Enzymes serve as biocatalysts for innumerable important reactions; however, their application has limitations, which could be overcome by using appropriate immobilization strategies. Here, a new support for immobilizing enzymes is proposed. This hybrid organic-inorganic support is composed of chitosan—a natural, nontoxic, biodegradable, and edible biopolymer—and sodium polyphosphate, which was the inorganic component. Lipase B from Candida antarctica (CALB) was immobilized in microspheres by encapsulation using these polymers. The characterization of the composites (by infrared spectroscopy, thermogravimetric analysis, and confocal Raman microscopy) confirmed the hybrid nature of the support, whose external part consisted of polyphosphate and core was composed of chitosan. The immobilized enzyme had the following advantages: possibility of enzyme reuse, easy biocatalyst recovery, increased resistance to variations in temperature (activity declined from 60°C and the enzyme was inactivated at 80°C), and increased catalytic activity in the transesterification reactions. The encapsulated enzymes were utilized as biocatalysts for transesterification reactions to produce the compound responsible for the aroma of jasmine.

One of the most broadly used models for membrane fouling is the Hermia model, which separates this phenomenon into four blocking mechanisms, each with an associated parameter n. These mechanisms are complete blocking (n=2), intermediate blocking (n=1), standard blocking (n=3/2), and cake formation (n=3/2). The original model, which was obtained through experimental data, is given by an Ordinary Differential Equation (ODE) dependent on n. At the time, this ODE was only solved for these four values of n, which limits the effectiveness of the model when adjusted to experimental data. This paper aims to not only mathematically prove the original Hermia model but also to broaden the scope of this model for any real number n by using the original ODE, the equations of fluid mechanics, and the properties of single and multivariable calculus. The extended Hermia model (EHM) is given by a power law for any n≠2 and is given by an exponential function at n=2. To better test the model, we have performed the model fitting of the EHM and compared its performance to the original four pore-blocking mechanisms in 6 micro- and ultrafiltration examples. In all examples, the EHM performed consistently better than the four original pore-blocking mechanisms. Changes in the blocking mechanisms concerning transmembrane pressure (TMP), crossflow rate (CFR), crossflow velocity (CFV), membrane composition, and pretreatments are also discussed.

(1) Background: Reduced magnesium (Mg) levels may be associated with a faster de-cline in renal function. The aim of this study was to evaluate the association of serum and uri-nary Mg levels with body composition and inflammatory markers; (2) Methods: Lon-gitudinal study with patients with chronic kidney disease undergoing non-dialysis treatment in stages 3A, 3B and 4. Venous samples were collected after a 12-hour night fast. Body composition was evaluated by Double X-Ray Emission Absorptiometry and Air Displacement Plethysmog-raphy; (3) Results: The sample consisted of 134 patients. In the adjusted linear regression model, uric acid, percentage of lean mass and ali-mentar intake of Mg were positively associated with the sergic Mg. Triglyceride levels, WC and fat mass percentage were negatively associated with serum Mg. For the Mg urinal, in the adjusted model, the eGFR (estimated glomerular filtration rate), IL (interleukin 6), food intake of Mg and the percentage of lean mass showed a positive correlation.; (4) Conclusions: Serum Mg levels were positively associated with lean mass and negatively with total and central body fat and urinary Mg was positively associated with IL6 and lean mass.

Water scarcity is a key challenge to global development. In Brazil, the Sao Francisco River Basin (SFB) has experienced water scarcity problems because of decreasing streamflow and increasing demands from multiple sectors. However, the drivers of decreased streamflow, particularly the potential role of surface-groundwater interaction, have not been yet investigated. Here, we assess long-term trends in baseflow, quickflow, and streamflow of the SFB during 1980–2015 and constrain the most likely drivers of observed decreases through trend analysis of precipitation (P), evapotranspiration (ET), and terrestrial water storage change (TWS). We found that over 82% of the observed decrease in streamflow can be attributed to a significant decreasing baseflow trend (< -20 m3 s-1 y-1) along the SFR with spatial agreement between decreased baseflow, increased ET, and irrigated agricultural land. We also noted a decrease in TWS across the SFB with trends exceeding -20 mm y-1. Overall, our findings indicate that decreasing groundwater contributions (i.e., baseflow) is providing the observed reduction in total SFR flow. A lack of significant P trends indicates that only P variability likely has not caused the observed baseflow reduction, mainly in the Middle and Sub-middle SFB. Therefore, groundwater and surface withdrawals may be likely a driver of water scarcity over the SFB.

Soil amendments with composted organic materials are recommended to increase soil organic matter (SOM), promote soil fertility, and the Circular Economy. Growing areas of hedged olive groves in southern Iberia peninsula generate huge amounts of olive leaves whose potential as soil organic amendment is not fully studied. An experimental field trial in a hedged olive grove (cv. “Cobrançosa”) was set up near Portalegre, Portugal, to test a compost of olive leaves plus sheep manure (2:1) applied in the row, at soil surface. Three nominal rates (0 or control, 2.5 and 5.0 kg m-2) were used, in a complete randomized block setup (3 treatments, 3 replicas, 9 plots) and soil chemical properties of layers 0-5, 5-15, and 15-30 cm deep were annually monitored. Results (mainly in layer 0-5 cm) showed increases in total N, carbon of the particulate organic matter (POM-C), permanganate oxidizable carbon (POX-C), extractable phosphorus, and zinc, after one year, and increases in soil organic carbon (SOC), C-stock, pHKCl, extractable phosphorus, and zinc, after two years. POX-C was the most sensitive SOM-related indicator, showing increases up to 30 cm deep after one year. This compost improved soil fertility, but should be monitored over longer periods of time, namely for SOC and extractable zinc content.

Here, one hundred patients (50 smokers and 50 non-smokers) clinically diagnosed with COVID-19 were studied. Yet, bioinformatics was used to predict epitopes on Tobacco mosaic virus coat protein (TMV-CP) to produce antibodies towards SARS-CoV-2. Death was three times higher in non-smokers than in smokers. However, biochemical parameters did not separate the groups. Bioinformatics analysis predicted the presence of B-cell epitopes in TMV-CP, suggesting the production of antibodies anti-TMV-CP in smoker patients. Smokers may develop severe forms of COVID-19, but survival was superior in the evaluated group than in non-smokers. Anti-TMV-CP antibodies, potentially present in smokers, might act as a pro-immune agent against SARS-CoV-2 at earlier stages of infection. These data are helpful for future studies assessing COVID-19 in smokers.

Natural host defensins, also sometimes termed antimicrobial peptides, are evolutionarily conserved. They have been studied as antimicrobials, but some pharmaceutical properties, undesirable for clinical use, led to the development of synthetic molecules with constructed peptide arrangements and/or peptides not found in nature. The leading development currently is synthetic small molecule nonpeptide mimetics, whose physical properties capture the characteristics of the natural molecules, and share their biological attributes. We studied brilacidin, an arylamide of this type, for its activity in vitro against fungi (40 clinical isolates, 20 species) that the World Health Organization has highlighted as problem human pathogens. We find antifungal activity at low concentrations for many pathogens, which indicates further screening for activity, particularly in vivo, is justified to evaluate this compound, and other mimetics, as attractive leads for development of effective antifungal agents.

Alzheimer's disease (AD) is a stealthy and progressive neurological disorder that is a leading cause of dementia in the global elderly population, imposing a significant burden on both the elderly and society. Currently, the condition is treated with medications that alleviate symptoms. Nonetheless, these drugs may not consistently produce the desired results and can cause serious side effects. Hence, there is a vigorous pursuit of alternative options to enhance the quality of life for patients. Ginkgo biloba (GB), an herb with historical use in traditional medicine, contains bioactive compounds such as terpenoids (Ginkgolides A, B, and C), polyphenols, organic acids, and flavonoids (quercetin, kaempferol, and isorhamnetin). These compounds are associated with anti-inflammatory, antioxidant, and neuroprotective properties, making them valuable for cognitive health. A systematic search across three databases using specific keywords GB in AD and dementia yielded 1702 documents, leading to the selection of 15 clinical trials for synthesis. In eleven studies, GB extract/EGb 761® was shown to improve cognitive function, neuropsychiatric symptoms, and functional abilities in both dementia types. In four studies, however, there were no significant differences between the GB-treated and placebo groups. Significant improvements were observed in scores obtained from the Mini-Mental State Examination (MMSE), Short Cognitive Performance Test (SKT), and Neuropsychiatric Inventory (NPI). While the majority of synthesized clinical trials show Ginkgo biloba has promising potential in these conditions, more research is needed to determine optimal dosages, effective delivery methods, and appropriate pharmaceutical formulations. Furthermore, a thorough assessment of adverse effects, exploration of long-term use implications, and investigation into potential drug interactions are critical aspects that must be carefully evaluated in future studies.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal man-agement. This study aims to develop and to validate two different models to predict the sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for planetary-scale environmental data analysis. The first model is a Bathtub Model based on the uncertainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and Modeling System software. The validation process performed in the Rio Grande do Sul coastal plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule formula implemented in GEE and is capable to determine the coastline retreat of a profile through the creation of a simple vector line from topo-bathymetric data. The model shows a very high cor-relation (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE platform seems to be an important tool for coastal management. The models developed have been openly shared, enabling the continuous improvement of the code by the scientific commu-nity.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence 15 of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal 16 management. This study aims to develop and to validate two different models to predict the 17 sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for plan-18 etary-scale environmental data analysis. The first model is a Bathtub Model based on the uncer-19 tainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and 20 Modeling System software. The validation process performed in the Rio Grande do Sul coastal 21 plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule for-22 mula implemented in GEE and is capable to determine the coastline retreat of a profile through the 23 creation of a simple vector line from topo-bathymetric data. The model shows a very high correla-24 tion (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE 25 platform seems to be an important tool for coastal management. The models developed have been 26 openly shared, enabling the continuous improvement of the code by the scientific community.

Preliminaries Convolutional Neural Network (CNN) applications have recently emerged in Structural Health Monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT (Lead Zirconate Titanate) based method and CNN. Likewise, applications using CNN along with the Electromechanical Impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of 4 types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

Background: Cleft lip and palate can affect the development of the maxilla, which may create a midfacial deficiency as well as an interference of the facial growth pattern and dentofacial esthetics. Objective: Estimate the chronological age of complete fusion of the spheno-occipital synchondrosis in cleft lip and/or palate patient´s and a control group, using cone beam computed tomography images. Methods: In this retrospective study, 125 patients were enrolled (cleft lip and/or palate group (n=91); control group (n=34)). Age comparison was made with Chi-square test and a Kaplan-Meyer analysis determined the median time to reach complete fusion of the spheno-occipital synchondrosis (p&lt;0.05). Results: The experimental group showed statistically significant differences in the median time for complete ossification between males and females (p=0.019). No statistically significant differences were found in the control group between males and females (p=0.104). The median time for complete ossification of the spheno-occipital synchondrosis was, for males, 15.0 years in both groups; for females, it was 14.0 years in the experimental group and 13.0 years in the control group. Conclusions: The present study showed no differences in the ossification of the spheno-occipital synchondrosis between individuals with and without cleft lip and/or palate.

Many of engineering’s problems are about to optimise equations, functions and process models equations which appear on common or even complex science’s cases. Most applied optimisation methods nowadays can only be used on particular cases, so the main objective of this article is to define a computational method that can optimise or even find target values for specified objective functions and variables, by just using computer effort. Chemical plants involve many differential equations and they can be optimised with more facility now. On this paper, there are shown as example, an optimisation for a chemical reactor, which is found the optimal temperature and volumetric flow rate of feed, for the given objective function being the composition of the desired product. Named Domain’s Sweep, it is an algorithm that evaluate given(s) mathematical function(s) and/or equation(s) by varying your(s) independent variable(s) through loops with a given step size and solving it after closing the degrees of freedom, and finally, with some condition statements, store all the optimum values of given or created objective functions with its respective independent variables. In another words, the user create an objective function and this method find the function’s maximum, minimum or a certain chosen target value, even if it does not have an inflection point in the given search interval of the independent variables.

