This paper presents a new methodology that provides the analysis of surface texture changes in areas adjacent to the volcano and its impact product of volcanic activity. To do this, algorithms from digital image processing such as the co-occurrence matrix and the wavelet transform are used. These methods are working on images taken by the Landsat satellite platform sensor 5 TM and Landsat 7 ETM + sensor, and implemented with the purpose of evaluating superficial changes that can warn of surface movements of the volcano. The results were evaluated by similarity metrics for grayscale images, and validated in two different scenarios that have the same type of eruption, but differ, essentially, in climate and vegetation. Finally, the proposed algorithm is presented, setting the parameters and constraints for implementation and use.

This paper presents new above-ground biomass (AGB) and biomass components equations for seventeen forest species in the temperate forests of northwestern Mexico. A data set corresponding to 1336 destructively sampled oak and pine trees was used to fit the models. Generalized method of moments was used to simultaneously fit systems of equations for biomass components and AGB, to ensure additivity. Additionally, the carbon content of each tree component was calculated by the dry combustion method, in a TOC analyser. The fitted equations accounted for on average 91, 83, 84 and 78% of the observed variance in stem wood and stem bark, branch and foliage biomass, respectively, whereas the total AGB equations explained on average 93% of the total observed variance in AGB. The inclusion of h or d²h as additional predictor in the d-only based equations systems slightly improved estimates of stem wood, stem bark and total above-ground biomass, and greatly improved the estimates produced by the branch and foliage biomass equations. The fitted equations were used to estimate AGB stocks at stand level from a database on growing stock from 429 permanent sampling plots. Three machine-learning techniques were used to model the estimated stand level AGB and carbon contents; the selected models were applied to map the AGB and carbon distributions in the study area, which yielded mean values of 129.84 Mg ha^-1 and 63.80 Mg ha^-1, respectively.

In the present study the synthesis of gold nanoparticles (AuNPs) loaded with methotrexate (MTX) has been carried out in order to obtain controlled size and monodispersed nanocarriers, around 20nm. Characterization study shows metallic AuNPs with MTX polydispersed on the surface. MTX is linked by a replacement of citrate by the MTX carboxyl group. The drug release profiles showed faster MTX release when it is conjugated, which leads to the best control of plasma concentration. Also, the enhanced release observed at pH 5 could take advantage of the pH gradients that exist in tumor microenvironments to achieve high local drug concentrations. AuNPs-MTX conjugates were tested by flow cytometry against lung (A-549) and colon (HTC-116) cancer cell lines. Results for A-549 showed a lighter dose-response effect than for colon cancer ones. This could be related to the presence of folate receptors in line HTC-116 on the contrary than line A-549, supporting the specific uptake of folate-conjugated AuNPs-MTX by folate receptor positive tumor cells. Conjugates exhibited considerably higher cytotoxic effects compared with the effects of equal doses of free MTX. Anexin V-PI test sustain as cell death mechanism apoptosis, which is normally disabled in cancer cells.

Taeniid infections pose a significant threat to both animal and public health, as certain tapeworms within this group can also infect humans, potentially leading to severe health conditions. Therefore, this calls for preventive and control measures, such as regular deworming of dogs. While the efficacy of deworming has been established in developed countries, there is a paucity of reported deworming practices in rural areas of developing countries, such as Tanzania. This study aims to understand determinants for helminth treatment-seeking behavior towards control of cestodial taeniid infections in rural settings in northern Tanzania. A cross-sectional study was undertaken in agropastoral and pastoral areas of northern Tanzania. Comprehensive data from household surveys in selected sub-villages were collected to ascertain dog ownership, dog deworming practices, and the availability of dewormers. Analytical methods were employed to discern how various determinants influenced deworming practices among dog owners in these communities. Awareness of dewormers specifically formulated for dogs emerged as a pivotal factor affecting dog deworming practices. Dog owners who were informed about appropriate dog dewormers were nearly two times more likely to engage in deworming compared to those with limited knowledge (OR = 1.78, 95% CI 1.77 - 4.18, p < 0.001). A majority 32(51.6%, 95% CI: 38.7-64.3) acknowledged accessing dewormers for livestock within their wards. Praziquantel a potent dewormer on cestodes was least known to most dog owners 2(13%, 95% CI: 2.3-41.6). Praziquantel, a potent and common dewormer effective against cestodial taeniid infections, remains relatively unknown in rural areas of northern Tanzania. Therefore, more awareness on appropriate deworming agents against taeniids in dogs should be raised in rural dog-keeping communities.

Introduction: The objective of this study was: compare the continuous infusion of cefepime with the intermittent infusion in patients with sepsis caused by Gram negative bacilli (GNB). Methods: Randomized 1:1 multicenter double-blinded placebo-controlled study with allocation concealment. Multicenter study in Intensive Care Units of Colombia. Patients with sepsis, severe sepsis or septic shock and GNB suspected bacteraemia. Cefepime was administered for 7 to 14 days over 30 m intermittently every 8 h over 24 h plus continuous saline solution (0.9%) (G1) or 3 g administered continuously plus saline solution every 8 h (0.9%) (G2). Percentage of clinical response at 3, 7, 14 days, relapse at 28 days and mortality at discharge were measured. Results: The recruitment was stopped by suggestion of the Institutional Review Board (IRB), following an FDA alert about cefepime. Thirty-two patients were randomized, 25 received the intervention and GNB bacteremia was confirmed in 16 (9 G1 and 7 G 2). Favourable clinical response in days 3,7,14 was 88.8%, 88.8%, 77.8 % (G 1) and was similar for G 2 (85.7%). There were not relapses and deaths in G2 while in G1 one relapse and 2 deaths were observed. Conclusions: The results of this study support the use of cefepime for the treatment of Gram-negative infections in critically ill patients but we could not demonstrate differences between continuous or intermittent administration, because of the small sample size, given the early suspension of the study.

Turner syndrome (TS) is a chromosomal disorder that is caused by a missing or structurally ab-normal second sex chromosome. Subjects with TS are at an increased risk of developing intrauterine growth retardation, low birth weight, short stature, congenital heart diseases, infertility, obesity, dyslipidemia, hypertension, insulin resistance, type 2 diabetes mellitus, metabolic syndrome, and cardiovascular diseases (stroke and myocardial infarction). The underlying pathogenetic mechanism of TS is unknown. The assumption that X chromosome-linked gene haploinsufficiency is associated with the TS phenotype is questioned since such genes have not been identified. Thus, other pathogenic mechanisms have been suggested to explain this phenotype. Morphogenesis encompasses a series of events that includes cell division, the production of migratory precursors and their progeny, differentiation, programmed cell death, and integration into organs and systems. The precise control of the growth and differentiation of cells is essential for normal development. The cell cycle frequency and the number of proliferating cells are essential in cell growth. 45,X cells have a failure to proliferate at a normal rate, leading to a decreased cell number in a given tissue during organogenesis. A convergence of data indicates an association between a prolonged cell cycle and the phenotypical features in Turner syndrome. This review aims to examine old and new findings concerning the relationship between a prolonged cell cycle and TS phenotype. These studies reveal a diversity of phenotypic features in TS that could be explained by reduced cell proliferation. The implications of this hypothesis for our understanding of the TS phenotype and its pathogenesis are discussed. It is not surprising that 45,X monosomy leads to cellular growth pathway dysregulation with profound deleterious effects on both embryonic and later stages of development. The prolonged cell cycle could represent the beginning of the pathogenesis of TS, leading to a series of phenotypic consequences in embryonic/fetal, neonatal, pediatric, adolescence, and adulthood life.

Voice cloning, an emerging field in the speech processing area, aims to generate synthetic utterances that closely resemble the voices of specific individuals. In this study, we investigate the impact of various techniques on improving the quality of voice cloning, specifically focusing on a low-quality dataset. To contrast our findings, we also use two high-quality corpora for comparative analysis. We conduct exhaustive evaluations of the quality of the gathered corpora in order to select the most suitable audios for the training of a Voice Cloning system. Following these measurements, we conduct a series of ablations by removing audios with lower SNR and higher variability in utterance speed from the corpora in order to decrease their heterogeneity. Furthermore, we introduce a novel algorithm that calculates the fraction of aligned input characters by exploiting the attention matrix of the Tacotron 2 Text-to-Speech (TTS) system. This algorithm provides a valuable metric for evaluating the alignment quality during the voice cloning process. We present the results of our experiments, demonstrating that the performed ablations significantly increase the quality of synthesised audios for the challenging low-quality corpus. Notably, our findings indicate that models trained on a 3-hour corpus from a pre-trained model exhibit comparable audio quality to models trained from scratch using significantly larger amounts of data.

This paper presents an approach to teacher identity heritage as result of the implementation of a research device created through Arts-based Research (ABR) methods, specifically with video elicitation and video essay used as research tools. Two main objectives were raised. The first one, to establish the real relevance of focusing performance on teacher identity. The second one, testing a new methodological proposal specifically designed for this purpose, but still useful in other contexts where heritage identity is as present as in teaching. The device, Body-camera, involves a process that allows new ways to understand the creation of identities using video to encourage the production of new meanings by visual and oral data. The participants were teachers in training during their internship period. Some notions about teachers' identity heritage were revealed, and also preserved, firstly, through personal perspectives by video elicitations and secondly, through collective perspectives by video essays. Both are video structures used in ABR which mix creative experience, memories, life experiences, relationships and links that shape the teachers’ professional identity.

The Eurasian grapevine (Vitis vinifera L.) is the most widely grown horticultural crop in the world and is important for the economy of many countries. In the wine production chain, grape varieties play an important role as they directly influence the authenticity and classification of the product. Identifying the different grape varieties is therefore fundamental for quality control and inspection activities, as well as for regulating production. Currently, ampelography and molecular analysis are the main approaches to identifying grape varieties. However, both methods have limitations. Ampelography is subjective and prone to errors and is experiencing enormous difficulties as ampelographers are increasingly scarce. On the other hand, molecular analyses are very demanding in terms of cost and time. In this scenario, Deep Learning (DL) and Machine Learning (ML) methods have emerged as a classification alternative to deal with the scarcity of ampelographs and avoid molecular analyses. In this study, the most recent and current methods for identifying grapevine varieties using DL classification-based approaches are presented through a systematic literature review. The classification pipeline of the 31 studies found in the literature was described, highlighting its pros and cons. Most of the studies used DL-based models trained with leaf images acquired in a controlled environment at a maximum distance of 1.2 metres to classify grape varieties. In addition, there is a large gap between practical applications and the datasets used: a great lack of varieties, limited data acquired in the field and a lack of tests on plants under adverse conditions. Potential directions for improving this area of research were also presented.

This work (in two parts) will review the recently developed predictive modeling methodology called “4th-BERRU-PM” and its applicability to energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” methodology, which yields best-estimate results with reduced uncertainties for the first fourth-order moments (mean values, covariance, skewness, and kurtosis) of the optimally predicted posterior distribution of model results and calibrated model parameters. The 4th-BERRU-PM uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters, thus incorporating, as particular cases, the results previously predicted by the second-order predictive modeling methodology 2nd-BERRU-PM, and vastly generalizing the results produced by extant data assimilation and data adjustment procedures. The 4th-BERRU-PM methodology encompasses the scopes of high-order sensitivity analysis (SA), uncertainty quantification (UQ), data assimilation (DA) and model calibration (MC). The application of the 4th-BERRU-PM methodology to energy systems is illustrated by means of the above-mentioned OECD/NEA reactor physics benchmark, which is modeled using the neutron transport Boltzmann equation involving 21976 imprecisely known parameters, the solution of which is representative of “large-scale computations.” The model result (“response”) of interest is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. Part 1 of this work illustrates the impact of high-order sensitivities, in conjunction with parameter standard deviations of various magnitudes, on the determination of the expected value and variance of the computed response in terms of the first four moments of the distribution of the uncertain model parameters. Part 2 of this work will illustrate the capabilities of the 4th-BERRU-PM methodology for combining computational and experimental information, up to and including forth-order sensitivities and distributional moments, for producing best-estimate values for the predicted responses and model parameters while reducing their accompanying uncertainties.

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

Carbon footprint mitigation actions have become a key issue for companies involved in urban freight transport. The electrification of the delivery fleets is one of the crucial sustainable actions carried out. To this end, it is necessary to explore several fleet renewal strategies over a finite planning horizon evaluating different types of electric powertrains for light commercial vehicles. The primary goal of this purpose-built analysis is to assist and boost the fleet managers' decisions when transitioning to alternative-fuelled vans, intending to maximize cost savings and reduce greenhouse gas emissions inventory. The model presented in this work is a Multi-Objective Linear Programming hybrid analysis taking into account the vehicle and energy supply infrastructure costs and the emissions reported in each inventory scope following the Greenhouse Gas Protocol sustainability guidelines. From this perspective, the conducted research offers a novel approximation to fleet management problems enabling a break-down cost analysis and an evaluation of the annual emissions company report. The comprehensive numerical simulations carried out over different study scenarios in Spain demonstrate that the optimization approach does not only show effective fleet renewal strategies but also identifies critical factors that impact the fleet's competitiveness, offering valuable insights for fleet managers and policymakers to meet carbon footprint reductions in a cost-effectively way.

