This paper presents an operational approach for detecting floods and establishing flood extent using Sentinel-1 radar imagery with Google Earth Engine. Flooded areas are identified using a change-detection method based on the normalized difference. The HAND algorithm is used to delineate zones for processing. The approach was tested and calibrated at small scale to identify optimal parameters for flood detection. It was then applied to the whole of the island of Madagascar after the cyclone Batsirai in 2022. The proposed method is enabled by the computing power and data availability of Google Earth Engine and Google Colab. The results show satisfactory accuracy in delineating flooded areas. The advantages of this approach are its rapidity, online availability and ability to detect floods over a wide area. The approach relying on Google tools thus offers an effective solution for generating a large-scale synoptic picture to inform hazard management decision-making. However, one of the method’s drawbacks is that it depends to a large extent on frequent radar imagery being available at the time of flood events and on free access to the platform. These drawbacks will need to be taken into account in an operational scenario.

Background: Only few pediatric reports exist regarding the prevalence, cause and evolution of liver and renal injury in patients with anorexia nervosa (AN). The aim of this study is to describe the prevalence and the risk factors of hepatic and renal failure at admission and during hospitalization, especially during refeeding in a cohort of hospitalized adolescents with AN.Methods: In a retrospective cohort study of adolescents with AN in a single hospital of Marseille from 2013 to 2019, we compared four groups on admission: elevated aminotransferases (AT)/normal AT and renal injury/no renal injury to analyze the differences between them (demographic factors, anthropometric factors, disease duration, initial prescribed calories, speed of refeeding, aminotransferase level, glomerular filtration rate). We observed the evolution of AT and renal injury for these four groups during refeeding (by the increase of kilocalories). Results: A total of 29 subjects with AN met eligibility criteria (age: 14.2 years, female (86.2%), BMI at admission (Z-score= -2.8 standard deviation (SD)) with elevated AT (20.7 %) and renal injury (13.8 %) on admission. Lower Z-score BMI (-4.05 vs -2 SD, p = 0.013), lower expected weight for height (69% vs 76%, p = 0.034) and longer disease duration (2.1 vs 0.9 years, p =0,032) were significantly associated with elevated liver enzymes at admission. Lower Z-score BMI (-3.35 vs -2.5 SD, p = 0.002), lower expected weight for height at admission (69% vs 74,5%, p = 0.002) and loss of weight before admission (0.66 vs à 0.20 kg per day, p = 0.002) were associated with renal injury at admission. Time nadir BMI (13.5 vs 6.5 days, p = 0.034) and duration of hospitalization (55 vs 41 days, p = 0.036) were longer in elevated enzymes on admission group. During refeeding, liver enzymes (95% confidence interval (CI), odds ratio (OR) aspartate aminotransferase: -0.07 [-0.11; -0.03] and OR alanine aminotransferase: -0.16 [-0.27; -0.06]) and renal injury (95% CI, OR creatinine: -0.013 [-0.017; -0.008]) have normalized with the increase of calories, with significant association.Conclusions: The results of this study suggest that degree of malnutrition is associated with liver and renal injury on admission. Theses failures disappeared with refeeding. In the future, prospective multicentric studies could examine evolution of renal and hepatic failure undergoing refeeding in large pediatric cohort of AN.

Purpose: Our study focused on Pied Grow and Cattle Egret, two commensal and ubiquitous birds feeding in dumps and frequenting our homes. Our aim was to identify the bacteria that birds bring to our homes and could be a potential risk to Congolese health. Method: We have done bacteriological analyses of bird feces for to explore its gut micriobiota composition. The feces were collected in the uricotelic cloaca by using a swab in 52 Cattle Egrets (Bubulcus ibis) and 23 Pied Crows (Corvus albus) from Kinshasa city. Results: Bacteriological analyses revealed the presence of Proteus vulgaris (3.8%) and Klebsiella pneumoniae (5.8%) on Cattle Egret, Salmonella sp. (8.7%), Klebsiella pneumoniae (8.7%), Pseudomonas aeruginosa (21.7%), Proteus vulgaris (30.4%) on Pied Crow, Citrobacter spp and Escherichia coli (100%) on both Cattle Egret and Pied Crow respectively. Conclusion: The presence of these pathogenic germs, suggesting these commensal and ubiquitous birds may be potential vectors of various Diseases which pose serious health problems in the region.

.With the increasing use of carbon fiber reinforced plastics in various area, carbon fiber composites based on prepregs have attracted attention in industries and academia research. However, prepreg manufacturing processes are costly, and the strength of structures varies depending on the orientation and defects (pores and delamination). For non-contact evaluation of internal defects, we proposed lock-in infrared thermography to investigate orientation angles after a compression test. We also conducted a drop-weight impact test to study the behaviour of the composites after impact according the fibers orientation for composite fabricated using unidirectional carbon fiber prepregs. Using CAI tests, we determined the residual compressive strength and confirmed the damage modes using a thermal camera. The results of the drop weight impact tests show that the specimen laminated at 0° suffered the largest damage because of susceptibility of the resin to impact. In contrast, the specimens oriented in of 0°/90° and +45°/–45° directions transferred more than 90% of the impact energy back to the impactor because of the lamination of fibers in the orthogonal directions. Furthermore, the specimens that underwent complete damage in the impact tests were subjected to the lock-in method and showed internal delamination and cut fibers. With the finite elements analysis, the damage of each ply could be observed. Moreover, the temperature differences in the residual compression tests were not significant.

To date, viruses closely related to SARS-CoV-2 have been reported in four bat species: Rhinolophus acuminatus, Rhinolophus affinis, Rhinolophus malayanus, and Rhinolophus shameli. Here, we analysed 343 sequences of the mitochondrial cytochrome c oxidase subunit 1 gene (CO1) from georeferenced bats of the four Rhinolophus species identified as reservoirs of SARS-CoV-2-like viruses. Haplotype networks were constructed in order to investigate patterns of genetic diversity among bat populations of Southeast Asia. No strong geographic structure was found for the four Rhinolophus species, suggesting high dispersal capacity. The ecological niche of SARS-CoV-2 like viruses was predicted using the four localities of bat SARS-CoV-2-like viruses and the localities where bats showed identical or very similar CO1 haplotypes than virus-positive bats. The ecological niche of SARS-CoV-like viruses was deduced from the localities where bat SARS-CoV-like viruses were previously detected. The results show that the ecological niche of SARS-CoV2-like viruses includes several regions of mainland Southeast Asia whereas that of SARS-CoV-like viruses is mainly restricted to China. In agreement with these results, human populations in Laos, Vietnam, Cambodia, and Thailand appear to be much less affected by the Covid-19 pandemic than other countries of Southeast Asia. In the climatic transitional zone between the two ecological niches (southern Yunnan, northern Laos, northern Vietnam, and possibly Hainan and Taiwan), genomic recombination between highly divergent viruses is more likely to occur. Since recombinant viruses can threaten the benefit of vaccination campaigns, these regions should be under surveillance.

