Skin inflammation associated with chronic diseases has the direct action of keratinocytes in its immunopathogenesis, leading to a series of immune responses. However, there are still no highly targeted treatments, and the development of more specific therapies is highly desirable. In the present work, nanocapsules containing quinizarin (QZ/NC) were developed, evaluating their act in a model of the keratinocytes inflammatory process in vitro with the action of Photodynamic Therapy, analyzing the permeation in a 3D skin model. Physicochemical, stability, cytotoxicity and permeation analyses of the nanomaterials were carried out. The nanocapsules showed desirable physicochemical characteristics and were stable during the analysis period, without spectroscopic modifications. The cell viability test carried out showed no cytotoxicity at the lowest QZ/NC concentrations. The permeation and cellular uptake studies showed the permeation of QZ/NC in 3D skin models and the intracellular incorporation and internalization of the drug, enhancing its action in the drug delivery technique. Promising results were presented using the model developed to induce the inflammatory process in vitro and applying the synthesized nanomaterial in conjunction with PDT, where a decrease in the levels of cytokines analyzed was observed, representing a possible new therapeutic approach for inflammatory skin diseases.

The management and proper use of the Urban Public Transport Systems (UPTS) constitutes a critical field that has not been investigated in accordance to its relevance and urgent idiosyncrasy within smart cities realm. Swarm Intelligence is a very promising paradigm to deal with such complex and dynamic systems. It presents robust, scalable and self-organized behaviour to deal with dynamic and fast changing systems. The intelligence of cities can be modelled as a swarm of digital telecommunication networks (the nerves), ubiquitously embedded intelligence, sensors and tags, and software. In this paper, a new approach based on the use of the Natural Computing paradigm and Collective Computation is shown, more concretely taking advantage of an Ant Colony Optimization algorithm variation and Fireworks algorithms in order to build a system that makes the complete control of the UPTS a tangible reality.

In this paper, we introduce the bicomplex Tetranacci and Tetranacci-Lucas quaternions. Moreover, we present Binet's formulas, generating functions, and the summation formulas for those bicomplex quaternions.

The increased availability of high resolution remote sensor data for precision agriculture 1 applications permits users to aquire deeper and more relevant knowledge about crops states that lead 2 inevitably to better decisions. The algorithm libraries being developed and evolved around these 3 applications rely on multi-spectral or hyper-spectral data acquired by using manned or unmanned 4 platforms. The current state of the art makes thorough use of vegetational indicies to guide the 5 operational management of agricultural land plots. One of the most challenging sub-problems is 6 to correctly identify and separate crop from soil. Thresholding techniques based on Normalized 7 Difference Vegetation Index (NDVI) or other such similar metrics have the advantage of being simple, 8 easy to read transformations of the data packed with useful information. Obvious difficulties arise 9 when crop/tree and soil have similar spectral responses as in case of grass filled areas in vineyards. 10 In this case grass and canopy are close in terms of NDVI values and thresholding techniques will 11 generally fail. Radiometric approaches could be integrated or replaced by a geometric approach that 12 is based on terrain data like Digital Surface Models (DSMs). These models are one of the ouputs 13 of orthorectification engines usually present in data acquired by using unmanned platforms. In 14 this paper we present two approaches based on DSM that are able to segment crop/tree from soil 15 while over gradient terrain. The DSM data are processed through a two dimensional data slicing or 16 reduction technique. Each slice is separately processed as a one dimensional time series to derive the 17 terrain and tree structures separately, here interpreted as object probability densities. In particular 18 the first approach is a Cartesian grid rasterization (CARSCAN) of the terrain and the second is its 19 immediate generalisation or radial grid rasterization of the DSM model (FANSCAN). The FANSCAN 20 recovers information from the original image at greater frequencies on the Fourier plane. These 21 approaches enable the identification of crop/tree from soil in case of slopes or hilly terrain without 22 any constraint on the displacement / direction of plant/tree row. The proposed algorithm uses pure 23 DSM information even if it is possible to fuse its output with other classifiers.

This paper presents a study and implementation of a convolutional neural network to identify and recognize humpback whale specimens from the unique patterns of their tails. Starting from a dataset composed of images of whale tails, all the phases of the process of creation and training of a neural network are detailed – from the analysis and pre-processing of images to the elaboration of predictions, using TensorFlow and Keras frameworks. Other possible alternatives are also explained when it comes to tackling this problem and the complications that have arisen during the process of developing this paper.

In this paper, we define Tetranacci and Tetranacci-Lucas matrix sequences and investigate their properties.

Introduction: Cholelithiasis and consumption of alcohol are the most frequent causes of acute pancreatitis (AP), accounting for about 30 to 40% of the cases, respectively. The frequency of acute biliary pancreatitis is high in a certain population in Brazil. Objective: To estimate the global frequencies of acute biliary pancreatitis (ABP), acute alcoholic pancreatitis (AAP) and the cases considered as acute idiopathic pancreatitis (AIP) in studies published from October 2006 to December 31, 2018. Methods: A systematic review of observational studies was performed from October 2006 to December 31, 2018. A meta-analysis by the random effects model was used to calculate the frequencies of global ABP, AIP and AAP and subgroups. Results: Forty-six studies representing 2,341,007 AP cases were included in 36 countries. The overall estimate for acute biliary pancreatitis (ABP) was 41.6% (95% CI 39.2-44.1), followed by acute alcoholic pancreatitis (AAP) with 20.5% (95% CI) 16.6- 24.6) and acute idiopathic pancreatitis (AIP) in 18.3% (95% CI 15.1-27.7). Conclusion: ABP is the most prevalent etiology of AP, being two times more frequent than second-placed pancreatitis. Latin America has a frequency for ABP much higher than the rest of the world. The importance of the etiologic diagnosis is the treatment of the cause for prevention of recurrence.

Starting 1994, the debate for regulating organic agriculture in Brazil was officially recognized in May 1999. Still, only in 2003, when the law # 10,831 was instituted, the country reached an effective milestone on organic production, both internally and externally. Since the industry regulation, the legislation underwent many modifications, constantly reviewing and adjusting. The current study examined the institutionalization of organic production in Brazil and what the current regulations can be used as a reference for those interested in this sector. The most recent update of Organic legislation and its implication on practice were also discussed. A case study on production of strawberry in organic system is presented and discussed.

In this paper, we introduce hybrid numbers with Fibonacci and Lucas hybrid number coefficients. We give the Binet formulas, generating functions, exponential generating functions for these numbers. Then we define an associate matrix for these numbers. In addition, using this matrix, we present two different versions of Cassini identitiy of these numbers.

In this paper, by using the q-integer, we introduce a new generalization of Fibonacci and Lucas sedenions called q-Fibonacci and q-Lucas sedenions. We present some fundamental properties of these type of sedenions such as Binet formulas, exponential generating fuctions, summation formulas, Catalan's identity, Cassini's identity and d'Ocagne's identity.

Polystyrene as a thin film on arbitrary substrates or pellets form defective graphene films or powders that can be dispersed in water and organic solvents. The materials were characterized by visible absorption, Raman and X-ray photoelectron spectroscopy, electron and atomic force microscopy and electrochemistry. Raman spectra of these materials show the presence of the expected 2D, G and D peaks at 2750, 1590 and 1350 cm^-1, respectively. The relative intensity of the G vs. the D peak is taken as a quantitative indicator of the density of defects in the G layer.

: Rasmussen’s encephalitis (RE) stands as a rare neurological disorder marked by progressive cerebral hemiatrophy and epilepsy resistant to medical treatment. Despite extensive study, the primary cause of RE remains elusive, while its histopathological features encompass cortical inflammation, neuronal degeneration, and gliosis. The underlying molecular mechanisms driving disease progression remain largely unexplored. In this case study, we present a patient with RE who underwent hemispherectomy and has remained seizure-free for over six months, experiencing gradual motor improvement. Further-more, we conducted molecular analysis on the excised brain tissue, unveiling a decrease in the expres-sion of cell cycle-associated genes coupled with elevated levels of BDNF and TNF-α proteins. These findings suggest potential involvement of cell cycle regulators in the progression of RE.

This paper addresses the evolving landscape of electricity markets in Europe, with a focus on the integration of Renewable Energy Communities as introduced by the Renewable Energy Directive 2018/2001. Residents within a postal code area are highly incentivized to join a community, which enables them to exchange energy among themselves at lower procurement costs. Thereby, energy management systems optimize the operation of respective energy systems, with electrical load forecasting playing a key role. Given that prosumers may switch between communities on a daily basis, electricity demands within these groups will vary, leading to data that is non-stationary, discontinuous as well as non-identical and independently distributed. To encounter this issue, we propose a sophisticated forecasting model that applies federated learning, using informations from various distributed communities to learn domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we ultimately evaluate their effectiveness. The findings suggest that our proposed framework is capable of effectively forecast non-stationary and discontinuous time series and that it can be applied to new, unseen data through the integration of knowledge from multiple sources.

Healthcare professionals constantly face situations that reflect ecosystems’ degradation. These can negatively affect their mental health. Research suggests that hope and resilience can play an important role in this scenario, since they are related to/predict mental health in highly heterogeneous samples (considering geography, age, profession, health, etc.). In this context, the aims of the present study are: to characterize and explore the relationship between hope, resilient coping and mental health of Portuguese healthcare professionals. Using Google Forms, 276 healthcare professionals answered to the GHQ-28, the (adult) Trait Hope Scale, and the Brief Resilient Coping Scale (cross-sectional study). The minimum and maximum possible scores were reached, with the exception of the maximum score of GHQ-28-Total. Regarding Hope, 19.6% scored below the midpoint (M=43.46, SD=11.97); 29.3% revealed low resilience (M=14.93, SD=4.05); and the average of 4 of the 5 Mental Health scores (exception: Severe Depression) indicates the probability of a psychiatric case. Hope correlated with Social Dysfunction and GHQ-28-Total; resilient coping proved to be a (weak) predictor of 4 of the 5 GHQ-28 indicators (exception: Severe depression). The results support the need to promote the sample's mental health, hope and resilient coping. They also suggest that stimulating resilient coping may contribute to improving healthcare professionals’ mental health.

Net CO2 emissions and sequestration from European forests are the result of removal and growth of flora. To arrive at aggregated measurements of these processes at a country's level, local observations of increments and harvest rates are up-scaled to national forest areas. Each country releases these statistics through their individual National Forest Inventory using their particular definitions and methodologies. In addition, five international processes deal with the harmonization and comparability of such forest datasets in Europe, namely the IPCC, SOEF, FAOSTAT, HPFFRE, FRA (definitions follow in the article). In this study, we retrieved living biomass dynamics from each of these sources for 27 European Union member states. To demonstrate the reproducibility of our method, we release an open source python package that allows for automated data retrieval and analysis, as new data becomes available. The comparison of the published values shows discrepancies in the magnitude of forest biomass changes for several countries. In some cases, the direction of these changes also differ between sources. The scarcity of the data provided, along with the low spatial resolution, forbids the creation or calibration of a pan-European forest dynamics model, which could ultimately be used to simulate future scenarios and support policy decisions. To attain these goals, an improvement in forest data availability and harmonization is needed.

All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller quad skate variant of hockey team sports, it is of great interest to automatically track player’s movements and positions, player’s sticks and, also, making other judgments, such as being able to locate the ball. In this work, we introduce a real-time pipeline composed by an object detection model, created specifically for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and quick motions, our deep learning object detection model effectively identifies and tracks, in real-time, important visual elements such as: ball; players; sticks; referees; crowd; goalkeeper; and goal. Using a curated dataset composed by a collection of videos of rink hockey, comprising 2525 annotated frames, we trained and evaluated the algorithm performance and compare it to state of the art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80%, and presents a good performance in terms of accuracy and speed, according to our results, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected one important event type in rink hockey games, the occurrence of penalties.