Medical brain image analysis is a necessary step in the Computers Assisted /Aided Diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of White Matter Hyperintensities (WMHs) of brain MRI images specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used the bibliometric networks. Out of a total of 140 documents we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and proposal of new models of deep learning to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with indicators (Dice Score, DSC: 0.99) were found, however with little practical application due to the uses of small datasets and lack of reproducibility. Therefore, the main conclusion is to establish multidisciplinary research groups to overcome the gap between CAD developments and their complete utilization in the clinical environment.

Soil erosion seriously affects vineyards. In this study, the influence of two plant covers on soil moisture and the effect of different physiographic conditions on runoff and sediment yields were evaluated in a rainfed vineyard formed by four fields (NE Spain) during 15 months. One field had spontaneous vegetation as plant cover and three fields had a cover crop of common sainfoin. The vineyards’ rows were dry and stable, whereas the inter-row areas were wet although very variable, and the corridors were wet and very stable. Soil moisture in the inter-row areas with Common sainfoin was much higher than in the rows (62% - 70%) whereas this difference was lower with spontaneous vegetation (40%). Two runoff and sediment traps (STs) were installed in two ephemeral gullies, and 26 time-integrated surveys (TIS) done. The mean and maximum runoff yields were 9.8 and 30.7 l TIS^–1 in ST2 and 13.5 and 30.2 l TIS^–1 in ST3. The mean turbidity was 333 and 19 g l^–1, and the maximum sediment yields were 41,260 and 2,778 g TIS^–1 in ST2 and ST3. Changes in the canopy covers (grapevines and plant covers) and rainfall parameters explained the runoff and sediment dynamics.

Statistical denoising techniques have been well studied, however many challenges still remain. This 9 article focuses on determining the least necessary number of significant samples in a statistical pop-10 ulation sampling, while minimizing the impact on the statistical detection of the fiber Bragg grating 11 (FBG) power spectra. This is achieved by using Bayesian decision making, small population statis-12 tics while excluding false detection. First, we investigate the impact of two-sided sliding window 13 technique applied near the cell under the test. The window size varies from 8 to 60 samples. The 14 considered maximum size of population sampling is connected with the number of wavelength 15 samples within the bandwidth of the symmetrical FBG power spectra peaks. Regarding the sym-16 metry of FBG power spectra peaks and the possible reduction of computational demands, we inves-17 tigate the impact of a one-sided sliding window. This enables to achieve less processing latency for 18 real time applications thus brings benefits to FBG-based fiber optical sensing. Both, two- and one-19 sided small population sampling techniques are then experimentally verified. Detection results are 20 analyzed in depth via calculating detection thresholds by using the small size window sliding along-21 side the waveband. From results achieved, it was found that the normality three-sigma rule does 22 not need to be followed while using the small population sampling. Experimental demonstrations 23 and analyses showed that the proposed concept of denoising and statistical threshold detection 24 based on the sliding window does not depend on the prior knowledge of probability distribution 25 functions of the FBG power spectra peaks level and background noise. We have also found that the 26 thresholds’ adaptability mainly depends on the statistical numerical characteristics of the above ad-27 ditive mixture of the FBG power spectra peaks signals and background noise.

The formulation and analytic solution of a new mathematical model with constitutive curvature for analysis of tunnel ventilation shaft wall is proposed. Based on the Mindlin-Reissner theory for thick shells, this model also takes into account the shell constitutive curvature and considers an expression of the shear correction factor variable (αn) in terms of the thickness (h) and the radius of curvature (R). The main advantage of the proposed model is that it has the possibility to analyze thin, medium and thick tunnel ventilation shafts. As a result, two comparisons were made: the first one, between the new model and the Mindlin-Reissner model without constitutive curvature with the shear correction factor (α_n=6/5) as a constant, and the other, between the new model and the tridimensional numerical models (solids and shells) obtained by finite element method for different slenderness ratios (h/R). The limitation of the proposed model is that it is to be formulated for a general linear-elastic and axial-symmetrical state with continuous distribution of the mass.

Agricultural expansion is altering the provision of ecosystem services and seriously affecting the well-being of the indigenous communities still living in forests. In this paper, we evaluate the impact of forest loss and degradation on the indigenous forest dependent communities of Eastern Salta, Argentina, between 2001 and 2015. First, we identified the demand area of ten final ecosystem services for 202 indigenous communities using participatory mapping data. Second, we calculated the remaining usage area using a deforestation geodatabase based on Landsat images. Third, we analyzed the significance of trends in forest productivity processing vegetation spectral indices from MODIS products. By last, we detected changes in the growing season length by evaluating monthly trends in spectral indices. Our results show a reduction of 21% in the area used by indigenous communities for capturing final ecosystem services, and significant negative trends in forest productivity for the demand area of 64% of the communities, indicating that the area of use is not only being reduced, but also remnant forest area is being degraded and the growing season is being shortened. These aspects indicate an important loss in the provision of ecosystem services that deeply affects the wellbeing of indigenous communities.

We enhanced the agro-hydrologic VegET model to include snow accumulation and melt processes and the separation of runoff into surface runoff and deep drainage. Driven by global weather datasets and parameterized by Land Surface Phenology (LSP), the enhanced VegET model was implemented in the cloud to simulate daily soil moisture (SM), actual evapotranspiration (ETa), and runoff (R) for the conterminous United States (CONUS) and the Greater Horn of Africa (GHA). Evaluation of the VegET model with independent data showed satisfactory performance, capturing the temporal variability of SM (Pearson correlation r: 0.22–0.97), snowpack (r: 0.86–0.88), ETa (r: 0.41–0.97), and spatial variability of R (r: 0.81–0.90). Absolute magnitudes showed some biases, indicating the need of calibrating the model for water budget analysis. The seasonal Landscape Water Requirement Satisfaction Index (L-WRSI) for CONUS and GHA showed realistic depictions of drought hazard extent and severity, indicating the usefulness of the L-WRSI for the convergence of evidence toolkit used by the Famine Early Warning System Network to monitor potential food insecurity conditions in different parts of the world. Using projected weather datasets and landcover-based LSP, the VegET model can be used not only for global monitoring of drought conditions, but also for evaluating scenarios on the effect of a changing climate and land cover on agriculture and water resources.

The growth in online child exploitation material is a significant challenge for European Law Enforcement Agencies (LEAs). One of the most important sources of such online information corresponds to audio material that needs to be analyzed to find evidence in a timely and practical manner. That is why LEAs require a next-generation AI-powered platform to process audio data from online sources. We propose the use of speech recognition and keyword spotting to transcribe audiovisual data and to detect the presence of keywords related to child abuse. The considered models are based on two of the most accurate neural-based architectures to date: Wav2vec2.0 and Whisper. The systems are tested under an extensive set of scenarios in different languages. Additionally, keeping in mind that obtaining data from LEAs is very sensitive, we explore the use of federated learning to have more robust systems for the addressed application, while maintaining the privacy of the data to LEAs. The considered models achieved a word error rate between 11% and 25%, depending on the language. In addition, the systems are able to recognize a set of spotted words with true positives rates between 82% and 98%, depending on the language. Finally, federated learning strategies show that they can maintain and even improve the performance of the systems when compared to centralized trained models. The proposed systems sit the basis for an AI-powered platform for automatic analysis of audio in the context of forensic applications within child abuse. The use of federated learning is also promising for the addressed scenario, where data privacy is an important issue to be managed.

This paper presents a new analytical method for obtaining new deformations compatibility equations or, new Saint-Venant’s identities, into the relative coordinate system with projected deformations by applying the hypothesis of the lineal shell theory in general flexion state. The method proposed generalizes the compatibility conditions established by A.L. Goldenveizer for the shell theory. On the other hand, the new equations include the deformations compatibility equations by other authors: Flügge, Saint-Venant, Love-Kirchhoff, Timoshenko, Goldenveizer, and Reissner-Mindlin. The results showed an increase of knowledge in general shell theory, and provide inverse and semi-inverse solutions, whose systems solution correspond to the hyper-statics degrees of the physical model, and not to their degrees of freedom.

Brain-computer interfaces (BCI) from electroencephalography (EEG) provide a practical approach to support human-technology interaction. In particular, motor imagery (MI) is a widely-used BCI paradigm that guides the mental trial of motor tasks without physical movement. Here, we present a deep learning methodology, named Kernel-based Regularized EEGNet (KREEGNet), leveled on Centered Kernel Alignment and Gaussian Functional Connectivity, explicitly designed for EEG-based MI classification. The approach proactively tackles the challenge of intra-subject variability brought on by noisy EEG records and the lack of spatial interpretability within end-to-end frameworks applied for MI classification. KREEGNet is a refinement of the widely accepted EEGNet architecture, featuring an additional kernel-based layer for regularized Gaussian functional connectivity estimation based on CKA. The superiority of KREEGNet is evidenced by our experimental results from binary and multi-class MI classification databases, outperforming the baseline EEGNet and other state-of-the-art methods. Further exploration of our model’s interpretability is conducted at individual and group levels, utilizing classification performance measures and pruned functional connectivities. Our approach is a suitable alternative for interpretable end-to-end EEG-BCI based on deep learning.

Several methods have been tried to estimate air temperature using satellite imagery. In this paper, the results of two machine learning algorithms, Support Vector Machine and Random Forest, are compared with Multivariate Linear Regression, TVX and Ordinary kriging. Several geographic, remote sensing and time variables are used as predictors. The validation is carried out using four different statistics on a daily basis allowing the use of ANOVA to compare the results. The main conclusion is that Random Forest with residual kriging produces the best results (R$^2$=0.612 $\pm$ 0.019, NSE=0.578 $\pm$ 0.025, RMSE=1.068 $\pm$ 0.027, PBIAS=-0.172 $\pm$ 0.046), whereas TVX produces the least accurate results. The environmental conditions in the study area are not really suited to TVX, moreover this method only takes into account satellite data. On the other hand, regression methods (Support Vector Machine, Random Forest and Multivariate Linear Regression) use several parameters that are easily calculated from a Digital Elevation Model, adding very little difficulty to the use of satellite data alone. The most important variables in the Random Forest Model were satellite temperature, potential irradiation and cdayt, a cosine transformation of the julian day.

In a bid to resolve lingering problems in cosmology, more focus is being tilted towards cosmological models in which physical constants of nature are not necessarily real constants, but varying with cosmic time. In this paper we have study cosmology in nonlinear electrodynamics with the Newton's gravitational constant $G$ not a constant but vary in form of power-law of the scale factor of the universe. The evolution of the scale factor $a (t)$ is studied in this model which depends on nonlinear electrodynamics fine tuning term of $\alpha$. Then we check the stability of the model using the speed of sound.

This work presents the mathematical framework of the Second-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (abbreviated as “2nd-CASAM-L”), which enables the most efficient computation of exactly obtained mathematical expressions of first- and second-order sensitivities of a generic system response with respect to functions (“features”) of model parameters. Subsequently, the first- and second-order sensitivities with respect to the model’s uncertain parameters, boundaries, and internal interfaces are obtained analytically and exactly, without needing large-scale computations. Within the 2nd-FASAM-L methodology, the number of large-scale computations is proportional to the number of model features (defined as functions of model parameters), as opposed to being proportional to the number of model parameters, which are considerably more than the number of features, being incomparably more efficient and more accurate than any other methods (statistical, finite differences, etc.) for computing exact expressions of response sensitivities (of any order) with respect to the model’s features and/or primary uncertain parameters, boundaries, and internal interfaces. The application of the 2nd-CASAM-L methodology is illustrated using a simplified energy-dependent neutron transport model of fundamental significance in nuclear reactor physics.

The administration of regional neuraxial analgesia for pain relief during labor is widely recognized as a safe and effective method involving medication delivery into the epidural or subarachnoid space in the lower back. This study proposes an innovative semantic image segmentation methodology emphasizing enhanced interpretability using convolutional Random Fourier Features and layer-wise weighted class-activation maps tailored explicitly for foot segmentation in regional analgesia monitoring. Namely, our contribution is twofold: i) a novel Random Fourier Features layer is introduced to deal with image data to enhance three well-known architectures (FCN, UNet, and ResUNet); ii) three novel quantitive measures are presented to evaluate the interpretability of a given deep learning model devoted to segmentation tasks. Our approach is rigorously evaluated on a demanding dataset of foot thermal images from pregnant women who received epidural anesthesia. Its small size and considerable variability characterize the dataset. Our validation results demonstrate that the proposed methodology not only achieves competitive foot segmentation performance but also significantly enhances the explainability of the process, rendering it well-suited for applications such as epidural insertion during labor.