The present work analyzes the behavior in terms of corrosion resistance of three reinforced concrete formulations over a period of 1 year. The samples were accompanied by creating a monitoring methodology using the Electrochemical Impedance Spectroscopy (EIE) technique, working only with the real component over time. Three mixtures were used, one conventional without pozzolanic addition (REF) and two others with pozzolanic additions, rice husk ash (RCA) and metakaolin (MK). Prototypes were created and exposed to the action of a 165g/l NaCl sodium chloride solution. The characterization of the materials was carried out by determining the chloride diffusion profile (ASTM 1556), analyzing images using tomography and with the support of analytical techniques such as x-ray fluorescence and x-ray diffraction. The monitoring methodology using the EIE demonstrated the positive effect of the insertion of pozzolans, rice husk ash (RCA) and metakaolin (MK), by delaying the process of chloride diffusion in the concrete, resulting in greater resistance to corrosion. The EIE also showed that the active mineral addition in concrete, of aluminum-silicic composition (MK), had a predominant protective effect on the steel//concrete interface against the attack of chloride ions.

The art work of this present paper is to show properly conditions and aspects to reach stability in the process control, as also to avoid instability, by using digital PID controllers, with the intention of help the industrial community. The discussed control strategy used to reach stability is to manipulate variables that are directly proportional to their controlled variables. To validate and to put the exposed principles into practice, it was done two simulations of a continuous fermentation tank process start-up with the yeast strain S. Cerevisiae NRRL-Y-132, in which the substrate feed stream contains different types of sugar derived from cane bagasse hydrolysate. These tests were carried out through the resolution of conservation laws, more specifically mass and energy balances, in which the fluid temperature and level inside the tank were the controlled variables. The results showed the facility in stabilize the system by following the exposed proceedings.

Cryptococcus neoformans is a multidrug-resistant human pathogenic yeast responsible for infections in immunocompromised patients. Here, Itraconazole (ITR), a commercial antifungal drug with low effectiveness against C. neoformans, was combined with different synthetic peptides Mo-CBP3-PepII, RcAlb-PepII, RcAlb-PepIII, PepGAT, and PepKAA. The mechanisms of action responsible for the synergistic effect were evaluated for the best combinations by Fluorescence Microscopy (FM). The synthetic peptides enhanced the activity of ITR by 10-fold against C. neoformans. Our results demonstrated that the combinations could induce pore formation in the membrane and overaccumulation of ROS on C. neoformans cells. Our findings indicate that our peptides successfully potentialize the activity of ITR by reducing it by 10-fold to reach antifungal activity against C. neoformans. Therefore, synthetic peptides are potential molecules to act as co-adjuvant agents in treating Cryptococcal infections.

The digital transition processes have demonstrated an enormous capacity to develop and implement sustainable solutions, which allow solving several problems such as poverty, high rates of species extinction and lack of equal opportunity. However, little attention is paid to the connection between the digital transition and sustainability. Thus, a systematic review of the bibliometric literature was developed to fill this knowledge gap and demonstrate the potential contributions of the digital transition to environmental, economic and social sustainability aspects. In environmental sustainability, the digital transition involves the application of technologies such as AI, big data analytics, IoT, and mobile technologies that are used to develop and implement sustainability solutions in areas such as sustainable urban development, sustainable production and pollution control. In economic sustainability, emerging digital technologies can drive transformation into the more sustainable circular economy, the digital sharing economy, and establish sustainable manufacturing and infrastructure design. In the digital transition to social sustainability, the studies analyzed demonstrate the need for multidimensional policy perspectives to address the current digital divide. For effective management of the digital transition that achieves sustainability goals, the study discusses alternative approaches that include innovation through experimentation, and dynamic and sustainable advantages achievable through temporary benefits.

Eutrophication, a global concern, impacts water quality, ecosystems, and human health. It’s crucial to monitor algal blooms in freshwater reservoirs, as they indicate the trophic condition of a waterbody through Chlorophyll-a (Chla) concentration. Traditional monitoring methods, however, are expen-sive and time-consuming. Addressing this hindrance, we developed models using remotely sensed data from the Sentinel-2 satellite for large-scale coverage, including its bands and spectral indexes, to estimate the Chla concentration on 149 freshwater reservoirs in Ceará, Brazil. Several machine learning models were trained and tested, including k-nearest neighbours, random forests, extreme gradient boosting, the least absolute shrinkage, group method of data handling (GMDH), and sup-port vector machine models. A stepwise approach determined the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH, achieving an R2 of 0.91, MAPE of 102.34%, and RMSE of 20.38 g/L, which are values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near infra-red bands.

Introduction: Body size is correlated with many aspects of an animal species' natural history, such as life span, abundance, dispersal capacity and diet breadth. However, contrasting trends have been reported for the relationship between body size and these ecological traits. Methods: Butterfly species from fruit-feeding guilds were used to investigate whether body size correlates with species abundances, dispersal, permanence, and diet breadth in a Neotropical savanna in Brazil (Cerrado). We used Blomberg’s K and Phylogenetic Generalized Least Squares models (PGLS) to measure phylogenetic signal strength in species traits, and to estimate size-dispersal-diet breadth associations while taking shared ancestry into account. Results: 539 individuals from 27 species were captured, and 190 individuals were recaptured, representing a 35% recapture rate. We found that body size negatively influenced butterfly abundance. In contrast, body size was positively associated with dispersal levels, distance traveled, number of traps visited, individual permanence, and diet breadth. These results indicate that larger butterflies have a greater proportion of dispersing individuals over longer distances, as they permanence were detected over longer periods than their smaller relatives. Moreover, larger butterflies are more generalized, based on the number of host plant families and genera they consume. Smaller butterflies demand fewer resources, which is reflected in their higher survival in small patches, and may explain their lower dispersal ability, and higher diet specialization. Nevertheless, lower dispersal ability, if not compensated by large population sizes, may threaten small-bodied species inhabiting environments with intense deforestation rates, such as the Cerrado. Conclusions: Body size positively influences dispersal and diet breadth in the fruit-feeding butterflies collected in this study.

This case report investigates 5 real cases which followed legal channels and were judged by Mato Grosso Court in Brazil. Audio systems served as elements of key evidence on those lawsuits. The goal here is to analyze the cases by using a methodology based on the forensic speaker verification by using the Ordinary Least Squares (OLS) algorithm and to compare results with analyses obtained on real cases. The comparative analysis is assessed for time elapsed for obtaining results, as well as results quality. In Brazil, the lawsuit duration is very important, since the Penal Code foresees prescription after a given time, and it may lead to impunity. Results show that the analysis, by using OLS, generates immediate, effective results when compared to those obtained with traditional methodologies on the studied Brazilian lawsuits.

The seeds of red clover are heteromorphic and two color morphs can be visually recognized, light purple and yellow, resulting from heterozygosity and recessive homozygosity at two loci. Here, we report the responses of seed imbibition, seed germination, and early seedling growth of the two morphs to distilled water, sodium chloride, and complete nutrient solution. The sensitivity of red clover seeds to treatments increased with the stage of development in what seems to be a cumulative process. No differences were found in seed imbibition between morphs or between treatments. In seedling growth, on the contrary, treatments were always effective, but differences between morphs were only observed in seeds that were treated with nutrient solution, whereas in the intermediate stage of seed germination, the effects by treatments were observed together with the appearance of differences between morphs in distilled water and in the treatment by sodium chloride solution. Simultaneously, the superior performance of the yellow morph that was found in germination, which appears to be a trait stable across cultivars of red clover seeds, turned into a superior performance of the light purple morph in seedling growth.

Advanced cutaneous melanoma is considered the most aggressive type of skin cancer with variable rates of treatment response. Nowadays, there are available some classes of immunotherapy and target therapy for its treatment. Immunotherapy can inhibit tumor growth and its recurrence by triggering host's immune system, while targeted therapy acts inhibiting specific molecules or signaling pathways. However, melanoma response to these treatments are highly heterogenous, and frequently become resistant. Epigenome (DNA/histone modification) contribute to cancer initiation and progression. Epigenetic alterations are divided into four levels of gene expression regulation: DNA methylation, histone modification, chromatin remodeling and non-coding RNAs regulation. The deregulation of lysine methyltransferase enzymes is associated with tumor initiation, invasion, development of metastases, changes in immune microenvironment and drug resistance. The study of lysine histone methyltransferase (KMT) inhibitors is important to cancer epigenetic mechanisms understanding and to biological processes. In addition to immunotherapy and target therapy, the research and development of KMT inhibitors is ongoing. Many studies are exploring the therapeutic implications and possible side effects of these compounds, besides their adjuvant potential to the approved current therapies. Importantly, as with any drug development, safety, efficacy, and specificity are crucial considerations when developing KMT inhibitors for clinical applications.

The regulation of multiple bacterial phenotypes was found to depend on different cyclic di-nucleotides (CDNs) that constitute intracellular signalling second messenger systems. Most notably, c-di-GMP, along with proteins related to its synthesis, sensing and degradation, was identified as playing a central role in the switching from biofilm to planktonic modes of growth. Recently, this world has been under expansion, with the discoveries of other signalling CNDs in bacteria (c-di-AMP and cGAMP) and also in eukaryotes, novel protein and RNA receptors of CDNs, and the numerous functions related to these molecules. In this work, we comprehensively review and analyse the structural biology data about the systems that bacteria use to synthesise and recognise CDNs, detailing their interactions at molecular level with their products/ligands. Additional interesting observations were made, including that different receptor types can bind CDNs in similar conformations and that, based on genomic data, different CDN second messenger systems may coexist in many organisms. The large amount of sequence and structural data available allows a broad view of the importance of CDNs in bacteria, but how cells coordinate these molecules to ensure adaptation to changing environmental conditions is still open for much further exploration.

In the software development process, the decision-maker (DM) has a range of problems inherent to its function. Wrong choices during software planning can bring great risk to the project. Therefore, the planning of software releases to be delivered to the customer should be well done. This is not an easy task because releases are made up of many requirements that contain complex variables that must be considered, such as precedence, cost, requirement stability, among other features that make the requirements-selection process challenging. To make this process less exhaustive, DM can use tools that facilitate this work. In software engineering, we can find fields of research specialists in this context, such as Search-Based Software Engineering (SBSE). The SBSE makes use of advanced metaheuristics to search for optimal solutions or the closest to it. In this work, we try to use another field of research to solve this same problem type, the Verbal Decision Analysis (VDA). To do this, we elaborate a workflow that will use the same source data, execute two solutions using the two search fields (SBSE and VDA) and compare the results. In the end, we evaluated and commented on the results.

In this work, we present a model of the atom that is based on a nonclassical logic called paraconsistent logic (PL), which has the main property of accepting the contradiction in logical interpretations without the conclusions being annulled. The proposed model is constructed with an extension of PL called paraconsistent annotated logic with annotation of two values (PAL2v), which is associated with an interlaced bilattice of four vertices. We use the logarithmic function of the Shannon entropy H_(s) to construct the paraconsistent equations and thus adapt a probabilistic model for representations in quantum physics. Through analyses of the interlaced bilattice, comparative values are obtained for some of the phenomena and effects of quantum mechanics, such as superposition of states, quantum entanglement, wave functions, and equations that determine the energy levels of the layers of an atom. At the end of this article, we use the hydrogen atom as a basis of the representation of the PAL2v model, where the values of the energy levels in six orbital layers are obtained. As an example, we present a possible method of applying the PAL2v model to the use of Raman spectroscopy signals in the detection of lubricating mineral oil quality.

Abstract: Understanding the reliability and sensitivity of jump testing is essential to determine the neuromuscular progress of athletes and make informed decisions. This study aimed to assess the reliability and sensitivity of countermovement jump (CMJ) and drop jump (DJ) tests metrics in female volleyball players. Sixteen semi-professional female volleyball players participated in the study. Intrasession and intersession reliability of CMJ and DJ metrics were evaluated using a randomized cross-over design. Dual force platform was used to collect data, and several dependent variables were calculated using forward dynamics. Intraclass correlation coefficients (ICC), coefficients of variation (CV) and minimum difference (MD) were calculated. The third attempt presented the highest values for both test, and changes in jump strategy in DJ. CMJ is consistent across sessions, while DJ also shows intersession differences (p<0.05) in 11 out of 15 variables. Most CMJ variables exhibit excellent reliability while DJ variables have lower reliability and higher variability. Concentric and eccentric impulses for DJ showed excellent reliability. Most CMJ variables showed excellent reliability and variability, DJ had lower reliability, higher variability, and lower sensitivity. These findings provide valuable information when assessing athletes' performance and highlight the reliability, sensitivity, and variability in jump performance for CMJ and DJ tests.