Multi-receiver encryption enables a sender to encrypt a message and transmit the ciphertext to a set of authorized users, while no one out this group of authorized users can decrypt the message. Multi-receiver encryption is of great importance in many sectors such as broadcast communication, cloud computing, wireless communications, networking applications, e-voting, lottery, and medical applications. This paper proposes an efficient multi-receiver public key encryption scheme based on Lucas sequences. The results of the computational analysis show that, projected scheme is better against renown attacks and prevailing anonymous multireceiver algorithms

In this study, the effect of increasing exposure time on microstructures, defectiveness, mechanical properties and corrosion behavior of selective laser melted sample Al-Si10-Mg powder have been investigated. The sample were processed at the same power (375 W) and scan speed (2000 mm/s) but with increasing exposure time. Exposure time equal to 40, 50 and 60 µs was applied. The features of the analyzed samples show that with increasing exposure time a better efficiency of the heat input has been obtained with larger size of melting pool and Si particles and lower defectiveness. Moreover, being the coarser microstructure associated a to lower level of voids, the average hardness is similar for the analyzed samples. The potentiodynamic curves of the samples show that the voids occurrence is more significant with respect to the scale of micro-structure on corrosion behavior being the sample processed at the highest exposure time the more resistant to corrosion. The experimental techniques used in the present study were Optical microscope, Sem microscope, Hardness and X-Ray Computed Tomography.

Red mud (RM) is composed of a waste alkaline solution (pH = 13.3) obtained from the production of alumina. It contains high concentrations of soluble hematite (Fe2O3), goetite (FeOOH), gibisite [Al(OH)3], a boemite (AlOOH), anatase (Tetragonal - TiO2), rutile (Ditetragonal dipyramidal - TiO2), hydrogarnets [Ca3Al2(SiO4)3−x(OH)4x], and perovskite (CaTiO3). It was shown to be an excellent catalytic mixture for biodiesel production. To demonstrate the value of RM, an environmentally friendly process of transesterification in aqueous medium using waste cooking oil (WCO), MeOH and waste alkaline solution (WAS) obtained from aluminum production was proposed. Triglycerides of WCO reacted with MeOH at 60 oC to yield mixtures of fatty acid methyl esters (FAMEs) in the presence of 0.019% (w/w) WAS/WCO using the WAS (0.204 mol L-1, predetermined by potentiometric titration) from aluminum production by the Bayer process. The use of the new catalyst (WAS) resulted in a high yield of the products (greater than 99% yield).

There are many new land use and land cover (LULC) products emerging yet there is still a lack of in-situ data for training, validation, and change detection purposes. The LUCAS (Land Use Cover Area frame Sample) survey is one of the few authoritative in-situ field campaigns, which takes place every three years in European Union member countries. More recently, a study has considered whether citizen science and crowdsourcing could complement LUCAS survey data, e.g., through the FotoQuest Austria mobile app and crowdsourcing campaign. Although the data obtained from the campaign were promising when compared with authoritative LUCAS survey data, there were classes that were not well classified by the citizens, and the photographs submitted through the app were not always of sufficient quality. For this reason, in the latest FotoQuest Go Europe 2018 campaign, several improvements were made to the app to facilitate interaction with the citizens contributing and to improve their accuracy in LULC identification. In addition to extending the locations from Austria to Europe, a change detection component (comparing land cover in 2018 to the 2015 LUCAS photographs) was added, as well as an improved LC decision tree and a near real-time quality assurance system to provide feedback on the distance to the target location, the LULC classes chosen and the quality of the photographs. Another modification was the implementation of a monetary incentive scheme in which users received between 1 to 3 Euros for each successfully completed quest of sufficient quality. The purpose of this paper is to present these new features and to compare the results obtained by the citizens with authoritative LUCAS data from 2018 in terms of LULC and change in LC. We also compared the results between the FotoQuest campaigns in 2015 and 2018 and found a significant improvement in 2018, i.e., a much higher match of LC between FotoQuest Go Europe and LUCAS. Finally, we present the results from a user survey to discuss challenges encountered during the campaign and what further improvements could be made in the future, including better in-app navigation and offline maps, making FotoQuest a model for enabling the collection of large amounts of land cover data at a low cost.

Abstract: Rotavirus (RV) it’s the agent of one of the main causes of severe viral gastroenteritis in the pediatric population, being responsible for a significant portion of deaths related to diarrhea among children under five years. RV are identified as a potential reference pathogen and it is known that the persistence and infectivity of the viral particles can be related to environmental factors. The aim of the study was to identify and correlate the presence of Rotavirus in collective and individual water sources (deep tubular wells, shallow tubular wells, shallow dug wells, springs, surface water, rainwater stored in cisterns and water trucks that were collected from surface water) of rural communities in the state of Goiás, with the seasons in which the collections were made (rainy and dry season). For this, 86 water samples in the dry period and 160 samples in the rainy period were collected. Concentration of water samples, extraction of viral genetic material, cDNA synthesis and molecular tests (qPCR) were performed. When analyzing the presence of RV in the samples in relation to the period studied, RV was found to be more prevalent in the dry season (54.7%) than in the rainy season (20%), showing a strong statistical association with the dry season (p-value < 0.001). The proportion of positive samples in the dry period, considering the type of source, resulted in higher in Shallow dug wells (p-value <0.001), higher in surface waters (p = 0.001) and higher in spring water (p-value = 0.02). The presence of pathogenic microorganisms in water is a public risk problem, enabling the emergence of outbreaks, endemics and epidemics. In the present research, there was an association among the presence of Rotavirus and the dry period of the year, when compared to the rainy period. Describing or identifying the seasonal behavior of pathogens is important for prevention and attention to water treatment and help in the monitoring of the virus in the water sources.

Laser powder bed fusion (LPBF) is an additive manufacturing process employed in many industries, for example for aerospace, automotive and medical applications. In these sectors, mainly nickel-, aluminum- and titanium-based alloys are used. In contrast, the mechanical engineering industry is interested in more wear-resistant steel alloys with higher hardness, both of which can be achieved with a higher carbon content, like in high-speed steels. Since these steels are susceptible to cracking, preheating needs to be applied during processing by LPBF. In a previous study, we applied a base plate preheating temperature of 500 °C for HS6-5-3-8 with 1.3 % carbon content. We were able to manufacture dense (p > 99.9 %) and crack-free parts from HS6-5-3-8 with a hardness > 62 HRC (as built) by LPBF. In this study, we investigate the influence of preheating temperatures up to 600 °C on hardness and microstructure dependent on part height for HS6-5-3-8. The microstructure was studied by light optical microscopy (LOM), scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD). The analysis of hardness and microstructure at different part heights is necessary because state-of-the-art preheating systems induce heat only into the base plate. Consequently, parts are subjected to temperature gradients and different heat treatment effects depending on part height during the LPBF process.

Climate change such as sea level rise, change in temperature, precipitation, and storminess are expected to impact significantly coastal lagoons. The nature and magnitude of these impacts are uncertain. The objective of the research is to determine the climate change impacts on mixing and circulation at Songkhla lagoon, Thailand. Songkhla lagoon is the largest lagoonal water resource in Thailand and Southeast Asia. The lagoon is a combined freshwater and estuarine complex of high productivity which represents an extraordinary combination of environmental resources believed to be unique in the region. This work is part of a Climate Change impact assessment framework. It is the validation phase (step 5) of the framework applying a case study. Delft 3D was used to simulate CC scenarios in the climate downscaling models, part of the previous framework steps. These results were compared to the current conditions to determine the main changes in mixing and circulation in the coastal lagoon. Three indicators were applied to quantify the impacts: flushing time, salinity intrusion and stratification. The results suggest an increase in water velocities at the inlet in future scenarios and a decrease of flushing time. Salinity and stratification showed more complex changes in futures scenarios.

The purpose of this work is to describe and explain the characteristics of pedagogy as a science of education, as an object, concepts and research method. The methodology assumed was the review of the history of pedagogy, especially the classics of pedagogy, those who built it with their experiences in laboratory schools. Here are listed the main concepts of pedagogy, its laws and principles; she establishes the opposition that exists between teaching-learning and education as formation of the personality; she also delimits the fields of the pedagogical method, of didactics or teaching-learning, and the methodology of educational labor that organizes the process and the educational context, the regime and the lifestyle, such as those experienced in the "colonies" or "communes" of children and adolescents, organized and directed by Neill and Makarenko. Since the present article does not derive from a primary source or from field work, as an empirical or experimental research, the results constitute the content of the article, as conclusions.

In this paper, inspired by recent articles of G. Anatriello and G. Vincenzi (see \cite{Ana}), and G. Cerda-Morales (see \cite{Cer1,Cer2}), we will introduce the third-order $\overline{h}$-Jacobsthal and third-order $\overline{h}$-Jacobsthal--Lucas sequences and their associated quaternions. The new results that we have obtained extend most of those obtained in \cite{Cer2}.

In this paper, we define and investigate modified p-Leonardo, p-Leonardo-Lucas and p-Leonardo sequences as special cases of the generalized Leonardo sequence. We present Binet's formulas, generating functions, Simson formulas, and the summation formulas for these sequences. Moreover, we give some identities and matrices related with these sequences. Furthermore, we show that there are close relations between modified p-Leonardo, p-Leonardo-Lucas, p-Leonardo numbers and Fibonacci, Lucas numbers.

Red mud (RM) is a residue obtained from the production of alumina. It contains a high concentration of metal oxides and waste concentrated sodium hydroxide solution (pH = 13). To increase the value of RM, an environmentally friendly process of transesterification using waste cooking oil (WCO), MeOH and concentrated sodium hydroxide solution (CSHS) from aluminum production was proposed. Triglycerides of WCO reacted with MeOH at 60 oC to yield mixtures of fatty acid methyl esters (FAMEs) in the presence of 2.03% (w/w) CSHS/WCO using the CSHS (0.204 mol L-1, predetermined by potentiometric titration) from aluminum production by the Bayer process or with the addition of 0.68% (w/w) fabric softener (3% w/w cetyltrimethylammonium chloride in solution) as a phase transfer (PTA) agent. The addition of PTA to the catalyst resulted in a better yield of the products (greater than 98% yield). A simplified mechanism is presented to account for the experimental results.

This paper presents a simple drone survey and image processing approach to identifying St. John's wort (Hypericum perforatum) on grazing land in the Hunter Valley NSW, Australia. St John’s wort is an invasive species (weed) that competes with pasture, poisons livestock, can downgrade wool with ‘vegetable fault’, and decreases property values. Identifying the locations of St John wort from the ground can be difficult due to topography, limited access, and/or larger land areas of mixed vegetation. In this study, a drone was used to survey a 174-ha grazing property in the Hunter Valley NSW (Australia). The images were stitched together using commercially available software. A unique Visual Atmospheric Resistance Index (VARI) attribute was identified and used to highlight the presence of St John’s wort in the survey area. These sub-areas were then digitised onto high resolution maps for future planning use by land managers.

Since the Paris Agreement in 2016, the goals of limiting climate change and moving toward climate resilience stand. With a share of about \SI{80}{\percent} of global \ce{CO2} emissions, the energy sector is an essential driver for these goals. A shift to low-carbon energy production and a decentralized system for more efficient energy transmission distribution is necessary. In this paper, we present our work on Modeling of Power Exchanges, Algorithms for \ac{LEM}, Competitiveness of \ac{CHP} and Energy Feedback Devices. The study was conducted considering technical, economic, social and regulatory framework. For easy integration into energy simulations or a \ac{DEMS}, a model for power exchanges was created that allows flexible input or deterministic price patterns. The algorithm handles the clearing of an \ac{LEM} by a district aggregator using limit orders with the goal of increasing the share of locally consumed electricity using economic incentives. An investigation was conducted into the operation of flexible \acp{CHP} in low-carbon power systems to balance the volatility of renewable energy. An energy signal light was developed as an energy feedback device, which is integrated into the \ac{DEMS} in a living lab and allows individual configuration. In summary, the results presented should be compared with those of other research approaches in the future and require qualitative and quantitative evaluation.