Remote sensing datasets are increasingly being used to provide spatially explicit large scale evapotranspiration (ET) estimates. The focus of this study was to estimate and thematically map on a pixel-by-pixel basis, the actual evapotranspiration (ET_a) of the Wonji Shoa Sugarcane Estate using the Surface Energy Balance Algorithm for Land (SEBAL), Simplified Surface Energy Balance (SSEB) and Operational Simplified Surface Energy Balance (SSEBop) algorithms. The results obtained revealed that the ranges of the daily ETa estimated on January 25, February 26, September 06 and October 08, 2002 using SEBAL were 0.0 - 6.85, 0.0 – 9.36, 0.0 – 3.61, 0.0 – 6.83 mm/day; using SSEB 0.0 - 6.78, 0.0 – 7.81, 0.0 – 3.65, 0.0 – 6.46 mm/day, and SSEBop were 0.05 - 8.25, 0.0 – 8.82, 0.2 – 4.0, 0.0 – 7.40 mm/day, respectively. The Root Mean Square Error (RMSE) values between SSEB and SEBAL, SSEBop and SEBAL, and SSEB and SSEBop were 0.548, 0.548, and 0.99 for January 25, 2002; 0.739, 0.753, and 0.994 for February 26, 2002;0.847, 0.846, and 0.999 for September 06, 2002; 0.573, 0.573, and 1.00 for October 08, 2002, respectively. The standard deviation of ET_a over the sugarcane estate showed high spatio-temporal variability perhaps due to soil moisture variability and surface cover. The three algorithm results showed that well watered sugarcane fields in the mid-season growing stage of the crop had higher ET_a values compared with the other dry agricultural fields confirming that they consumptively use more water. Generally during the dry season, ET_a is limited to water surplus areas only and in wet season, ET_a was high throughout the entire sugarcane estate. The evaporation fraction (ETrF) results also followed the same pattern as the daily ET_a over the sugarcane estate. The total crop and irrigation water requirement and effective rainfall estimated using the Cropwat model were 2468.8, 2061.6 and 423.8 mm/yr for January 2001 planted and 2281.9, 1851.0 and 437.8 mm/yr for March 2001 planted sugarcanes, respectively. The mean annual ET_a estimated for the whole estate were 107 Mm³, 140 Mm³, and 178 Mm³ using SEBAL, SSEB, and SSEBop, respectively. Even though the algorithms should be validated through field observation, they have potential to be used for effective estimation of ET in the sugarcane estate.

In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conduct a comparative analysis of our model against existing quantum models such as Quantum Neural Networks and their classical counterparts, elucidating its unique contributions. Through empirical results, we aim to underscore the effectiveness of our approach in optimizing energy consumption in practical scenarios.

Autonomous weapons systems (AWS), sometimes referred to as “killer robots”, are receiving evermore attention, both in public discourse as well as by scholars and policymakers. Much of this interest is connected with emerging ethical and legal problems linked to increasing autonomy in weapons systems, but there is a general underappreciation for the ways in which existing law might impact on these new technologies. In this paper, we argue that as AWS become more sophisticated and increasingly more capable than flesh-and-blood soldiers, it will increasingly be the case that such soldiers are “in the power” of those AWS which fight against them. This implies that such soldiers ought to be considered hors de combat, and not targeted. In arguing for this point, we draw out a broader conclusion regarding hors de combat status, namely that it must be viewed contextually, with close reference to the capabilities of combatants on both sides of any discreet engagement. Given this point, and the fact that AWS may come in many shapes and sizes, and can be made for many different missions, we argue that each particular AWS will likely need its own standard for when enemy soldiers are deemed hors de combat. We conclude by examining how these nuanced views of hors de combat status might impact on meaningful human control of AWS.

This study aims to better understand students who attend the Basic Vocational Training Cycles (FPB) regarding the measures to ensure diversity. This quantitative research project approximates the perception of students concerning their passage through their previous studies and their satisfaction and goals after finishing the school year. The sample con-sists of 352 students from Cordoba (Spain). A questionnaire has been used which follows the CIPP model. After the exploratory factor analysis was completed with the different groups of items and their descriptive analysis, various tests were carried out to consider the hypotheses (Pearson's correlation (r), one-factor analysis of variance and repeated ANOVA measures). The results indicate that the educational interest of the students is ac-ademic and professional. Likewise, there is no relationship detected between the appear-ance of socio-professional goals and average academic levels and the attributions with re-spect to repetitions of previous courses, although these goals vary depending on the satis-faction shown by the students with respect to the cycles. We conclude that the course of the FPB influences the adoption of decisions regarding academic-professional projects.

This paper presents a novel approach to power management techniques in energy storage system (ESS) interoperability with the energy power system (EPS) by inserting a SCADA controlled directional protection relay for improving the mode switching sequence. According with latest European standards, all necessary measures for re-synchronization and reconnection for health work of the system must be taken. The proposed approach intends to improve the “interface” between storage systems and electric power grid by using a directional relay which provides interoperability improvement by automatic recognizing and differentiating the switching sequence of the mode of operation from the fault mode. Simulated results from a small scale electrical generating system are provided. The results demonstrate that the proposed power management solution is technically ideal to achieve high quality energy while retaining the stability of the power grid network. The interoperability of PHS systems integrated with the electric power grid usually leads to perturbations in terms of frequency of the generated voltage, and for improving it we designed an automatic treating and re-connecting software module. This work regards the importance of producing electricity from green and renewable energy sources, while retaining the stability and reliability of the power grid. ESS systems are widely used to help balancing of intermittent forms of load demand on the electrical power grid at specific key moments. In this article, the pumped hydro storage (PHS) technologies are proposed as an economical way to utilize smoothly the off-peak energy or surplus energy, by pumping water to a reservoir at a higher level. During peak load periods, the stored water is discharged through the reversible pump-turbine to generate electricity to meet the peak demand controlled by designed SCADA application.

Purpose: Expanded carrier screening (ECS) informs couples of their risk of having offspring affected by certain genetic conditions. Limited data exists assessing the actions and reproductive outcomes of at-risk couples (ARCs). We describe the impact of ECS on planned and actual pregnancy management in the largest sample of ARCs studied to date. Methods: Couples who elected ECS and were found to be at high risk of having a pregnancy affected by at least one of 176 genetic conditions were invited to complete a survey about their actions and pregnancy management. Results: Three hundred ninety-one ARCs completed the survey. Among those screened before becoming pregnant, 77% planned or pursued actions to avoid having affected offspring. Among those screened during pregnancy, 37% elected prenatal diagnostic testing (PNDx) for that pregnancy. In subsequent pregnancies that occurred in both the preconception and prenatal screening groups, PNDx was pursued in 29%. The decision to decline PNDx was most frequently based on the fear of procedure-related miscarriage, as well as the belief that termination would not be pursued in the event of a positive diagnosis. Conclusions: ECS results impacted couples’ reproductive decision-making and led to altered pregnancy management that effectively eliminates the risk of having affected offspring.

Albert Einstein, shortly after publishing his General Relativity Theory (GRT) applied it to the entire universe in a paper of 1917. Einstein assumed that such a universe was static in time and possessed a uniform distribution of matter on the largest scales, obtaining a static and finite universe of spherical spatial curvature of radius R. Although this model was refuted by Edwin Hubble's discovery of the expansion of the universe, it is still often studied as a theoretical framework for some problems, such as the twin paradox. This paper will show that Einstein's static universe model leads to a privileged frame of reference, so that any observer moving at the speed v will be able to measure speeds of light other than c depending on the direction of beam transmission. The above will refute the validity of the application to this closed and static universe of the Special Relativity Theory (SRT) proposed by Einstein in 1905 in his article . In addition, a new synchronization convention and new coordinate transformations will be deduced, the "generic" transformations, which will replace those of Lorentz. Finally, the twin paradox in this universe will be solved by making use of them.

This paper proposes a novel data processing framework dedicated to bedload monitoring in underwater environments. After calibration, by integration the total energy in the nominal bandwidth, the proposed experimental setup is able to accurately measure the mass of individual sediments hitting the aluminum plate. This require a priori knowledge of the vibration trasients in order to match a predefined dictionary. Based on unsupervised hierarchical agglomeration of complex vibration spectra, the proposed algorithms allow to accurately localize the transients corresponding to the shocks created by sediment impacts on a steel plate.

The Independent Component Analysis (ICA) has been recently introduced as a reliable alternative to identify canonical scattering mechanisms within Polarimetric Synthetic Aperture Radar (PolSAR) images. This manuscript addresses two important aspects when applying such methods on real data, namely speckle filtering and statistical classification with ICA. A novel PolSAR data processing framework is introduced by adjusting the Lee's sigma filter to the particular nature of the Touzi's polarimetric decomposition. In its current form, it allows the use of the ICA mixing matrix in the derived speckle filter. An extension of the Fromont at al. iterative segmentation is introduced, equally. This proposed framework is tested using P band airborne PolSAR data acquired for the ESA campaign TropiSAR campaign.

The paper presents the results of the theoretical and practical research on developing the infrastructure for assessment of overall risk (explosion / occupational / terrorist attack) associated with unwanted events such as major accidents that can occur at explosive storehouses for civilian use. The scientific research outlined in this article was carried out within the Nucleu Project PN 16 43 02 15 - “Research on increasing safety levels at technical facilities for storage of explosives for civil use”.

The linkage between the salt wedge, tidal patterns, and the Magdalena River discharge is established by assessing the ensuing parameters: stratification (ϵ), buoyancy frequency (β), potential energy anomaly (φ), Richardson number (RL), and bottom turbulent energy production (P). The salinity, temperature, density, and water velocity data utilized were derived from MOHID 3D, a previously tailored and validated model for the Magdalena River Estuary. To grasp the dynamics of the river, a flow regime analysis was conducted during both wet and dry climatic seasons of the Colombian Caribbean. The utilization of this model aimed to delineate the estuary's spatial reach, considering flow rates spanning from 2000 to 6500 m³/s across two tidal cycles. This approach facilitates the prediction of the position, stability, and stratification degree of the salt front. Among the conclusions drawn, it is highlighted that: 1. The river flow serves as the principal conditioning agent for the system, inducing a strong estuary response to weather stations; 2. The extent of wedge intrusion and the river discharge exhibit a non-linear, inversely correlation; 3. Tidal waves cause differences of up to 1000 m in the horizontal extent of the wedge; 4. Widespread channel erosion occurs during the rainy season when the salt intrusion does not exceed 2 km; 5. Flocculation processes intensify during the transition between dry and wet seasons; 6. The stability of the salt layering and the consolidation of the FSI-TMZ are contingent upon the geometric attributes of the channel.

Type 2 Diabetes Mellitus and Alzheimer's disease are two diseases with a high incidence today that share similar pathophysiological mechanisms suggesting a potential causal relationship between them. In fact, Alzheimer-associated Mellitus Diabetes is also known as Type 3 Diabetes Mellitus and constitutes a diabetes intricately linked to Alzheimer's disease. Central to this association is insulin resistance, a key factor leading to decreased insulin sensitivity, hyperglycemia, and impaired glucose uptake. Because of this, the brain is altered resulting in energy deficits and neuronal damage associated with neurodegenerative disorder characteristic of Alzheimer's disease. Both diseases exhibit common biochemical and physiological characteristics such as inflammation, mitochondrial dysfunction, oxidative stress, and the formation of advanced glycation end products. Understanding the complex pathophysiology of Type 3 Mellitus Diabetes and identifying common features shared with Diabetes and Alzheimer's disease are essential steps towards potential therapeutic interventions targeting the insulin resistance pathway. The authors of this review on the relationships between type 2 diabetes and Alzheimer's disease have developed two works. In the first (Relationships between Type 2 Diabetes Mellitus and Alzheimer's Disease. I. Common pathogenic mechanisms), authors explore the shared (physiological, genetics and molecular) pathological changes occurring in Type 2 Mellitus Diabetes and Alzheimer's disease as well as pathways such as AMPK, PPARγ, cAMP, and PI3K/Akt as potential targets for managing Type 3 Diabetes Mellitus. By exploring these intricate mechanisms, the goal is to uncover their interdependencies and facilitate the discovery of innovative pharmacological and nutritional (epigenetics) strategies. In the second paper (Relationships between Type 2 Diabetes Mellitus and Alzheimer's Disease. II Potential therapies) we describe possible nutritional intervention, particularly dietary patterns and phytochemicals that can enhance the effects of pharmaceutical drugs designed against pathological mechanisms involved in the complex dual disease Type 2 Diabetes Mellitus and Alzheimer's disease (or in Type 3 Diabetes Mellitus as other authors consider).

‘Arrayana’ mandarin fruits have a short postharvest life and are sensitive to chilling injury (CI) during cold storage. Brassinosteroids (BR) have been used as a sustainable technology to alleviate CI in fruits and improve postharvest quality. This study evaluated the effect of applying the 24-epibrasinolide analogue (EBR), at doses of 5 mg L-1; DI-31 analogue, at 5 and 10 mg L-1; and control, on the main physical and biochemical characteristics of 'Arrayana' mandarin stored at 4°C for 40 days and, subsequently, 7 days at room temperature (shelf life). The application of EBR and DI-31 analogues reduced the appearance of CI in the exocarp of 'Arrayana' mandarin fruits by reducing electrolyte leakage, maintaining membrane integrity, and increasing antioxidant activity and phenol content at the end of cold storage and shelf life. This was especially pronounced with 5 mg L-1 of EBR. Similarly, the BR maintained the postharvest quality of mandarins by reducing weight loss, respiratory intensity and chlorophyll degradation; increasing β-carotene; and maintaining titratable acidity, and soluble solids. Our research reports for the first time CI tolerance in Arrayana mandarin using natural (EBR) and spirostanic (DI-31), analogues and illustrates the tolerance functionality of the DI-31 analog on CI in fruit postharvest.