Hereditary Hemorrhagic Telangiectasia (HHT), also known as Osler-Weber-Rendu syndrome, is a rare and inherited vascular disorder, characterized by the development of arteriovenous malformations (AVMs) in various organs and telangiectasia (small AVM) in the mucocutaneous. The majority of HHT patients have haploinsufficiency of genes involved in the transforming growth factor-beta (TGFβ) signaling pathway, including endoglin (ENG), activin receptor-like kinase 1 (ALK1, also known as ACVRL1), or SMAD4. Active angiogenesis is also required for telangiectasia and AVM development. Anti-angiogenic strategies have been tested in patients and animal models extensively. However, the exact mechanisms for telangiectasia and AVM development remain unclear. In this review, we discuss recent advances in identifying disease mechanisms, and potential therapeutic targets.

Research indicates that farmers’ demographic characteristics and production practices have safety and health implications. However, current systems do not identify organic farmers independently from conventional farmers, and literature on how organic and conventional farmers compare is very limited. We conducted a secondary analysis of 2012 Census of Agriculture data to compare organic and non-organic farms and principal operators (POs) in New Mexico (NM). Organic farms were smaller in size, and POs of farms with organic sales were significantly younger (55.8±9.5 vs. 60.5±5.5 years) and less experienced (19.5±6.8 vs. 25.2±6.8 years). Significant differences were also found in POs ethnicity, race, and primary occupation. More farms with organic sales had a female PO compared to farms with non-organic sales (27% vs. 19%). Other significant differences related to work arrangements, household income, living conditions, and access to Internet. National surveys and regional studies may not accurately typify and describe the local organic producer, which is essential in order to advance policy, develop health interventions, and properly address occupational safety and risk among organic farmers. This study makes a unique contribution to understanding the importance of surveillance and collecting place-based data that are specific to the organic producer.

The viewpoint and reaction of a country towards climate change are shaped by its political, cultural, and scientific backgrounds, in addition to the distinct characteristics of its evolving climate and the anticipated and actual consequences of the phenomenon in the times ahead. A region's climate has a significant impact on how water is managed and used, mostly in the primary sector (agroforestry systems, livestock, and agriculture), and both the distribution of ecosystem types and the amount and spreading of species on Earth. As a result, the environment and agricultural practices are affected by climate, so evaluating both distribution and evolution is extremely pertinent. Towards this aim, the climate distribution and evolution in the S. Francisco River basin (SFRB) is assessed in three periods (1970−2000, 1981−2022) in the past and 20412060 in the future from an ensemble of GCMs under two SSPs (Shared Socioeconomic Pathways), SSP2-4.5 and SSP5-8.5. The Köppen-Geiger (KG) climate classification system is analyzed, and climate change impacts are inferred for this watershed located in central-eastern Brazil, covering an area equivalent to 8% of the country. Results predict the disappearance of the hot-summer (Csa) and warm-summer (Csb) Mediterranean climates, and a reduction/increase in the tropical savanna with dry winter (Aw)/dry summer (As). A striking increase in the semi-arid hot (BSh-steppe) climate is predicted with a higher percentage (10%) under SSP5-8.5. The Source and the mouth of SFRB are projected to endure the major impacts of climate change that is followed by a predicted increase/decrease in temperature/precipitation. Future freshwater resource availability, and quality for human use will all be impacted. Consequences on ecosystems, agricultural and for the socioeconomic sectors within the SFRB might deepen the current contrasts between regions, urban and rural areas, and even between population groups, thus translating to a larger extent, the inequality that still characterizes Brazilian society. Maps depicting land use and cover changes in the São Francisco River basin from 1985 to 2022 highlight tendencies such as urbanization, agricultural expansion, deforestation, and changes in shrubland and water bodies. Urban areas fluctuated slightly, while cropland significantly increased from 33.57% to 45.45%, and forest areas decreased from 3.88% to 3.50%. Socioeconomic data reveals disparities among municipalities: 74.46% with medium Human Development Index (HDI), 0.59% with very high HDI, and 9.11% with low HDI. Most municipalities have a Gross Domestic Product (GDP) per capita below US$ 6,000. Population distribution maps show a predominance of small to medium-sized urban and rural communities, reflecting the basin's dispersed demographic and economic profile. To build routes towards a shared sustainable goal—namely, to adapt and reduce the expected climate change impacts in SFRB—it is imperative that integrated measures be conducted with the cooperation of stakeholders, local population, and decision-makers.

The transport of iron(III) from Fe(III)-Mn(II)-HCl mixed solutions through a flat-sheet supported liquid membrane is investigated, being the carrier phase of Cyanex 923 (commercially available phosphine oxide extractant) dissolved in Solvesso 100 (commercially available diluent), as a function of hydrodynamic conditions, concentration of manganese and HCl in the feed phase, and carrier concentration in the membrane phase. A transport model is derived that describes the transport mechanism, consisting of diffusion through a feed aqueous diffusion layer, a fast interfacial chemical reaction, and diffusion of the Fe(III)-Cyanex 923 complex across the membrane phase. The membrane diffusional resistance (Δm) and feed diffusional resistance (Δf) are calculated from the model, and their values are 145 s/cm and 361 s/cm, respectively. It is apparent that the transport of iron(III) is mainly controlled by diffusion through the aqueous feed boundary layer, being the thickness of this layer calculated as 2.9x10-3 cm. Since Mn(II) is not transported through the membrane phase, the present system allows to the purification of this manganese-bearing solutions.

Even in the first quarter of XXI century, the presence of organic dyes in wastewaters is a normal occurrence in a series of countries, and being these compounds toxics, their removal from these waters is of a necessity. Among the separation technologies, adsorption processing appeared as one of the most widely used to reach this goal. The present work reviewed the most recent approaches (first half of 2021 year) about the use of a variety of adsorbents on the removal of, also, a variety of organic dyes of different nature.

Mercury is a toxic metal, thus, it is an element which has more and more restrictions in its uses, but despite the above, the removal of this metal, from whatever the form in which it is encountered (zero valent metal, inorganic or organic compounds), and from different sources, is of a widespread interest. In the case of Hg(II), or Hg²⁺, the investigations about the treatment of Hg(II)-bearing liquid effluents (real or in most cases synthetic solutions) appear not to end, and from the various separation technologies, adsorption is the most popular among researchers. In this topic, and in the 2019 year, more than 100 publications had been devote to this field: Hg(II)-removal-adsorption. This work examined all of them.

On the system Au(III)-HCl-A324H⁺Cl^-, liquid-liquid extraction experiments were used to define the extraction equilibrium and the corresponding extraction constant, and the facilitated transport of this precious metal, from HCl solutions, across a flat-sheet supported liquid membrane was investigated, using the ionic liquid as carrier, as a function of hydrodynamic conditions, concentration of gold(III) (0.01-0.1 g/L), and HCl (0.5-6 M) in the feed phase, and carrier concentration (0.023-0.92 M) in the membrane. An uphill transport equation was derived considering aqueous feed boundary layer diffusion and membrane diffusion as controlling steps. The aqueous diffusional resistance (Δf) and the membrane diffusional resistance (Δm) were estimated from the proposed equation, being their values 241 s/cm an 9730 s/cm, respectively. The performance of the present carrier was compared against results yielded by other ionic liquids, and also it was investigated the influence that other metals had on gold(III) transport both from binary or quaternary solutions. Gold was finally recovered from receiving solutions as zero valent gold nanoparticles.

The transport of indium(III), from HCl solutions, across a supported liquid membrane in flat-sheet configuration was investigated, being the carrier the ionic liquid HA324H+Cl- (derived from the tertiary amine Hostarex A324 and hydrochloric acid). Different variables affecting the metal transport: hydrodynamic conditions in the source and receiving phases, metal and HCl concentrations in the source phase, and carrier concentration in the membrane phase, were investigated. Also the transport of indium(III) using carriers of various nature: ionic liquids, alcohol, ketone, phosphine oxide and phosphoric ester, was compared. The metal transport was modelled describing the transport mechanism as: diffusion across the source diffusion layer, a fast interfacial chemical reaction, and diffusion of the InCl4--carrier complex through the membrane support. Diffusional parameters for the transport of indium(III), from the experimental data and the model, were estimated.

The transport of indium(III), from HCl solutions, across a supported liquid membrane in flat-sheet configuration was investigated, being the carrier the ionic liquid HA324H⁺Cl^- (derived from the tertiary amine Hostarex A324 and hydrochloric acid). Different variables affecting the metal transport: hydrodynamic conditions in the source and receiving phases, metal and HCl concentrations in the source phase, and carrier concentration in the membrane phase, were investigated. Also the transport of indium(III) using carriers of various nature: ionic liquids, alcohol, ketone, phosphine oxide, etc., was compared. The metal transport was modelled describing the transport mechanism as: diffusion across the source diffusion layer, a fast interfacial chemical reaction, and diffusion of the InCl₄^--carrier complex through the membrane support. Diffusional parameters for the transport of indium(III), from the experimental data and the model, were estimated.

The present investigation deals with the adsorption of chromium(III) from alkaline media using multi-walled carbon nanotubes. The adsorption of Cr(III) has been studied under various experimental conditions: stirring speed of the aqueous solution, initial metal and adsorbent concentrations, NaOH concentration in the aqueous solution, and temperature. The rate law indicated that chromium adsorption is well represented by the particle diffusion model, whereas the adsorption process fits to the pseudo-second order kinetic model within an exothermic character. Equilibrium data fit to the Langmuir type-2 equilibrium isotherm in an spontaneous process. Chromium(III) can be eluted from metal-loaded nanotubes using acidic solutions, from which fine chromium(III) oxide pigment can be ultimately yielded.

Overweight/obesity is a growing pandemic nowadays that affects many organs and tissues. We have investigated whether a high-lipid diet provokes an imbalance between type 1 and type 2 angiotensin II (Ang II) receptors signaling, leading to liver alterations associated with previously described cardiovascular and kidney disturbances. Chronic administration of a high-lipid diet can provoke an hepatocardiorenal syndrome as the result of activation of the Ang II→type 1 receptor axis, which is completely counteracted by Ang-(3–4) the allosteric enhancer of the Ang II→type 2 receptor pathway.

The marketing industry is rapidly evolving, necessitating educational programs to adapt and equip graduates with relevant skills and knowledge. This systematic literature review with bibliometric analysis (SLRBA) explores the integration of Problem-Based Learning (PBL) in marketing education. PBL is an instructional strategy that empowers students to solve real-life problems by integrating theory and practice, thus fostering critical thinking and decision-making skills. Our review synthesizes data from 71 sources, highlighting the definition of PBL, strategies to promote its implementation in marketing education, and the benefits and challenges associated with this approach. The findings indicate that PBL enhances student engagement, provides hands-on experience, and prepares students for real-world marketing challenges. Strategies such as using social media, serious games, e-learning, and feedback mechanisms are discussed for their effectiveness in promoting active learning and student participation. Despite its advantages, PBL faces challenges such as resource intensiveness, student resistance, and information integrity issues. This review underscores the importance of PBL in bridging the gap between theoretical knowledge and practical application, ultimately preparing marketing students to meet the demands of the industry. The paper concludes with recommendations for future research and best practices for implementing PBL in marketing education.

In this work, we report the magnetocaloric effect (MCE) in a quantum dot corresponding to an electron interacting with an antidot, under the effect of an Aharonov-Bohm flux subjected to a parabolic confinement potential. We use the Bogachek and Landman model, which additionally allows the study of quantum dots with Fock-Darwin energy levels for vanishing antidot radius and flux. We find that the Aharonov-Bohm flux (AB-flux) strongly controls the oscillatory behaviour of the MCE, thus acting as a control parameter for the cooling or heating of the magnetocaloric effect. We propose a way to detect AB-flux by measuring temperature differences.

This study aims to determine the role of Industry 4.0 sensors in digital transformation. Using a systematic literature review with a bibliometric analysis, we examine a sample of 52 studies from the SCOPUS database to identify research activity on this topic until December 2022. We find that 2022 was the year with the highest number of peer-reviewed documents on the subject, with 18 publications. We also examine current themes and find that sensors can connect multiple systems and devices, thus enabling machine-to-machine communication and facilitating great precision during product designing, development, and production, thus enabling customization and improving quality. However, despite these benefits, the current sensor technologies face multiple challenges that undermine their maximum adoption.