If environmental exposures are shown to cause an adverse health outcome, reducing exposure should reduce the disease risk. Links between exposures and outcomes are typically based on ‘associations’ derived from observational studies, and causality may not be clear. Randomised controlled trials to ‘prove’ causality are often not feasible or ethical. Here the history of evidence that tobacco smoking causes lung cancer – in observational studies – is compared to that of low sun exposure and/or low vitamin D status as causal risk factors for the autoimmune disease, multiple sclerosis. Evidence derives from in vitro and animal studies, as well as ecological, case-control and cohort studies, in order of increasing strength. For smoking and lung cancer, the associations are strong, consistent, and biologically plausible – the evidence is coherent or ‘in harmony’. For low sun exposure/vitamin D as risk factors for MS, the evidence is weaker, with smaller effect sizes, but coherent across a range of sources of evidence, and biologically plausible. The association is less direct – smoking is directly toxic and carcinogenic to the lung, but sun exposure/vitamin D modulate the immune system, which in turn may reduce the risk of immune attack on self-proteins in the central nervous system. Opinion about whether there is sufficient evidence to conclude that low sun exposure/vitamin D increase the risk of multiple sclerosis, is divided. General public health advice to receive sufficient sun exposure to avoid vitamin D deficiency (<50nmol/L) should also ensure any benefits for multiple sclerosis.

Incorporating renewables in the power grid presents challenges for stability, reliability, and operational efficiency. Integrating Energy Storage Systems (ESS) offers a solution by managing unpredictable loads, enhancing reliability, and serving the grid. Hybrid storage solutions have gained attention for specific applications, suggesting higher performance in some respects. This paper compares the performance of hybrid energy storage systems (HESS) to single battery, evaluating their energy supply cost and environmental impact through optimization problems. The model generates an optimized dispatch, minimizing cost or environmental impact of supplying energy to a generic load while considering battery degradation. Seven technologies are assessed, with an example applied to an industrial site contrasting vanadium redox flow (VRFB) and lithium batteries considering the demand of a local load (building). The results also indicates that efficiency and degradation curves have the highest impact in the final costs and environmental functions on the various storage technologies assessed. For the simulations of the example case, a single system only outperforms the hybrid system in cases where lithium efficiency is higher than approximately 87% and vanadium is lower approximately 82%.

There is now evidence that, based on cytokine profiles, bipolar disorder (BD) is accompanied by simultaneous activation of the immune-inflammatory response system (IRS) and the compensatory immune-regulatory system (CIRS), and that both components may be associated with the staging of illness. Nevertheless, no BD studies have evaluated the IRS/CIRS ratio using CD (cluster of differentiation) molecules expressed by peripheral blood activated T effector (Teff) and T regulatory (Treg) subpopulations. This study examined T cell subsets both before and after ex vivo anti CD3/CD28 stimulation using flow cytometric immunophenotyping in 25 euthymic BD patients and 21 healthy controls as well as human cytomegalovirus (HCMV)-specific IgG antibodies. BD is associated with a significantly lowered frequency of baseline (unstimulated) CD3+CD8+CD71+ and CD4+CD25+FOXP3 and increased CD4+CD25+FOXP3+CD152+ frequencies and with lowered stimulated frequencies of CD3+CD8+CD71+, CD4+CD25+FOXP3+CD152+ and CD4+CD25+FOXP3+GARP cells and, consequently, by an increased stimulated Teff/Treg ratio. Moreover, the number of manic, but not hypomanic or depressive episodes, is significantly and negatively associated with the stimulated proportions of CD3+CD4+CD154+, and CD69+ and CD71+ expression on CD4+ and CD8+ cells, while duration of illness (≥ 10 years) is accompanied by a depleted frequency of stimulated CD152+ Treg, and CD154+ and CD71+ CD4+ T cells. BD and anti-human cytomegalovirus (HCMV) IgG levels significantly interact to decrease the expression of CD4+CD25+FOXP+GARP T phenotypes. In conclusion, BD is characterized by deficits in immune-regulatory functions while the staging of illness is characterized by additional impairments is Teff and Treg activation. HCMV seropositivity may contribute to an immune-risk phenotype associated with BD.

In this paper, we study sums of finite products of Chebyshev polynomials of the first kind and Lucas polynomials and represent each of them in terms of Chebyshev polynomials of all kinds. Here the coefficients involve terminating hypergeometric functions ₂F₁ and these representations are obtained by explicit computations.

This article presents an in-depth review of Virtual Power Plant (VPP), its organization in the energy system and its prospects in the face of the promising future of the increasing bidirectional complexity exposed in the current scenario. A new paradigm for the operations of modern power distribution and transmission systems requires greater grid flexibility that is accompanying an extensive change in the structure of electricity markets, the fruit of the development of the renewable industry and of the growing photovoltaic systems popularized, but in contrast due to the intermittent nature cause variable uncertainties in the power system. Innovative concepts like VPP are becoming a reality, establishing an efficient and effective mechanism. The objectives and components are described in a comprehensive way, and some of the most important are pointed out and presented in detail to contribute with a description of the energy systems and the implicit research needed for sustainability and resilience in the eminent energy scenario with this technology. In addition, the literature and studies of this technology already indicate a direction of this new tool as a promising solution to manage the uncertainties of the renewable energies.

In this age of mass media and, in particular, social media-driven perception of reality, coupling disease and prophylactic opinion dynamics models can provide better insights into disease evolution than using a disease model alone. We develop in this work two disease-opinion dynamics models based on the epidemiology of the new coronavirus disease (COVID-19) and the availability or not of imperfect vaccines. We assume that susceptibility to infection decreases with the level of prophylactic attitude (personal hygiene, social distancing), and changes in prophylactic attitudes of susceptible individuals occur in response to perceived disease prevalence and vaccination coverage and efficacy in the population. We derive and discuss the disease-free equilibriums and reproduction numbers in the introduced models. We further assess the impacts of the distribution of opinions at disease introduction, the ability to detect presymptomatic, asymptomatic and symptomatic positive COVID-19 cases, the behavioural responses to the outbreak and the introduction of vaccination, and the effects of distortions of disease prevalence by public policy and mass media on disease dynamics. The insights highlighted from the proposed models are expected to make informative contributions to public policy in a context of opinion fluxes in response to perceived disease prevalence.

Purpose To analyse diagnostic and therapeutic impact of molecular imaging TNM (miTNM) stage obtained with 18F-DCFPyL versus 18F-Fluorocholine in head to head comparison in biochemical recurrence (BCR) prostate cancer (PCa). Material and methods Patients with BCR PCa after radical treatment with previous 18F-Fluorocholine-PET/CT (negative or oligometastatic disease) were derived to 18F-DCFPyL-PET/CT. Patients were classified attending to: grade group, EAU classification, PSA, PSA doubling time (PSAdt) and PSA velocity (PSAvel). The overall detection rate (DR) and miTNM stage according to PROMISE criteria were assessed for both radiotracers and also correlation (Kappa). The influence of PSA and kinetics on both PET/CT (DR and miTNM) and predictive value of unfavourable kinetics on miTNM were determined. Cut-off PSA, PSAdt and PSAvel values able to predict PET/CT results were determined. Change in miTNM and treatment derived of 18F-DCFPyL information with respect to 18F-Fluorocholine was also evaluated. Results We studied 138 patients. 18F-DCFPyL showed a higher DR than 18F-Fluorocholine (64.5% vs 33.3%). 18F-DCFPyL and 18F-Fluorocholine detected T in 33.3% versus 19.6%, N in 27.5% versus 13.8%, M in 30.4% versus 8.7%. Both tracers DR showed significant associations with PSA and PSAvel. Significant association was only found between miTNM and PSA on 18F-Fluorocholine-PET/CT (p=0.033). For 18F-Fluorocholine and 18F-DCFPyL PET/CT a PSAdt cut-off of 4.09 and 5.59 months, respectively, were able to predict M stage. 18F-DCFPyL changed therapeutic management in 40/138 patients. Conclusion 18F-DCFPyL provides a higher DR and superior miTNM staging than 18F-Fluorocholine in restaging BCR, especially with high PSA and unfavourable PSA kinetics, showing a fair agreement to 18F-Fluorocholine.

The Eliezer and Gray physical interpretation of the Ermakov-Lewis invariant is applied as a guiding principle for the derivation of the special relativistic analog of the Ermakov-Milne-Pinney equation and associated first integral. The special relativistic extension of the Ray-Reid system and invariant is obtained. General properties of the relativistic Ermakov-Milne-Pinney are analyzed. The conservative case of the relativistic Ermakov-Milne-Pinney equation is described in terms of a pseudo-potential, reducing the problem to an effective Newtonian form. The non-relativistic limit is considered as well. A relativistic nonlinear superposition law for relativistic Ermakov systems is identified. The generalized Ermakov-Milne-Pinney equation has additional nonlinearities, due to the relativistic effects.

Introduction: Decompensated heart failure (HF) is a complex and debilitating syndrome, which constitutes a severe emergency condition with high morbidity and mortality. The kidneys play fundamental roles in the pathophysiology of HF and, in the context of decompensations, acute kidney injury (AKI) has a bilateral cause-and-effect relationship, which can significantly worsen prognosis. However, the interaction between AKI and decompensated HF is poorly understood. Objective: This study aimed to assess the occurrence of AKI in patients hospitalized due to decompensated HF and to analyze its prognostic impact during hospitalization. Methods: Prospective single-center observational study that included patients hospitalized due to decompensated HF in a tertiary-level teaching hospital, conducted between July 2017 and January 2020. Patients who developed AKI during hospitalization were compared with those who did not develop it, until hospital discharge or death. AKI was defined as a serum creatinine increase greater than or equal to 0.3 mg/dl in 48 hours, a 1.5-fold increase in baseline creatinine in seven days or urinary volume <0.5 ml/kg/h during six hours, according to the Acute Kidney Injury Network (AKIN) criteria. The endpoints analyzed were death, need for invasive mechanical ventilation (IMV) and length of hospital stay. The Wilcoxon, Mann-Whitney and unpaired student t tests were used. Results: Ninety-nine patients were included, with a mean age of 65.4 ± 14 years, of which 47 (47.5%) were male and 52 (52.5%) were female. Reduced ejection fraction was observed in 77.8% of patients, whilst 22.2% had a diagnosis of HF with preserved EF. The decompensation clinical classifications were: dry and warm = 7 (7.1%), wet and warm = 72 (72.7%), wet and cold = 15 (15.1%) and dry and cold = 5 (5.1%). The average left ventricular ejection fraction was 38.3% ± 15. AKI ocurred in 22 patients (22.2%). Comparison between patients who evolved with and without AKI showed higher mortality (36.4% vs 10.4%, p = 0.004) and the need for IMV (54.5% vs 13%, p = 0.0001) in the first group. There was no significant difference regarding the length of in-hospital stay (22.9 ± 19 vs 18.8 ± 16 days, p = 0.26). Conclusions: The occurrence of AKI was frequent in patients with decompensated HF requiring hospitalization, affecting approximately one out of five patients. This complication was significantly associated with increased mortality and the need for IMV during hospitalization.

(S)-Atenolol ((S)-2-(4-(2-Hydroxy-3-(isopropylamino)propoxy)phenyl)acetamide) has been synthesized in >99% ee with use of Candida antarctica lipase B from Syncozymes (Shanghai, China), in a kinetic resolution of the corresponding racemic chlorohydrin. Deprotonation of the starting phenol building block was base catalyzed. The enantiopurity of the chlorohydrin building block remained unchanged upon subsequent amination. All four steps in the synthesis protocol have been optimized compared to previously reported methods, which makes this new protocol more sustainable and in accordance with green chemistry principles compared to previously reported protocols.

A E. Crassipes is considered a problem in different aquatic ecosystems, due to its abundance could become a solution to design and build economic and efficient treatment plants, and especially for the production of biofuels such as bioethanol. The objective of this research is to design and implement a sustainable development process between phytoremediation and bioethanol production with E. crassipes, evaluating the incidence of chromium adhered to the biomass of this plant in the production of bioethanol. Materials and methods: A system was installed to evaluate the phytoremediation with E crassipes with water loaded with chromium, determining the effectiveness of this plant to remove this heavy metal even if it is alive in a body of water. After this process, we proceeded to bring the biomass loaded with chromium to bioreactors to evaluate the production of bioethanol, assessing three types of biomass, one without chromium adhered and the other two with chromium adhered to its plant structure. There was an impact of the ethanol production of the E crassipes due to the presence of chromium, but this production can be taken into account for the assembly of an integral system of phytoremediation and bioethanol production, making the most of this biomass.