The objective of this work was to evaluate the blending and grinding influence over the nutritional, sensorial and sustainable aspects of coffee. To do so, a systematic review of the literature was accomplished. The database for the selection of relevant papers was the Portal de Periódicos da Capes, with remote access via CAFe. For the elaboration of the research, a chronological criterion with period restriction was used, considering the period between 2008-2022, to access all possible works related to the theme of this work. The following terms were used: blending; grinding; coffee; nutrition-al; sensorial; sustainability. To filter the searches, the association of these terms was also used by means of links and word associations. In the terminology, the Boolean operator AND was used to interconnect the terms used. Roasting degree, grinding, and amount of each coffee species impacts the nutritional and sensorial aspects of coffee. And the determination of each blending influences the sustainability of the environment, economic and social aspects of the coffee chain.

Humanity, culture, societal development and language issues are mutually reinforcing much as they are intricately interwoven in a non-ceasing dynamic interrelationships within the matrix defined by language standardization or development, acquisition and use that mostly take up central place as unifying and propelling forces in language discourse. Within the confines of globalization which is neither homogenization nor convergence; but the beginning of drawing on the strength of indigenous capabilities to create knowledge ecology that would enliven socio-economic and scientific development of the world in far reaching interconnectivities and relationships across every clime, issues pertaining to language development, acquisition and use become imperative. It is against this background that this paper examines the development/standardization, acquisition and use of Esan language as one of several Nigerian indigenous languages to promote scientific and technological development through knowledge creation, preservation and dissemination. The obsession of using western model and epistemological outlook to achieve appropriate scientific and technological development to the utter neglect of home grown and culturally distilled efforts were also examined. In the light of the dare consequences and the harsh conditions globalization imposes on developing nations, this paper highlights standardization and use of indigenous languages in chatting appropriate trajectory for effectual developmental efforts in fast shrinking contemporary world.

Non-typhoid salmonellosis is a common and problematic foodborne zoonotic disease in which pork and pork products can be an important potential source of infection. In order to prevent this disease important efforts to monitor the situation in the main source, livestock, are conducted in most developed countries. In the European Union EFSA and ECDC compile information at the member state level, even though important differences in production systems and surveillance systems exist. Here, Salmonella surveillance systems in one of the main sources of foodborne salmonellosis, swine, and humans in Spain were reviewed to identify potential gaps and discuss potential ways of integration under a One Health approach. Despite the extensive information generated through the surveillance activities source attribution can be only routinely performed through ad-hoc outbreak investigations, and national reports on human outbreaks do not provide sufficiently detailed information to gain a better understanding of the epidemiology of the pathogen. Human and animal monitoring of Salmonella would benefit from a better exchange of information and collaboration. Analysis of spatio-temporal trends in livestock and humans could help to identify likely sources of infection and to target surveillance efforts in areas with higher prevalence or where specific strains are found.

Landscapes evolve due to climatic conditions, tectonic activity, geological features, biological activity, and sedimentary dynamics. These processes link geological processes at depth to surface features. Consequently, the study of landscapes can reveal essential information about the geochemical footprint of ore deposits at depth. Advances in satellite imaging and computing power have enabled the creation of large geospatial datasets, the sheer size of which necessitates automated processing. We describe a methodology to enable the automated mapping of landscape pattern domains using machine learning (ML) algorithms. From a freely available Digital Elevation Model, derived data, and sample landclass boundaries provided by domain experts, our algorithm produces a dense map of the model region in Western Australia. Both random forest and support vector machine classification achieve about 98\% classification accuracy with reasonable runtime of 48 minutes on a single core. We discuss computational resources and study the effect of grid resolution. Larger tiles result in a more contiguous map, while smaller tiles result in a more detailed, and at some point, noisy map. Diversity and distribution of landscapes mapped in this study support previous results. In addition, our results are consistent with the geological trends and main basement features in the region.

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

Objective: To carry out a bibliometric analysis study of systematic reviews and meta-analyses in dentistry, divided into different thematic areas. Material and method: A search was conducted in the Science Citation Index-Expanded on the core collection of Web of Science, they were selected from the area of Dentistry and Oral Surgery and Medicine categories, the data was downloaded on April 20, 2022 and The 100 most cited articles from each of the thematic areas were selected. Results: An increase in this type of articles was observed in the last decade in the areas of pediatric and medical dentistry and oral pathology. The thematic area that received the most citations was periodontics. The two authors with the most citations are Zwahlen, Marcel and Pjetursson, Bjarni Eluar. The countries that receive the most citations are European, along with the USA and China. The topics that are most published in this type of article deal with Cancer with 50 publications, caries treatment with 25 and fluoridation with 1. The entities that finance this type of articles the most are private companies (26.76%). Conclusions: Together with an exponential increase in the number of publications in dentistry, there has been an increase in the number of publications in systematic reviews. The areas publishing the most articles and having the most citations are Periodontics and Implantology, despite the fact that the most studied topic is cancer.

This review presents an updated overview of research concerning Leishmania protein structures, primarily sourced from the Protein Data Bank (PDB), that play a role in the metabolic pathways of the Leishmania parasite. Furthermore, we assess the current progress in the identification and development of bioactive chemical agents aimed at addressing this substantially overlooked tropical disease. We have analyzed experimental data obtained from in vitro, in vivo and in silico sources. This data has been categorized into four main areas: a) vector taxonomy and geographic distribution; b) parasite taxonomy and geographic distribution; c) enzymatic functions of proteins engaged in parasite/host interactions throughout various developmental stages (such as oxidoreductases, transferases, hydrolases, lyases, isomerases, ligases, and cytokines); and d) established and experimental treatments employing bioactive chemical compounds. Our objective is to establish a foundational point of reference for research efforts concentrating on the elucidation of interaction mechanisms and the processes of ligand-protein activation/inactivation, specifically linked to Leishmania infections. Consequently, we emphasize enzymes recognized for their involvement in the biochemical pathways incited during Leishmania infection episodes. This review encapsulates the current understanding, offering insights to inform and direct future explorations aimed at targeting proteins and pathways to enhance the management of Leishmania-related diseases.

In the present work we propose a dynamical mathematical model of the lung cells inflammation process in response to SARS-CoV-2 infection. In this scenario the main protease Nsp5 enhances the inflammatory process, increasing the levels of NF kB, IL-6, Cox2, and PGE2 with respect to a reference state without the virus. In presence of the virus the translation rates of NF kB and IkB arise to a high constant value, and when the translation rate of IL-6 also increases above the threshold value of 7 pg mL^-1 s^-1 the model predicts a persistent over stimulated immune state with high levels of the cytokine IL-6. Our model shows how such over stimulated immune state becomes autonomous of the signals from other immune cells such as macrophages and lymphocytes, and does not shut down by itself. We also show that in the context of the dynamical model presented here, Dexamethasone or Nimesulide have little effect on such inflammation state of the infected lung cell, and the only form to suppress it is with the inhibition of the activity of the viral protein Nsp5.To that end, our model suggest that drugs like Saquinavir may be useful. In this form, our model suggests that Nsp5 is effectively a central node underlying the severe acute lung inflammation during SARS-CoV-2 infection. The persistent production of IL-6 by lung cells can be one of the causes of the cytokine storm observed in critical patients with COVID19. Nsp5 seems to be the switch to start inflammation, the consequent overproduction of the ACE2 receptor, and an important underlying cause of the most severe cases of COVID19.

Player’s performance in an intense sport such as basketball is known to be related to attributes like speed, agility, and power. This study presents a comparative analysis of associations between anthropometric assessment and physical performance in different age-group elite youth basketball players, while simultaneously identifying the predictors for speed and agility in these players. U14 (n=44), U15 (n=45) and U16 (n=51) players were tested for anthropometry, lower body power, speed, and agility. U16 players were found to be taller, heavier, more muscular than U14 and U15 players. Also, the U16 group showed better performance in all performance tests. Age had a significant positive correlation with countermovement (CMJ) and drop jump (DJ) performance in U14 players, and a significant negative correlation with 10m and 20m sprint times in U15 group. CMJ and DJ emerged as the most significant predictors for sprint and agility variables, respectively. Body fat percentage was found to be a significant predictor for the speed and agility tests in all age groups, but a negative lower-body power predictor. Therefore, besides all sport-specific and fitness tests, it is essential to place emphasis on the percentage of body fat when designing players’ individualized training programs, and during team selection.

Background: As noninvasive prenatal screening usage grows in the general obstetrics setting, proper patient education on the screen’s benefits and limitations is needed. Objective: Describe the use of a technology platform designed for large-scale dissemination of noninvasive prenatal screening information and results. Study Design: The technology platform functioned as follows: Patients were emailed a link to an noninvasive prenatal screening general-education video upon laboratory receipt of a test requisition. Providers were then notified upon availability of patients’ results. If noninvasive prenatal screening results were negative, the patient was sent an automated email with instructions to access results through a secure portal where she could watch tailored informational videos, request “on-demand” or scheduled genetic counseling, or decline any further services. If genetic counseling was elected, a summary of the session was sent to the ordering provider and patient upon completion. If noninvasive prenatal screening results were positive, either the ordering provider or a board-certified genetic counselor contacted the patient directly to communicate test results and provide counseling. The number and type of results issued through the platform, the number and type of genetic counseling consultations completed, and factors associated with requesting laboratory-delivered genetic counseling were tracked and analyzed for a 39-month period. Results: Over the study period, 67,122 noninvasive prenatal screening results were issued through the platform, and 4,673 patients elected genetic counseling consultations; 95.2% (n=4,450) of consultations were for patients receiving negative results. Over 70% (n= 3,370) of consultations were on-demand rather than scheduled. Median consultation time was 14 minutes for positive results and six minutes for negative results. A positive screen, advanced maternal age, family history, previous history of a pregnancy with a chromosomal abnormality, and other high-risk pregnancy were associated with the greatest odds of electing laboratory-delivered genetic counseling. Conclusions: By combining web education, automated notifications, and genetic counseling, we implemented a service that effectively facilitates results disclosure for ordering providers. These data demonstrate the capability to deliver noninvasive prenatal screening results, education, and counseling—congruent with management guidelines—to a large population, which is imperative to quality care as uptake increases.

Background: The Ibizan Hound is a canine breed native to the Mediterranean region, where leishmaniasis is an endemic zoonosis. Several studies indicate a low prevalence of this disease in dogs, whereas other canine breeds present a high prevalence. However, the molecular underlaying mechanisms yet remains unknown. Methods: In this study, we analyze the haplotypes of genes encode cytokines related to immune response of Leishmania infantum infection in twenty-four Boxer and twenty-four Ibizan hound apparently healthy using CanineHD DNA Analysis BeadChip including 165,480 mapped positions. Results: The results show that several haplotypes of genes encoding Interleukin 6 (IL6) and Interferon gamma (IFNG) are related to Interferon gamma (IFN-γ), and Interleukins (IL) 2 and 18 serum levels. Our results indicate that the regulation of immune response is different in the two canine breeds analyzed and are related to the haplotype compositions of the genes encoding these cytokines. Conclusions: Future studies are needed to elucidate whether these differences and haplotypes are related to different phenotypes in immune response and expression gene regulation to L. infantum infection in dogs.

The latest World Atlas of Desertification represents a turning point in the diagnosis of desertification. While it forgoes desertification mapping due to the intrinsic complexity of the phenomenon and the impossibility of measuring it using a single indicator, it introduces the Convergence of Evidence paradigm, which identifies socioeconomic and biophysical variables whose behavior allows pointing out those areas prone to desertification. The Spanish National Action Program Against Desertification (PAND), back to 2008, already implemented a similar approach to identify five “desertification landscapes” within Spain using both socio-economic and climatic information. The PAND was not only pioneering but also, unfortunately, accurate. Desertification in Spain has continued to worsen and the first two decades of the 21st century have consolidated an agri-food model whose dynamics have exacerbated the desertification processes identified in the PAND. Despite its scientific value, the PAND lacked a proper action plan and was completely detached from the diagnosis. As a result the diagnosis it provided was not followed by effective actions to halt desertification in Spain. The Spanish Government's recent declaration of climate and environmental emergency requires a new strategy to combat desertification. This commitment is an excellent opportunity to update the diagnosis of the situation and, more crucially, to unify the different desertification sectoral policies and actions under a single front. We provide here elements for a roadmap to be designed around the pressures, impacts, and drivers that define today's Spanish desertification landscapes to manage them effectively and avoid their further degradation.