We assessed the prevalence and correlates of stunting among children aged 6-23 months from poor households in Rwanda. A cross-sectional study was conducted among 817 mother-child dyads living in poor households from five districts with a high prevalence of stunting. We used descriptive statistics to determine the prevalence of stunting, bivariate analysis, and a multivariate logistic re-gression model to measure the strength of the Association between childhood stunting and exposure variables. Stunting was at 34.1%. Children aged 19-23 months (AOR = 4.410, 95% CI: [1.911-10.173], p-value=0.01), children aged 13-18 months (AOR=2.788, 95% CI: [1.302-5.968], p-value=0.08), children from households that do not have a vegetable garden (AOR=2.165, 95% CI: [1.201-3.905], p-value<0.01) were more likely to be stunted. On the other hand, children whose mothers were not exposed to physical violence (AOR= 0.145, 95% CI: [0.074-0.287], p-value<0.001), children whose fathers were working (AOR=0.036, 95% CI: [0.005-0.242], p-value=0.001), children whose both parents were working (AOR=0.208, 95% CI: [0.051-0.851], p-value=0.029) and children whose mothers had good hand-washing practice (AOR=0.181, 95% CI: [0.091-0.362], p-value<0.001) were less likely to be stunted. Our findings underscore the importance of integrating the promotion of hand-washing practices, owning vegetable gardens, and intimate partner violence prevention in the interventions to fight stunting.

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

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

Optical backbone networks constitute the fundamental infrastructure employed today by network operators to deliver services to users. As network capacity is a key factor influencing optical network performance, it is important to understand how topological and physical properties impact its behavior and to have the capability to estimate its value. In this context, we propose here a method to evaluate the network capacity that relies on the optical reach to account for physical layer aspects, in conjunction with constrained routing techniques for traffic routing. As this type of routing can lead to traffic blocking, particularly due to the limitation on the number of wavelengths per fiber, we also propose a fiber assignment algorithm designed to deal with this problem. We apply this method to a set of randomly generated networks using a modified Waxman model, and for a network with 60 nodes, in a scenario without blocking, we obtain capacities of about 2.5 Pbit/s for a symbol rate of 64 Gbaud and about 5 Pbit/s for a symbol rate of 128 Gbaud. Remarkably, this duplication in the total network capacity is achieved by an increase in the total fiber length of only about 51%.

The 26 June 1917 tsunamigenic earthquake in Samoa is considered the largest historical event on record to have impacted this region in terms of earthquake magnitude and intensity. Yet, very little is known about the scale and distribution of tsunami impacts compared with the recent 2009 event which originated about 150 km east along the subduction zone bend of the Northern Tonga Trench (NTT). In this study we set out to: 1) reconstruct the 1917 tsunami from source to inundation to understand its hazard risk characteristics in the Samoan islands of Savai’i and Upolu; and 2) assess the hazard implications of tsunamis sourced from different locations along the subduction zone bend of the NTT on present-day exposure of coastal assets relative to the 2009 tsunami benchmark. We use the BG-Flood numerical modelling suite to produce model outputs representing inundation extent and hazard depth intensities at spatially flexible grid resolution (10 m, 20 m and 40 m). These are validated using available tide gauge records in Apia harbour and limited observations of runup that were derived from historical records. We then combine the inundation model with available digital distributions of buildings in the RiskScape multi-hazard risk analysis software, to produce exposure metrics for understanding the likely impacts on present-day coastal asset and population distributions if a similar tsunami were to occur. Results of the tsunami modelling indicate variable modelled-to-observed consistency using available source models, wave and runup validation data. Discrepancies in recorded vs modelled wave arrival time at Apia of between 30—40 mins are observed, with modelled runup underestimated in southeast Upolu Island compared with the rest of the country where runup observations are available (e.g., Savai’i Island). These differences likely reflect complexities in the tsunami source mechanism which might not currently be represented in our modelling. Nevertheless, our results suggest that a larger proportion of people would be exposed in Savai’i island (71% of exposure total), compared with Upolu island if a characteristic 1917-type event were to occur. While this study provides the first detailed inundation model of the 1917 tsunami in the Samoan region, the observed discrepancies suggest that further investigation is required to constrain potential tsunami source complexities which might not be accounted for in this study. Notwithstanding these limitations, our findings help to reinforce an appreciation of the risk to the greater Samoan region faced by local tsunamis sourced at different locations along the subduction zone bend of the NTT.

Renewable energy sources are increasingly crucial for meeting global energy demands in an eco-friendly manner, particularly as the world shifts away from fossil fuels and faces higher volatility of the market prices. Portugal, a country with limited fossil fuel reserves and high energy dependency, has heavily invested in renewables in recent years. However, renewables like sunlight, wind, and water are inherently unsteady due to climate influences, resulting in energy production fluctuations. To mitigate this issue, energy storage systems are being deployed to stabilize renewable energy supply during production lulls. This project aims to assess an energy storage system and define technical specification in order to maximize profitability at the Finerge-owned Alto Douro wind farm. To achieve this, we've conducted an energy demand analysis, followed by simulations to assess system performance in relation to energy use and profit, while varying parameters such as power and efficiency. We've also compared commercial proposals from different battery manufacturers utilizing various technologies. Our findings suggest that high-power, quick charge-discharge lithium-ion batteries are the most suitable solution, given their efficiency and modularity. These attributes are vital for achieving current and future profitability.

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

Phylogenetic trees of coronaviruses are difficult to interpret because they undergo frequent ge-nomic recombination. Here, we propose a new method, named coloured genomic bootstrap (CGB) barcodes, to highlight the polyphyletic origins of human sarbecoviruses and understand their host and geographic ori-gins. The results indicate that SARS-CoV and SARS-CoV-2 contain genomic regions of mixed an-cestry originating from horseshoe bat (Rhinolophus) viruses. First, different regions of SARS-CoV share exclusive ancestry with five Rhinolophus viruses from Southwest China (RfYNLF/31C: 17.9%; RpF46: 3.3%; RspSC2018: 2.0%; Rpe3: 1.3%; RaLYRa11: 1.0%) and 97% of its genome can be related to bat viruses from Yunnan (China), supporting its emergence in Rhinolophus species of this province. Second, different regions of SARS-Cov-2 share exclusive ancestry with eight Rhi-nolophus viruses from Yunnan (RpYN06: 5.8%; RaTG13: 4.8%; RmYN02: 3.8%), Laos (RpBA-NAL103: 3.3%; RmarBANAL236: 1.7%; RmBANAL52: 1.0%; RmBANAL247: 0.7%), and Cam-bodia (RshSTT200: 2.3%), and 98% of its genome can be related to bat viruses from northern Laos and Yunnan, supporting its emergence in Rhinolophus species of this region. Although CGB barcodes are very useful to retrace the origins of human sarbecoviruses, further investigations are needed to better apprehend the diversity of coronaviruses in bats from Cambo-dia, Laos, Myanmar, Thailand and Vietnam.

Epithelial ovarian cancer (EOC) remains the most lethal gynecologic malignancy, largely due to metastasis and drug resistant recurrences. Fifteen percent of ovarian tumors carry mutations in BRCA1 or BRCA2, rendering them vulnerable to treatment with PARP inhibitors such as olaparib. Recent studies have shown that TGFβ can induce “BRCAness” in BRCA wild-type cancer cells. Given that TGFβ is a known driver of epithelial to mesenchymal transition (EMT), and the con-nection between EMT and metastatic spread in EOC and other cancers, we asked if TGFβ and EMT alter susceptibility of EOC to PARP inhibition. Epithelial EOC cells were transiently treated with soluble TGFβ and their clonogenic potential, expression and function of EMT and DNA repair genes, and response to PARP inhibitors compared with untreated controls. A second epithelial cell line was compared to its mesenchymal derivative for EMT and DNA repair gene expression and drug responses. We found that TGFβ and EMT resulted in downregulation of genes responsible for homologous recombination (HR) and sensitized cells to olaparib. HR efficiency was reduced in a dose-dependent manner. Furthermore, mesenchymal cells displayed sensitivity to olaparib, cis-platin, and the DNA-PK inhibitor Nu-7441. Therefore, treatment of disseminated, mesenchymal tumors may represent an opportunity to expand clinical utility of PARP inhibitors and similar agents.