When there is uncertainty in the value of parameters of the input random components of a stochastic simulation model, two-level nested simulation algorithms are used to estimate the expectation of performance variables of interest. In the outer level of the algorithm (n) observations are generated for the parameters, and in the inner level (m) observations of the simulation model are generated with the value of parameters fixed at the value generated in the outer level. In this article, we consider the case in which the observations at both levels of the algorithm are independent, showing how the variance of the observations can be decomposed into the sum of a parametric variance and a stochastic variance. Next, we derive central limit theorems that allow us to compute asymptotic confidence intervals to assess the accuracy of the simulation-based estimators for the point forecast and the variance components. Under this framework, we derive analytical expressions for the point forecast and the variance components of a Bayesian model to forecast sporadic demand; and we use these expressions to illustrate the validity of our theoretical results by performing simulation experiments using this forecast model.

A major challenge in the clinical management of patients with mesial temporal lobe epilepsy (MTLE) is identifying those who do not respond to antiseizure medication (ASM), allowing for the timely pursuit of alternative treatments, such as epilepsy surgery. Here, we investigated changes in plasma metabolites as biomarkers of pharmacoresistance in patients with MTLE. Furthermore, we used the metabolomics data to gain insights into the mechanisms underlying MTLE and response to ASM. We performed an untargeted metabolomic method using magnetic resonance spectroscopy and multi- and univariate statistical analyses to compare data obtained from plasma samples of 28 patients with MTLE compared to 28 controls. The patients were further divided according to response to ASM: 20 patients were refractory to treatment, and eight were responsive to ASM. We only included patients using carbamazepine in combination with clobazam. We compared the group of patients with controls and found that the profiles of glucose (p = 0.01), saturated lipids (p = 0.0002), isoleucine (p = 0.0001), β-hydroxybutyrate (p = 0.0003), and proline (p = 0.02) were different in patients compared to controls (p &lt; 0.05). In addition, lipoproteins (p = 0.05), lactate (p = 0.05), glucose (p = 0.05), unsaturated lipids (p = 0.05), isoleucine (p = 0.05), and proline (p = 0.05), could discriminate between the two groups of patients classified according to response to ASM. The identified metabolites were linked to different biological pathways related to cell energy metabolism, and pathways linked to inflammatory processes and the modulation of neurotransmitter release and activity in MTLE. In contrast, we found that pyruvate metabolism may be linked to resistance to ASM. In conclusion, in addition to insights into the mechanisms underlying MTLE and the response to treatment with ASM, our results suggest that plasma metabolites may be used as biomarkers of disease and response to ASM in patients with MTLE. These findings warrant further studies exploring the clinical use of metabolites to assist in decision-making when treating patients with MTLE.

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

This article analyzes laws, normative reforms and regulations surrounding the practices and mechanisms of control over the consumption, cultivation and commercialization of cannabis. On the one hand, I investigate regulatory practices that deny rights for the consumption of cannabis classified as recreational. On the other hand, I study regulatory practices that extend rights for the production, consumption and commercialization of cannabis classified as medicinal and industrial. The empirical moment is composed of four stages and methodological tools. First, I conduct an analysis of state policies. Then, I analyze nine laws and codes on cannabis regulation from 1970 to the present. Next, I perform five semi-structured interviews. Finally, I develop two life stories of former people deprived of their liberty for cannabis cultivation and trade. In this way, I trace forms of regulation on multiple demands, knowledge, interests and beliefs. This research focuses on state regulation. Cannabis regulatory regimes are conceived as constructions of political, social and economic truths, meanings and rights. In sum, the answer to the question of how collective demands around cannabis are regulated in Ecuador stipulates that state action reinforces stigmatization and criminalization. In addition, it legitimizes moral entrepreneurs and counter-movements around cannabis.

Given the importance of the development of urban infrastructure and environmental impacts produced by the civil construction waste (CCW), it is important to correct the handling of CCW with objective solutions that are more environmentally friendly. In that sense the present study aimed to determine indicators that make it possible to estimate the amount of CCW generated from construction sites in the city of Londrina, Parana State, Brazil. The generation of CCW was estimated in a general way, regarding the composition of its mixture, correlated to the gross areas of the buildings studied and their generated volumes of RCC. This generating rate was evaluated in a general way and specifies two types of sites: the new residential and new non-residential constructions. The data required for the development of these indicators was obtained through extensive survey and interviews carried out at the environment secretariat of the City Hall. The generating rate of CCW obtained for non-residential buildings was 0.2052m3/m2 or 170.44kg/m2, for new residential sites was 0.2054m3/m2 or 170.60kg/m2 and for new commercial or non-residential construction sites, it was 0.20453 m3/m2 or 169.85kg/m2. It was also possible to estimate the amount generated annually per inhabitant in the municipality, which is 0.60m3/inhabit.year or 498.55kg/inhabit.year.

Keywords: emerging pollutants; pharmaceuticals; plant uptake; soil behavior; soil remediation.

Filter Media (FM) sourced from recycled organic and mineral material offers a low cost and effective means of treating urban stormwater. Using recycled materials rather than from an increasingly scarce source of virgin materials (typically sandy loam soil) can ensure a sustainable long-term economy and environment. This paper presents results from the laboratory analysis and mathematical modeling to highlight the performance of recycled organic and mineral material in removing nutrients and metals from stormwater. Analysis included physical and chemical characterisation such as particle size distribution, saturated hydraulic conductivity (Ksat), bulk density, effective cation exchange capacity, and pollutant removal performance. Design mixes (DM), comprising a combination of organic and mineral materials, were characterised and used to develop/derive modelling design within the Model for Urban Stormwater Improvement Conceptualisation (MUSIC v6) [1]. Comparison is made to the Adoption Guidelines for Stormwater Biofiltration Systems - Summary Report [2] which were based on the Facility for Advancing Water Biofiltration (FAWB) guidelines to assist in the development of biofiltration systems, including the planning, design, construction and operation of those systems. An observed outcome from over two decades of biofiltration guideline development has been the exclusion of alternative biofilter materials due to claims of excessive leaching. Results from this study indicate that high nutrient and metal removal rates can be achieved over a range of hydraulic conductivities using design mixes of recycled organic and mineral materials that have a demonstrated equivalence to existing guideline specifications.

Clinical training in medical schools in Spain is performed by rotations in university hospitals. During these internships, students are expected to acquire and master basic procedural skills. However, the assessment tools available rarely check whether these skills are completely acquired by the students. We have used an e-portfolio to determine the optimal number of times the students need to repeat a procedure to be able to perform it independently. The results were compared with the actual performance during the internships. An e-portfolio collected qualitative information about the internships. Quantitative information was also requested about the number of times each clinical skill was performed. Later, a survey asked these students and their teachers the optimal number of times each skill should be repeated before it could be considered fully acquired. The questionnaire was answered by 98.6% of the students and 70.3% of their teachers. Out of the 21 clinical skills and procedures selected, both students and their tutors agreed in a similar optimal value in 16 of them; only in five of them, teachers thought that students needed a greater number of times than that selected by the students. When these optimal values were compared with the actual values recorded in the portfolio during the internships, it was found that about half of the clinical skills were carried out less frequently than expected, thus providing an important feedback about the internships. Quantitative information collected in portfolios reveals a moderate mismatch between students and tutors perceptions of their training needs.

COVID-19 generated a scenario for global health with multiple systemic impairments. This retrospective study evaluated the clinical, radiological, and pulmonary functional evolution in 302 post-COVID-19 patients. Regarding post-COVID-19 pulmonary symptoms, dry cough, dyspnea, and chest pain were the most frequent. Of the associated comorbidities, asthma was more frequent (23.5%). Chest Tomography (CT) initially showed a mean pulmonary involvement of 69.7%, and the evaluation in the subsequent months showed an improvement in the evolutionary image, and with less than six months post-pathology, there was a commitment of 37 .7%, from six to twelve months, 20% and after 12 months, 9.9%. And as for most of the sample, 50.3% of the patients presented CT normalization in less than six months after infection, 23% normalized between six and twelve months, and 5.2% normalized the images after twelve months, with one remaining. Percentage of 17.3% who maintained post-COVID-19 pulmonary residual sequelae. Regarding spirometry, in less than six months after the pathology, 59.3% of the patients already showed a regular exam; 12.3% normalized their function within six to twelve months, and 6.3% concluded a normal exam after twelve months of post-pathology evaluation. Only 3.6% of the patients still showed some alteration in this period.

The combined influence of the North Atlantic Oscillation (NAO) and the East Atlantic (EA) patterns on the covariability of temperatures and precipitation in 35 stations of the Iberian Peninsula during the period 1950-2019 is analysed in this work. Four EA-NAO composites were defined from teleconnection patterns positive and negative phases: EA+NAO+, EA+NAO-, EA-NAO+, and EA-NAO-. Daily data of maximum and minimum temperature were used to obtain seasonal means (TX, and TN, respectively), and the covariability of these variables with accumulated seasonal rainfall (R) was studied comparing results obtained for different NAO and EA composites. Main results indicate slight differences in the spatial coverage of correlation coefficients between R and temperature variables, except in spring when the generalized negative relationship between R and TX under EA+NAO+ and EA-NAO- disappears under EA-NAO+ and EA+NAO- composites. This result may be useful to interpret and discuss historical reconstructions of Iberian climate.

Carnivorous waterbirds have been reported to enhance wetlands diversities, control pests, bio-indicators of ecological conditions, and act as indicators of potential disease outbreaks. They also provide important ecosystem services to indigenous communities residing along wetlands. Regardless of their importance, global waterbirds populations are declining. Despite their ecological contribution to wetlands functions and ecosystem services, piscivore waterbirds have been re-ported to compete with piscivorous fish and fishermen. The aim of this study was to compare piscivorous waterbirds species and functional diversity between fish protected areas (FPA) and non-fish protected area (NFPA) in two tributaries of Zambezi River in Namibia. We also measured the degree to which patterns of guild structure between piscivore waterbirds and fish were similar between FPA and non-FPA. Species diversities were determined by calculating the Shannon Diversity Index. We calculated taxonomic diversity, functional diversity and community-based trait diversity indices for each tributary. Generally, species diversities were not significantly different between FPA and NFPA (P > 0.05). However, all functional diversities indices calculated were significantly different between FPA an NFPA (P < 0.05 for all comparisons). This was partly due to food item, weight and movement type that were associated with each site.

Mandarins occupy the third group among citrus produced in Brazil. Primarily aimed at the fresh fruit market, commercial orchards have suffered significant losses in production, due to the high incidence of diseases, such as alternaria brown spot (ABS) and huanglongbing (HLB). The fact that 80% of the orchards are composed of two varieties, Ponkan mandarin and Murcott tangor, are highly susceptible to both diseases, demand intensive chemical control of the diseases in the field, which has resulted in a significant increase in production costs and a negative environmental impact. A total of one hundred and seventy-three mandarin accessions, including several species and hybrids, belonging to the Collections of the Instituto Agronômico de Campinas (IAC), were evaluated in the field, in an endemic location for the diseases. Evaluations of the incidence and severity of the diseases showed that there is a range of genotypes tolerant to ABS with potential to replace the current commercial varieties, mainly within the clementina and Willow leaf mandarin groups. Although with differences in incidence and susceptibility to HLB, there are no varieties tolerant to this disease, which significantly impacts the loss of fruit quality.