This paper presents performed Ansys numerical simulation on WindFloat structures. It contributes to the methodology of calculation based on CFD instruments to study the movements of the waves on semi-submersible stationary structures and highlights the importance of using RAO functions. RAO numerical approach developed can be applied to design semisubmersible WindFloat structures in order to fulfill essential requirements regarding operational safety and design. Numerical methodology was designed in accordance marine loads and the I.T.T.C. recommendations. The numerical RAO results for regular wave movements include many elements for the semi-submergible structure. Close results to open sea operation can be achieved by constant improvement in numerical simulation methodologies. The work opens the way for further hydrodynamic development on irregular waves for realistic hydrodynamic and structural response of the actual semi-submersibles at sea. The results demonstrate the accuracy of RAO function approach for specific WindFloat in Jonswap spectrum.

At present, business strategies in SMEs (Small and medium enterprises) are crucial for consolidation in highly competitive markets, in achieving a better image and in business profitability. One of the strategies that have the most success and business success are sustainable practices and social responsibility such as: ISO 14001 and ISO 26001. The literature related to sustainable business is based mainly on the theory of resources and capabilities, and in theory based on Stakeholders. These currents state that companies should focus on profitable strategies to ensure significant and long-term results, in order to achieve organizational and financial results for stakeholders. In this work, the sample consists of 215 companies from the commerce, services and industry sectors, located in the southern region of the State of Sonora in Mexico. The objective of the work is to analyze the influence of ISO 14001 and 26001 standards on the image and profitability of SMEs. The statistical analysis of the data has been carried out through the linear regression technique by OLS (Ordinary Least Squares). The findings prove that the ISO 14001 standard is the one that most influences the improvement of the business image and the level of profitability of the SME. In addition, we discovered that ISO 26001 has a partial influence on the image and profitability of the SME.

High efficiency and safety are critical factors in ensuring optimal performance and reliability of systems and equipment across various industries. Fault Monitoring (FM) techniques play a pivotal role in this regard by continuously monitoring system performance and identifying the presence of faults or abnormalities. However, traditional FM methods face limitations in fully capturing the complex interactions within a system and providing real-time monitoring capabilities. To overcome these challenges, Digital Twin (DT) technology has emerged as a promising solution to enhance existing FM practices. By creating a virtual replica or digital copy of a physical equipment or system, DT offers the potential to revolutionize fault monitoring approaches. This paper aims to explore and discuss the diverse range of predictive methods utilized in DT and their implementations in FM across industries. Furthermore, it will showcase successful implementations of DT in FM across a wide array of industries, including manufacturing, energy, transportation, and healthcare The utilization of DT in FM enables a comprehensive understanding of system behavior and performance by leveraging real-time data, advanced analytics, and machine learning algorithms. By integrating physical and virtual components, DT facilitates the monitoring and prediction of faults, providing valuable insights into the system’s health and enabling proactive maintenance and decision-making.

This work continues the review and illustrative application to energy systems of the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” (4th-BERRU-PM) methodology. The 4th-BERRU-PM methodology uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, in-cluding fourth (and higher) order sensitivities of computed model responses with respect to model parameters. The 4th-BERRU-PM methodology yields the fourth-order MaxEnt posterior distribution of experimentally measured and computed model responses and parameters, in the combined phase-space of model responses and parameters. The 4th-BERRU-PM methodology encompasses fourth-order sensitivity analysis (SA) and uncertainty quantification (UQ), which were reviewed in the accompanying work (Part 1), as well as fourth-order data assimilation (DA) and model calibration (MC) capabilities, which will be reviewed and illustrated in this work (Part 2). The applicability of the 4th-BERRU-PM methodology to energy systems is illustrated by using the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark, which is modeled using the linear neutron transport Boltzmann equation, involving 21976 im-precisely known parameters. This benchmark is representative of “large-scale computations” such as those involved in the modeling of energy systems. The result (“response”) of interest for the PERP benchmark is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. The impact of the high-order sensitivities of the response with respect to the PERP model parameters is quantified for “high-precision” parameters (2% standard deviations) and “medium-precision” parameters (5% standard deviations). Analyzing the best-estimate results with reduced uncertainties for the 1st- through 4th-order moments (mean values, covariance, skewness, and kurtosis) produced by the 4th-BERRU-PM methodology for the PERP benchmark indicates that, even for systems mod-eled by linear equations (e.g., the PERP benchmark), retaining only first-order sensitivities is insufficient for reliable predictive modeling (including SA, UQ, DA, MC). At least second-order sensitivities should be retained in order to obtain reliable predictions.

: BACKGROUND: The prevalence of smoking among students is a growing concern, particular-ly among medical students. Being associated with stress, competitiveness, and various social in-fluences, smoking can have detrimental effects on self-esteem and self-efficacy and may create a vicious cycle that may contribute to students’ well-being and academic performance out-comes.METHODS: A cross-sectional study on 456 medical students was conducted via an online survey using structured questionnaires on socio-demographic characteristics, smoking habits, nicotine dependence, self-esteem, and self-efficacy. RESULTS: The prevalence of smoking was 31.1% The scores of self-esteem and self-efficacy were positively correlated with smoking habits (W1=0.957, W2=0.975, P<0.05). Students presented rather low smoking addiction with male stu-dents having slightly higher scores than female students on self-esteem and self-efficacy, mili-tary students had higher self-esteem scores than civilians with physical activity having a posi-tive correlation with non-smoking behavior. CONCLUSION: According to our results, self-esteem and self-efficacy were slightly positively correlated with smoking habits. Furthermore, there was no statistically significant association between other social factors and smoking. Our results may develop a theoretical basis for medical students’ psychological variables studying for further implementation of university educational programs and preventive interventions em-powering students towards an overall better quality of life, addressing, among others, smoking behaviors.

We conducted this systematic review with the objective to understand the efficacy and safety of proton pump inhibitors [PPIs] in the pediatric population. We have used PubMed to identify randomized controlled trials [RCTs] published between 1st June 2010 and 30th June 2023, in patients from birth to 18 years old, with gastroesophageal reflux disease [GERD], who received treatment with any PPI. The literature yielded 76 articles and 13 met inclusion criteria. For infants, PPIs were equal to placebo in reducing GERD symptoms in 4 articles. In 1, the number of GER episodes and esophageal acid exposure were lower in infants who received PPIs and left lateral position, but there was no significant improvement in symptoms. In another publication, the combination of PPIs and feeding modifications [FM] was not more effective than PPIs alone. For children and adolescents, PPIs were effective in improving symptoms and achieving mucosal and histological healing, which was subsequently maintained. To conclude, PPIs are not effective to reduce symptoms related to GERD in infants, but are effective in older children, along with histological remission. Generally, PPIs are well tolerated, but it is important to not forget the possible adverse events [AE], especially if they are used for a long time.

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

In this study, a critical review was carried out using the Web of ScienceTM Core Collection database to analyse the scientific literature published to date to identify lines of research and future perspectives on the presence of chemical pollutants in beer brewing. Beer is one of the world's most popular drinks and the most consumed alcoholic beverage. However, a widespread challenge with potential implications for human and animal health is the presence of physical, chemical and/or microbiological contaminants in beer. Biogenic amines, heavy metals, mycotoxins, nitrosamines, pesticides, acrylamide, phthalates, bisphenols, microplastics and, to a lesser extent, hydrocarbons (aliphatic chlorinated and polycyclic aromatic), carbonyls, furan-derivatives, polychlorinated biphenyls and trihalomethanes are the main chemical pollutants found during the beer brewing process. Pollution sources include raw materials, technological process steps, the brewery environment and packaging materials. Different chemical pollutants have been found during the beer brewing process, from barley to beer. Brewing steps such as steeping, kilning, mashing, boiling, fermentation and clarification are critical in reducing the levels of many of these pollutants. As a result, their residual levels are usually below the maximum levels allowed by international regulations.

The concept of “hyperferritinemic syndrome” defines a complex pathophysiological and clinical entity, characterized by a systemic hyperinflammatory state, generally associated with elevated levels of proinflammatory cytokines (hypercytokinemia) and high serum levels of ferritin (hyperferritinemia), traditionally recognized as an iron-binding plasma protein involved in the storage of iron in a biologically available form for different physiological cellular processes. Recent studies have shown that ferritin has other important functions as well, operating both as a proinflammatory mediator and as a immunosupressive agent, its elevated levels in the serum being detected in various conditions associated with inflammatory states, such as infectious diseases, autoimmune and autoinflammatory disorders and, in some cases, in malignancies. Catastrophic antiphospholipid syndrome (cAPS), systemic juvenile idiopathic arthritis (sJIA) and adult-onset Still’s disease (AOSD) and acquired forms of hemophagocytic lymphohistiocytosis (HLH), which includes macrophage activation syndrome (MAS), have been associated with hyperinflammatory states and dramatic elevations of serum ferritin, generating hyperferritinemic syndromes. Severe forms of coronavirus disease-2019 (COVID-19), in which MAS may frequently develop, has also been linked to the development of a hyperferritinemic syndrome. This review offers several insights into the physiological and pathophysiological roles of ferritin in several (hyper-)inflammatory diseases, but also into the causal entities, the underlying pathophysiological mechanisms, clinical manifestations and diagnostic features of somewhat obscure, yet potentially fatal pathological conditions, reunited under the concept of hyperferritinemic syndromes.

Projected future sea level rise and marine storminess is a serious threat for beaches as they induce beach flooding and erosion. Among other factors, wave runup play an important role in beach evolution and must be robustly assessed. However, little attention has been paid to the uncertainties associated to its characterization and how do they compare to other sources of uncertainty. As a first step towards this goal, we have quantified the impact of several sources of error in the estimation of wave runup on sandy beaches. Understanding what factors are more influential in the quality of the results will help to determine the main sources of uncertainty in beach flooding projections. To reach that goal, a calibrated state-of-the-art numerical modelling system has been setup for two beaches in the Mallorca islands (NW Mediterranean). The system has been forced with the best available information of nearshore incoming waves and has been validated against observations to define the benchmark quality. To determine the key factors affecting the quality of the system’s results, different systems configurations have been tested with different degrees of complexity.Our results show that using the most sophisticated modelling system with the best information on boundary conditions, bottom bathymetry, and submerged vegetation leads to a swash RMSE comparable to the standard deviation of the observed swash. We have also found that the choice of lateral boundary conditions (i.e., source of information for the incoming waves) can double the RMSE and induce large biases. Our results also show that using a simple empirical approach usually underestimates the wave runup. However, in locations with vegetated seabed there is a compensation error, and the empirical approach can lead to acceptable results if forced by nearshore waves. In addition, we have compared the error estimates obtained with uncertainties associated to projected sea level rise. Our findings suggest that the uncertainty associated with wave runup modelling should be considered in the assessment of the total uncertainty of future beach flooding, since the analysis performed indicates that this uncertainty can account for between 16% to 60% of the uncertainties linked to mean sea level projections.

This paper proposes a definition of fractional line integral, generalising the concept of fractional definite integral. The proposal replicates the properties of the classic definite integral, namely the fundamental theorem of integral calculus. It is based on the concept of fractional anti-derivative used to generalise the Barrow formula. To define the fractional line integrals the Gr\"unwald-Letnikov and Liouville directional derivatives are introduced and their properties described. The integral is defined first for broken line paths and afterwards to any regular curve

Photogrammetry involves aerial photography of the earth’s surface and subsequently processing the images to provide a more accurate depiction of the area (Orthophotography). It’s used by the Spanish Instituto Geográfico Nacional to update road cartography but requires a significant amount of manual labor due to the need to perform visual inspection of all tiled images. Deep Learning techniques (artificial neural networks with more than one hidden layer) can perform road detection but it is still unclear how to find the optimal network architecture. Our system applies grammar guided genetic programming to the search of deep neural network architectures. In this kind of evolutive algorithm all the population individuals (here candidate network architectures) are constrained to rules specified by a grammar that defines valid and useful structural patterns to guide the search process. Grammar used includes well-known complex structures (e.g. Inception-like modules) combined with a custom designed mutation operator (dynamically links the mutation probability to structural diversity). Pilot results show that the system is able to design models for road detection that obtain test accuracies similar to that reached by state of the art models when evaluated over a dataset from the Spanish National Aerial Orthophotography Plan.

The quantification of sea surface temperature (SST) through space platforms has revolutionized how we obtain information at a global level. However, the main disadvantage of obtaining SST with satellite images consists of its inherent coarse spatial resolution. One solution could be the use of downscaling algorithms to create sequences of matrices at a higher resolution. We used the same SST data source from the MODIS-Aqua sensor at three spatial resolutions of 9 km, 4.5 km, and 1 km in the Gulf of California. Based on an open-source algorithm, the original SST images were downscaled to 4.5 km, 1 km, 500 m, 250 m, and 125 m per pixel scales. Results indicate a strong linear relationship between the original SST-MODIS data and the modeled data for all spatial resolutions. This study demonstrates the feasibility of an open-source downscaling algorithm to enhance the spatial resolution of SST images in a marginal sea.