Coastal flooding exacerbated by climate change is recognised as a major global threat which is expected to impact more than a quarter of people currently residing in Pacific Islands countries. While most research in the last decade has focused on understanding the dynamics and impacts of future coastal flooding from extreme sea levels, relative sea level rise (RSLR) effects on tsunami hazards are not well understood. Far-field or distant-sourced tsunamis tend to have relatively lower impacts in Pacific Island states compared with locally sourced events, but there is limited understanding on how the impact of far-field tsunamis changes over time due to RSLR. Using the hydrodynamics software BG-Flood, we modelled the Tōhoku tsunami from propagation to inundation in Samoa under incremental SLR to examine the effects that RSLR has on changing the exposure of the built environment (e.g., buildings) to a far-field tsunami. Outputs of maximum tsunami inundation and flow depth intensities which incorporate incremental SLR were then combined with digital representations of buildings and depth-damage functions in the RiskScape multi-hazard risk modelling software to assess the changes in building exposure over time. Results indicate that present day buildings exposure in Samoa to a Tōhoku-oki type far-field tsunami will increase by approx. 600% with 1 m RSLR by 2080–2130, and approx. 2,350% with 2 m RSLR by 2130–2140. These findings provide a useful baseline for tsunami hazard risk assessment under changing sea level conditions in analogous island environments.

Count data analysis presents unique challenges due to its discrete nature, often exhibiting excessive zeros and overdispersion. To address these complexities, count models, such as Poisson regression and Negative Binomial regression, have been developed, enabling modeling and prediction of count-based phenomena. Additionally, it’s important to notice that zero inflation, a phenomenon commonly observed in count data, requires specialized techniques for robust analysis. This article provides an overview of count data and count models, explores zero inflation, introduces Likelihood Ratio Tests, and explains how the Vuong Test can be used as a model selection criteria. Furthermore, we created a Vuong Test implementation from scratch using the Python programming language. This implementation enhances the accessibility and applicability of the Vuong Test in real-world scenarios, providing a valuable contribution to the academic community, since Python didn’t have an implementation of this statistical test.

Virtual reality (VR) can play a key role in automotive marketing research, lowering costs and shortening the time it takes to bring a new product to market. However, there are still few VR applications that support automotive customers' experiences during the early stages of product development. Through a systematic review of literature and patents, this study aims to identify the challenges and opportunities for the application of virtual reality in car clinics, and to categorize them into attributes. We searched through the knowledge databases of PatentScout, ScienceDirect, Springer, and IEEEXplore. We found 72 patents with a high concentration in a few inventors. The United States of America presented the greatest number of records and the most common applications related to the apparatus for automatically reading respondents' reactions in a virtual environment. In terms of articles, we found 19 research papers that discussed sixteen categories identified as challenges and opportunities for automotive marketing research: 1) cost, 2) location to customers, 3) flexibility in interactions, 4) model transportation, 5) depth perception, 6) haptic perception, 7) motion, 8) movement perception/ physical collision, 9) color and texture, 10) sound feedback, 11) product interaction/manipulation, 12) visual-spatial, 13) graphic quality, 14) intuitiveness, 15) cybersecurity and 16) cybersickness. We conclude that the automotive industry can employ virtual reality for marketing research, but relevant elements such as hardware and software definition, stimulus quality, and research objectives, among others, must be considered.

We present a novel approach to quantum theory construction that involves maximizing the Shannon entropy of quantum measurements relative to their initial preparation. By constraining the maximization problem with a phase that vanish under measurements, we obtain quantum mechanics (vanishing U(1)-valued phase), relativistic quantum mechanics (vanishing Spin^c(3,1)-valued phase), and quantum gravity (also a vanishing Spin^c(3,1)-valued phase, but with a dilation constraint replacing the normalization constraint). The first two cases are equivalent to established theory, whereas the later case yields a quantum theory of accelerated reference frames, in which a quantized version of the Einstein field equation lives. Specifically, the spacetime interval is promoted to an observable, effectively building the metric tensor from the underlying quantum structure. Moreover, the SU(3)xSU(2)xU(1) gauge symmetries of the Standard Model arise naturally without additional assumptions. Finally, the solution is consistent only with 3+1 spacetime dimensions, as it encounters obstructions in all other dimensional configurations. This framework integrates quantum mechanics, relativistic quantum mechanics, quantum gravity, spacetime dimensionality, and particle physics gauge symmetries from a simple entropy maximization problem constrained by a vanishing phase.

All fundamental Planck scale symmetries are restored on a global level when a new charge is postulated in a finite, closed, Euclidean discrete space. Gravity emerges as a residual effect of the electromagnetic force in this scenario, resulting in a deterministic toy universe driven by a single input parameter. The model is developed using a constructive approach. Randomness is identified using a Chaintin argument. Aleph0 definite value is tied to the size of the universe. This is not an interpretation of Quantum Mechanics, but a deeper attempt to describe nature.

All fundamental Planck scale symmetries are restored on a global level when a new charge is postulated in a finite, closed, Euclidean discrete space. Gravity emerges as a residual effect of the electromagnetic force in this scenario, resulting in a deterministic toy universe driven by a single input parameter. Randomness is identified using a Chaintin argument. Aleph0 definite value is tied to the size of the universe. This is not an interpretation of Quantum Mechanics, but a deeper attempt to describe nature.

The paper presents the design stages of a single-phase Silicon Carbide bidirectional DC-AC converter. This includes the LCL filter design responsible to meet grid connection requirements. A 3kW laboratory prototype of the power converter is built employing a low-cost phase locked loop and its results are presented. The design of the low-cost phase locked loop and its implementation are depicted in some detail.

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

Shrimps are under the influence of several environmental factors such as fluctuation of physical and chemical parameters of the water affected by variations in rainfall, temperature, salinity, and pH. These factors have also been identified as risk factors for shrimp disease outbreaks. Despite the high levels of production, shrimp producers suffered significant economic losses in years, mainly due to the presence of diseases that now plague the industry. In particular, viral diseases have had and will continue to have profound impact on industry growth. In response to stress such environmental or pathophysiological, cells are able to up regulating selectively the expression of a protein group known as Heat Shock Proteins (HSPs). In a recent search at the literature we observed a close relation between HSP70 with apoptotic proteins and others stress proteins such HSP60 and HSP90. Moreover, the response of shrimp to viral stress was examined, some of which are correlated to the reactions of HSP70. Thus, the aim of this review is to describe the current knowledge on the status of stress responses in shrimps, particularly HSP70 responses, triggered by viruses.