Mass customization, small batch sizes, high variability of product types and a changing product portfolio during the life cycle of an industrial plant are current trends in the industry. Due to an increasing decoupling of the development of software and hardware components in an industrial context, compatibility problems within industrial control systems arise more and more frequently. In this publication, a strategy concept for compatibility testing is derived and discussed by means of literature review and applied research. This 4-phased strategy concept identifies incompatibilities between software and hardware components in the industrial control environment and enables test engineers to detect problems at an early stage. By automating the compatibility test on an external I-PC, the test can be run both when new software is installed on the industrial controller and when the controller is restarted. Thus, changes to the components are constantly detected and incompatibilities are avoided. Furthermore, early incompatibility detection can ensure that a system remains permanently operational. Based on a discussion, additionally strategies are identified to consolidate the robustness and applicability of the presented concept.

Several review articles about megachurch studies have been published recently concentrating their work on USA, Europe, and other parts of the world, with just a few references about Latin American megachurches. For that reason, this article aims to identify some of the characteristics of Latin American Evangelical megachurches by looking at relevant literature, especially that produced in the region, in Spanish and Portuguese, which is usually overlooked by researchers in Global North. Since this research field is still limited in Latin America, areas, where further work is necessary, are identified. Three general catalysts for the emergence of megachurches in the region, church growth methodologies, Pentecostalization, and theologies of growth, serve as guides to organize the review process. The discussion shows several potential areas of research in a variety of fields such as theology, ecclesiology, organizational theory, leadership, gender studies, and ethics, are proposed from the review.

Traumatic cardiac arrest has a high mortality. We report the case of a 65 years old women who presented a cardiac arrest following traffic accident. Examinations concluded to a tension gastro thorax with a delayed diagnosis. Naso gastric tube insertion was for many authors a temporary care while waiting for surgery. We report on the diagnostic difficulties found in the literature and a recent review of published cases.

In this work we explore the utilization of plasmonic resonance (PR) in silver nanowires to enhance the performance of organic solar cells. Plasmonic resonance is a phenomenon in which nanoscale conductive materials exhibit oscillation of conduction electrons, resulting in the creation of an electric field. Enhancing light absorption is crucial for improving organic solar cell efficiency and incorporating metallic nanostructures to induce surface plasmon resonance (SPR) shows promise in achieving this goal. We discuss the two key mechanisms of plasmonic effects: far-field scattering and near-field resonance modes. Far-field scattering extends the optical path of incident light, while near-field plasmonic effects involve localized surface plasmon resonance (LSPR) and plasmonic cavity modes, enhancing absorption by strengthening electric fields near the nanostructures. Silver nanowires are the focus of this study, and finite-difference time-domain (FDTD) simulation software is used to investigate their plasmonic resonance behavior in a ZnO/Silver nanowires/ZnO (ZAZ) electrode structure. The simulations reveal the dominance of LSPR in this configuration, with intense electric fields inside the nanowire and propagation into the surrounding medium, offering opportunities for enhanced light absorption in the organic solar cell's active layer.

Alternaria brown spot (ABS) is a disease caused by the fungus Alternaria alternata f. sp. citri, which results in lesions on fruits, leaves, and branches of several mandarin varieties and their hybrids. Due to the high cost of fungicide application, alternative methods for controlling ABS need to be studied. Therefore, this study aimed to evaluate the use of essential oils (EOs) from different mandarin varieties to mitigate the effects of ABS. The inhibitory effect of different concentrations (1, 2, 4, 8, and 16 μL mL-1) of Fremont IAC 543 mandarin, IAC 2019Maria mandarin, Murcott IAC 221 tangor, and Late IAC 855 willowleaf. EOs on the in vitro mycelial growth of the fungus A. alternata was evaluated. Additionally, the curative and preventive effects of these EOs on ABS symptoms on detached leaves of Murcott IAC 221 tangor were also assessed. The EO of IAC 2019Maria mandarin induced less mycelial growth, and consequently, greater inhibition of the growth of the fungus A. alternata at a concentration of 16 μL mL-1. This EO was more effective in control than the other oils tested. In the detached leaf experiment, both the curative and preventive treatments at a concentration of 16 μL mL-1 showed lower values of disease severity.

Recent technological advancements such as IoT and Big Data have granted industries extensive access to data, opening up new opportunities for integrating Artificial Intelligence (AI) across various applications to enhance production processes. We can cite two critical areas where AI can play a key role in industry such as product quality control and predictive maintenance. This paper presents a survey of AI applications in the domain of Industry 4.0, with a specific focus on product quality control and predictive maintenance. Experiments were conducted using two datasets, incorporating different machine learning and deep learning models from the literature. Furthermore, this paper provides an overview of the AI solution development approach for product quality control and predictive maintenance. This approach includes several key steps, such as data collection, data analysis, model development, model explanation, and model deployment.

Single Image Super Resolution (SSIR) is a problem in computer vision where the goal is 1 to create high-resolution images from low-resolution ones. It has important applications in fields 2 such as medical imaging and security surveillance. While traditional methods such as interpolation 3 and reconstruction-based models have been used in the past, deep learning techniques have recently 4 gained attention due to their superior performance and computational efficiency. This article proposes 5 an Autoencoder based Deep Learning Model for SSIR, in particular, a light model that uses fewer 6 parameters without compromising performance. The down-sampling part of the Autoencoder 7 mainly uses 3 by 3 convolution and has no subsampling layers. The up-sampling part uses transpose 8 convolution and residual connections from the down sampling part. The model is trained using a 9 subset of the VILRC ImageNet database. The model is evaluated using quantitative metrics PSNR, 10 SSIM as well as qualitative measures such as perceptual quality. PSNR and SSIM figures as high as 11 76.06 and 0.93 are reported.

Following the development of tyrosine kinase inhibitors (TKI), the survival of patients with chronic myeloid leukaemia (CML) drastically improved. With the introduction of these agents, CML is now considered a chronic disease, for some patients. Taking into consideration the side effects, toxicity, and high cost, discontinuing TKIs became a goal for patients with chronic phase CML. Patients who achieved deep molecular response (DMR) and discontinued TKI, remained in treatment-free remission (TFR). Currently, the data from the published literature demonstrate that 40-60% of patients achieve TFR, with relapses occurring within the first six months. In addition, almost all patients who relapsed regained a molecular response upon re-treatment, indicating TKI discontinuation is safe. However, there is still a gap in the understanding the mechanisms behind TFR, and whether there are prognostic factors that can predict the best candidates who qualify for TKI discontinuation with a view to keeping them in TFR. Furthermore, the information about a second TFR attempt and the role of gradual de-escalation of TKI before complete cessation is limited. This review highlights the factors predicting success or failure of TFR. In addition, it ex-amines the feasibility of a second TFR attempt after the failure of the first one, and the current guidelines concerning TFR in clinical practice.

The plants Agave sisalana (sisal) and Punica granatum (pomegranate) are rich in saponins, which are secondary metabolites known for their therapeutic properties, including anti-inflammatory effects. Although, Brazil is the world's leading sisal producer, approximately 95% of the leaves are discarded after fiber extraction. Similarly, pomegranate peel waste is abundant in Brazil. To address the need for safe and effective anti-inflammatory treatments. This study aimed to create a topical mucoadhesive gel containing a combination of sisal (RS) and pomegranate residue ex-tracts (PR). In vitro experiments examined isolated and combined extracts, as well as the result-ing formulation, focusing on: 1) phytochemical analysis (total saponin content); 2) cytotoxicity (MTT assay); and 3) pharmacological assessment of anti-inflammatory activity (phagocytosis, macrophage spreading, and membrane stability).Results revealed saponin concentrations in grams per 100 grams of dry extract as follows: SR - 29.91 ± 0.33, PR - 15.83 ± 0.93, Association (A) - 22.99 ± 0.01, Gel Base (G1) - 0.00 ± 0.00, and Association Gel (G2) - 0.52 ± 0.05. In MTT tests for isolated extracts, cytotoxicity values (µg/mL) were 3757.00 for SR and 2064.91 for PR. Converse-ly, A and G2 exhibited no cytotoxicity, with increased cell viability over time. All three anti-inflammatory tests confirmed the presence of this activity in SR, PR, and A. Notably, G2 demon-strated an anti-inflammatory effect comparable to dexamethasone.In conclusion, the gel contain-ing SR and PR holds promise as a novel herbal anti-inflammatory treatment. Its development could yield economic, social, and environmental benefits by utilizing discarded materials in Brazil.

The vehicular ad hoc network (VANET) when augmented with internet is called internet of vehicles (IoV). In the IoV, vehicles have on board units (OBUs), use communication technologies and customized software to communicate with infrastructure around them. The vehicles can have permanent connectivity and global network awareness based on cloud computing. IoV technologies are still being assessed to ensure that the network is protected from attacks, supports a dynamic vehicular mobility scenarios and meets the low latency communication requirements between the vehicles and the surrounding infrastructure. To meet these strict Quality of Service (QoS) demands, Unmanned Aerial Vehicles (UAVs) play a vital role in IoV ecosystems. An UAV can serve as a mobile roadside unit (mRSU) to transmit data between vehicles, stationery RSUs and other UAVs. The UAVs have better line of sight (LOS) connections which result in better channel states and lower path loss than fixed RSUs. Although the inclusion of UAVs in vehicular networks has improved the system performance by providing better resource management and routing solutions, the traditional optimization techniques with their limitations are often not applicable. Therefore, in recent years, research community is integrating Artificial Intelligence (AI) and Machine Learning (ML) into UAV based IoV ecosystem to better manage resource allocation, routing and mobility management issues to optimize the overall network performance. In this survey paper we review the existing research done in AI/ML based UAV-IoV networks and communications with focus on resource management and routing from year 2019 to 2023. We have studied different AI techniques, their training attributes and architectures in various research works. The limitations of AI-based approaches in terms of required computational resources, availability of real world data and AI models’ complexity for the UAV-IoV environment are also discussed. Finally, the future directions for UAV-IoV research opportunities that can leverage the full potential of AI are presented.

Within the framework of multiblock data analysis, a unified approach of supervised methods is discussed. It encompasses multiblock redundancy analysis (MB-RA) and multiblock partial least squares (MB-PLS) regression. Moreover, we develop new supervised strategies of multiblock data analysis, which can be seen as variants of one or the other of these two methods. They are respectively refered to as multiblock weighted redundancy analysis (MB-WRA) and multiblock weighted covariate analysis (MB-WCov). The four methods are based on the determination of latent variables associated with the various blocks of variables. They are derived from clear optimization criteria whose aim is to maximize either the sum of the covariances or the sum of squared covariances between the latent variable associated with the response block of variables and the block latent variables associated with the various explanatory blocks of variables. We also propose indices to help better interpreting the outcomes of the analyses. The methods are illustrated and compared based on simulated and real datasets.

In the field of forensic imaging, it is important to be able to extract a “camera fingerprint” from one or a small set of images known to have been taken by the same camera (image sensor). Ideally, that fingerprint would be used to identify an individual source camera. Camera fingerprint is based on certain kind of random noise present in all image sensors that is due to manufacturing imperfections and thus unique and impossible to avoid. PRNU (Photo-Response Non-Uniformity) has become the most widely used method for SCI (Source Camera Identification). In this paper, we design a set of “attacks” to a PRNU based SCI system and we measure the success of each method. We understand an attack method as any processing that alters minimally image quality and that is designed to fool PRNU detectors (or, generalizing, any camera fingerprint detector). The PRNU based SCI system was taken from an outstanding reference that is publicly available.

Peri-implant diseases are defined as pathological inflammatory reactions in the tissue surrounding the osseointegrated implants. They are classified into two categories: mucositis - defined as peri-implant soft tissue inflammation and peri-implantitis - bone loss in the peri-implant region. A clinical case of a 61-years-old woman with an implant in the 46 anatomical position and a 5-year follow-up is presented. A probe depth of 5mm and a bone defect length of 35% was observed. The Implacure® Protocol was applied, with the main objective of eliminating the biofilm present on the exposed implant surface. There are multiple approaches to treat peri-implant diseases. While non-surgical treatment is essential for mucositis control, the treatment of peri-implantitis surgical treatment should be considered. Regenerative bone reconstruction promotes bone repair in the defect area and reduces bleeding during probing. To achieve that, autologous fibrin combined with Cerasorb M® was used. The proposed approach in the exposed clinical case involves the application of Implacure® Protocol, whose combination of the physical decontamination technique, together with the use of chlorhexidine and orthophosphoric acid, added with the combination Piperacillin + Tazobactam together with hyaluronic acid, provide a base that allows to regenerate bone using platelet-rich fibrin with Cerasorb M® and increase the survival time of the implant.