Infant obesity has become one of the most serious global health challenges of our time. The combined prevalence of overweight and obesity has rapidly increased worldwide during the last two decades, especially in some developing countries where obesity is reaching levels on a par with some industrialized countries, or even higher. This fast growth has occurred especially in countries in the midst of rapid social-economic transitions. Most international comparisons focus on the adult population while analyses focusing on the child population are more limited. Using the methodology developed by Phillips and Sul [1], this paper studies the worldwide evolution of infant body mass index (BMI), overweight and obesity prevalence for a large sample of countries during the period 1975-2016. Our results indicate that the figures for BMI or the prevalence of obesity in different countries do not converge, while the opposite is the case for overweight prevalence in children. Furthermore, there is a non-linear relationship between obesity and income or human capital, indicating that low and middle-income countries require a strong initiative for health policies targeting obesity prevention.

The obtention of simple formulae for the Lebesgue constants arising in the the classical Fourier series approximation is presented. Both even and odd cases are treated enlarging Fejér result. Asymptotic formulae are also obtained.

The duality is one of the most interesting properties of the Laplace and Fourier transforms associated to the integer order derivative. Here, we will generalize it for fractional derivatives and extend the results to the Mellin, Z and discrete-time Fourier transforms. The scale and nabla derivatives are used.

This article analyzes 6 probability distributions from the Generalized Pareto family, with 3, 4 and 5 parameters, with main purpose to identify other distributions from this family with applicability in flood frequency analysis compared to the distribution already used in the literature from this family such as Generalized Pareto Type II and Wakeby. This analysis is part of a larger and more complex research carried out in the Faculty of Hydrotechnics regarding the elaboration of a norm for flood frequency analysis using the linear moments method. In Romania, the standard method of parameter estimation is the method of ordinary moments, thus the transition from this method to the method of linear moments is desired. All the necessary elements for the distributions use are presented like, the probability density functions, the complementary cumulative distribution functions, the quantile functions, the exact and approximate relations for estimating parameters, for both methods of parameters estimation. All these elements are necessary for a proper transition between the two methods, especially since the use of the method of ordinary moments is done by choosing the skewness of the observed data depending on the origin of the maximum flows. A flood frequency analysis case study, using annual maximum and annual exceedance series, was carried out for the Prigor river, to numerically present the analyzed distributions. The performance of this distributions is evaluated using relative mean error, relative absolute error and linear moments diagram.

This article presents six probability distributions from the Gamma family with three parameters, for the flood frequency analysis in hydrology. The choice of the Gamma family of statistical dis-tributions was driven by its frequent use in hydrology. In the Faculty of Hydrotechnics, the im-provement of the estimation of maximum flows and including the methodological bases for the realization of a regionalization study with the linear moments method with the corrected pa-rameters was researched, being an element of novelty. The linear moments method is better than MOM because it avoids the choice of skewness depending on the origin of the flows, practiced in Romania. The L-moments method conforms to the current trend for estimating the parameters of statistical distributions. Observed data from hydrometric stations are of relatively short length, so the statistical parameters that characterize them are of a sample that requires correction. The correction of the statistical parameters is proposed, using the method of least squares based on the inverse functions of the statistical distributions expressed with the frequency factor for L-moments. All the necessary elements for their use are presented like, quantile functions, the exact and ap-proximate relations for estimating parameters and frequency factors. A flood frequency analysis case study was carried out for the Ialomita river, to verify the proposed methodology. The per-formance of this distributions is evaluated using Kiling-Gupta and Nash-Sutcliff coefficients.

Estimating the parameters of statistical distributions generally involves solving a system of nonlinear equations or a nonlinear equation, being a technical difficulty in their usual application in hydrology. This article presents improved approximations and, in some cases, new approximations for the estimation with the method of ordinary moments and the method of linear moments, which are useful for the direct calculation of the parameters, because the errors in the approximate estimation are similar to the use of iterative numerical methods. Thirteen statistical distributions of two and three parameters frequently used in hydrology are presented, for which parameter estimation was laborious. Thus, the approximate estimation of the parameters by the two methods is simple but also precise and easily applicable by hydrology researchers. The approximate forms characterized by very small errors are the result of the research conducted within the Faculty of Hydrotechnics to update the romanian normative standards in the hydrotechnical field.

The bilateral tempered fractional derivatives are introduced generalising previous works on the one-sided tempered fractional derivatives and the two-sided fractional derivatives. An analysis of the tempered Riesz potential is done and showed that it cannot be considered as a derivative.

This study investigated the development of Aedes aegypti density in houses of the urban locality of Poblado Miguel Alemán Valdes, in the Sonora state of Mexico, after application of 1% propoxur paint as full wall coverage (IP) and targeted indoor painting (IP 1m) in comparison to IRS with propoxur 70% WP (full wall coverage). The 1% propoxur paint was applied by the homeowners by brushing and rolling at the recommended dose of 1L/8 m², equivalent to 1.5 g a.i./m², while IRS was conducted by professionals with Propoxur 70% WP at a dose of 1 g a.i./m². Adult mosquito surveys were conducted in a random sample of houses in each block one week before the interventions and at week 1, month 1 to 4, month 6, month 9 and month 12 post-interventions. All three propoxur based treatments provided similar reductions (43.7%, 44.9% and 41.3% for IP, IP 1m and IRS respectively) in the fraction of houses positive in female Aedes aegypti resting indoor and outdoor as one year average of 8 follow up surveys. Indoor resting density of Aedes females during the one-year evaluation was reduced by 77.5% through IP followed by IP 1m with 64.2% reduction and 30% reduction with IRS. Culex mosquitoes’ interior density was affected as well by the insecticide treatments with similar average reductions for IP 1m (50.0%) and IRS (57.8%) in comparison with control. Aedes breeding was impacted by the insecticide paint in similar extent for both interventions, expressed by a substantial reduction of the House Index (20.1% IP, 31.2% IP 1m) and especially the Container Index (51.8% IP, 61.7% IP 1m) during the one-year surveys in comparison to control. In contrast, IRS treated block experienced an increase in both indexes. However, despite IP and IP 1m impacted in Aedes adult and immature indexes with noticeable reductions, the differences in all cases were not significative among the different insecticide treatments. The low sample size and mosquito population levels may have influenced the statistical outcomes. More than 80% of the interviewed residents were satisfied with the effectiveness of the paint and IRS treatments. The determination of the blood cholinesterase activity of tested individuals after the use of the carbamate paint and IRS in this study did not exceed acceptable inhibition limits. This study suggests that the application of propoxur paint by homeowners as full house coverage or as targeted indoor painting can be a safe and accepted intervention method for density reduction of Aedes aegypti populations in urban environments.

The two major global immunisation agenda framings (Missed Opportunity for Immunisation, MOI vs Immunisation Defaulting) are interchangeably and inappropriately used in public health research and practice with flawed or misleading strategies recommended and adopted in various settings globally. This is evident in the fact that many opportunities to adopt evidence/findings from immunisation coverage research in policy are grossly missed. Ineffectiveness of inappropriate interventions from biased evidence can discourage and mislead the governance to make radical decisions by discretion. This could be the reason for the inability of low-and middle-income countries to vaccinate 80% of their children and otherwise; this also poses a global health threat to capable nations. The current guideline and information on MOI and immunisation defaulting appear insufficient and a little clarification on it would assist forerunners in immunisation to achieve measurable progress in ensuring good coverage especially in low-and middle-income countries. Consequently, this paper is aimed at addressing this issue in immunisation practice with appropriate recommendations. Optimistically, this will stimulate further discussions, streamline differences, and gear global immunisation governance on the subject matter, to achieve the target coverage by 2030 in low-and middle-income countries.

The purpose of this study is to estimate the mean transitioning probabilities from a Healthy state to malaria positive uncomplicated state or to malaria positive severe state. It also classifies the various transitioning probabilities of moving through the various states based on some baseline characteristics. Malaria test results for 2019 over a 12-month period were collected from the University of Ghana school clinic. An H-U model for the study was developed and the transition rates from the cross-sectional data are indicated. With two states Healthy (H) and Uncomplicated (U) forming a state space, there were four possible transitions. The results show that the probability of transitioning from a Healthy state to a malaria positive state is 0.03% while the probability that an individual will remain at Healthy state (H) after the test is 99.73%. It was found that if an individual is already positive and has taken medication the probability that its second test came out negative is 6.45% while the chances that it will remain positive but uncomplicated is 93.55%. The study also showed that in the long run, about 95.98% of persons who visited the student clinic with malaria symptoms recorded negative tests for malaria parasite while about 4% recorded positive for malaria. In terms of disaggregation by gender, it was realized that the number of reported negative test results were higher for females (97.08%) than for males (96.13%). However, the infection rate is higher for males (3.87%) than females (2.92%). It is recommended that in as much as the University of Ghana has two health centers (a clinic and hospital), there should be a centralized system to track students’ health so research done would not be biased.

In response to the COVID-19 pandemic, and resultant cancellation of geoscience fieldwork, as well as outstanding accessibility issues inherent in conducting fieldwork, we developed a virtual geological fieldtrip (VFT) to the Huronian age deposits in the Whitefish Falls area, Ontario, Canada. This region is a geologically significant site in which many Ontario universities conduct undergraduate teaching due to the high-quality exposures. In this contribution we describe and comment on the development of this openly available resource, the motivations in doing so, the challenges faced, its pedagogical impact and relevance, as well as provide suggestions to others in the development of such resources. Our multimedia VFT combines 360° imagery, georeferenced data on integrated maps, and multi-scale imagery (aerial/drone, outcrop, and thin section images). The VFT was built using the Esri Storymaps platform, and thus offers us the opportunity to review the effectiveness of building such resources using this medium, as well as our approach to doing so. We conclude that the Esri Storymaps platform provides a sound medium for the dissemination of multimedia VFTs, but that some aspects of in-person fieldwork remain hard to replicate. Most notably, ‘hands on experience’ and specific activities such as geological mapping. In addition, while VFTs alleviate some accessibility barriers to geoscience fieldwork, substantial barriers remain that should remain the focus of both pedagogical and geoscience work.

Environmental legislation on waste management coupled with the potential for nutrients recovery are key factors encouraging the use of advanced treatment technologies to manage biosolids waste. In this context, phosphorus recovery from a sewage sludge treated by a wet oxidation process was carried out. High organic matter (up to 85% in COD) and Total Solids content (up to 75%) removal values were achieved at elevated temperature (up to 300 ºC) and pressure (up to 200 bar) conditions. The liquid and solid fractions found in oxidation process effluent contain amounts of phosphorus that can be recovered. This research aims to maximize its valorization in both liquid and solid fractions. In the liquid effluent, phosphorus was recovered (up to 90 mg P L-1) by chemical precipitation as struvite (MgNH4PO4∙6 H2O), a slow-release fertilizer. In this case, P recoveries greater than 95% were achieved. Also, the solid fraction, analyzed after filtration and drying (68 mg P gsolid-1), was treated by acid leaching, to achieve up to 60% phosphorus recovery. All phosphorus extracted was in orthophosphate form.

Background: Vascular inflammation plays a crucial role in peripheral arterial disease (PAD), although the role of the mediators involved has not yet been properly defined. The aim of this work is to investigate gene expression and plasma biomarkers in chronic limb-threating ischemia (CLTI). Methods: Using patients from the GHAS trial, both blood and ischemic muscle samples were obtained to analyze plasma markers and mRNA expression, respectively. Statistical analy-sis was performed by using univariate (Spearman, t-Student, X2) and multivariate (multiple lo-gistic regression) tests. Results: 35 patients were available at baseline (29 for mRNA expression). Baseline characteristics (mean): Age:71.4±12.4 (79.4% male); TNF-α:10.7±4.9; hs-CRP:1.6±2.2; Neutrophil-to-lymphocyte ratio (NLR):3.5±2.8. Plasma TNF-α was found elevated (≥8.1) in 68.6% of patients, while high hs-CRP (≥0.5) in 60.5%. Diabetic patients with high level of inflammation showed significantly higher levels of NOX4 expression at baseline (p=0.0346). Plasma TNF-α had a negative correlation with eNOS expression (-0.5, p=0.015) and hs-CRP with VEGF-A (-0.63, p=0.005). The expression of NOX4 was parallel to that of plasma TNF-α (0.305, p=0.037), especial-ly in DM. Cumulative mortality at 12-month was related to NLR ≥3 (p=0.019) and TNF-α ≥8.1 (p=0.048). The best cut-off point for NLR to predict mortality was 3.4. Conclusions: NOX4 and TNF-α are crucial for the development and complications of lower limb ischemia, especially in DM. hs-CRP could have a negative influence on angiogenesis too. NLR and TNF-α represent suita-ble markers of mortality in CLTI. These results are novel because they connect muscle gene expres-sion and plasma information in patients with advanced PAD, deepening the search of new and ac-curate targets for this condition.