Accurate channel estimation is of utmost importance for massive MIMO systems to provide significant improvements in spectral and energy efficiency. In this work, we present a study on the distribution of a simple but yet effective and practical channel estimator for multi-cell massive MIMO systems suffering from pilot-contamination. The proposed channel estimator performs well under moderate to aggressive pilot contamination scenarios without previous knowledge of the inter-cell large-scale channel coefficients and noise power, asymptotically approximating the performance of the linear MMSE estimator as the number of antennas increases. We prove that the distribution of the proposed channel estimator can be accurately approximated by the circularly-symmetric complex normal distribution, when the number of antennas, M, deployed at the base station is greater than 10.

Sustainability addresses environmental and social issues affecting this and future generations. When family businesses perceive that the community is disrupted, recognize an environmental problem and respond by implementing new environmental policies or regulations, the family business’s socio-emotional values press to transition to a more sustainable production system, such as the ‘Circular Economy.’ Drawing on the Dubin (1978) methodology—a paradigm for building models through deduction—we design a sustainable model, which shows family businesses’ responses to changes in the environment. It explains the reasons why family firms transition to the Circular Economy, based on the theory of Socio-Emotional Wealth (SEW). We check the model through the case study of the food retail leader in the Spanish market—Mercadona—which applies policies about energy, resources and waste to become a Circular Economy business model. Because of the strong family character of Mercadona, this case can be useful for the decision-making of other family businesses.

Many estuarine ecosystems and the fish communities that inhabit them have undergone substantial changes in the past several decades, largely due to multiple interacting stressors that are often of anthropogenic origin. Few are more impactful than droughts, which are predicted to increase in both frequency and severity with climate change. In this study, we examined over five decades of fish monitoring data from the San Francisco Estuary, California, U.S.A, to evaluate the resistance and resilience of fish communities to disturbance from prolonged drought events. High resistance was defined by the lack of decline in species occurrence from a wet to a subsequent drought period, while high resilience was defined by the increase in species occurrence from a drought to a subsequent wet period. We found some unifying themes connecting the multiple drought events over the fifty-year period. Pelagic fishes consistently declined during droughts (low resistance), but exhibit a considerable amount of resiliency and often rebound in the subsequent wet years. However, full recovery does not occur in all wet years following droughts, leading to permanently lower baseline numbers for some pelagic fishes over time. In contrast, littoral fishes seem to be more resistant to drought and may even increase in occurrence during dry years. Based on the consistent detrimental effects of drought on pelagic fishes within the San Francisco Estuary and the inability of these fish populations to recover in some years, we conclude that freshwater flow remains a crucial but not sufficient management tool for the conservation of estuarine biodiversity.

Familial Alzheimer’s disease (FAD) can be caused by mutations in PSEN1 that encodes presenilin-1, a component of the gamma-secretase complex that cleaves amyloid precursor protein. Alterations in calcium (Ca2+) homeostasis and glutamate signaling are implicated in the pathogenesis of FAD, however it has been difficult to assess in humans whether or not these phenotypes are the result of amyloid or tau pathology. This study aimed to assess early calcium and glutamate phenotypes of FAD by measuring the Ca2+ response of induced pluripotent stem cell (iPSC)-derived neurons bearing PSEN1 mutations to glutamate and the ionotropic glutamate receptor agonists NMDA, AMPA and kainate compared to isogenic control and healthy lines. The data shows that in early neurons, even in the absence of amyloid and tau phenotypes, FAD neurons exhibit increased Ca2+ responses to glutamate and AMPA, but not NMDA or kainate. Together this suggests that PSEN1 mutations alter Ca2+ and glutamate signaling as an early phenotype of FAD.

This study aims to analyze the interconnection between the concepts of digital economy and sustainability. These concepts have become popular due to awareness of climate change and increased development and adoption of technologies. Researchers, business leaders and policymakers are exploring the many ways digital technologies can be used to address sustainability issues. Using a systematic literature review with a bibliometric analysis, we examined a sample of 92 studies indexed in SCOPUS to identify research activity on this topic through April 2023. We found that 2022 had the highest number of peer-reviewed papers on the subject, with 31 publications. During the research, we also identified multiple opportunities for this interconnection, such as developing renewable energy and sustainable technological solutions, smart cities and sustainable urbanization, and sustainable consumption. These opportunities enabled by digital technologies allow companies to adopt sustainable business strategies and develop sustainable products. Despite these opportunities, the digital economy presents several challenges that can hinder efforts to achieve sustainability goals, such as increased e-waste, high energy consumption and rising carbon emissions, digital divide, job insecurity, growing monopolies, and data protection. These issues must be addressed to enable optimal use of the opportunities presented in the digital economy to promote sustainability.

The initial development of seahorse juveniles is characterized by low digestion capabilities. Stable isotope analysis is an effective tool in studies of trophic food webs and animal feeding patterns. The present study provides new insights for the understanding of growth and food assimilation in early developing seahorses following a laboratory diet switch. The study was performed in early life stages of the seahorse Hippocampus reidi by assessing the influence of diet shift on changes and turnovers in carbon (δ13C) and nitrogen (δ15N) stable isotope in juveniles. Newborn seahorses were fed for 60 days following two feeding schedules (A6 and A11) based initially on copepods Acartia tonsa and subsequently on Artemia nauplii (since days 6 and 11, respectively). After prey shift, we determined δ13C and δ15N turnover rates as functions of change in either body mass (fitting model G) and days of development (fitting model D), contributions of metabolism and growth to those turnover rates, and diet-tissue discrimination factors. Survival, final dry weight and final standard length for diet A11 were higher compared to diet A6. The shift from copepods to Artemia lead to fast initial enrichments in δ13C and δ15N. Afterwards, the enrichment was gradually reduced until reaching the isotopic equilibrium with diet. In most cases, both fitting models performed similarly. The isotopic analysis revealed that 100% of tissue turnover was attributed to growth in diet A11, whereas 19-25% was endorsed to metabolism in diet A6. Diet-tissue discrimination factors were estimated for the first time in seahorse juveniles, resulting in higher estimates for diet A11 (2.9 ± 0.7‰ for δ13C; 2.5 ± 0.2‰ for δ15N) than in diet A6 (1.8 ± 0.1‰ for δ13C; 1.9 ± 0.1‰ for δ15N). This study highlights the relevance of feeding on copepods and their effect on isotopic patterns and discrimination factors in seahorse juveniles after a dietary shift. Regarding the application of the results achieved to feeding schedules in the rearing of H. reidi, a long period of feeding on copepods during the first days of development is highly recommended.

The fourth industrial revolution, also known as Industry 4.0, has led to an increased transition towards automation and reliance on data-driven innovations and strategies. Companies in the manufacturing sector are heavily integrating Industry 4.0 technologies, including cloud compu-ting, cyber-physical systems (CPS), big data analytics, horizontal and vertical system integration, the internet of things (IoT), and additive manufacturing (3d printing). The interconnected systems and processes have significantly increased operational efficiency, enhanced organizational capacity to monitor and control functions, reduced costs, and improved product quality. One significant way that companies have achieved these benefits is by integrating diverse sensor technologies within these innovations, allowing critical data collection on products, equipment performance, and maintenance. The sensors connect systems and machines, allowing them to communicate and track operations for enhanced operations. While numerous research has been conducted to show the role of sensors in industry 4.0, limited studies show how these innovations can leverage product design to maximize benefits and opportunities. Given the rapidly changing market conditions, Industry 4.0 requires new products and business models to ensure companies adjust to the current and future changes. These requirements call for the evolutions in product design processes to accommodate design features and principles applicable in the current dynamic business environment. This research paper employs a systematic bibliometric literature review (LRSB) methodology to explore and synthesize data on how Industry 4.0 and sensors can leverage product design. The results show that various product design features create opportunities to be leveraged to guarantee the success of Industry 4.0 and sensor technologies. However, the research also identifies numerous challenges that undermine the ongoing transition towards intelligent factories and products.

The anterior cruciate ligament (ACL) injury is the most serious injury in judo. Therefore, its prevention is of great importance. The main mechanism of injury is the collapse of the knee in valgus, which usually occurs when there are deficits of strength and neuromuscular control of the core, external rotators and hip abductors, as well as limitations of ankle and hip mobility. In most sports there is a difference in the prevalence of this injury between both sexes, being more common in women. Therefore, in the present study we observed this possible intersexual disparity in the difference of movement patterns among elite judokas according to their sex, in order to identify those athletes with a higher risk of ACL injury. In addition, it was not observed differences between sexes in the performance of the Single Leg Squat test (SLS test); There was worse neuromuscular control of the non-dominant leg in men and women; No differences were observed in in the range of motion of ankle dorsiflexion and hip external rotation between men and women, but there were differences in internal rotation, a movement whose restriction may be related to an increased risk of ACL injury.

Changes in food quality can dramatically impair zooplankton fitness, especially in eutrophic water bodies where cyanobacteria are usually predominant. Cyanobacteria are considered a food with low nutritional value, besides some species can produce bioactive secondary metabolites reported as toxic to zooplankton. Considering that cyanobacteria can limit survival, growth and reproduction of zooplankton, we hypothesized that the adverse effects of the dietary exposure of Daphnia species to saxitoxin-producing cyanobacteria depend on the relative abundance of nutitious food source in the diet. Life table experiments were conducted with different diets: 1) without nutritional restriction, where neonates were fed with diets at a constant green algae (Monoraphidium capricornutum) biomass as a nutritious food source, and increasing cyanobacterial concentration (STX-producing Raphidiopsis raciborskii), and 2) with diets consisting of different proportions of M. capricornutum (nutritious) and R. raciborskii (poor food) at a total biomass 1.0 mg C L 1. In general, the presence of high proportions of cyanobacteria promoted a decrease in Daphnia somatic growth, reproduction and intrinsic rate of population increase (r) in both diets with more pronounced effects in the nutritionally restricted diet (90% R. raciborskii). Two-way ANOVA revealed significant effects of species/clone and treatments in both assays, with significant interaction between those factors only in the second assay. Regarding the grazing assay, only D. laevis was negatively affected by increased cyanobacterial proportions in the diet. In the life table assay with constant edible food, a reduction in the reproduction parameters and the intrinsic rate of population increase (r) of all species was observed, In conclusion, we found adverse effects of the toxic cyanobacterial strain R. raciborskii on Daphnia fitness, regardless of the constant amount of nutritious food available, proving the toxic effect of R. raciborskii and the nutritional quality of the food has a greater influence on the fitness of these animals.

Abstract— Flight Control System is an integrated avionics system equipped with the minimum required components for an autonomous flight. This paper focuses on the Hardware Design of the Flight Control System and presents specific details of the components and its interface. The system architecture is based on Field Programmable Gate Array and Digital Signal Processor. Employing these two processors in the flight control system would improve the Flight Control System performance in terms of fast sequential processing of high-level control algorithms. In addition to Field Programmable Gate Array and Digital Signal Processor, the flight control computer system will also make use of Global Positioning System and Micro Electro Mechanical System sensors. The project will be implemented using Altera’s System On Programmable Chip builder, currently known as Qsys – Platform Designer implemented in Quartus-II. The system employs Nios-II processor which is 32-bit soft-core embedded-processor architecture designed especially for the Altera’s family of Field Programmable Gate Array. From conceptualization to final design, this paper presents the functionality of the different modulus and complex interfaces employed in this Flight Control System.

The main challenges in oil platforms are how to define optimal setpoints and decision-making for controlling variables due to many existing dynamical production uncertainties while conceding safe operation and minimizing discharge to sea and atmosphere without killing plantwide inventory. A novel scheduling model to optimize the whole essential units of an oil platform production gathering topside and subsea variables is disclosed. A proper topside and subsea scheduling model enables and creates harmony between optimal production, supply chain, costs, and market dynamics, satisfying demands, environmental and operations constraints leading to stability. The interest of the present research lies in determining the optimal topside and subsea operational settings and decision-making, establishing maximum E&P performance and production control setpoints while predicting and manipulating reservoir lifespan and its revitalization. This objective is attained through a novel multiperiod large-scale model for the planning and operational production scheduling and model predictive control (MPC) in oil platforms complying with sustainability, profitability, platform design capacities, and offloading of oil and gas to supply chains or to pipeline exportation. The present model details heat and mass transfer, PVT, flow profiles along a planning horizon and can be used to any oil platform since comprises all the essential unit operations for oil, gas, CO₂, H₂S and water. The model maximizes the total oil production over any planning horizon. The model is versatile, and decision-making can be either linear or nonlinear but rather a MILP or LP as best choice on optimizing large-scale systems. If the model is applied with auto rescheduling on site within variable hours or minutes, it becomes a real-time optimization schedule approach. Due to its high-velocity performance and robustness, a novel multi-objective function strategy, acting as an LP generic plantwide MPC with industrial scope to maximize production while controlling the process is presented, and as an example is here used to control slug flow to avoid equipment trips and inventory instability, at the same time production is maximized. The output from the decision-making was compared to actual plant data, and the results proved compliance to the design capacity with process safety and sustainability. Comparing to official Brazil's government data for oil, the case study showed superiority to the same size platforms, like FPSOs P-75 and MV32, e.g., more than 101.5% more capacity to produce oil in beginning of campaign after first oil, and 10.5% during campaign. The present work indicates that: 1) the production of millions of barrels is being delayed or left aside; 2) not scheduling production harms the environment and diminish process safety; 3) not scheduling production can create stochastic supply chain deliveries instead of organizing it by deterministic offloading days; 4) scheduling is essential to manipulate, revitalize and monitor reservoir's pressure and content under uncertainty; 5) the oil platform can be automatic optimized by a plantwide MPC reducing human activity/dependency; 6) scheduling gives more transparency to stakeholders and contractors by forecasting business data and capability, and can design, develop or enable new businesses.