The normal and Poisson distribution assumptions in the normal-Poisson mixed effects regression model are often too restrictive for many real count data. Several works have independently relaxed the Poisson conditional distribution assumption for counts or the normal distribution assumption for random effects. This work couples some recent advances in these two regards to develop a skew t–discrete gamma regression model in which the count outcomes have full dispersion flexibility and random effets can be skewed and heavy tailed. Inference in the model is achieved by maximum likelihood using pseudo-adaptive Gaussian quadature. The use of the proposal is demonstrated on a popular owl sibling negotiation data. It appears that, for this example, the proposed approach outperforms models based on normal random effects and the Poisson or negative binomial count distribution.

Most existing flexible count regression models allow only approximate inference. Balanced discretization is a simple method to produce a mean-parametrizable flexible count distribution starting from a continuous probability distribution. This makes easy the definition of flexible count regression models allowing exact inference under various types of dispersion (equi-, under- and overdispersion). This study describes maximum likelihood (ML) estimation and inference in count regression based on balanced discrete gamma (BDG) distribution and introduces a likelihood ratio based latent equidispersion (LE) test to identify the parsimonious dispersion model for a particular dataset. A series of Monte Carlo experiments were carried out to assess the performance of ML estimates and the LE test in the BDG regression model, as compared to the popular Conway-Maxwell-Poisson model (CMP). The results show that the two evaluated models recover population effects even under misspecification of dispersion related covariates, with coverage rates of asymptotic 95% confidence interval approaching the nominal level as the sample size increases. The BDG regression approach, nevertheless, outperforms CMP regression in very small samples (n = 15 − 30), mostly in overdispersed data. The LE test proves appropriate to detect latent equidispersion, with rejection rates converging to the nominal level as the sample size increases. Two applications on real data are given to illustrate the use of the proposed approach to count regression analysis.

Climate changes caused by anthropogenic actions can directly and indirectly affect living beings, including parasites and their hosts. Changes such as water temperature, pH, distribution of nutrients in the aquatic environment are some examples that can interfere with the fish community, whether in open water or in production systems. In this review, we will show how climate change can affect the adaptation of hosts and parasites in the aquatic environment and how these parasites can be used for environmental monitoring.

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

In this article the galactic rotation curve is analyzed as an effect of an accelerated reference frame. This phenomenon is the strongest evidence for the so called dark matter. We show that a non-inertial reference frame could explain the experimental data. We also show that general relativity is not enough to complete explain that which encouraged alternatives paths such as the MOND approach. Considering the effect of dark matter as a realization of accelerated reference frames is a simple but powerful hypothesis.

Introduction: Biofuels, biohydrogen and bioethanol have properties that stand out from other fossil fuels, are colorless, odorless, and insipid, therefore, they are free of contaminants. Its production can be generated from the biomass of the aquatic plant Eichhornia crassipes, since this alternative is timely and viable due to its high energy composition. This plant is a problem in wetlands, rivers and other hydrosystems, due to its abundance and dominance over other species of aquatic plants, and which in effect causes an ecological imbalance. Objective: To evaluate the production of biohydrogen and bioethanol from the biomass of the E. crassipes plant. Materials and methods: Lignocellulosic material was used, realizing different physical, chemical and biological processes such as: reduction of size, lignin removal, acid hydrolysis, fermentation and distillation. a laboratory scale production system was designed and built where two bioreactors were used in the process. Results and discussion: the production of bioethanol and biohydrogen showed a production index of more than 40 mg of bioethanol per gram of bio-mass of E. crassipes and in relation to biohydrogen, 65 mmol / L was obtained. Conclusion: With the biomass of E. crassipes it is possible to obtain a high index of bio-fuel production, since it is possible to take advantage of its physical characteristics and its high proliferation in hydrosystems, in this way it constitutes an optimal alternative to produce biohydrogen and bioethanol at a high scale.

Cosmic strings can arise from spontaneous phase transitions during the inflationary period and are describable as disclination topological defects in spacetime. Analytical integration of the geodesic equations for a particle around a cosmic string reveals distinct orbit patterns dependent on the disclination intensity $\beta_{0}$. After its interaction with the cosmic string, the particle's behavior becomes random. To categorize these patterns, we describe the geodesic motion as iterated maps of a discrete parameter. Through the stabilization values of these functions, we correlate the scattering angle and the number of loops for the particle with $\beta_{0}$. Therefore, we interpret the gravitational field mathematically as an instructor of a particle's next position, given its previous one. We find that the radial function in the small displacement limit is similar to the logistic map. This leads us to conjecture the potential of the discrete parameter being a multiple of Planck's time, suggesting a method for the geometric discretization of spacetime around a cosmic string.

Understanding the relationships between land cover/urban structure patterns and air pollutants is key to sustainable urban planning and development. In this study, we employ a mobile monitoring method to collect PM_2.5 and BC data in the city of Philadelphia, PA during the summer of 2019 and apply the Structure of Urban Landscapes (STURLA) methodology to examine relationships between urban structure and atmospheric pollution. We find that, while PM_2.5 and BC vary by STURLA class, many of the differences in pollutant concentrations between classes are not significant. However, we also find that the proportions in which STURLA components are present throughout the urban landscape can be used to predict urban air pollution. Among frequently sampled STURLA classes, gpl hosted the highest PM_2.5 concentrations on average (16.60 _± 4.29 µg/m³), while tgbwp hosted the highest BC concentrations (2.31 _± 1.94 µg/m³). Furthermore, STURLA combined with machine learning modeling was able to correlate PM_2.5 (R²= 0.68, RMSE 2.82 µg/m³) and BC (R² = 0.64, RMSE 0.75 µg/m³) concentrations with the urban landscape and spatially interpolate concentrations where sampling did not take place. These results demonstrate the efficacy of the STURLA methodology in modeling relationships between air pollution and land cover/urban structure patterns.

Introduction Acalypha indica (AI) is an Ayurvedic medicinal plant. Some of this plant’s phytochemicals have been reported to cause oxidative stress in red blood cells. However, we know very little about those chemicals due to a dearth of literature. We present a case in which a patient with a previously unidentified glucose-6-phosphate dehydrogenase (G6PD) deficiency experienced acute hemolysis after ingesting fresh AI plant leaves. Case Presentation A 65-year-old man presented with jaundice, pallor, and haematuria following ingestion of the "Kuppamenia mallum" dish. His peripheral blood picture showed bite cells and Heinz bodies. A positive Brewer's test raised the clinical suspicion of G6PD deficiency. Low values of the G6PD level assessment at the follow-up visit confirmed the diagnosis. Conclusion Although AI is regarded as a medicinal plant, acute hemolysis can also occur after consumption in G6PD-deficient patients. This case emphasises the need for further research to identify the phytochemicals and their concentrations that can induce toxicity.

Software defect prediction models enable test managers to predict defect-prone modules and assist with delivering quality products. A test manager would be willing to identify the attributes that can influence defect prediction and should be able to trust the model outcomes. The objective of this research is to create software defect prediction models with a focus on interpretability. Additionally, it aims to investigate the impact of size, complexity, and other source code metrics on the prediction of software defects. This research will also assess the reliability of cross-project defect prediction. Well-known machine learning techniques such as support vector machines, k-nearest neighbors, random forest classifiers, and artificial neural networks were applied to publicly available PROMISE datasets. The interpretability of this approach has been demonstrated by SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) techniques. The developed interpretable software defect prediction models showed reliability on independent and cross-project data. Finally, the results demonstrate that static code metrics can contribute to the defect prediction models, and the inclusion of explainability assists in establishing trust in the developed models.

In recent years, the abundance of streaming data has posed significant challenges for traditional machine learning algorithms due to the dynamic nature and high volume of data. To address these challenges, this paper presents an adaptive supervised learning framework designed for data streams, leveraging the power of reproducing kernel Hilbert spaces (RKHS) while incorporating a data sparsity constraint. The proposed framework aims to adaptively update the model to efficiently handle the continuous arrival of streaming data while ensuring accurate and reliable predictions. The primary objective is to exploit the RKHS framework, which provides a rich mathematical structure for learning in high-dimensional feature spaces. By utilizing RKHS, the model can capture complex patterns and nonlinear relationships in the streaming data. Furthermore, the framework incorporates a data sparsity constraint to address the issue of limited resources and computational efficiency. The constraint promotes the selection of a subset of relevant features, reducing the dimensionality of the problem and enhancing the scalability of the learning algorithm. This constraint not only improves computational efficiency but also mitigates the effects of noisy or irrelevant features, leading to more robust and accurate predictions. To achieve adaptability, the proposed framework employs an online learning approach that incrementally updates the model parameters as new data arrives. This allows the model to adapt to concept drift and changing data distributions, ensuring its relevance and effectiveness over time. The adaptation process is guided by a mechanism that balances the exploitation of current knowledge with the exploration of new information, enabling the model to gradually evolve and refine its predictions. Experimental evaluations on various benchmark datasets demonstrate the efficacy of the proposed framework in handling streaming data. The results indicate that the adaptive supervised learning approach in RKHS with a data sparsity constraint outperforms traditional batch learning methods and exhibits superior accuracy, scalability, and adaptability in dynamic data stream scenarios. Overall, this research contributes to the development of adaptive supervised learning methods for data streams, highlighting the effectiveness of RKHS and the significance of incorporating a data sparsity constraint. The proposed framework offers a promising solution to handle the challenges posed by streaming data, paving the way for real-time, efficient, and accurate learning in diverse application domains.

In this paper is presented the design and syntheis of a dynamic output feedback neural network controller for a non-holonomic mobile robot. First the dynamic model of a non-holonomic mobile robot is presented, in which these constraints are considered for the mathematical derivation of a feasable representation for this kind of robot. Then two control strategies are provided based on kinematic control for this kind of robot. The first control strategy is based on driftless control, this means, that considering that the velocity vector of the mobile robot is orthogonal to its restriction a dynamic output feedback and neural network controller is designed in order that the control action would be zero only when the velocity of the mobile robot is zero. The Lyapunov stability theorem is implemented in order to find a suitable control law. Then another control strategy is designed for trajectory tracking purposes, which similar to the driftless controller, a kinematic control scheme is provided but suitable to implement in more sophisticated hardware. In both control strategy a dynamic control law is provided along with a feedforward neural network controller, so in this way, by the Lyapunov theory the stability and convergence to the origin of the mobile robot position coordinates is ensured. Finally, two numerical experiment are presented in order to validate the theoretical results synthetized in this research study. Discussions and conclusions are provided in order to analyze the results found in this research study.

Autotaxin (ATX) is a secreted lysophospholipase D, catalysing the conversion of lysophosphatidylcholine (LPC) to bioactive lysophosphatidic acid (LPA). LPA acts through two families of G protein-coupled receptors (GPCRs) controlling key cellular responses, and is implicated in many physiological processes and pathologies. ATX has therefore been established as an important drug target in the pharmaceutical industry. Structural and biochemical studies of ATX have shown that it has a bimetallic nucleophilic catalytic site, a substrate-binding (orthosteric) hydrophobic pocket that accommodates the lipid alkyl chain, and an allosteric tunnel that can accommodate various steroids and LPA. Here we first review what is known about ATX-mediated catalysis, crucially in light of allosteric regulation. We then present the known ATX catalysis-independent functions, including binding to cell-surface integrins and proteoglycans. In light of these data we then discuss the four types of ATX inhibitors, as classified depending on their binding to the orthosteric and/or the allosteric site. Finally, we analyse the binding mode of known members of all four types and discuss how mechanistic differences might differentially modulate the activity of the ATX-LPA signalling axis, and clinical applications including cancer.