Research rarely explores LGBTQ+ youth bullying in the context of culture-specific outcomes (e.g., LGBTQ+ identity development) and what can mitigate the impact of peer stressors. This study used a concurrent mixed methods design to explore how experiences of peer victimization predicted LGBTQ+ youth’s identity development (i.e., stigma sensitivity, concealment motivation, and difficult process) and whether social support and outness served as protective, moderating factors. The mixed-methods approach provides a culture-specific context via qualitative inquiry to inform whether the quantitative findings align with how youth qualitatively discuss their experience of bullying, negative outcomes, and social support. Our sample consisted of 349 LGBTQ+ youth 14-17 years old who completed a survey (quantitative sample), and a subset of 39 LGBTQ+ youth who completed a semi-structured interview (qualitative sample). Our quantitative findings indicated that greater overall peer victimization was positively related to LGBIS-revised subscales of stigma sensitivity, concealment motivation, and difficult process, where both outness and social support moderated such relations. Qualitatively, victimized youth also reported stigma sensitivity and concealment motivation, while also endorsing how being out and having a support system played a role in their experience of being victimized. These qualitative findings align with our quantitative findings that classmate support mitigated the effects of peer victimization on difficulty of coming out. Implications for practitioners and researchers are provided.

Background: Proteins targeted by the Ubiquitin Proteasome System (UPS) are identified for degradation by the proteasome, which has been implicated in the development of neuro-degenerative diseases. Major histocompatibility complex (MHC) molecules present peptides broken down by the proteasome and are involved in neuronal plasticity, regulating synapse number and axon regeneration in the central or peripheral nervous system during develop-ment and in brain diseases. The mechanisms governing these effects are mostly unknown, but evidence from different compartments of the cerebral cortex indicates the presence of im-mune-like MHC receptors in the central nervous system. Methods: We have used human induced pluripotent stem cells (iPSC) differentiated to neural stem cells and then to motor neurons as a developmental model to better understand the structure of the proteasome in developing motor neurons. We perform a proteomic analysis of starting human skin fibroblasts, their matching iPSC, differentiated neural stem cells and motor neurons that highlighted significant differences in the constitutive proteasome and immunoproteasome subunits during development towards motor neurons from iPSC. Results: Proteomic analysis showed that the catalytic proteasome subunits expressed in fi-broblasts differ to those in neural stem cells and motor neurons. Western blot analysis con-firmed the proteomic data, particularly the decreased expression of Beta5i (PSMB8) subunit immunoproteasome. Conclusion: The constitutive proteasome subunits are upregulated in iPSC from HFF and the immunoproteasome subunit beta 5i expression is higher in MN than NSC suggesting a im-munoproteasome phenotype in MN. The immunoproteasome may have implications on motor neuron development and neurodevelopmental diseases that warrants further investi-gation.

Coronaviruses are pathogens recognized for having an animal origin, commonly associated with terrestrial environments. However, although in a few cases, there are reports of their presence in aquatic organisms like fish, frogs, waterfowls and marine mammals. None of these cases has led to human health effects when contact with these infected organisms has taken place, whether they are alive or dead. Aquatic birds seem to be the main group carrying and circulating these types of viruses among healthy bird populations. Although the route of infection for CoVID-19 by water or aquatic organisms has not yet been observed in the wild, the relevance of its study is highlighted because there are cases of other viral infections known to have been transferred to humans by aquatic biota. It is encouraging to know that aquatic species, such as fish, marine mammals, and amphibians, shows very few cases of coronaviruses and that some other aquatic animals may also be a possible source of cure or treatment against then, as some evidence with algae and marine sponges suggest.

We study in this paper the time evolution of stock markets using a statistical physics approach. We consider an ensemble of agents who sell or buy a good according to several factors acting on them: the majority of the neighbors, the market ambiance, the variation of the price and some specific measure applied at a given time. Each agent is represented by a spin having a number of discrete states q or continuous states, describing the tendency of the agent for buying or selling. The market ambiance is represented by a parameter T which plays the role of the temperature in physics: low T corresponds to a calm market, high T to a turbulent one. We show that there is a critical value of T, say T_c, where strong fluctuations between individual states lead to a disordered situation in which there is no majority: the numbers of sellers and buyers are equal, namely the market clearing. The specific measure, by the government or by economic organisms, is parameterized by $H$ applied on the market at the time t₁ and removed at the time t₂. We have used Monte Carlo simulations to study the time evolution of the price as functions of those parameters. In particular we show that the price strongly fluctuates near T_c and there exists a critical value H_c above which the boosting effect remains after H is removed. Our model replicates the stylized facts in finance (time-independent price variation), volatility clustering (time-dependent dynamics) and persistent effect of a temporary shock. The second party of the paper deals with the price variation using a time-dependent mean-field theory. By supposing that the sellers and the buyers belong to two distinct communities with their characteristics different in both intra-group and inter-group interactions, we find the price oscillation with time. Results are shown and discussed.

Since 2017 Cartagena UNESCO World Heritage has threatened to be categorized as "in Danger" by UNESCO. This research analyzes two main critical aspects, Governance and Current state of the Site. Regarding Governance, the study aims to identify strengths, weaknesses, opportunities and treats in the UNESCO Site. Exemplary heritage management systems are studied to propose an adaptable management approach specifically for Cartagena. On the other hand, a comprehensive analysis is conducted utilizing a photographic report that highlights the major issues arising from inadequate heritage management. Hyperspectral images obtained from a previous study are employed to identify vegetation and asbestos-cement roofs within the cultural heritage Site. The study reveals that the ambiguity surrounding decision-making authority for heritage management, distributed between the Mayor's Office and the Ministry of Culture, is a primary challenge. This fragmentation has resulted in duplicated efforts and a lack of coordinated action, significantly compromising the conservation and protection of the cultural heritage Site. Moreover, twelve current shortcomings of cultural heritage in Cartagena are identified through authors' five-year on-site regular visits, photographic reports and observation. To address the prevailing concerns, a new line of command for cultural heritage management is proposed as the most effective means of tackling these challenges. Additionally, general recommendations are presented to mitigate the existing problems and prevent the classification of Cartagena's heritage as "at risk" by UNESCO in the near future. This research provides a scientific perspective, drawing upon years of experience studying heritage and residing in the city, devoid of political influences or conflicts of interest.

N-(diisopropylphosphanyl)benzamide, PhC(O)NHPiPr2, has been synthesized in good yield following two alternative procedures that employ benzamide as the starting material. The first one is a two-step preparation, in which N-(trimetilsilyl)benzamide is reacted with PiPr2Cl, to give the title compound in good yield, whereas the second one is a straightforward synthesis which converts benzamide into N-(diisopropylphosphanyl)benzamide by reaction with PiPr2Cl in the presence of N,N-dimethylpyridin-4-amine (DMAP) and triethylamine. NMR spectroscopy and X-ray diffraction analyses have been performed to characterize the new compound and elucidate its molecular structure in the solid state. N-(diisopropylphosphanyl)benzamide adds to the limited family of amido-substituted phosphines, RC(O)NHPR’2, which can be classified as bidentate hybrid P,O-ligands, both in their neutral and anionic forms, the latter achievable by deprotonation of the NH group.

Natural carotenoids are secondary metabolites that exhibit antioxidant, anti-inflammatory and anti-cancer properties. These types of compounds are in high demand by pharmaceutical, cosmetic, textile and food industries, leading to the search for new natural sources of carotenoids. In recent years, the production of carotenoids from bacteria has become of great interest for industrial applications. In addition to carotenoids with C40-skeletons, some bacteria have the ability to synthesize characteristic carotenoids with C30-skeletons. In this regard, a great variety of methodologies for the extraction and identification of bacterial carotenoids has been reported and this is the first review that condenses much of this information. To understand the diversity of these carotenoids, we present their biosynthetic origin in order to focus on the methodologies employed in their extraction and characterization. Special emphasis has been made on high-performance liquid chromatography-mass spectrometry (HPLC-MS) for the analysis and identification of bacterial carotenoids. We end up this review showing their potential commercial use of bacterial carotenoids. This review is proposed as a guide for the identification of these metabolites, which are frequently reported in new bacteria strains.

(1) Background: the use of force by Public and Private Security Forces is currently an issue of great relevance because of the potential injuries that any excessive use of force by either the active or passive subjects or a deficit in the real mastery of the appropriate Physical Intervention Techniques (PIT) may cause. For this reason, certain traditionally used Physical Intervention Techniques have been questioned by scientific research studies and punished by justice. On the other hand, certain media have dealt with this matter in a biased and unfair manner by broadcasting videos where the use of force by police officer is displayed out of context. As a consequence, this problem has been brought under the spotlight, causing general uneasiness of the communities and rapidly spreading over social networks while favoring all sorts of parallel judgments. (2) Research method: A suit equipped with 19 Inertial Measurement Units (IMU) and a Biomechanics of Bodies software application for the Marras’ analysis of the data collected on trajectory, trunk twisting velocity, its sagittal angle, load, nature and severity of the injuries associated to the different intervention techniques that have been examined. (3) Results: according to the data registered, the implementation of Operational Tactical Procedures (OTP) reduces the probability of injuries and leads to a more satisfactory outcome. (4) Conclusions: the implementation of Operational Tactical Procedures, to-gether with the awareness on the risks associated to the excessive use of force by Public and Private Security Forces and Bodies, could reduce the risk of injuries suffered by both officers and citizens.

: The performance of the students in academics depends on many different factors. One of these factors is the economic status of their families. Education in public schools, though it is free, some parents cannot afford to send their children to school due to financial constraints. Education as a free is every individual's right in society, but it becomes a privilege because of a lack of financial support. This research work is a comparative study of students' or learners' performance in mathematics among students from working and non-working. This research article's ultimate objective is to investigate and compare a significant difference in their performance in the subject mathematics. The research methodology utilizes a t-test to analyze and synthesize primary data sources collected via interview and academic records, and diverse literature on the study area. However, the research findings revealed no significant difference in the two groups (the working and non-working group of learners). Therefore, it is recommended that secondary schools should be provided with adequate books and facilities and faculty school interventions and programs and promote the usage of SIM or Strategic Intervention Materials for learning and memory enhancement among learners.

Psychotherapy is one of the evidence-based clinical interventions for the treatment of depression in older adults with dementia. Randomized Controlled Trials are often the first methodological choice to gain evidence, yet they are not applicable to a wide range of humanistic psychotherapies. Amongst all, the efficacy of the Gestalt therapy (GT) is under-investigated. The purpose of this paper is to present a research protocol aiming to assess the effects of a GT-based intervention on people with dementia (PWD) and the indirect influence on their family carers. The study implements the Single-Case Experimental Design with Time-Series Analysis that will be carried out in Italy and Mexico. Ten people in each country, who received a diagnosis of dementia and present depressive symptoms, will be recruited. Eight or more GT sessions will be provided whose fidelity will be assessed by the GT Fidelity Scale. Quantitative outcome measures are foreseen for monitoring participants’ depression, anxiety, quality of life, carers’ burden, and the caregiving dyad mutuality, at baseline and follow-up. The advantages and limitations of the research design are considered. If GT will result effective in the treatment of depression in PWD, it could enrich the range of evidence-based interventions provided by healthcare services.

In recent years there has been increasing interest in the discovery of new natural herbal remedies for the treatment of common conditions such as diabetes and inflammatory diseases. In this context, this paper analyzed the antidiabetic and anti-inflammatory potential of two plants of the genus Artemisia: Artemisia absinthium (wormwood) and Artemisia vulgaris (mugwort) and Trigonella foenum-graecum (fenugreek) plants less studied from this point of view. Thus, hydroal-coholic extracts (50% EtOH v/v, 10% mass) were made, which were processed by membrane technologies, micro- and ultrafiltration, in order to concentrate the biologically active principles. The extracts were then analyzed in terms of content in active principles of interest (polyphenols, flavones). The antidiabetic activity of the extracts was analyzed by testing their ability to inhibit α-amylase and α-glucosidase and the anti-inflammatory activity by testing the ability to inhibit hyaluronidase (HYA) and lipoxygenase (LOX). Thus, the T. foenum-graecum extracts showed a high inhibitory activity on α-amylase 3.22 ± 0.3 g / mL (compared to the standard used - IC50 acarbose = 3.5 g / mL) and a high inhibitory activity on LOX: 19.69 ± 0.52 g/mL (compared to all standards used). The concentrated extract of Artemisia vulgaris showed increased activity of inhibition of α-amylase - IC50 = 8.57 ± 2.31g/mL and moderate activity of inhibition of HYA - IC50 = 17.18 g/mL compared to ibuprofen, the standard used - IC50 = 5.73 g/mL. The concentrated extract of Artemisia absinthium showed a pronounced activity of inhibition of LOX - IC50 = 19.71 g / mL, compared to the standard - rutin - IC50 = 22.34 g/mL).

Wildfires present a significant risk to societies globally due to population growth, con- centrated activities, climate change, and extreme environmental conditions. To establish effective fire suppression and management policies, it is crucial to determine whether the distance between ignition points and urban areas is increasing or decreasing. This study analyses 101,597 fires in Spain and California between 2007 and 2015, using the Mann-Kendall test to determine trends. The study analyses the location and cause of fires to determine their distance from human constructions over time at two spatial scales. The findings suggest that wildfires are not moving away from buildings and, in some areas, are moving significantly closer.