Water conflicts have been a significant issue in Brazil, especially in the Sao Francisco River basin. Subseasonal forecasts, up to a 60-day forecast range, can provide information to support decision-makers in managing water resources in the river basin, especially before drought events. Predictability in the subseasonal forecasts range is a research topic as this range contains mixed dependence on weather and seasonal phenomena. Numerical models may inherit this mixed dependence on their skills. The objective of this paper is to evaluate the Eta Model simulations to reproduce the drought events between the years 2011 and 2016. Two sets of 60-day simulations were produced; one started in September (SO) and the other in January (JF) each year. These months were chosen to evaluate the model skill to reproduce the onset and the middle of the rainy seasons in Central Brazil, where the Upper Sao Francisco River is located. The SO simulations reproduced the observed spatial distribution of precipitation but underestimated the amounts. Precipitation errors exhibit large variability across the subbasins. The JF simulations also reproduced the observed precipitation distribution but overestimated it in the Upper and Lower subbasins. The JF simulations better captured the interannual variability of precipitation. The 60-day simulations were discretized in six 10-day accumulations to assess the intra-monthly variability. They showed that simulations can capture the onset of the rainy season and the small amounts of the rainy months that occurred in these severe drought years.

This present work explores the performance of a thermal-magnetic engine of Otto type, considering as a working substance an effective interacting spin model corresponding to the q− state clock model. We obtain all the thermodynamic quantities for the q = 2, 4, 6, 8 cases in a small lattice size (3×3 with free boundary conditions) by using the exact partition function calculated from the energies of all the accessible microstates of the system. The extension to bigger lattices was performed using the mean-field approximation. Our results indicate that the total work extraction of the cycle is highest for the q=4 case, while the performance for the Ising model (q=2) is the lowest of all cases studied. These results are strongly linked with the phase diagram of the working substance and the location of the cycle in the different magnetic phases present, where we find that the transition from a ferromagnetic to a paramagnetic phase extracts more work than one of the Berezinskii–Kosterlitz–Thouless to paramagnetic type. Additionally, as the size of the lattice increases, the extraction work is lower than smaller lattices for all values of q presented in this study.

The aim of this work was to describe the physiological limitations induced under drought conditions in young sweet orange trees, including the crop capability to recuperate its physiological status once re-watering, and after a water withholding. The trial was conducted during two consecutive months (July and August) in two-years old sweet orange trees. Three irrigation treatments were arranged: A full irrigation treatment; (FI); a low-frequency deficit-irrigation treatment (LFDI); and a sustained-deficit irrigation (SDI) treatment. The obtained results showed that for Stem values below -1.5 MPa, significant decreases in gas exchange rates, evidencing diffusive limitations to drought stress. Additionally, there was evidence of increased osmolyte synthesis, compatible as a defensive response to prevent oxidative damage. Likewise, the isohydric behaviour observed also resulted in significant decreases in gs even for moderate stress situations. In addition, it was observed that in the LFDI treatment, the stress levels reached caused a significant increase in the levels of Pro and MDA, which were not equal to those detected in FI during the recovery periods, which leads us to assume that potential values below -1.8 MPa would be causing significant oxidative damage with no capacity for subsequent recovery, and probably affecting the final yield.

The fourth industrial revolution (4IR) can offer significant benefits to the construction sector, improving productivity, efficiency, collaborative efforts, and product quality while promoting safety and sustainability. However, research on the application of 4IR technologies in construction is scarce in developing countries. It is crucial to understand the ability of construction companies to adopt new technologies and identify factors influencing the success of technology implementation. In this study, a questionnaire-based survey was conducted with construction professionals to evaluate the level of technological development of the construction market in an emerging economy, assess the potential for innovation implementation, and identify factors that might influence technological development. Although respondents recognized the importance of digital transformation, the results showed that most innovations are in the early stages of implementation in the construction sector, particularly technologies that are essential for the consolidation of the 4IR. Understanding the factors underlying this reality can support the development of strategies to foster innovation and harness the advantages of the 4IR in the construction industry in developing countries.

In this paper, we identify issues and possible solutions in the key area of large-scale antenna systems, also know as Massive Multiple Input Multiple Output (MIMO) systems. Additionally, we propose the use of Massive MIMO technology as a means to tackle the uplink mixed-service communication problem. Under the assumption of an available physical narrowband shared channel (PNSCH), the capacity of the MTC network and, in turn, that of the whole system, can be increased by grouping Machine-Type Communication (MTC) devices into clusters and letting each cluster share the same time-frequency physical resource blocks. We study the feasibility of applying sub-optimal linear detection to the problem of detecting a large number of MTC devices sharing the same time-frequency resources at the uplink of a base station (BS) equipped with a large number of antennas, M. In our study, we derive the achievable lower-bound rates for the studied sub-optimal linear detectors and show that the transmitted power of each MTC device can be reduced as M increases, which is a very important result for powerconstrained MTC devices running on batteries. Our simulation results suggest that, as M is made progressively larger, the performance of sub-optimal linear detection methods approach the matched filter bound, also known as perfect interference-cancellation bound.

Transcription is carried out in most eukaryotes by three multimeric complexes (RNA polymerases I, II and III). However, plants contain two additional RNA polymerases (IV and V), which have evolved from RNA polymerase II. RNA polymerases II, IV and V contain both common and specific subunits that may specialize some of their functions. In this study, we conducted a search for the genes that putatively code for the specific subunits of RNA polymerases IV and V, as well as the corresponding of RNA polymerase II in olive. Based on the homology with the genes of Arabidopsis thaliana, we identified 13 genes that putatively code for the specific subunits of polymerases IV and V, and 16 genes that code for the corresponding specific subunits of polymerase II in olive. The transcriptomic analysis by RNA-Seq revealed that the expression of the RNA polymerases IV and V genes was induced during the initial stages of fruit development. Given that RNA polymerases IV and V are involved in the transcription of long non-coding RNAs, we investigated their expression and observed relevant changes in the expression of this type of RNAs. Particularly, the expression of the intergenic and intronic long non-coding RNAs tended to increase in the early steps of fruit development, suggesting their potential role in this process. The positive correlation between the expression of RNA polymerases IV and V subunits and the expression of non-coding RNAs supports the hypothesis that RNA polymerases IV and V may play a role in fruit development throughout the synthesis of this type of RNAs.

Industry 4.0 or the Fourth Industrial Revolution is driven by innovative technologies that have profound effects on both production systems and business models. This revolution is characterized by the addition of disruptive technologies and methods. These aspects of Industry 4.0 have a significant impact on marketing and have led to an evolution to ensure that marketing activities align with technological advancements and address consumers' current needs. The purpose of this paper is to formulate and discuss future research avenues for marketing considering the changes brought by Industry 4.0. The approach taken in the paper is to review the relevant literature and focus on the key themes most important for future research on industry 4.0 and marketing. Therefore, a systematic literature review was conducted based on SCOPUS indexing online database of scientific articles, the most important peer-reviewed peer in the academic world. The paper finds that there are a number of research avenues for marketing researchers to conduct investigations on, but the most important areas are five marketing principles in Industry 4.0: cooperation, conversation, co-creation, cognitivity, and connectivity.

Adequate public spaces and urban green areas are key criteria for urban development and infrastructure implementation of healthy cities. Latterly, there has been an increasing number of research methods using artificial intelligence (AI) to monitor, quantify, and control the state of these spaces with an aim towards pioneering research in Urban Studies. However, in informal areas, open-data access tends to lack adequate and updated information, making it difficult to use AI methods. Hence, we propose a methodology for restricted open data collection and preparation for future use in machine learning or spatial data science models for similar areas. To that extent, we examine two peripheral and low-income neighborhoods in Quito, Ecuador, La Bota and Toctiuco, to analyze their public spaces, urban green areas, points of interest, and road networks, and how they address healthy cities criteria. We develop an original methodological approach that combines an index of proximity, accessibility, quantity, and quality of these spaces with geospatial and network analysis techniques. Results indicate that the connectivity and structure of these spaces are centralized and nodal, representing exclusion and segregation. This work provides insights on potential healthy spaces, and information to urban planners and policymakers in decision-making for healthy urban infrastructure.

This study presents a new kinetic scheme for the mass yield prediction of waste lignocellulosic biomasses treated by Hydrothermal Carbonization (HTC). The proposed reactions are based on the decomposition, solubilization, and polymerization of each main fraction of the biomass: cellulose, hemicellulose, and lignin. The ash content was assumed to be inert. The kinetic parameters have been obtained by non-linear adjustment using a data set with 220 experimental runs collected from the literature. The results indicate that the pre-exponential factors range was from 7.33 x10¹ to 1.412x10⁵ min^-1, and activation energies were between 33.75 y 225.3 kJ/mol. A good fit is achieved between the observed and predicted data with an R² of 0.81 and an RMSE of 7.7 %. The proposed scheme was validated with the experimental data obtained by the HTC of sawdust (Pinus radiata) and rapeseed (Brassica napus). The experiments were carried out at temperatures of 190, 220, and 250 ºC and reaction times of 0, 30, 60, 90, and 120 min. The predicted values showed an average error of 2.3 and 3.5 %, respectively. Therefore, the kinetic scheme is a useful tool in the conversion analysis of waste biomass treated by HTC.

The signature of chaotic systems can be characterized either by the sensitivity of the initial conditions or by the change of its parameters. This feature can be used for manufacturing high sensitivity sensors. Sensors based on chaotic circuits have already been used for measuring water salinity, inductive effects, and both noise and weak signals. This article investigates an impedance sensor based on the Van der Pol and Duffing damped oscillators. The calibration process is a key point and therefore the folding behavior of signal periods was also explored. A sensitivity of 0.15 kΩ/Period was estimated over a range from 89.5 to 91.6 kΩ. This range can be adjusted according to the application by varying the gain of the operational amplifier used in this implementation. The development of this type of sensor might be used in medical and biological engineering for skin impedance measurements, for example. This type of chaotic sensor has the advantage of sensing small disturbances and then detect small impedance changes within biological materials which, in turn, may not be possible with other detectors.

Over the last two decades, a considerable amount of equipment has been acquired (spreaders, seeders, sprayers, among others) to respond to the challenges of Precision Agriculture (PA) concept. Most of this equipment has been purchased at a high cost. However, many of them, despite still being functional and equipped with sensors, actuators, and electronic processing units capable of adjusting to variations in speed, have become obsolete in terms of communication, and incompatible with new monitoring and control systems based on the “Isobus” protocol. This work aims to present a solution for updating the control system (“Ferticontrol”) of a “Vicon RS-EDW” spreader with variable rate application (VRA), making it compatible with the “InCommand” system from “Ag Leader”. The solution includes low-cost “Arduino” and “Raspberry Pi” microcontrollers and open-source software. The development shows that it is possible to implement a solution that is accessible to farmers in general. It also provides a niche business opportunity for young researchers to set up small technology-based enterprises associated with universities and research centers. These partnerships guarantee permanent innovation and represent a decisive step towards modern, technological, competitive, and sustainable agriculture.