COVID-19 is a well-known respiratory disease spreading worldwide since January 2020, causing many deaths and massive pressure on health systems. This pandemic’s appearance compromised the health services’ sustainability and quality as many procedures were postponed or even canceled out, with an expected increase of adverse events like nosocomial infections, in-hospital deaths, and the worsening of the patients’ clinical status. Therefore, assessing whether the healthcare providers’ performance has changed is paramount. In this case, we evaluated the public hospitals in Portugal as the leading secondary health care providers in the country, which in turn has been highly affected by the pandemic. To this end, we developed a network Data Envelopment Analysis model serially relating efficiency and effectiveness. Using 2,795 observations from public hospitals and 18 variables, we observed consistent drops in efficiency when the pandemic started, followed by a recovery to levels above the pre-pandemic ones. Regarding effectiveness, we observed a positive trend during the evaluated period. In short, these results allow us to conclude that, apart from the great resilience of public healthcare providers, the Portuguese State was not prepared for a pandemic like COVID-19, but still the actions taken (including massive vaccination) were beneficial.

A novel investigation into the structure-function relationship was undertaken to enhance understanding of the mechanism of action of the vasoactive peptide angiotensin II (AngII) using unconventional substitutions such as AIB (α-aminobutyric acid), CHA (cyclohexylalanine acid), IAP (isonipecotic acid), TOAC (2,2,6,6-tetramethylpiperidine-1-oxyl-4-amine-4-carboxylic acid) at position 3, and DMT (2,6-dimethyl-Tyrosine) at position 4. Biological potency assays were conducted on guinea pig ileum, and structural features were analyzed using circular dichroism (CD) and molecular dynamics (MD). CHA3-AngII and AIB3-AngII analogs retained some agonistic activity, whereas IAP3-AngII and TOAC3-AngII derivatives were inactive. CD results indicated low conformational flexibility for AIB3-AngII and CHA3-AngII, and greater conformational restrictions for IAP3-AngII and TOAC3-AngII, consistent with computational modeling analyses. DMT4-AngII exhibited significant agonistic activity compared to the inactive Phe4AngII. Molecular modeling suggested a potential interaction between the hydroxyl group of Tyr4 and the amide bond between positions 7 and 8. AngII showed greater conformational restriction compared to DMT4-AngII and Phe4-AngII, underscoring the importance of tyrosine at position 4.

Conditions under which compositions of component systems form a well-defined system-of-systems here are formulated at a fundamental level. Statement of what defines a well-defined composition and sufficient conditions guaranteeing such a result offers insight into exemplars that can be found in special cases such as differential equation and discrete event systems. For any given global state of a composition, two requirements can be stated informally as: 1) the system can leave this state, i.e., there is at least one trajectory defined that starts from the state, and 2) the trajectory evolves over time without getting stuck at a point in time. Considered for every global state, these conditions determine whether the resultant is a well-defined system and if so, whether it is non-deterministic or deterministic. We formulate these questions within the framework of iterative specifications for mathematical system models that are shown to be behaviorally equivalent to the Discrete Event System Specification (DEVS) formalism. This formalization supports definitions and proofs of the afore-mentioned conditions. Implications are drawn at the fundamental level of existence where the emergence of a system from an assemblage of components can be characterized. We focus on systems with feedback coupling where existence and uniqueness of solutions is problematic.

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

The success of Toxoplasma gondii (intracellular parasite) host cell invasion relies on the apical complex, a specialized microtubule cytoskeleton structure associated with secretory organelles. The genome encodes three isoforms of both α- and β-tubulin which are altered by specific post-translational modifications (PTMs), changing the biochemical/biophysical proprieties of microtubules, and modulating their interaction with associated proteins. Tubulin PTMs are a powerful and evolutionarily conserved mechanism to generate tubulin diversity, forming a biochemical ‘tubulin code’ that can be ‘read’ by microtubule-interacting factors. The T. gondii tubulin PTMs are: α-tubulin acetylation, α-tubulin detyrosination, Δ5α-tubulin, Δ2α-tubulin, α- and β-tubulin polyglutamylation, and α- and α-tubulin methylation. Tubulin glutamylation is a key candidate to assist microtubule remodeling in Toxoplasma, being involved in the regulation of microtubule stability, dynamics, interaction with motor proteins, and severing enzymes. The correct balance of tubulin glutamylation is achieved by the coordinated action of polyglutamylases and deglutamylating enzymes. In this work we will review and discuss the current knowledge on T. gondii tubulin glutamylation. By in silico identification of mammalian protein orthologs we explored and updated the identification of putative proteins related to glutamylation, contributing to a better understanding of the role of tubulin glutamylation in T. gondii.

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

Human keratinocytes and melanoma can stick together to form clusters after eight days in co-culture. As in dynamic system concepts, one can consider cluster formation as the system attractor, cell seeding as the initial condition, and density change over time as a path within the basin of attraction. Herein, Cellular Automata, which is a class of Agent-Based Models, and Boolean Networks are discrete modeling methods used in population dynamics such that running the dynamics of cellular automata backward reveals an underlying network in terms of basin of attraction, also known as global dynamics. Thus, we hypothesize that one can estimate the local dynamic of an agent-based model from the basin of attraction of a boolean network. Here, we propose an approach to estimating these agent-based model rules, which consists of comparing the density states within each state transition and reaching a consensus among state transitions belonging to a basin of attraction of a boolean network. The objectives of this study are: (1) to infer a boolean network from the co-culture growth curve; (2) to estimate the rules of agent-based models; and (3) to implement spatial dynamics simulations. The binarization of the growth curve shows high population density after four days; the estimated agent-based model rules were compatible with cell proliferation and migration in agreement with literature, so we had to propose individual rules for survival and death. Spatial dynamics shows that (1) keratinocytes exhibit a higher density in neighborhoods where melanoma is present; (2) the chance of keratinocyte migration increases until the fourth day, then decreases, and the probability of survival increases substantially after four days; and (3) cells with low proliferation capacity die and free space for those with high ones. Our approach suggests that the attractor state of cell co-culture is induced mainly by an increase in keratinocyte migration and survival. Our approach has the potential to offer valuable clues about microenvironmental interactions or configurations that drive population dynamics.

In the end of April nearly 100 cases of children aged between 6 month and 9 years with Kawasaki like disease were reported (mostly in Europe) probably linked to COVID-19. With the increasing awareness of this condition the number of cases reported is increasing worldwide. We aim to sum up the known data about this new entity based on published data (in a case report, a series of 8 cases and in newspapers and society statement) and using our knowledge of classical Kawasaki disease. It seems to be a post infectious disease with an onset between 2-4 weeks after the infection, probably in genetically predisposed children aged between 6 month to 17 years. A very rough estimation of incidence based on current data from Bergamo, Italy, and New York State and a lot assumption is between 0.016% (95% CI:0.013-0.02%) - 0.31% (95% CI: 0.2-0.47%) of infected children. Clinical signs overlaps with Kawasaki disease in some children, but another feature is prominent gastrointestinal manifestations. For the 9 detailed patients most had incomplete presentation for Kawasaki disease (with a mean 1.7 (+/-1.2) criteria per patient for the 5 non fever criterion) and only one had a classical form. In some cases, presentation is closer to toxic shock syndrome or isolated myocarditis. Persistent fever seems to be constant and biological exploration are consistent with inflammation (elevated CRP, ferritin and D-Dimers). Management is described as supportive and children seem to improve rapidly, but can require cardiac or respiratory support. In date of 11 may 2020 there is 4 deaths confirmed linked to these new entities (1 in UK and 3 in New York). Paediatricians and general practitioners need to be aware of these possible evolution following COVID-19 infection. However it seems to be rare and children are probably still spared from most morbidities and mortality linked to COVID-19 infection .There are need of published detailed cohorts to better delineate these entities.