Twin pregnancies are highly undesirable in dairy cattle; they compromise the health and wellbeing of a cow, and its incidence dramatically impairs the farm economy. Recently, a genomic prediction for twin pregnancies has been developed. The objective of this study was to assess cow, environmental and management risk factors affecting the incidence of twin pregnancies in high-producing dairy cows in their first lactation, with special emphasis placed on the genomic prediction values for twin pregnancy. Our study population of primiparous cows proved valuable in identifying factors other than genomic predictive values influencing the twin pregnancy rate. The odds ratio for twin pregnancy was 0.85 (p &lt;0.0001) for each unit of a prediction value increase, 3.5 (p = 0.023) for cows becoming pregnant during the negative photoperiod, and 0.33 (p = 0.016) for cows producing ≥42 kg of milk at AI, compared to the remaining cows producing &lt;42kg of milk. As a general conclusion, the practical implication of our findings is that genomic prediction values can identify the risk of twin pregnancy at herd level. Given the cumulative effect of genomic selection, selecting for animals with reduced genetic risk of twin pregnancies can contribute to reduce its incidence in dairy herds.

The analysis and simulation of water quality in distribution networks is a complex issue of great concern today. The analysis of the evolution of water age, as a simple indicator of water quality in the network, is of great interest in both design and operation phases. Understanding the factors that have the strongest influence on water quality is key to developing adequate strategies aimed at preserving it. This paper firstly analyses the factors with biggest influence in the tanks water age, to support the selection of the most appropriate configuration from the point of view of water quality during design phases. Then, the main considerations when modelling tanks following the different mixing models considered in Epanet have been presented. Also, a real tank behaviour has been characterised through field measurements and Epanet simulations in order to determine the best fitted mixing model.

The theory of the origin of carcinomas suggests that neoplasias from epithelial tissues are the consequence of the reactivation of developmental programs. It proposes a model in which the epithelial cells undergo cellular transitions due to replicative senescence and inflammation towards a mesenchymal-undifferentiated phenotype with cancerous behavior. The conserved pattern of histological progression and the molecular biology of carcinomas is congruent with the view of cancer as a developmental disease. In support of the theory, the experimental literature regarding the molecular, cellular, and histopathological mechanisms associated with epithelial carcinogenesis were aligned according to the premises of the hypothesis. Thereby identified a generic process in the carcinogenesis of the breast, endometrium, prostate, colon, lung, pancreas, bladder, liver, and cervix. Then, is provided a methodology overview of modeling in systems biology derived from previous research testing the hypothesis. The results illustrate the value of the complex systems approach to recover behavior that cannot be inferred only by traditional methods. Specifically, the model suggests that the consistency in the cell types and the directionality of the observed cellular transitions during epithelial carcinogenesis arise from structural constraints in the molecular networks associated with the carcinomas. Overall, the results of the dynamical analysis agree with the premises of the hypothesis and provide an insightful perspective of the potential mechanisms underlying the cellular plasticity displayed during epithelial carcinogenesis. In an era of big data and yet few advances in the underlying causes of chronic diseases, the manuscript also aims to inspire molecular biologists to integrate existing empirical evidence with systems biology modeling in the pursuit of understanding.

The origin of cancer remains one of the most important enigmas in modern biology. The prevailing paradigm has failed to grasp a comprehensive view of the disease. Naturally, therapies developed under the current assumptions are inadequate and cancer is practically an incurable disease. Meanwhile, descriptive studies continuously extend the molecular complexity of cancer without an equivalent advancement in its understanding. Furthermore, they tend to accumulate inconsistencies inexplicable under the classical view. This paper presents a compelling theory of the origin of carcinomas. By hypothesis, a series of generic events in epithelial tissues promoted by cellular aging and inflammation enables the reactivation of developmental programs. The origin of carcinomas in vivo is described as the time-ordered cell state transitions undergone by epithelial cells in the hyperplasia due to replicative senescence and inflammation towards a mesenchymal undifferentiated endogenous cell state with cancerous behavior. In support of the theory, the molecular, cellular, and histopathological evidence is critically reviewed. A plausible model for the origin of carcinomas is presented to explain the mechanism underlying carcinogenesis from an evolutive and developmental perspective. The implications of the hypothesis in the current strategies for cancer prevention and treatment are discussed along with rational alternatives and some predictions for possible experimental validation.

Plastics are naturally hydrophobic materials so, in order to employ flotation for the separation of plastic mixtures, the use of appropriate wetting agents is mandatory. In this work, the effect of pretreatment with alkaline solutions of sodium hydroxide on the floatability of four plastics (PET, PS, PMMA and PVC) was studied. The influence of NaOH concentration, treatment time and temperature of the alkaline solution, and influence of particle size was analyzed. Results showed that alkaline treatment had a strong effect on PET floatability, some effect on floatability of PMMA and PVC and no effect on floatability of PS. Plastics floatability decreased with the increase of NaOH concentration, temperature and treatment time of the alkaline solution. Based on flotation behavior of simple plastics, flotation separation after alkaline treatment of bi-component mixtures of PET with PS and PVC was achieved efficiently. The best separation was obtained for PET/PS mixture, a floated with a grade of 98% in PS and a sunk with a grade of 100% in PET. PET/PMMA mixture led to the worst separation. For PET/PMMA and PET/PVC mixtures, flotation separation improved with the decrease of the particles size.

Tyrosinases (TYR) play an important role in oxidizing phenols and catechols, leading to the formation of catechols and ortho-quinones, respectively. In mammals, TYR catalyzes the conversion of L-Tyrosine to quinone, a pivotal step in melanogenesis, which generates melanin pigments responsible for protecting against UV radiation and oxidative stress. Given TYR's importance in melanin-related disorders and cosmetic applications, the inhibition of TYR has garnered attention for controlling melanin production. However, current inhibitors like hydroquinone, arbutin, and kojic acid (KA) have limitations in terms of side effects and effectiveness. A novel class of TYR inhibitors featuring arylpiperidine and arylpiperazine components has demonstrated potent inhibitory activity. This study presents a computational analysis of these inhibitors using molecular docking, molecular dynamics (MD) simulations, and the Linear Interaction Energy (LIE) method was used to estimate binding free energies. The arylpiperidine and arylpiperazine inhibitors exhibit remarkable stability within the TYR active site during simulations. The van der Waals interactions are notably sustained, indicating effective inhibition mechanisms. The results from LIE calculations closely align with experimental binding affinities. In summary, these results contribute to the understanding of TYR inhibition and underscore the potential of arylpiperidine and arylpiperazine derivatives as effective agents for modulating melanin production in both cosmetic and pharmaceutical applications. Overall, this study sheds light on the intricate interactions between inhibitors and TYR, opening avenues for the development of novel therapies and interventions.

The primary sources of particulate matter are linked to human activity. Certain particulate emissions, particularly those of a finer nature, can significantly impact human health, making them one of the most concerning pollutants. Ultrafine particles (UFP), which have a diameter of less than 100 nm, are of particular concern due to their impact on human health and the difficulty in controlling them. The concentration of ultrafine particles (UFP) in the workplace is a growing concern and is classified as an emerging risk. Workers may be exposed to UFP through inhalation, skin absorption, ingestion, or a combination of these routes. This study aims to determine the levels of UFP exposure among workers in environments with varying direct particle emission patterns. Measurements were conducted to compare the results with the levels recommended by the WHO. The study monitored industrial workplaces with direct particulate matter emissions, such as a carpentry and a bakery, as well as social sector sites without or almost without direct particle emissions, such as a school and a health clinic. One conclusion drawn from this study is that all tasks and occupations are susceptible to high levels of UFP, exceeding WHO recommended values in virtually all monitored environments. Therefore, monitoring and controlling UFP is crucial. Further in-depth studies on this subject are also necessary..

Artificial intelligence’s rapid advancement has enabled various applications, including intelligent video surveillance systems, assisted living, and human-computer interaction. These applications often require one core task: video-based human action recognition. Research in human video-based human action recognition is vast and ongoing, making it difficult to assess the full scope of available methods and current trends. This survey provides an in-depth exploration of the vision-based human action recognition field, comprehensively offering the available techniques and their evolution, highlighting the cutting-edge ideas driving its development. We also analyze the most used keywords in research papers over the past years to identify trends and predict possible future directions. Hence, this concise survey helps researchers understand the breadth of existing approaches, evaluate current research trends, and stay up-to-date on potential developments.

This study investigates the power production and blade fatigue of a three-turbine array subjected to active yaw control (AYC) in full-wake and partial-wake configurations. A framework of two-way coupled large-eddy simulation (LES) and aeroelastic blade simulation is applied to simulate the atmospheric boundary-layer (ABL) flow through the turbine array and the structural responses of the turbine blades. Mean power outputs and blade fatigue loads are extracted from the simulation results. By exploring the feasible AYC decision space, we find that (a) in the full-wake configuration, the local power-optimal AYC strategy with positive yaw angles endures less flapwise blade fatigue and more edgewise blade fatigue than the global power-optimal strategy; (b) in the partial-wake configuration, applying positive AYC in certain inflow wind directions achieves higher optimal power gains than that in the full-wake scenario and reduces the blade fatigue from the non-yawed benchmark. Through a theoretical analysis based on the blade element momentum theory, we reveal that the aforementioned differences in flapwise blade fatigue between the positively and negatively yawed turbine are due to the differences in the azimuthal distributions of the local relative velocity on blade sections, resulting from the combined effects of vertical wind shear and blade rotation. Furthermore, the difference in the blade force between the positively and negatively yawed front-row turbine induces different wake velocity and turbulence distributions, causing different fatigue loads on the downwind turbine exposed to the wake.

(1) Background: This text emphasizes concerns regarding the safety of Spanish police officers at-tributed to inadequate training, employing biomechanics for the analysis and mitigation of risks during Use of Force training. The elevated global prevalence of musculoskeletal disorders under-scores the imperative for effective occupational health measures. The study concentrates on evalu-ating injury risks in novice police officers during Use of Force training. (2) Research method: The movements of police officers were captured using a Rokoko Smartsuit Pro equipped with 19 sen-sors. The Biomechanics of Bodies software processed the data, providing REBA and Marras risk scores. Statistical tests were conducted, and Operational Tactical Procedures (OTP) were inte-grated into police training. A diverse group of officers participated, categorized by training level and equipped with the Rokoko Smartsuit. (3) Results: The study evaluates police force training us-ing REBA and Marras methods. REBA scores indicate a moderate to high injury risk, suggesting effective technique design. ANOVA reveals no significant differences between training levels. Marras analysis, characterized by non-normal distribution, unveils varied risk perceptions among groups via the Kruskal-Wallis test. (4) Conclusions: The analysis of both the REBA and Marras methods implies the effectiveness of the OTP technique in reducing injury risks across dif-ferent training levels. The ANOVA test indicates no significant differences in REBA Maximum Scores between the groups. However, the Kruskal-Wallis test for Marras Probability of high-risk values suggests distinctions in medians between training levels. The combined analysis visually illustrates variations in membership regions between expert, intermediate, and novice groups.

Electric Transformers make up some of the most critical components of a power system. Their reliable operation is critical for an efficient power grid as well as economic viability for the transformer operators and owners. Power transformers, like other High Voltage (HV) electrical equipment experience aging and insulation degradation due to chemical, mechanical and electrical forces during their operation. Partial Dischargers (PD) are localized electrical discharges that develop within insulation systems of HV electrical equipment such as transformers. In transformers, PD occurs in different forms and various locations and insulation both internal and external of the transformer. PD originates as small pulses but tends to increase in size and intensity which can result in complete insulation failure. Monitoring partial discharges has proven to provide valuable information on the state of the insulation systems of the power transformer, allowing transformer operators to make calculated decisions for the maintenance and life of plant plans. This systematic literature review aims to systematically examine the use of machine learning techniques in classifying PD in transformers to present a complete indicator to allow for future research in the field. The systematic review surveyed a total of 81 research literature published from 2010 to 2023 that fulfilled a specific methodology which was developed as part of this study. The results revealed that supervised learning has been the most widely used Artificial Intelligence (AI) algorithm utilized, primarily in the form of Support Vector Machine (SVM). Regarding PD, the survey revealed that most researchers tend to use numerous types of PD and compare them to one another. Furthermore, the use of artificial PD defect models to simulate the occurrence of PD is widely used versus the use of actual power transformers. Most of the literature tends to not specify the physical characteristics of PD, such as the magnitude of PD, PD inception voltage and PD extinction voltage.

