cognition in schizophrenia, suggesting that semantic memory deficits, particularly in non-categorical associations, play a important role in social cognitive impairments. This study provides new insights into the cognitive underpinnings of schizophrenia, emphasizing the need for further research to explore these relationships and their implications for cognitive models and therapeutic interventions.

Global energy scenario is becoming vital for the sustainable economic development of a region or country. Current changes in energy production, mainly due to scarcity and geopolitical factors, have proven the need for changes in energy mix towards a lower energy dependence. In that sense, a considerable amount of biomass waste is generated in many regions, because of agro-industrial activities, whose management could contribute to energy production. The aim of this work was to study the determination of combustion conditions in a biomass stove by using different raw materials as fuels, such as pine, poplar, and plum tree pellets. For that purpose, power, excess air, and biomass content were optimized, among other parameters, and fumes were analyzed with a Testo 335 analyzer, recording temperatures with temperature probes. As a conclusion, high yields were found for the optimized parameters of the studied biomass (ranging from 91.1 % for poplar pellet to 92.34 % for pine pellet), making these three biomass fuels suitable for combustion in the abovementioned stove. Also, increasing biomass flow by 25 % in the stove contributed to a higher efficiency of the process, especially in the case of plum tree pellets.

Keywords: glycerin; Ni-based catalyst; lanthanum; semi-industrial scale; hydrogen production; biorefinery; monitoring.

The use of sustainable energy sources is a feasible alternative for fossil fuel products, which can represent a positive factor to alleviate many current environmental issues. In that sense, not only the sustainable industrial development is important, but also sustainable practices at local level, including households. Specifically, heating and cooking are one of the most important activities that require energy in households, where the role of biomass could be interesting, as it can provide an energy source with lower environmental impact. However, there is room for improvement in biomass stoves, whose adaptability to specific wastes, as well as their improvement in efficiency, should be accomplished. The aim of this work was to assess the improvement of combustion performance of a domestic stove by the implementation of a programmable logic converter for a better adaptation to different biomass samples (holm oak, pear tree, poplar and sugarcane bagasse pellets). Thus, the working regime of the stove was controlled, especially concerning the screw conveyor (to regulate pellet feeding) and flue gas fan (to change oxygen supply). As a result, better combustion performances were obtained, heat capacities from 5.66 to 8.67 kW for the selected samples. On the other hand, different improvements of the combustion stove to avoid blockages, for instance, are proposed as further steps.

In order to monitor the diversity of terrestrial biomes, the Earth Exploration Satellite Services demand a high data rate for downlink transmissions while sharing their frequency spectrum with, and suffering interferences from, cellular Base Stations. Both highlight the dilemmas of spectral efficiency (bps/Hz) and spectral coexistence. Our research aims to mitigate interference on low orbit satellite downlinks using Cognitive Radio (CR) and Adaptive MODulation and CODing (MODCOD) techniques. To fulfill this: 1) we present CR approaches to enhance spectrum exploitation. Next, 2) we detail Adaptive MODCOD (ACM) technique to increase RF power and spectral efficiencies. Then, 3) we suggest the solution by combining 1) and 2). Afterwards, 4) we analyze it by monitoring the signal to interference plus noise ratio and the CR/MODCOD strategy. Finally, 5) we provide case studies to demonstrate the strategy’s effectiveness through the practical operation of satellite services, and 6) we conduct a bench test of the communication system.

Smart cities will have a strong impact on the future of renewable energies as terms like sustainability and energy saving will be more common. In this sense, both of wind and hydrokinetic compact-size turbines, can play an important role in urban communities by providing energy to nearby consumption points in an environmentally suitable way. This work presents the experimental evaluation for a vertical-axis turbine Darrieus type, operating in an open-field wind tunnel and a confined water channel. Power and characteristic curves have been obtained for all test conditions, also the effect of turbine blockage has been evaluated under blockage values ranging from 6.8% to 35%. The peak power coefficient for the confined flow condition reached a value of 0.31 which is 1.5 times higher than the peak one for the experimental open field condition at the same Reynolds number and a blockage of 20%. Finally, two blockage correction equations have been applied to the water channel tests, which gave values quite similar to the results obtained from the wind tunnel.

Today, mechanical tracking systems have been downsized to allow them to be used in the field of airborne laser communications and in the military domain. Risley systems are used for this purpose, which work by directing a beam of light to a given target point, this procedure is commonly known as the inverse problem. In this paper, an analytical method, the geometric method, has been designed and developed to determine the beam steering in a Risley system and solve the inverse problem. The method focuses on different geometric shapes, like circumference or ellipse, that are described when the beam passes through the second prism. The accuracy and efficiency of the geometric method has been analysed and found to be faster than the two-step method. Furthermore, the geometric method has been implemented in an iterative process and an accuracy of up to 1 pm has been achieved. This high accuracy would allow the geometric method to be applied in fields such as lithography, stereolithography or 3D printers.

The discovery of more bioactive compounds for non-invasive administration has been the goal of research groups focused on pharmacotherapy. Phytonutrients have always been attractive for researchers because they are a significant source of bioactive phytochemicals, but it is challenging to determine which components show high biomedical activity. However, based on the chemical structure of these active compounds, their physicochemical properties can be calculated to predict the probability of gastrointestinal (GI) absorption after oral administration. Indeed, different researchers have proposed several rules (e.g., Lipinski's, Veber's, Ghose's, and Muegge's rules) to attain these predictions for synthetic compounds. Most phytochemicals do not fully comply with these rules even though they show high bioactivity and high GI absorption experimentally. Here, we provide a detailed methodology using web-based platforms to determine the physicochemical properties of five phytochemicals in the phytonutrients ginger, echinacea, and tobacco. Furthermore, we analyzed the calculated data and established a protocol based on the combination and integration of these rules, plus other extended parameter ranges, to reliably predict the GI absorption of natural compounds. The presented data and methodology can be beneficial for evaluating bioactive natural compounds as potential drug candidates and predicting their oral bioavailability in patients.