The anti-money laundering (AML) process has failed both in identifying suspicious cases in due time as in assisting the AML analysts in decision making. Starting from a new generic anti-fraud approach, this article presents the main aspects related to the development of a multi-agent system that goes beyond the capture of suspicious transactions, seeking to assist the human expert in the analysis of suspicious behaviour. First, a transactional behavioural profile of clients is obtained in a data mining process. A set of rules, obtained through data mining over a real database, in conjunction with specific rules based on legal aspects and in the expertise of the AML analysts make up the agents' knowledge base. The cases for which the system was unable to suggest a decision are flagged as requiring more detailed analysis. The system analysed 6 months of real transactions and indicated several suspicious profiles, a set of these suspects was investigated by the AML analysts who proved the suspicion of several cases, including some that had not been identified by the systems in execution.

In mobile robotics, laser scanners have a wide spectrum of indoor and outdoor applications, both in structured and unstructured environments, due its accuracy and precision. Most works that use this sensor have their own data representation and their own case-specific modeling strategies, and no common formalism is adopted. To address this issue, this manuscript presents an analytical approach for identification and localization of objects using a 2D LiDARs Our main contribution lies in formally defining laser sensor measurements and their representation, the identification of objects, their main properties and their location in a scene. We validate our proposal with experiments in generic semi-structured environments common in autonomous navigation, and we demonstrate its feasibility on multiple object detection and identification, following strictly its analytical representation. Finally, our proposal further encourages and facilitates the design, modeling and implementation of other applications that use laser scanners as a distance sensor.

Incremental control strategies provide undeniable advantages for controlling Uncrewed Aerial Vehicles (UAVs) due to their reduced model dependency and accurate tracking capacities. This is of particular relevance for hybrid UAVs, specially for tail-sitters, which perform complex manoeuvres when transitioning to and from aerodynamic flight. This paper summarizes the research undertaken in the application of the Incremental Nonlinear Dynamics Inversion (INDI) and Incremental Backstepping (IBKS) to a small tail-sitter UAV. After the application of these control strategies in a simulated scenario, the implementation in a Micro-Controller Unit was validated in a Hardware-in-the-Loop scenario. Following such validation, indoor experimental trials were undertaken in order to evaluate the capacity of these controllers to stabilize and control the real aircraft. The stabilization in the flight trials was successfully implemented and allow to conclude that both INDI and IBKS are control strategies suited to control a tail-sitter in vertical flight, providing accurate tracking capabilities under smooth actuation. Furthermore, it was found that the IBKS is significantly more computationally demanding than the INDI, although having a global proof of stability that is of interest in aircraft control.

The purpose of the study was to evaluate the success of procedural conscious sedation, using inhaled equimolar nitrous oxide-oxygen (NOIS – EMONO), in patients undergoing routine dental and oral surgery procedures in a Swiss university hospital setting. Materials and methods: The authors conducted a retrospective cohort study of patients that underwent NOIS-supported procedures between 2018 and 2022 at the oral surgery department of the University Hospital of Geneva (HUG), Switzerland. The primary outcome was the measurement of the procedure success and efficacy as defined by the European Society of Anesthesiology. Secondary objectives included the analysis of the types of treatments performed, their indications, patient behavior, and the patient - clinician satisfaction score. Results: 55 patients were included in the study, 85% underwent surgical procedures, the remaining 15% underwent restorative and preventive procedures. The overall treatment success rate was 98.2%, and 97.9% for surgically treated patients. 62% of patients appeared relaxed, calm, and serene, while 16% expressed pain or fear during the procedure. Infiltrative administration of local anesthesia caused stress in 22% of patients, this was significantly lower in sub cohorts who received local topical anesthetics (0%) or a combination of systemic and local topical analgesics (7%). Patients (75%) and clinicians (91%) were satisfied with the procedure. Conclusion: Inhaled equimolar nitrous oxide-oxygen procedural sedation used during dental procedures and oral surgery results in high treatment success and satisfaction rates. The administration of additional topical anesthetics helps reduce the anxiety and stress related to infiltrative anesthesia. Further dedicated studies and prospective trials are needed to confirm these findings.

Drylands are arid and semiarid ecosystems, where the lack of rains imposes harsh conditions for the survival of organisms. These ecosystems are also susceptible to degradation and desertification, and their conservation depends on the understanding of the ecological functioning of vegetation and soil. In drylands, the vegetation is spatially structured as a mosaic of patches (vegetation) and interpatches (bare soil), a consequence of plant-plant interactions (facilitation and competition). Empirical data and modeling approaches reinforce the role of ecological facilitation for the maintenance of all organisms in drylands. However, the actual range of facilitation is still poorly known. Here, we explored data of meso- and micro-arthropods found in soil as bioindicators to infer the range of facilitation provided by plants to soil. We regarded data of abundances and species densities (independent samples) collected in random patches and bare soil places as dependent variables. Data of patch size and distances between bare soil and patches were arranged in a single shuffle, producing a 1-d coordinate system centered at the border of the patch. Discrete portions of this system are taken to calculate averages and variances of abundance and species density, and we investigated how soil communities variate across the patch border. We employ techniques of signal analysis to reduce the data noise and obtain a smooth and continuous behavior, which allowed us to fit a logistic function. Our findings indicate that soil communities suddenly change from simple patterns to numerous and diverse communities in bare soil regions, meaning that the influence of vegetation on soil goes beyond the patch border. We interpreted the variations in fauna as a consequence of the positive influence provided by plants (or its lack) on surrounding bare soil. We observe a fast decaying of fauna quantities at 0.35 m outside the patch border, a threshold that reveals the mean range of facilitation provided by plants. However, the changes in soil communities outside the patches seem not necessarily related to the efficiency of soil processes mediated by arthropods, which seem to be more active inside large patches. Concluding, we found a minimum patch-size (radius ~ 0.5 m) able to maintain high diverse communities in soil, and an average distance of vegetation influence along the patch border (halo between 0.35 and 0.50 m). This information can be interpreted in terms of facilitation provided by plants to soil conditions, which seems to differ from the quantitative functionality of soil processes. Our findings contribute to the general understanding of the ecological functioning of drylands, as well as to better plan conservation actions.

Malaria remains an important and challenging infectious disease, and novel antimalarials are required. Benzyl isothiocyanate (BITC), the main breakdown product of benzyl glucosinolate, is present in all parts of Tropaeolum majus L. (T. majus) and has antibacterial and antiparasitic activities. To our knowledge, there is no information on the effects of BITC against malaria. The present study evaluates the antimalarial activity of BITC and T. majus seeds, leaves, and stems aqueous extracts. We used flow cytometry to calculate the growth inhibition (GI) percentage of the extracts and BITC against unsynchronized cultures of chloroquine-susceptible Plasmodium falciparum (P. falciparum) 3D7-GFP strain. Extracts and/or compounds with at least 70% GI were validated by IC50 estimation against P. falciparum 3D7-GFP and Dd2 (chloroquine-resistant strain) unsynchronized cultures by flow cytometry, and the resistance index (RI) was determined. T. majus aqueous extracts showed some antimalarial activity higher in seeds than in leaves or stems. BITC’s GI was comparable to chloroquine’s. BITC’s IC50 was similar in both strains, thus a cross-resistance absence with aminoquinolines was found (RI < 1). BITC presented features that could open new avenues for malaria drug discovery.