This study investigates the relationship between clean and dirty energy markets, specifically focusing on clean energy stock indexes and their potential as hedging assets and safe havens during periods of global economic uncertainty. The research analyzes five clean energy indexes and five dirty energy indexes from May 2018 to May 2023, considering events like the global pandemic and the Russian invasion of Ukraine. The main objective is to examine the causal relationship among different stock indexes pertaining to dirty and clean energy by using the Granger causality test (VAR Granger Causality/Block Exogeneity Wald Test) to determine whether clean energy indexes can predict future prices of dirty energy indexes. However, the findings reveal that clean and dirty energy indexes do not exhibit hedging characteristics or serve as safe havens during times of economic uncertainty, rejecting the research question. These results have important implications for investment strategies, as assets lacking safe haven characteristics may not preserve portfolio efficiency in uncertain times. The study's insights provide valuable guidance for investors, poli-cymakers, and participants in energy financial markets. It highlights the need to adapt investment approaches and seek alternative options to navigate uncertain economic conditions effectively.

The production of high-quality wines is one of the primary goals of modern oenology. In this regard, it is known that the potential quality of a wine begins to be determined in the vineyard, where the quality of the grape, initially, and later that of the wine, will be influenced by the soil properties. Given the spatial variability of the fundamental soil properties related to the potential grape production, such as texture, soil organic matter content, or cation exchange capacity, it seems that a uniform management of a vineyard is not the most optimal way to achieve higher grape quality. In this sense, the delineation of zones with similar soil characteristics to implement site-specific management is essential, reinforcing the interest to incorporate technologies and methods to determine these homogeneous zones. A case study was conducted in a 3.3 ha vineyard located near Évora, South of Portugal. A non-contact sensor (DUALEM 1S) was used to measure soil apparent electrical conductivity (ECa) in the vineyard, and later, a kriged ECa map was generated. ECa and elevation maps were utilised to delineate homogeneous zones (management zones, MZs) in the field through a clustering process. MZs were validated using some soil properties (texture, pH, organic matter -OM, phosphorous -P2O5, potassium -K2O, sum of the exchange bases -SEB; and cation exchange capacity -CEC) which were determined from 20 soil samples taken in the different MZs. Additionally, validation was also performed using Rasch measures, which were defined based on the formula-tion of the objective and probabilistic Rasch model, integrating the information from the afore-mentioned soil properties at each sampling location. The comparison of the MZs was more evident with the use of the Rasch model, as only one value was to be employed in each MZ. The use of ECa constitutes an efficient technological tool for implementing site-specific man-agement in viticulture, which allows for the improvement of decision-making processes by con-sidering the inherent spatial variability of the soil.

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

As digitalization expands across all sectors, the economic toll of software defects on the U.S. economy reaches up to $2.41 trillion annually. High-profile incidents like the Boeing 787-Max 8 crash have shown the devastating potential of these defects, highlighting the critical importance of software testing within quality assurance frameworks. However, due to its complexity and resource intensity, the exhaustive nature of comprehensive testing often surpasses budget constraints. This research utilizes a machine learning (ML) model to enhance software testing decisions by pinpointing areas most susceptible to defects and optimizing scarce resource allocation. Previous studies have shown promising results using cost-sensitive training to refine ML models, improving predictive accuracy by reducing false negatives through addressing class imbalances in defect prediction datasets. This approach facilitates more targeted and effective testing efforts. Nevertheless, the generalizability of these models across different projects (cross-project) and programming languages (cross-language) remained untested. This study validates the model's applicability across diverse development environments by integrating various datasets from distinct projects into a unified, using a more interpretable ML approach. The results demonstrate that ML can support software testing decisions, enabling teams to identify up to seven times more defective modules with the same testing effort as a benchmark.

The abundant use of lithium-ion batteries (LIBs) in a wide variety of electric devices and vehicles will generate a large number of depleted batteries, which contain several valuable metals such as Li, Co, Mn, and Ni present in the structure of the cathode material (LiMO2). The present work investigates chemical, technological, and environmental aspects in the treatment of such wastes, development of a methodology for the extraction of lithium, cobalt, nickel, manganese, and graphite by a carbochlorination pyrometallurgical process. Mixtures of cathode and anode materials (called black mass, mixed oxides of Li, Co, Ni, Mn, and graphite) from different LIBs, carbon black (as reducing agent), and CaCl2 (as chlorinating agent) were used. Non-isothermal thermogravimetric tests up to 850°C and isothermal tests at 700°C of the mixtures in an inert atmosphere were carried out. It was experimentally observed that the LiMO2-C-CaCl2 reaction takes place at 700°C. LiCl, Ni, and Co were obtained as final products, and to a lesser extent, CoO, NiO, and MnO2. CaCO3 was also obtained as a by-product. The obtained results show that carbochlorination is an efficient and effective alternative route for the extraction and recovery of metals from different LIBs, focused on the sustainability and circular economy

Since its inception in December 2019, Covid-19 has challenged the global scientific community. Some treatments were used in this infection, but doubts still persist regarding the use of medications ¹. More severe cases complicate with endothelial dysfunction, excess thrombin synthesis and decreased fibrinolysis which, associated with hypoxemia, lead to a hypercoagulability state ² and some authors indicate the use of anticoagulants for these ³. For mild cases, doubts remain regarding this indication. We will report the case of a patient with mild symptoms of covid-19, complicated by inferior mesenteric vein thrombosis.

This work presents a new algorithm to resolve the problem of distribution network reconfiguration using the Tabu Search metaheuristic and voltage drop. The proposed method presents a different way to obtain the initial solution using the voltage drop, as well as a new way to generate a neighborhood of candidate solutions, based on randomness and the voltage drop, to perform a smooth and steady descent execution of the algorithm. The proposed algorithm has the main objective to reduce the active power losses in distribution networks, but it also achieves an refinement in the voltage profile. The proposed method was tested in two distribution systems, the 33-Bus and 94-Bus systems, commonly found and tested in the literature. The results of the tests show that the proposed method presents a low computational cost, achieves good solutions, and is better than the other methods found in the literature.

The Hollow fiber membrane contactor (HFMC) is an intensification process that combines membrane and absorption technologies, being a technology in expansion, so the dynamics of this process is crucial for an increase in industrial scale-up. In this manuscript a rigorous dynamic model of an HFMC is developed and simulated in Python software for the absorption of post-combustion CO2 using a monoethanolamine (MEA) solution, which was validated using available data in literature in steady state. Dynamic simulations were performed to analyze the model's capability to predict the effect of disturbances on the responses of CO2 capture ratio and loading. Four distinct experiments were conducted by step changes and pulse disturbances at the following variables: gas and liquid volumetric flow rates, CO2 composition in flue gases, and CO2 lean load. The gas phase showed a faster response compared to the liquid. The CO2 capture ratio and rich loading presented opposite responses when disturbing both liquid flows, and for the composition and lean loading variations, the capture ratio presented an inverse response characteristic, while the loading showed a delayed response. As the model proved to be capable of returning to the reference state, it can potentially be used for control studies purposes.

Since the 1960s Rare earths (REs) applications gradually have expanded to everyday life. REs have great strategic importance in industrial and technological development, so it is expected an increase in their demand. Among the REs the European Commission considered Cerium and Lanthanum as critical raw materials. This research article studies the adsorption of Ce and La onto two carbon nanomaterials, multiwalled carbon nanotubes (MWCNT) and carboxylic functionalized multiwalled carbon nanotubes (MWCNT_ox). The latter has slightly more affinity for REs than MWCNT. The recovery percentage for Ce were 89 and 98% and in the case of for La were 99 and 92% using 0.8 g of MWCNT and 0.2 g of MWCNT_ox respectively. The adsorption process fits a pseudo second-order kinetic model and the Langmuir isotherm best represented the metal uptake.

The aim of this study is to evaluate three different strategies to improve classification accuracy in a highly fragmented semiarid area. i) Using different classification algorithms: Maximum Likelihood, Random Forest, Support Vector Machines and Sequential Maximum a Posteriori, with parameter optimisation in the second and third cases; ii) using different feature sets: spectral features, spectral and textural features, and spectral, textural and terrain features; and iii) using different image-sets: winter, spring, summer, autumn, winter+summer, winter+ spring+summer; and a four seasons combination. A 3-way ANOVA is used to discern which of these approaches and their interactions significantly increases accuracy. Tukey-Kramer contrast using a heteroscedasticity-consistent estimation of the kappa covariances matrix was used to check for significant differences in accuracy. The experiment was carried out with Landsat TM, ETM, and OLI images corresponding to the period 2000-2015. A combination of four images was the best way to improve accuracy. Maximum Likelihood, Random Forest and Support Vector Machines do not significantly increase accuracy when textural information is added, but do so when terrain features are taken into account. On the other hand, Sequential Maximum a Posteriori increases accuracy when textural features are used, but reduces accuracy substantially when terrain features are included. Random Forest using the three feature subsets and Sequential Maximum a Posteriori with spectral and textural features had the largest kappa values, around 0.9.

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.

In this work, we report the magnetocaloric effect (MCE) in two systems of non-interactive particles, the first corresponds to the Landau problem case and the second, the case of an electron in a quantum dot subjected to a parabolic confinement potential. In the first scenario, we realize that the effect is totally different from what happens when the degeneration of a single electron confined in a magnetic field is not taken into account. In particular, when the degeneracy of the system is negligible, the magnetocaloric effect cools the system, while in the other case, when the degeneracy is strong, the system heats up. For the second case, we study the competition between the characteristic frequency of the potential trap and the cyclotron frequency to find the optimal region that maximizes the ΔT of the magnetocaloric effect, and due to the strong degeneration of this problem, the results are in coherence with those obtained for the Landau problem. Finally, we consider the case of a transition from a normal MCE to an inverse one and back to normal as a function of temperature. This is due to the competition between the diamagnetic and paramagnetic response when the electron spin in the formulation is included.

Background: The gold standard for the diagnosis of sleep bruxism (SB) and obstructive sleep apnea (OSA) is Polysmnography (PSG). At the end of the apnea episodes there is frequently a final hy-permotor muscle activity that could act as a confusion factor in the diagnosis of SB with the elec-tromyography portable devices. The aim of this study was to compare the concordance on the number of episodes of SB in a population with OSA, between the diagnosis obtained by PSG, an-alyzed manually by a neurophysiologist and that obtained manually and automatically by a portable electromyography (EMG) and electrocardiography (EKG) device. Methods: Twenty-three subjects underwent one night of polysomnographic study with simulta-neous recording with the EMG-EKG device. The variables referring to the number of episodes and the SB index measured with both tools and analyzed in the manual and automatic modes were compared. Masticatory muscle activity was scored according to published criteria. The sample was segmented by severity of OSA according to AASM criteria. ANOVA, correlations, and the Bland–Altman plot were used to quantify the agreement between both methods. The concordance was calculated through the ICC. Results: The total events of SB per night in the PSG study were on average (8.17), lower than the one obtained with EMG-EKG manual analysis (14.13) and automatic (29.26). Both the SB PSG and Manual EMG-EKG episodes decrease from non-OSA (PSG = 16 ± 13,55, EMG-EKG = 16,83 ±11,58) to severe OSA (PSG = 3,14 ± 4,26, EMG-EKG = 9,86 ± 8,09). However, in the case of automatic EMG-EKG mode: the number of SB episodes in severe OSA doubled (41,23 ± 12,50) with respect to non OSA (24,50 ± 12,19). On average: the EMG-EKG device Automatic analysis measures 21.08 units more than PSG. The results with the manual EMG-EKG analysis improved. Conclusion: There is no concordance between the results obtained in the PSG neurophysiologic analysis and those obtained by means of the EMG-EKG device automatic and manual analysis for the diagnosis of SB in a population mostly with OSA. The OSA could act as a confusion factor in the diagnosis of SB with the electromyography portable devices, but further study is needed.

This study involved a comprehensive examination of sensory attributes in Bísaro dry-cured loins, including odor, androsterone, scatol, color, fat color, hardness, juiciness, chewiness, flavor intensity, and flavor persistence. Analysis of 40 samples revealed a wide variation in these attributes, ensuring a robust margin for multivariate calibration purposes. The respective near-infrared (NIR) spectra unveil distinct peaks associated with significant components such as protein, lipid, and water. Support Vector Regression (SVR) models were methodically calibrated for all sensory attributes, with optimal results using multiplicative scattering correction pre-treatment, MinMax normalization, and radial base kernel (non-linear SVR model). The predictive models exhibited acceptable results, characterized by R-squared values close to 1 (0.9616 - 0.9955) and low RMSE values (0.0400 – 0.1031). The prediction set's relative standard deviation (RSD) remained under 5%. Comparisons with prior research revealed significant improvements in prediction accuracy, particularly when considering attributes like pig aroma, hardness, fat color, and flavor intensity. This research underscores the potential of advanced analytical techniques to improve the precision of sensory evaluations in food quality assessment.