In the field of robotics education, introductory courses would ideally utilize heavy industrial arms for hands-on learning. This would provide students with valuable experience in joint programming, which involves direct control of each joint motor in the arm to accomplish desired path planning and differential movements. This practice requires consideration of the physical dynamics of the arm. However, the use of heavy industrial arms has several drawbacks. They are large and expensive, require specialized maintenance, can pose safety risks, and they typically do not allow for direct control of the joint motors. One solution is to use a small, lightweight, toy-like arm. While these are cost-effective and provide direct access to the joint motors without posing safety risks, their lightweight construction means they exhibit minimal dynamic behavior, which limits their educational value. Another solution involves a robotic arm simulator that uses a virtual arm. However, a virtual arm doesn't offer the same level of hands-on engagement and excitement as a physical arm. We propose a hybrid solution that combines a small physical arm with a virtual arm. This approach provides students with the experience of working with an industrial arm, but without the associated difficulties. We demonstrate how this approach successfully improved the completion rate of joint programming assignments, offering a promising solution for practical robotics education.

New data on the climate in southern Spain from 1792 to 1808 are analyzed in this work. The data source is the newspaper Correo Mercantil de España y sus Indias, where summaries of the weather conditions in Spain were published at weekly resolution. The study is focused on southern provinces, providing 2788 new records, some of them corresponding to areas without data previously recorded. The analysis indicates the predominance of cold-dry winters, cold-wet springs, warm-dry summers, and variable conditions in autumn, from west (cold-wet) to east (warm-dry). Some examples of these situations are presented.

As companies increasingly undergo digital transformation, the role of talent management pro-cesses becomes pivotal in enhancing overall organizational performance. The research addresses the significance of talent management in corporate digital transformation, examining whether variations in the digitalization of these processes can be attributed to company size. A qualitative approach was employed, utilizing a questionnaire, collecting responses from 202 organizations across diverse sectors. Findings reveal disparities in digitalization throughout the talent man-agement process, with pronounced presence in the attracting, selecting, and rewarding phases, but diminishing in deployment and development, further declining in planning. A positive cor-relation between company size and adoption of specific digital platforms was observed. Larger enterprises exhibit greater utilization of digital platforms in talent deployment and development. Moreover, corporate communication tools are consistently utilized in the rewarding phase, irre-spective of company size. These findings offer practical insights for organizations to optimize their digitalization strategies based on their scale, thereby contributing to more effective and tailored digitalization endeavors. The uniqueness of this research lies in its exploration of the in-fluence of company size on the digitalization of talent management processes and its potential to explain variations across different stages of these processes.

The main objective of this study was to study the effect of occupational stress on turnover intentions, as well as whether this relationship is mediated by burnout levels and moderated by motivation. The sample of this study consisted of 603 participants, all employees of the Portuguese Tax and Customs Authority, which is part of the Ministry of Finance and belongs to the Direct State Administration. The results indicate that occupational stress (with managers, with career and remuneration and with work overload) has a positive and significant effect on turnover intentions and that this relationship is mediated by burnout. Motivation (intrinsic and identified) has a negative and significant effect on turnover intentions. Intrinsic motivation moderates the rela-tionship between occupational stress (with managers, with colleagues, with career and pay and with family problems) and turnover intentions. These results indicate that among the dimensions of occupational stress, the most critical are stress with managers, work overload and career and pay. These are factors that the Direct State Administration should be concerned about in trying to minimize their causes.

Integrated crop-livestock production (ILP) is an interesting alternative for more sustainable soil use. However, more studies are needed to analyze soil pore properties under ILP at the micrometer scale. Thus, this study proposes a detailed analysis of soil pore architecture at the micrometer scale in three dimensions. For this purpose, samples of an Oxisol under ILP subjected to minimum tillage (MT) and no-tillage (NT) with ryegrass as cover crop (C) and silage (S) were investigated. The soil micromorphological properties of the soil were analyzed using X-ray microtomography. The MT(C) system showed the highest values of porosity (c. 20.4%), connectivity (c. 32.8×103), volume (c. 26%), and number of pores (c. 32%) in a rod-like shape. On the other hand, the MT(S), NT(C), and NT(S) systems showed higher tortuosities (c. 2.2, c. 2.0, and c. 2.1) and lower pore connectivities (c. 8.3×103, c. 6.9×103, and c. 6.2×103), especially in S use. Ellipsoidal and rod-shaped pores predominated over spheroidal and disc-shaped pores in all treatments. The results of this study show that the use of ryegrass as a cover crop improves soil physical properties, especially in MT. For S use, the type of soil management (MT or NT) did not show any differences.

Disorders associated with substance abuse are a major public health crisis with few treatment options. According to World Health Organization (WHO) ethanol is the most widely used drug in the world, and it represents a risk factor for the advent of disease, disability, and eventually death. Foetal Alcoholic Spectrum Disorders (FASD) is a diagnostic term to describe the range of effects that can occur in an individual whose mother drank alcohol during pregnancy. These effects encompass both physical, mental, behavioural and further lifelong disabilities. Besides, ethanol can harm the gut microbiota. Gut microbiome is firstly acquired from the mother and it is crucial for intestinal homeostasis during hosts’ lifetime. It is responsible for producing metabolites that benefits and protects the host from harm microbial colonization. Knowledge about the interactions between human gut microbes and the developing nervous system is still scarce. Nevertheless, animal models have shown that gut bacteria and microbial metabolites are strongly associated with Central Nervous System (CNS) homeostasis. Endotoxins such as Lipopolysaccharides (LPS) are hypothesized to have a major role in neurodegeneration, however, conclusions must be taken with care due to differences in sensitivity between humans and mice. In this review we focus on the role of gut microbiota on the neurodevelopment of mice when ethanol consumption is one of the major stressors during prenatal period. We detail the range of the endotoxin hypothesis in describing endotoxins’ contribution to neurodegeneration and the influence that kynurenine pathway has on the process.

This paper introduces the exponential time differencing (ETD) technique as a numerical method to solve efficiently American vulnerable options pricing. We address several challenges, including removing cross-derivative terms through appropriate transformations, treating early-exercise opportunities using the penalty method, and substituting fixed boundary conditions with corresponding one-sided finite differences. The proposed method is shown to be both accurate and efficient through numerical experiments, which also compare the results with existing methods and analyse the numerical stability and convergence rate.

Entering University is an important transition for students. Requires adaptation and can influence academic success. Psychological disorders might emerge when difficulties like anxiety occur due to insecurity about emotions in a student's life. We are currently living in post-pandemic conditions where pressure is increasing. The project "Toolbox: University Student – Web-interactive Platform", a platform for learning anxiety management, was developed to address this problem among university students. The main objective is to understand whether using the platform (based on cognitive behavioral theory) has effectively managed anxiety among students. It is a case study with a sample of 31 students from two Portuguese Universities, aged between 19 and 47 years. They were divided into two groups: intervention (n=17) and control (n=13). The results show that participants subjected to intervention with the interactive platform decreased their anxiety.

Angiotensin-converting enzyme 2 (ACE2) is an important player of the renin-angiotensin-aldosterone system (RAAS) in regulating the conversion of angiotensin II into angiotensin (1-7). While expressed on the surface of human cells, such as lung, heart, kidney, neurons, and endothelial cells (EC), ACE2 is the entry receptor for SARS-CoV-2. Here, we would like to highlight that ACE2 is predominant on the EC membrane. Many of coronavirus disease 2019 (COVID-19) symptoms have been associated with the large recruitment of immune cells, directly affecting EC. Additionally, cytokines, hypoxia, and complement activation can trigger the activation of EC leading to the coagulation cascade. The EC dysfunction plus the inflammation due to SARS-CoV-2 infection may lead to abnormal coagulation, actively participating in thrombo-inflammatory processes resulting in vasculopathy and indicating poor prognosis in patients with COVID-19. Considering the intrinsic relationship between EC and the pathophysiology of SARS-CoV-2, EC-associated therapies such as anticoagulants, fibrinolytic drugs, immunomodulators, and molecular therapies have been proposed. In this review, we will discuss the role of EC in the lung inflammation and edema, in the disseminate coagulation process, ACE2 positive cancer patients, and current and future EC-associated therapies to treat COVID-19.

This work aims to investigate the effect of process temperature and catalyst content by pyrolysis and thermal catalytic cracking of (organic matter + paper) fraction from municipal household solid waste (MHSW) on the yields of reaction products (bio-oil, bio-char, H2O, and gas), acid value and chemical composition of bio-oils, and characterization of bio-chars, in laboratory scale. The collecting sectors of MHSW in the municipality of Belém-Pará-Brazil were chosen based on geographic and socio-economic database. The MHSW collected and transported to the segregation area. The gravimetric analysis of MHSW carried out and the fractions (Paper, Cardboard, Tetra Pack, Hard Plastic, Soft Plastic, Metal, Glass, Organic Matter, and Inert) separated. The selected organic matter and paper submitted to pre-treatment of crushing, drying, and sieving. The experiments carried out at 400, 450, and 475 °C and 1.0 atmosphere, and at 475 °C and 1.0 atmosphere, using 5.0, 10.0, and 15.0% (wt.) Ca(OH)2, in batch mode. The bio-oil characterized for acid value. The chemical functions present in bio-oil identified by FT-IR and the composition by GC-MS. The bio-char characterized by SEM, FT-IR and XRD. The variance in mass (wt.%) for organic fraction of municipal household solid waste, between 56.21 and 67.45% (wt.), lies with the interval of 56% (wt.) and 64% (wt.) of OFMHSW for middle and low income countries. The pyrolysis of MHSW fraction (organic matter + paper) show bio-oil yields between 2.63 and 9.41% (wt.), aqueous phase yields between 28.58 and 35.08% (wt.), solid phase yields between 35.29 and 45.75% (wt.), and gas yields between 16.54 and 26.72% (wt.). The bio-oil yield increases with pyrolysis temperature. For the catalytic cracking, the bio-oil and gas yields increase slightly with CaO content, while that of bio-char decreases, and the H2O phase remains constant. The GC-MS of liquid reaction products identified the presence of hydrocarbons (alkanes, alkenes, alkynes, cycloalkanes, and aromatics) and oxygenates (carboxylic acids, ketones, esters, alcohols, phenols, and aldehydes), as well as compounds containing nitrogen, including amides and amines. The acidity of bio-oil decreases with increasing process temperature and with aid Ca(OH)2 as catalyst. The concentration of hydrocarbons in bio-oil increases with increasing Ca(OH)2-to-MHSW fraction ratio due to the catalytic deoxygenation of fatty acids molecules, by means of de-carboxylation/de-carbonylation, producing aliphatic and aromatic hydrocarbons.

The origin of cancer remains one of the most important enigmas in modern biology. This paper presents a hypothesis for the origin of carcinomas in which cellular aging and inflammation enable the recovery of cellular plasticity that may ultimately result in cancer. The process is described as the result of dedifferentiation undergone by epithelial cells in hyperplasia due to replicative senescence towards a mesenchymal cell state with potential cancerous behavior. In support of the hypothesis, the molecular, cellular, and histopathological evidence was critically reviewed and reinterpreted when necessary to postulate a plausible generic model for the origin and progression of carcinomas. In addition, the implications of this theoretical framework for the current strategies of cancer treatment are discussed against recent evidence of the molecular events underlying the epigenetic switches involved in the resistance of breast carcinomas. Subsequently, is proposed an epigenetic landscape for their progression and a potential mechanism to restrain the degree of dedifferentiation and malignant behavior. Finally, is suggested a novel understanding of the involution and carcinogenesis of tissues associated with aging as a perspective that might inspire integrative approaches in the study and management of chronic diseases.