The Portuguese health system has a universal, public, general National Health Service (NHS), tending towards free healthcare access. Created in 1979, this delivery model developed from the integration and complementarity between the different response levels (primary, hospital, continuing, and palliative care). However, over these 40 years, the initially centralized system underwent a decentralization process with the creation of Regional Health Administrations in the five mainland administrative regions. Since then, the entire NHS has settled around this new organization. The most recent step started in 2018 with decentralizing primary health care skills to 190 municipalities. This paper presents the various critical issues involved in the latest gradual decentralization process in health, intending to bring services closer to the citizens, and more focused on their needs. The article identifies and discusses the implications of this experience based on the steps foreseen in the already published legal texts.

Anthracnose, caused by the fungus Colletotrichum lindemuthianum, poses a significant and widespread threat to the common bean crop. The use of plant genetic resistance has proven to be the most effective strategy for managing anthracnose disease. The Amendoim Cavalo (AC) Andean cultivar has resistance against multiple races of C. lindemuthianum, which is conferred by the Co-AC gene. Fine mapping of this resistance gene to common bean chromosome Pv01 enabled the identification of Phvul.001G244300, Phvul.001G244400, and Phvul.001G244500 candidate genes for further validation. In this study, we assessed the relative expression of Co-AC candidate genes, as well as other putative genes in vicinity of this locus and known resistance genes, in the AC cultivar following inoculation with the race 73 of C. lindemuthianum. Gene expression analysis revealed significantly higher expression levels of Phvul.001G244500. Notably, Phvul.001G244500 encodes a putative Basic Helix-Loop-Helix transcription factor, suggesting its involvement in the regulation of defense responses. Furthermore, we observed a significant modulation of the expression of defense related genes PR1a, PR1b, and PR2, in a time-course experiment. These findings contribute to the development of improved strategies for breeding anthracnose-resistant common bean cultivars, thereby mitigating the impact of this pathogen on crop yields and ensuring sustainable bean production.

The valorization of the large amount of crude glycerol formed from biodiesel industry, is of primordial necessity. One possible direction with high interest to the biorefiniery sector is the production of fuel additives as solketal, though the acetalization of glycerol with acetone. This is a chemical process that conciliates high sustainability and economic interest, since solketal contributes to the fullfilment of a Circular Economy Model through its use in biodiesel blends. The key to guarantee high efficiency and high sustainability for solketal production is the use of a recovery and recyclable heterogeneous catalyst. Some examples are already reported in the literature (first example in 2012), and this review intends to conciliate the information spreaded in this topic and established the most important reacional parameters and the main catalytic characteristics strategies to achieve high solketal production in short reaction time.

The efficient and sustainable management of hospitals is an increasingly relevant challenge in the healthcare sector. With limited financial resources, regulatory pressures and increasing demands for quality services, hospitals are faced with the want to approve sound policies to certify financial and operational sustainability. Management accounting is critical in this instance. In Portugal the DL nº 284, of July 26, 1999, allow us to confirm the need to reinforcement of profitability and efficiency in the provision of healthcare. Management accounting is an essential tool for monitoring and controlling costs, making strategic decisions, efficiently allocating resources and evaluating performance in the hospital environment. It allows a comprehensive view of financial and operational aspects, providing valuable information for management and strategic planning. However, the sustainability of hospital management accounting is a critical and complex topic that deserves special attention. Hospitals face several challenges, such as the complexity of hospital costs, the integration of information systems and the measurement of healthcare value. These challenges can compromise the effectiveness of management accounting and its ability to provide relevant and reliable information for decision making. As a result, it is required to address the sustainability of hospital management accounting by identifying the specific challenges faced in this area and exploring opportunities to overcome them. By addressing these challenges and seizing opportunities, hospitals can strengthen their position in the healthcare sector, improve their operational efficiency, optimize resource allocation and ensure the provision of quality services to patients. In this article, the challenges faced by hospital management accounting will be analysed, such as the complexity of costs, the integration of information systems and the measurement of value in healthcare. Additionally, opportunities to improve financial and operational management will be highlighted through the adoption of advanced technologies, an emphasis on value-based management and collaborative partnerships. By exploring these themes, this essay aims to make a contribution. to the advancement of hospital management accounting and the sustainability of hospitals in the current healthcare scenario.

The International Maritime Organization (IMO) has recently revised its strategy for shipping de-carbonization, deepening the ambition to reduce annual greenhouse gas emissions until 2050. The accomplishment of this strategy requires the large-scale deployment of alternative maritime fuels, whose diversity and technical characteristics impose transition challenges. While several studies address the production of these fuels, a notable gap lies in the analysis of the required adaptations in vessels and ports for their usage. This study aims to fill this gap through a comprehensive re-view of material compatibility, storage in ports/vessels, and bunkering technology. Firstly, we an-alyze key aspects of port/vessel adaptation: physical and chemical properties; energy conversion for propulsion; fuel feeding and storage; bunkering procedures. Then, we perform a maturity as-sessment, placing each studied fuel on the technological readiness scale, revealing the most prom-ising options regarding infrastructure adaptability. Finally, we develop a case study for Brazil, whose economy is grounded on maritime exports. Findings indicate that multi-product ports may have potential to serve as multi-fuel hubs, while the remaining ports are inclined to specific fuels. In terms of vessel categories, we find that oil tankers, chemical ships and gas carriers are the most ready for conversion in the short-term

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.

In this paper, we investigated the capacitive effect and the electromagnetic coupling on the measurement chain induced by the gas gun or the powder gun during impact experiments. Reduced bandwidth and noise have been noticed on experimental signals. Rogowski coil measurements have been added on the cables to characterize the electromagnetic coupling. The perturbation currents on the cables have been quantified depending on the configuration. The gauge, the transmission line and the conditioning system have been modeled. The calculations have reproduced the electrical wave arrival time, the transmission line transfer impedance, the conditioning system transfer impedance and it shows the bandwidth limitation. A capacitive effect with the piezoresistive manganin gauge embedded into the sample has been identified depending on the experimental setup.

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

Antimony (Sb) has gained significance as a critical raw material (CRM) within the European Union (EU) due to its strategic importance in various industrial sectors, particularly in the textile industry for flame retardants and as a component of Sb-based semiconductor materials. Moreover, Sb is emerging as a potential alternative for anodes used in lithium-ion batteries, a key element in the Energy transition. This study focused on exploring the feasibility of identifying and quantifying Sb mineralizations through the spectral signature of soils using reflectance spectroscopy, a non-invasive remote sensing technique, and by employing deep learning algorithms such as Convolutional Neural Networks (CNNs). Common signal preprocessing techniques were applied to the spectral data, and the soils were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Despite achieving high R-squared values, the study faces a significant challenge of generalization of the model to new data. Despite the limitations, this study provides valuable insights into potential strategies for future research in this field.

The computer network world is changing and the NetDevOps approach has brought the dynamics of applications and systems into the field of communication infrastructure. Businesses are changing and businesses are faced with difficulties related to the diversity of hardware and software that make up those infrastructures. The "Intent-Based Networking - Concepts and Definitions" document describes the different parts of the ecosystem that could be involved in NetDevOps. The recognize, generate intent, translate and refine features need a new way to implement algorithms. This is where artificial intelligence comes in.