Empirical Orthogonal Functions (EOF) analysis between the anomalies of Lake Titicaca water levels (WLs) and the Pacific Interdecadal Oscillation (IPO) reveals a correlation coefficient of 0.71 between the WLs and the principal component (PC1). These findings demonstrate the high accuracy of the IPO in representing the positive and negative phases of the water levels. By utilizing wavelet functions, we identified multi-decadal variability with periods ranging between 20 and 30 years. The statistical techniques employed indicate a relationship between IPO and ENSO; however, they operate on different timescales of climate variability. The IPO exhibits longer-term variability, typically spanning 20-30 years, while ENSO events occur on average every 2-7 years. Nevertheless, the IPO has the ability to modulate the strength and frequency of El Niño and La Niña events, and vice versa. During the positive phase of the IPO, there is a tendency for more frequent and intense El Niño events, accompanied by fewer La Niña events. This can be attributed to warmer sea surface temperatures in the central and eastern Pacific, which create favorable conditions for El Niño development. Conversely, during the negative phase of the IPO, there is a tendency for more frequent and intense La Niña events, with fewer occurrences of El Niño events, due to cooler sea surface temperatures in the central and eastern Pacific.

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

By the use of the tertiary amine A327 and 1 M HCl solution as precursors, the ionic liquid A327H+Cl- was generated and used to investigate its performance in the transport of Au(III) form hydrochloric acid medium. The influence of the stirring speed (600-1800 min-1), ionic liquid concentration (1.25-50% v/v) in the membrane phase and gold concentration (0.01-0.15 g/L) in the feed phase on metal transport have been investigated. An equation which included both equilibrium and kinetics parameters was derived, and the membrane diffusional resistance (Δm) and feed phase diffusional resistance (Δf) was estimated as 9.5x106 s/cm and 307 s/cm, respectively. At carrier concentrations in the 5-50% v/v range and gold concentrations in the 0.01-0.15 g/L range, metal transport is controlled by diffusion of metal species through the feed boundary layer, whereas at the lowest carrier concentrations, membrane diffusion is predominant. From the receiving solutions, gold can be recovered as gold nanoparticles.

Monitoring is an essential component in ecosystem management and leveraging existing data sources for multiple species of interest can be one effective way to enhance information when making management decisions. Here we analyzed juvenile Chinook Salmon (Oncorhynchus tshawytscha) bycatch data that has been collected by the recently established Enhanced Delta Smelt Monitoring program (EDSM), a survey designed to estimate the abundance and distribution of the San Francisco Estuary’s (estuary) endangered Delta Smelt (Hypomesus transpacificus). Two key aspects of the EDSM program distinguish it from other fish surveys in the estuary: a stratified random sampling design and the spatial scale of its sampling effort. We integrated the EDSM dataset with other existing surveys in the estuary and used an occupancy model to assess detection probability differences across gear types. We saw no large-scale differences in size selectivity, and while detection probability varied among gear types, cumulative detection probability for EDSM was comparable to other surveys due to the program’s use of replicate tows. Based on our occupancy model and sampling effort in the estuary during spring of 2017 and 2018, we highlighted under-sampled regions that saw improvements in monitoring coverage due to EDSM. Our analysis also revealed that each sampling method has its own benefits and constraints. Although the use of random sites with replicates as conducted by EDSM can provide more statistically robust abundance estimates relative to traditional methods, the use of fixed stations and simple methods such as beach seine may provide a more cost-effective way of monitoring salmon occurrence in certain regions of the estuary. Stronger inference on salmon abundance and distribution can be made by leveraging the strengths of each survey’s method. Careful consideration of these trade-offs is crucial as the management agencies of the estuary continue to adapt and improve their monitoring programs.

Hydrogen gas is seeping from the sedimentary basin of São Franciso, Brazil. The seepages of H₂ are accompanied by helium whose isotopes reveal a strong crustal signature. Geophysical data indicates that this intra-cratonic basin is characterized by i) a relatively high geothermal gradient, ii) deep faults delineating a horst and graben structure and affecting the entire sedimentary sequence, iii) an archean to paleoproterozoïc basements enriched in radiogenic elements and displaying mafic and ultramafic units, and iv) a possible karstic reservoir located 400 m below the surface. The high geothermal gradient could be due to a thin lithosphere enriched in radiogenic elements, which can also contribute to a massive radiolysis process of water at depth, releasing an important amount of H₂. Alternatively, ultramafic rocks that may have generated H₂ during their serpentinization are also documented in the basement. The seismic profiles show that the faults seen at the surface are deeply rooted in the basement, and can drain deep fluids to shallow depths in a short time scale. The carbonate reservoirs within the Bambuí group which forms the main part of the sedimentary layers are crossed by the fault system and represent good candidates for temporary H₂ accumulation zones. The formation by chemical dissolution of sinkholes located at 400 m depth might explain the presence of sub-circular depressions seen at the surface. These sinkholes might control the migration of gas from temporary storage reservoirs in the upper layer of the Bambuí formation to the surface. The very high fluxes of H₂ escaping out of these structures which have been recently documented are, however, in disagreement with the newly developed H₂ production model in the Precambrian continental crust. They either question the validity of these models or the measurement methodology.

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

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

The literature is scarce in the evaluation of Behçet’s disease (BD) among individuals during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Then, an epidemiological study was conducted using the data from 46 hospitalized patients with BD and coronavirus disease (COVID)-19 representing a prevalence of 2,11 patients per 100,000 inhabitants. From the study sample, 58.7% (27 patients) were female, and 67.4% (31 patients) were White. The main symptoms were fever (40; 87.0%), cough (38; 82.6%), dyspnea (35; 76.1%), and peripheral saturation below 95% (35%, 76.1%). The need for intensive care unit support occurred for 39.1% (18) patients. Also, 21.7% (10) patients needed invasive ventilatory support and 47.8% (22) needed non-invasive ventilatory support. A total of 73.9% (34) of patients lived, and 26.1% (12) of patients died. Only the presence of respiratory discomfort [37.9% vs. 5.9%] was more common among those who died. Patients with BD were not at an increased risk of worse COVID-19 outcomes in Brazil.

In recent years the field of computer science has experienced great changes, due to the incredible advances in the field of artificial intelligence. Deep Learning models are responsible for most of them since the biggest milestones occurred in 2012 when AlexNet won the image classification challenge called ImageNet. These models have demonstrated great performances in different types of complex tasks like image restoration, medical diagnosis or object recognition. Their disadvantages are related to their high data dependency, which forces experts in the field to follow a precise methodology to obtain accurate models. In this paper, we describe a complete workflow that begins with the management of the raw data until the in-depth interpretation of the performance of the models. This should be taken as a high-level consultation document describing good practices that should be applied. Apart from the step-by-step methodology, we present different use cases that correspond to the two main problems of the field: classification and regression.

Asthma is the most prevalent cause of chronic respiratory diseases. Herein, we evaluate asthma burden in Mexico based on results from the Global Burden of Disease (GBD 2019) study 2019. Using data from the GBD 2019, we estimated asthma prevalence, incidence, mortality, and disability-adjusted lived years (DALYs) counts and crude and age-standardized rates per 100,000 people with a 95% uncertainty interval (UI) by sex and age at the national and subnational levels in Mexico from 1990 to 2019. At the national level, asthma affected 3.35 million (95% UI, 2.59-4.37) people, with 606.0 thousand (433.0-811.1) new incident cases and 1,655 (3-1,931) deaths during 2019. Asthma caused a slightly higher burden in females and affected mainly age groups between 1 and 14 years of age. The burden of asthma gradually decreased from 1990 to 2010. However, during the last decade (2010-2019), prevalence increased by 8.2%, as did incidence by 11.3%, whereas mortality and DALYs decreased by 23.3 and 1.6%, respectively. Finally, the burden of asthma displayed a heterogeneous pattern of disease at the subnational level. In conclusion, asthma causes a significant health loss in Mexico that differentially affects the population distributed among the states of the country, thus causing health disparities that should be addressed to provide sustainable asthma diagnosis and control to reduce its burden, especially at early stages of life.

The isolation and characterization of chitin (CHI) obtained from Vespa velutina (CHIVV) is described. Moreover, a trapping procedure is presented to selectively catch the invasive species. The chitin contents of dry Vespa velutina was observed to be 11.7 %. The physicochemical properties of CHIVV was characterized by Fourier transform infrared spectroscopy (FTIR), solid-state NMR (ssNMR), elemental analysis (EA), scanning electron microscopy (SEM) and thermogravimetric analysis (TG). Obtained CHIVV is close to pure (43, 47% C, 6.94% H, and 6.85% N) and full acetylated with a value of 95.44%. Also, lifetime and kinetic parameters such as activation E and the frequency factor A using model-free and model-fitting methods, were determined. For CHIVV the solid state mechanism that follows the thermodegradation is of type F2 (Random nucleation around two nuclei). Vespa velutina chitin should not be used at temperatures above 60ºC, since its half-life would be only one year, and from an industrial point of view it would not be profitable. Based on certain factors such as the current and probable continued abundance of Vespa velutina and the quality of the product obtained, the invasive Asian hornet is a promising alternative source of chitin.

We study an air-fluidized granular monolayer, composed in this case of plastic spheres, which roll on a metallic grid. The air current is adjusted so that the spheres never loose contact with the grid, so that the dynamics may be regarded as pseudo two-dimensional (or two-dimensional, if the effects of sphere rolling are not taken into account). We find two surprising continuous transitions, both of them displaying two coexisting phases. Moreover, in all cases, we found the coexisting phases display strong energy non-equipartition. In the first transition, at weak fludization, a glassy phase coexists with a disordered fluid-like phase. In the second transition, a hexagonal crystal coexists with the fluid phase. We analyze, for these two-phase systems, the specific diffusive properties of each phase, as well as the velocity correlations. Surprisingly, we find a glass phase at very low packing fraction and for a wide range of granular temperatures. Both phases are characterized also by a strong anti-correlated velocities upon collision. Thus, the dynamics observed for this quasi two-dimensional system unveils phase transitions with peculiar properties, very different from the predicted behavior in well know theories for their equilibrium counterparts.

International basins are divided into sub-basins that are managed at the national and local levels, within the territory of each country, before reaching the level of international concern. The management of international basins could be developed using an integrated water management, based on multi-level governance model in which many actors are involved, from local to international scale. However, in the La Plata River Basin this model is not working, even with international agreements such as the La Plata Basin Treaty, which was signed in 1969. The understanding of political interests behind the law helps to explain its inefficiency. Therefore, considering the lack of a systemic view in an international watershed, it could be possible to have accumulating effects caused by the transformations of watercourses in the sub-basins. Then, the 2014 water crisis in the State of São Paulo, Brazil, is an example of the possible consequences arising from the lack of an integrated water management in the La Plata River Basin. Finally, this article demonstrates that the only way to have water resources utilization efficiency in an international basin is considering it as an integrated ecosystem, in order that any local problem may affect the whole basin.

Keywords: environmental impacts of hotels; environmental management in lodging facilities; corporate sustainability.

The 21st century has seen the launch of new space-borne sensors based on LiDAR (light detection and ranging) technology developed in the second half of the 20th century. LiDAR was initially developed to integrate laser-focused imaging with the capability to determine distances through the measurement of signal return times, utilizing suitable sensors and data acquisition electronics. Nowadays, these sensors have transformed into robust instruments, offering novel opportunities for mapping terrain, canopy heights, and estimating above-ground biomass (AGB) across local to regional scales. This work aims to analyze the scientific impact of these sensors on large-scale for-est mapping to retrieve 3D canopy information, monitor forest degradation, estimate AGB, and model key ecosystem variables such as primary productivity and biodiversity. In this way, a worldwide bibliometric analysis of this topic was carried out based on up to 412 publications in-dexed in the Scopus database during the period 2004-2022. The results showed that the number of published documents increased exponentially in the last five years, coinciding with the commis-sioning of two new LiDAR space missions: Ice, Cloud and Land Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI). These missions are providing data since 2018 and 2019, respectively. The journal that demonstrated the highest productivity in this field was "Remote Sensing," and among the leading contributors, the top five countries in terms of publica-tions were the USA, China, the UK, France, and Germany. In the realm of prominent research in-stitutions, France boasted six, the USA had four, China had three, while the UK and Canada each had one. The upward trajectory in the number of publications recorded from 2004 to 2022 catego-rizes the subject under investigation as a highly trending research topic, particularly within the context of enhancing the administration of forest resources and engaging in global climate treaty frameworks mandating the surveillance and reporting of carbon stocks in forests. The recent launch in August 2022 of the Terrestrial Ecosystem Carbon Monitoring Satellite (TECMS; China State Administration of Forestry and Grassland), along with the planned launch in the coming years of up to three new space sensors, such as the Multi-footprint Observation LiDAR and Im-ager (Japan Aerospace Exploration Agency), the BIOMASS P-band Synthetic Aperture Radar (SAR) (European Space Agency), and the LiDAR Surface Topography (LIST; NASA), will greatly contribute to expanding the ability to map and monitor forest systems at very large scales. In this context, the integration of space-borne data, including imagery, SAR, and LiDAR, is anticipated to steer the trajectory of this research in the upcoming years.