Implementing the methodology of clinical simulation in the nursing degree course is a necessity in the European framework of higher education to acquire competences. The objectives of this research were to evaluate the strategies and techniques used during the simulations, identify the contents learned, and determine which of them are transferred to the nursing practice. We performed an observational, descriptive, and cross-sectional study from the nursing students’ perspective during the 2020-21-year course. On the one hand, our results show that the DASH scale helped us to obtain an internal validity of the simulations obtaining a mean score of 6.61 out of 7. On the other hand, the Ad Hoc scale, based on the competences were acquired in the simulations were transferred to the care practices. In conclusion, it is possible to improve care practices by integrating knowledge through clinical simulations.

The disruption in healthcare attention to people with alcohol dependence, along with psychological decompensation consequence of lockdown derived from COVID-19 pandemic, could have a negative impact on people who suffers from alcohol abuse disorder. Retrospective observational study of 9,966 men aged ˃16 years registered as having diagnosis of alcohol abuse disorder in the electronic medical records (EMR) of the Aragon Regional Health Service (Spain). Of those who were not infected during the study period (9,576), clinical (Glutamate-oxaloacetate -GOT-, Glutamate pyruvate -GPT-, creatinine, glomerular filtration, systolic blood pressure -SBP-, diastolic blood pressure -DBP-, total cholesterol, LDL, HDL, triglycerides, and body mass index -BMI-), pharmacological (dose per inhabitant per day -DHD- of drugs used in addictive disorders, benzodiazepines and antidepressants) and health resource use variables (primary and specialized care) were considered. Student´s T-test for matched samples was performed. After carrying out the Levene´s test, the Student´s T-test was used to analyze the changes in clinical variables between alcohol abuse disorder patients with and without COVID-19. Only creatinine and LDL show a significant but clinically irrelevant changes just after and 6 months after the end of strict lockdown. The total number of DHDs for all drugs included in the study (except for Benzodiazepines), decreased. In the same way, the use of health services by these patients also decreased. The impact of COVID-19 among this group of patients has been moderate. The reorganization of health and social services after the declaration of the state of alarm in our country, made possible the maintenance of care for this vulnerable patients.

Ralstonia solanacearum is a pathogen that causes bacterial wilt producing severe damage in staple solanaceous crops. Traditional control has low efficacy and/or environmental impact. Recently, the bases of a new biotechnological method by lytic bacteriophages vRsoP-WF2, vRsoP-WM2 and vRsoP-WR2 with specific activity against R. solanacearum were established. However, some aspects remain unknown, such as the survival and maintenance of the lytic activity after submission to a preservation method as the lyophilization. To this end, viability and stability of lyophilized vRsoP-WF2, vRsoP-WM2 and vRsoP-WR2 and their capacity for bacterial wilt biocontrol have been determined against one pathogenic Spanish reference strain of R. solanacearum in susceptible tomato plants in different conditions and making use of various cryoprotectants. The assays carried out have shown satisfactory results with respect to the viability and stability of the bacteriophages after the lyophilization process, maintaining high titres throughout the experimental period, also with respect to the capacity of the bacteriophages for the biological control of bacterial wilt, controlling this disease in more than 50% of the plants. The results offer good prospects for the use of lyophilization as a conservation method for the lytic bacteriophages of R. solanacearum in view of their commercialization as biocontrol agents.

Historically, Physalis physalis massive stranding events have been infrequent and poorly documented. However, their occurrence can have significant impacts on human health and the stability of coastal ecosystems. In this study we analyze the largest P. physalis stranding event historically recorded affected Cuba’s northwestern coast in December 2022. We counted all colonies during the massive event along ~ 3 km coast, within a 5 m stripe. Density, dimorphic form (left/right-handed), and colony size were quantified using 0.25 m2 quadrat placed every 50 m, 10 m from shoreline. Eighty-five people were stung, with 38 having strong allergic reactions, resulting in a health risk. Over ten thousand colonies were recorded, with the highest mean colony density (29.3 per m2) ever reported. The massive stranding coincided with the lowest Arctic Oscillation index (-2.59) in the past 11 years during December, likely linked to climate change. This led to the emergence of northeasterly winds reaching speeds of up to 24 km/h. Wind direction and speed, coupled with the dominance of left-handed colonies (71.4%), suggest the Sargasso Sea as the possible source of the bloom. The high prevalence of juvenile P. physalis colonies (68 %) likely aligns with the autumn breeding season in the northern hemisphere. The potential causes of P. physalis blooms are still poorly understood. Systematic monitoring of the distribution and abundance of this species should be a research priority considering the potential risk to human health and that the blooms could become more frequent and affect other areas in the Atlantic, given climate change and increasing eutrophication of the oceans.

Nowadays, cyber-physical systems (CPSs) are composed of more and more agents and the demand for designers to develop ever larger multi-agent systems is a fact. When the number of agents increases, several challenges related to control or communication problems arise due to the lack of scalability of existing solutions. It is important to develop tools that allow control strategies evaluation of large-scale systems. In this paper, it is considered that a CPS is a heterogeneous robot multi-agent system that cooperatively performs a formation task through a wireless network. The goal of this research is to evaluate the system's performance when the number of agents increases. To this end, two different mixed reality frameworks developed with the open-source tools Gazebo and Webots are used. These frameworks enable combining both real and virtual agents in a realistic scenario allowing scalability experiences. They also reduce the costs required when a significant number of robots operate in a real environment, as experiences can be conducted with a few real robots and a higher number of virtual robots by mimicking the real ones. Currently, the frameworks include several types of robots being the aerial robot Crazyflie 2.1 and differential mobile robots Khepera IV those used in this work. To illustrate the usage and performance of the frameworks, an event-based control strategy for rigid formations varying the number of agents is analyzed. The agents should achieve a formation defined by a set of desired Euclidean distances to their neighbors. To compare the scalability of the system in the two different tools, the following metrics have been used: formation error, CPU usage percentage, and the ratio between the real-time and the simulation time. The results show the feasibility of using Robot Operating System (ROS) 2 in distributed architectures for multi-agent systems in mixed reality experiences regardless of the number of agents and their nature. However, the two tools under study present different behaviors when the number of virtual agents grows in some of the parameters, and such discrepancies are analyzed.

Background: While greenness has been associated with lower depression, the generalizability of this association in arid landscapes remains undetermined. We assessed the association between depression and greenness among nursing students living in El Paso, Texas (the Chihuahuan desert). Methods: Depression was measured with the Patient Health Questionnaire-9 scale, and greenness with the normalized difference vegetation index (at buffer sizes =250m, 500m, 1000m). Using data from the National Land Cover Database two additional measures of land patterns were analyzed: grayness and brownness. Structural equation models were used to assess the relationships of these land patterns to depression and quantify the indirect effects of peer alienation. Results: After adjusting for individual characteristics, at buffers 250 m greenness was associated with a decrease in the Incidence Rate Ratios (IRR) of depression by 49% (IRR, 0.51; 95%CI, 0.12-2.10), greyness with increases by 64% (IRR, 1.64; 95%CI, 1.07-2.52) and brownness with decreases by 35% (IRR, 0.65; 95%CI, 0.42-0.99). At buffer 250 m peer alienation explained 17.43% (95% CI, -1.79-36.66) of the association between depression and brownness, suggesting a pathway to depression. Conclusions: We did not observe an association between depression and residential greenness in El Paso, Texas. However, we did observe a protective association between brownness and depression as well as an adverse association with grayness. These results have theoretical implications as based on commonly used frameworks in this literature and adverse association of brownness (and the lack of greenness) and depression was expected.

Industry 4.0 is in continuous technological growth that benefits all sectors of industry and society in general. This article reviews the concept of Digital Twin (DT) and the interest of its application in photovoltaic installations. A comparison is made on how other authors use the DT approach in photovoltaic installations to improve the efficiency of the renewable energy generated and consumed, the energy prediction and the reduction of the operation and maintenance costs of the photovoltaic installation. It is reviewed how, by providing real-time data and analysis, DTs enable more informed decisions in the solar energy sector. The objectives of the review are to study the digital twin technology and to analyze its application and implementation in photovoltaic systems.

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

Background: In the 2022 National Youth Tobacco Survey disposable e-cigarette devices are shown to be the most widely used e-cigarette devices amongst U.S. youth. In this paper we report the results of research designed to estimate the prevalence of use of BIDI® Stick branded e-cigarettes amongst youth (aged 13 to 17), and under-age young adults (aged 18 to 20) in the U.S. Methods: Cross sectional online survey of a nationally representative sample of 1,215 youth (13 17 years) recruited via the IPSOS probability-based KnowledgePanel and 3,370 young adults aged 18 to 24 - amongst whom 1,125 were aged 18 to 20. Results: Amongst youth aged 13 to 17, 0.91% [95% CI: 0.44-1.68] or 190,000 [95% CI: 90,000-350,000] youth reported having ever used a BIDI® Stick branded product and 0.04% [95% CI: 0.00-0.38] or less than 10,000 youth reported currently using a BIDI® Stick branded product. Amongst those young adults aged 18 to 20, 3.90% [95% CI: 2.49-5.81] or 470,000 [95%CI: 300,000-700,000] reported having ever used a BIDI® Stick product whilst 0.60% [95% CI: 0.17-1.55] or 70,000 [95% CI: 20,000-180,000] reported they now use a BIDI® Stick product “every day” or “some days”. Conclusions: The low prevalence of youth and underage adult current use of the BIDI® Stick e cigarette suggests that this product is not responsible for the recent growth in the use of disposable e-cigarettes by youth within the U.S. as demonstrated by the 2022 National Youth Tobacco Survey.

As the global population grows, the demand for food increases and puts a strain on food production systems and agricultural productivity, causing soil degradation. Soil Quality Indexes (SQIs) have been developed to maintain and improve soil quality. However, due to the variety of soils and SQIs, analyzing and comparing results has been historically difficult. Therefore, in this study, we carried out a systematic review with meta-analysis focused on soil quality studies of agricultural soils under intensive agriculture using the unified weighted additive SQI methodology (SQIU). We analyzed 65 quality observations obtained from 22 studies. Chemical indicators were the most prevalent in the SQIUs, followed by physical and biological indicators. Conventional soil management had negative effects on soil quality (–7.55%). From the factors analyzed, the minimum database had a significant effect on the soil quality results, but not the number of indicators that made up the SQIU. The SQIU made up of chemical-biological indicators (CB) presented negatively overestimated measurements of soil quality (–32.53%), exaggerating the damage to the analyzed soils. The indicators that correlated most strongly with the size of the effect on agricultural soil quality were the cation exchange capacity (CEC), carbon to nitrogen ratio (C/N), and microbial biomass carbon (MBC). The SQIU is a feasible tool to interpret the quality of agricultural soils around the world, as it makes it possible to obtain a simple and generalized view of soil conditions.

The objective of this study were to conduct association mapping for drought tolerance at the seedling stage and yield-related traits. 60 cowpea accessions were used in the study. Single-nucleotide polymorphisms (SNPs) discovered through genotyping by sequencing (GBS) were used for genotyping. Association mapping was conducted using single-marker regression (SMR) in Q Gene, and general linear model (GLM) and mixed linear model (MLM) built in TASSEL. The population of the cowpea accessions were analysed using STRUCTURE 2.3.4 and the peak of delta K in the greenhouse showed seven population types, whereas the peak of delta K in the glasshouse indicated the presence of six population types. One SNP marker, 14083649|F|0-9 was associated with NP with a p value <0.001. Fifty SNP markers were associated with PWT at p <0.001. Four SNP markers, 14074781|F|0-16, 100047392|F|0-36, 14083801|F|0-28 and 100051488|F|0-49 were associated with AVSPD at p <0.001. SNP markers, 14074781|F|0-16, 14083801|F|0-28 and 100051488|F|0-49 were associated with PL at P <0.001. Five SNP markers, 100047392|F|0-36, 14083801|F|0-28, 100072738|F|0-34, 14076881|F|0-49 and 14076881|F|0-49 were associated with PWDTH at p <0.001. The 65 SNP markers identified can be used in cowpea molecular breeding to select for AVSPD, NP, PL, PWDTH, PWT, and RR through marker assisted selection (MAS).

Background A diet rich in red meat, and poor in fruits and vegetables, can hinder the body's ability to detoxify, therefore, an alternative vegetarian diet maintained over a certain period, determines the elimination of excess toxins and accumulated residues, helping the body to regenerate and recharge energy. Following this simple rule, there are a multitude of benefits for our health. Significance of the autophagy process An even better regeneration of the body takes place during the black fast, in which senescent cells, (dead cells that no longer exercise any function, but instead produce inflammation in the body), are eliminated by autophagy, and in their place, the cells are stimulated stem. Aging can be associated with various pathological conditions if autophagy is not promoted by a healthy lifestyle. In elderly people, cells can show DNA mutations and accumulations of protein aggregates that impair the body's physiological processes. Several studies have shown that the activation of autophagy slows down the ageing process. Conclusions Aging, and its associated conditions of illness, are due to genomic instability caused by DNA damage, telomere shortening and epigenome changes that modulate gene expression.