Nature-based Solutions (NbS) have rapidly been gaining traction across the research, policy, and practice spheres, touted as transformative actions to jointly address biodiversity loss and climate change. However, there are multiple, alternative ways to imagine NbS in those three spheres. To inform the NbS discourses across these three spheres, we critically reflect on the prevailing framing of NbS and consider the potential of a different framing of NbS to support transformations towards regenerative relationships between humans and nature. Such reflection is urgently needed to ensure that research, policy, and practice delivers on the transformative ambitions of NbS. We propose a novel “core framing” of NbS, charting two pathways for how such a framing can support a human value-based transformation – first through influencing individual beliefs and values, and second through the communication and application of the NbS concept in research, policy, and practice. We argue that for NbS to support transformation, it must support a reframing of human-nature relationships, one where the interdependencies between people and nature are recognized as essential for social and environmental well-being. We elaborate on how such a framing is key to support inclusivity and collaboration between diverse research perspectives, policy objectives across scales, and implementation practices, to deliver successful NbS.

A Robust Optimization framework with original concepts and fundamentals also admitting a fusion of ideals from relative regret models and static robust optimization, containing conservatism concepts is disclosed. The algorithm uses a fine-tune strategy to tune the model so the robustness and a target ideality can be mutually achieved with a specified risk. The framework comprises original concepts, a mathematical approach and an algorithm. The statistical treatment of the data with the original concepts from the framework make it able to make short, middle or long-term decision-making setting. The framework has high tractability since the algorithm forces the creation of a setting that makes a robust optimization with the specified risk. The framework can be applied in linear and nonlinear mathematical models since that the objective function is monotonic in the domain of the active convex region. Several examples are solved to best understand the framework and all results demonstrated high tractability and performance. There is a wide range of applications. Along all the text, there is a profound discussion about its philosophy, objective, original concepts, fields of application, statistical and probabilistic fundamentals.

We report the case of a 61-year-old woman who was referred to the Oral Surgery and Implantology Unit of the University Hospitals of Geneva to take care edentulous sites after dental avulsions of teeth 11 and 21 following traumatic shock due to endotracheal intubation under general anaesthesia. The dental history revealed that the patient had a history of generalized chronic periodontitis that had been treated several years earlier. Treatment consisted, for the first time to our knowledge, of immediate insertion of implants at sites 11 and 21 with simultaneous bone augmentation in a single surgical procedure and immediate restorations.

A healthy 47-year-old woman consulted the Oral Surgery and Implantology Unit of the University Hospitals of Geneva with a request to treat her painful tooth 11 and replace the missing tooth 21. The dental history revealed that the patient had lost teeth 21 and 22 due to advanced caries. On clinical examination, tooth 11 showed an ill-fitting prosthetic crown with overhanging margins, an increased localized probing depth of 8 mm in the disto-vestibular area and sensitivity to percussion. The edentulous site 21 showed horizontal bone atrophy. Radiological examination revealed a well-defined unilocular radiotransparent lesion surrounded by a thin radiolucent border, located at the apex of tooth 11 and measuring 10 x 8 mm. The treatment consisted of extraction of 11, enucleation of the apical lesion, insertion of implants at sites 11 and 21 with simultaneous bone augmentation in a single surgical procedure with aesthetic and functional results at 3-year follow-up without any complications. Our case highlights that immediate implant placement in case of cystic periapical lesion represents a good valid alternative to standard treatment.

Epidemiological surveillance and phylogenetic studies rely nowadays on processing and analysing huge volumes of data. Processing tasks consist on running and refining a series of intertwined computational tasks. And, despite of existing several web applications for data processing and interactive visualization for phylogenetic studies, integrating many different tools and algorithms, their execution is total or partially on the client side, making them unsuitable for dealing with huge volumes of data. Studies are often also not easy to reproduce. On the other hand, in recent years, data-centric workflow systems have been proposed, allowing to deal better with increasingly larger datasets. The integration of these systems within phylogenetic tools will allow to scale them as required, and will contribute also to promote studies reproducibility. We propose then the FLOWViZ middleware for facilitating the integration of a state of the art data-centric workflow system, Apache Airflow, within web applications for phylogenetic analyses. This framework abstracts contracts and a core API for defining tools and workflows, where tools are assumed to be containerized. FLOWViZ has been tested and evaluated within the PHYLOViZ web application, a tool supporting phylogenetic inference and data visualization.

The primary cilium plays a pivotal role during embryonic development of vertebrates. It acts as a somatic signaling hub for specific pathways, such as sonic hedgehog signaling. In humans, mutations in genes that cause dysregulation of ciliogenesis or ciliary function lead to severe developmental disorders called ciliopathies. Beyond its obvious role in early morphogenesis, growing evidence points towards an essential function of the primary cilium in neural circuit formation in the central nervous system. However, very little is known about a potential role in the formation of the peripheral nervous system. Here, we investigated the presence of the primary cilium in neural crest cells and their derivatives in the trunk of the developing chicken embryo in vivo. We found that neural crest cells, sensory neurons, and boundary cap cells all bear a primary cilium during key stages of early peripheral nervous system formation. Moreover, we described differences in the ciliation of neuronal cultures of different populations from the peripheral and central nervous system. Our results offer a framework for further in vivo and in vitro investigations on specific roles that the primary cilium might play during peripheral nervous system formation.

The predictability of wind information in a given location is essential for the evaluation of a wind power project. Predicting wind speed accurately improves the planning of wind power generation, reducing costs and improving the use of resources. This paper seeks to predict the mean hourly wind speed in anemometric towers (at a height of 50 meters) at two locations: a coastal region and one with complex terrain characteristics. To this end, the Holt-Winters (HW), Artificial Neural Networks (ANN) and Hybrid time-series models were used. Observational data evaluated by the Modern-Era Retrospective analysis for Research and Applications-Version 2 (MERRA-2) reanalysis at the same height of the towers. The results show that the hybrid model had a better performance in relation to the others, including when compared to the evaluation with MERRA-2. For example, in terms of statistical residuals, RMSE and MAE were 0.91 and 0.62 m/s, respectively. As such, the hybrid models are a good method to forecast wind speed data for wind generation.

Construction work is physically demanding. The tasks involved in this professional occupation physically exhaust workers and put them at high risk of injury. This work aims to analyze muscle fatigue in civil construction workers, with and without physical activity historical practice. For this purpose, the muscle activity of the Biceps Femoris, Trapezius Transversalis, Erector Spinae Longissimus, was evaluated using surface electromyography. Eleven male civil construction workers aged 26 to 52 years (38.00 ± 9.60), of whom 7 were untrained (N =7) and 4 were trained (N=4) participated in this study. Each subject completed two assessments at two different times. At the first assessment moment, a questionnaire was completed, and the maximum repetition of the deadlift exercise was assessed. At the second assessment, the subjects were assessed while performing the deadlift in four different situations: 50%RM at rest, 80%RM at rest, 80%RM at fatigue, and 50%RM at fatigue. The trained construction workers have higher levels of muscle activity, and there are no significant differences in muscle activation between the fatigued and non-fatigued sets. There appears to be a strong influence of physical exercise on muscle activity and fatigue in civil construction workers.