The main goal of the paper is to compare the effects of the wave spectrum, computed using the Discrete Interaction Approximation (DIA) and the Webb–Resio–Tracy (WRT) methods, on statistical wave properties such as skewness and kurtosis. The statistical properties are obtained by integrating the three-dimensional free-surface Euler equations with a high-order spectral method combined with a phenomenological filter to account for the energy dissipation due to breaking waves. In addition, we investigate the minimum spatial domain size required to obtain meaningful statistical wave properties. The numerical simulations are performed over a physical domain of size 4.13 km × 4.13 km. The results indicate that statistical properties must be computed over an area of at least 4 km2. The results also suggest that selecting a more computationally expensive WRT method does not affect the statistical values to a great extent. The most noticeable effect is due to the energy dissipation filter that is applied. It is concluded that selecting the WRT or the DIA algorithm for computing the wave spectrum needed for the numerical simulations does not lead to major differences in the statistical wave properties. However, more accurate energy dissipation mechanisms due to wave breaking are needed.

In this work we present a new unsupervised and language-independent methodology to detect relations of textual generality, for this, we introduce a particular case of textual entailment (TE), namely Textual Entailment by Generality (TEG). TE aims to capture primary semantic inference needs across applications in Natural Language Processing (NLP). Since 2005, in the TE recognition (RTE) task, systems are asked to automatically judge whether the meaning of a portion of the text, the Text - T, entails the meaning of another text, the Hypothesis - H. Several novel approaches and improvements in TE technologies demonstrated in RTE Challenges are signalling of renewed interest towards a more in-depth and better understanding of the core phenomena involved in TE. In line with this direction, in this work, we focus on a particular case of entailment, entailment by generality, to detect relations of textual generality. In-text, there are different kinds of entailment, yielded from different types of implicative reasoning (lexical, syntactical, common sense based), but here we focus just on TEG, which can be defined as an entailment from a specific statement towards a relatively more general one. Therefore, we have T→GH whenever the premise T entails the hypothesis H, being it also more general than the premise. We propose an unsupervised and language-independent method to recognize TEGs, from a pair ⟨T,H⟩ having an entailment relation. To this end, we introduce an Informative Asymmetric Measure (IAM) called Simplified Asymmetric InfoSimba (AISs), which we combine with different Asymmetric Association Measures (AAM). In this work, we hypothesize the existence of a particular mode of TE, namely TEG. Thus, the main contribution of our study is to highlight the importance of this inference mechanism. Consequently, the new annotation data seems to be a valuable resource for the community.

Chondroitin is a glycosaminoglycan that has gained widespread use in nutraceuticals and pharmaceuticals, mainly for treating osteoarthritis. Traditionally, it has been extracted from animal cartilage but recently, biotechnological processes have emerged as a commercial alternative to avoid the risk of viral or prion contamination and offer a vegan-friendly source. Typically, these methods involve producing the chondroitin backbone using pathogenic bacteria and then modifying it enzymatically through the action of sulfotransferases. Despite the challenges of expressing active sulfotransferases in bacteria, the use of eukaryotic microorganisms is still limited to a few works using Pichia pastoris. To create a safer and efficient biotechnological platform, we have constructed a biosynthetic pathway for chondroitin production in S. cerevisiae as a proof-of-concept. Up to 125 mg/L and 200 mg/L of intracellular and extracellular chondroitin were produced, respectively. Furthermore, as genome-scale models are valuable tools for identifying novel targets for metabolic engineering, a stoichiometric model of chondroitin-producing S. cerevisiae has been developed and used in optimization algorithms. Our research has yielded several novel targets, such as uridine diphosphate(UDP)-N-acetylglucosamine pyrophosphorylase (QRI1), glucosamine-6-phosphate acetyltransferase (GNA1) or N-acetylglucosamine-phosphate mutase (PCM1) overexpression that might enhance chondroitin production, which will guide future experimental research to develop more efficient host organisms for the biotechnological production process.

Solid-state batteries (SSBs) have emerged as a promising alternative to conventional lithium-ion batteries, with notable advantages in safety, energy density, and longevity, yet the environmental implications of their lifecycle, from manufacturing to disposal, remain a critical concern. This review paper examines the environmental impacts associated with the production, use, and end-of-life management of SSBs, starting with the extraction and processing of raw materials which highlights significant natural resource consumption, energy use, and emissions. A comparative analysis with traditional battery manufacturing underscores the environmental hazards of novel materials specific to SSBs. The review also assesses the operational environmental impact of SSBs by evaluating their energy efficiency and carbon footprint in comparison to conventional batteries, followed by an exploration of end-of-life challenges including disposal risks, regulatory frameworks, and the shortcomings of existing waste management practices. A significant focus is placed on recycling and reuse strategies, reviewing current methodologies like mechanical, pyrometallurgical, and hydrometallurgical processes, along with emerging technologies that aim to overcome recycling barriers, while also analyzing the economic and technological challenges of these processes. Additionally, real-world case studies are presented, serving as benchmarks for best practices and highlighting lessons learned in the field. In conclusion, the paper identifies research gaps and future directions for reducing the environmental footprint of SSBs, underscoring the need for interdisciplinary collaboration to advance sustainable SSB technologies and contribute to balancing technological advancements with environmental stewardship, thereby supporting the transition to a more sustainable energy future.

This article explores the relationship between green energy and cryptocurrencies in the sustainable energy finance sector. The research findings contribute to our understanding of the application of green economy practice, enabling investors in financial markets, policymakers, and stakeholders to make informed decisions and develop specific strategies. Adopting the green economy paradigm makes it possible to promote collaboration and innovation by integrating ethical and responsible principles that can improve the overall quality of processes and boost sustainable growth. Cryptocurrencies have been widely used as financial instruments over the last decade. Given the development of the cryptocurrency market and the growing awareness of greener and more energy-efficient tokens, the green economy has become a popular topic for understanding economic and political issues. However, the literature still lacks clear evidence on how cryptocurrencies interact with green energies. Therefore, this study examines the long- and short-term relationships between dirty cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), clean cryptocurrencies such as Cardano (ADA), Ripple (XRP), Stellar (XLM), and green energies such as ISE Clean Edge Global Wind Energy, S&amp;P Global Clean Energy, S&amp;P TSX Renewable Energy and Clean Technology, Solactive China Clean Energy, in the period from January 2020 to September 2023. The results show that diversification is key, with clean cryptocurrencies such as ADA, XLM and XRP offering diversification opportunities alongside "dirty" cryptocurrencies such as BTC and ETH. Although sustainable energy indices show mixed evidence in the long and short term, they remain relevant for those who focus on clean energy investments. It is also becoming increasingly relevant for investors in sustainable portfolios to assess their environmental impact, especially for energy-intensive cryptocurrencies, and it is advisable to explore sustainable blockchain technologies.

Non-standard diesel blends can be harmful to the environment and human health. In this context, a simple analytical method to estimate the biodiesel mixture ratio in diesel was developed based on the impedance spectroscopy (IS) associated with the interdigitated sensors. In this article, four different interdigitated sensors, variable comb spacing (G), were simulated using the COMSOL Multiphysics software. Based on finite element simulations, four interdigitated electrode architectures by manufactured and evaluated. According to the X-ray powder diffraction technique, the deposition of the conductive layer (Au°) over the surface of the dielectric substrate (SiO2) did not alter its phase composition. In the analysis of AFM and SEM, it was possible to observe irregular edges on the electrodes, possibly related to thin layers' manufacturing process and mechanical stability. Another characteristic observed in the AFM images was the height of the step of the gold layer of the sensor. Several cross-sections were obtained, and the mean step value is 225.71 ± 0.0032 nm. Although there are differences in the roughness, the whole sensor has nanometric roughness. Based on the finite element method simulation performed, it can be assumed that the geometric parameters more suitable for the manufacturing of the electrode are: W = 20 µm, L = 1000 µm, G = 50 µm e N = 40 digits. The electrical characterization performed by impedance spectroscopy showed that we could differentiate between biodiesel and diesel fuels and their pseudo-binary mixtures in the low-frequency region.

In this paper, we revisit the q-states clock model for small systems. We present results for the thermodynamics of the q-states clock model from $q=2$ to $q=20$ for small square lattices $L \times L$, with L ranging from $L=3$ to $L=64$ with free-boundary conditions. Energy, specific heat, entropy and magnetization are measured. We found that the Berezinskii-Kosterlitz-Thouless (BKT)-like transition appears for $q>5$ regardless of lattice size, while the transition at $q=5$ is lost for $L<10$; for $q\leq 4$ the BKT transition is never present. We report the phase diagram in terms of $q$ showing the transition from the ferromagnetic (FM) to the paramagnetic (PM) phases at a critical temperature T$_1$ for small systems which turns into a transition from the FM to the BKT phase for larger systems, while a second phase transition between the BKT and the PM phases occurs at T$_2$. We also show that the magnetic phases are well characterized by the two dimensional (2D) distribution of the magnetization values. We make use of this opportunity to do an information theory analysis of the time series obtained from the Monte Carlo simulations. In particular, we calculate the phenomenological mutability and diversity functions. Diversity characterizes the phase transitions, but the phases are less detectable as $q$ increases. Free boundary conditions are used to better mimic the reality of small systems (far from any thermodynamic limit). The role of size is discussed.

This study analyzes the eating behavior and factors associated with the presence of disordered eating attitudes in patients undergoing bariatric surgery. It is a cross-sectional, descriptive, and analytical study conducted at a hospital in the Amazon region of Brazil. The Disordered Eating Attitude Scale reduced version (DEAS-s) was used to assess the risk of eating disorders and the Three-Factor Eating Questionnaire (TFEQ-R21) to characterize eating behavior. A total of 205 patients participated, with a mean age of 37.5 ± 8.6 years, the majority being female (93.7%; p&lt;0.001), the mean BMI was 45.3±6.7 kg/m2. It was found that cognitive restraint had the highest mean (52.6±19.9; p&lt;0.001). As for the DEAS-s, the question with the highest mean was "spending one or more days without eating or consuming only liquids to lose weight" (2.80±1.99). Female participants had a higher score in the emotional eating (p=0.016). Disordered eating attitudes showed a correlation with emotional eating and uncontrolled eating. These results suggest that candidates for bariatric surgery may have susceptibility to eating disorders. The importance of a multidisciplinary team in monitoring during the preoperative period is highlighted.

There is growing recognition of the adverse effects of body image dissatisfaction (BID) and eating disorder (ED) symptoms on adolescent health. The aim of this study was to estimate the prevalence of ED symptoms, BID, and their relationship in adolescents from public schools in Southern Brazil. A total of 782 schoolchildren (male: n=420, female: n=362); age: 15 ± 0,4 years) answered a self-administrated questionnaire to identify sociodemographic data. Children´s Figure Rating Scale was adopted to identify body image and Eating Attitudes Test (EAT-26) was applied to investigate ED symptoms. Inferential statistics and hierarchical model-controlled logistic regression were used for association between variables. Most of the schoolchildren reported being satisfied with their bodies. However, we observed a higher prevalence of dissatisfaction among girls for being overweight and thinness among boys. Female students and students from schools located in the central area of the city showed higher chances of developing ED symptoms, and the absence of symptoms of ED appeared to act as a protective factor against BID in schoolchildren. Results of this study show the need to reflect on these factors that influence the development of ED and non-acceptance of their own body in a population concerned with their physical appearance.