Reliability assessment in traditional power distribution systems has played a key role in power system planning, design, and operation. Recently, new information and communication technologies have been introduced in power systems automation and asset management, making the distribution network even more complex. In order to achieve efficient energy management, the distribution grid has to adopt a new configuration and operational conditions that are changing the paradigm of the actual electrical system. Therefore, the emergence of the cyber-physical systems concept to face future energetic needs requires alternative approaches for evaluating the reliability of modern distribution systems, especially in the smart grids environment. In this paper, a reliability approach that makes use of failure modes of power and cyber network main components is proposed to evaluate risk analysis in smart electrical distribution systems. We introduce the application of Failure Modes and Effects Analysis (FMEA) method in future smart grid systems in order to establish the impact of different failure modes on their performance. A smart grid test system is defined and failure modes and their effects for both power and the cyber components are presented. Preventive maintenance tasks are proposed and systematized to minimize the impact of high-risk failures and increase reliability.

EMG analysis is becoming increasingly important in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, electromyographic signals can be contaminated by various types of noise, interference and artifacts, which can lead to misinterpretation of the data acquired using this method. Even assuming best practices, the collected signal may still be altered by such contaminants. The aim of this paper is to review methods employed to reduce contamination of single channel EMG signals. This review is limited to methods performed directly on the measured EMG signal and those that allow total reconstruction of the EMG signal. Subtraction methods used in the time domain, denoising methods performed after signal decomposition and hybrid methods are assessed. It is defended that individual methods may be more or less suitable for a particular application depending on contaminant(s) present in the signal and on the specific requirements of the application.

The aerospace industry develops prognosis and health management algorithms to ensure better safety on board. Particularly for in-flight controls where jamming is dreaded. For that, vibration signals are monitored to predict future defect occurrences. However, time series are not labeled according to severity level, and the user can only assess the system health from the data mining procedure. To that extent, we developed a clustering algorithm using a deep neural network core. We encoded the time series into pictures to be feed into an artificially trained neural network: U-NET. From the segmented output, one-dimensional information on cluster frontiers is extracted and filtered without any parameter selection. Then, a kernel density estimation finally transforms the signal into an empirical density. Ultimately, a Gaussian mixture model extracts the latter independent components. The method empowered us to reveal different degrees of severity faults in the studied data, with their respective likelihood without prior knowledge. We compared it to state-of-the-art machine learning algorithms. However, internal clustering results evaluation for time series is an open question. As state-of-the-art indexes were not producing relevant results, we built a new indicator to fulfill this task. We applied the whole method to an actuator consisting of an induction machine linked to a ball screw. This study lays the groundwork for future training of diagnosis and prognosis structures in the health management framework.

Lymphedema is a chronic and debilitating disease that affects up to 250 million patients worldwide. Recent advances in understanding its pathophysiology, along with improved diagnosis and microsurgical techniques, have enhanced our ability to cope with the challenging task of treating this disease. This review provides an overview of the disease from a surgeon's point of view, including existing imaging modalities used for preoperative assessment, as well as surgical procedures used in its treatment. Advantages and drawbacks of various existing modalities used for pre or intraoperative assessment of lymphatic vessels are discussed. Lymphedema treatment has shifted from palliative debulking procedures (liposuction and direct excision) to those aimed at restoring lymphatic flow and countering the pathophysiology of the disease (lymphaticovenous anastomosis and vascularized lymph node transfer). A combination of both approaches can result in a synergistic benefit for patients and are discussed in this review. Despite recent advances, some controversies persist, and further studies are needed to better define surgical treatment algorithms.

Background: Sensor usage in the classical scientific process has allowed new experimentation in medicine. We report on the design process of a MEMS sensor being used for muscle trauma evaluation in diagnosing acute compartment syndrome (ACS), a medical ailment costing society billions of dollars per year. Objectives: Modeling the disease with scientific process allows a more complete under-standing of the disease. The goal was to formulate a hypothesis that could be tested to aid in making the diagnosis of ACS accurately. Methods: Scientific process was observed throughout the disease modelling process. Background information was improved and clarified, new pre-clinical models were de-signed and verified, a hypothesis built on pressure measurement with MEMS sensors was carried out, and the testing of the model as verified against previous clinical data was accomplished. Results: Scientific process resulted in hypothesis generation around the relationship of intracompartmental pressure measurement and the disease process and therapy. This resulted in new understanding of ACS, accurate modelling, and sensor design resulting in a MEMS device that has an extremely high sensitivity and specificity (over 99%) in treating and diagnosing the disease. Conclusion: MEMS sensor technology defines the new gold standard of implanting a sensor in a muscle compartment that allows accurate diagnosis of ACS with continuous trends in pressure.

Bio-based glass-forming materials are now considered for thermal energy storage in building applications. Among them, Xylitol appears as a biosourced seasonal thermal energy storage material with high potential. It has a high energy density, a high and stable undercooling allowing storing solar energy at ambient temperature thus, reducing thermal losses and the risk of spontaneous nucleation (i.e., the risk of losing the stored energy). Generally when the energy is needed, the discharge triggering of the storage system is very difficult as well as reaching a sufficient power delivery. Both are indeed the mains locks for the use of pure Xylitol in seasonal energy storage. Different techniques have been hence considered to crystallize highly undercooled Xylitol. Nucleation triggering of highly undercooled pure Xylitol by using an air lift reactor has been proven here. This method should allow reaching performances matching with building applications (i.e., at medium temperatures, below 100 °C). The advantages of this technique compared to other existing techniques to activate the crystallization are discussed. The mechanisms triggering the nucleation are investigated. The air bubble generation, transportation of nucleation sites and subsequent crystallization are discussed to improve the air injection operating conditions.

An important indicator of human-related pollution in watersheds is dissolved oxygen (DO). The DO is highly dependent on both space and time characteristics of the watershed and is directly linked to eutrophication, which impairs the development of both the aquatic fauna and flora, also negatively impacting the water quality. Aspiring to reach a more accurate and precise forecasting approach to predict levels of DO, the present work proposes new graph-based and transformer-based deep learning models. The models were trained and validated for the Credit River Watershed, and the results were compared with both benchmarking and literature-found approaches. The proposed Graph Neural Network Sample and Aggregate (GNN-SAGE) model was the best-performing approach, reaching coefficient of determination (R2) and Root Mean Squared Error (RMSE) values of 97% and 0.34 ppm, respectively. The findings from the Shapley additive explanations (SHAP) indicated that the GNN-SAGE benefited from spatiotemporal information from the surrounding stations, improving the model’s results, and that temperature is a major input attribute for determining future DO levels. The results established that the proposed GNN-SAGE model stands as a state-of-the-art solution for DO forecasting, with potential for real-time water quality applications.

The aim of this study was to examine the post-injury psychological impact looking for to avoid sources of health issues. We have analyzed differences in the stress, anxiety and depression levels between male and female football players who have suffered at least one injury during the last season played respect to similar players who have not been injured. A descriptive cross-sectional design was used. The participants were 187 football players and data on the player’s injuries were collected. A Self-reported Questionnaire collected number, type and severity of injuries sustained during the previous sports season (twelve months), and also the player’s current situation being injured or not injured. To evaluate the psychological variables, the DASS-21 questionnaire was used. Results indicated that the male injured players presented anxiety levels higher than those who were non-injured. When we observe the female players’ data, the stress levels of the non-injured ones are higher than those of the injured players. Regarding the non-injured athletes, results showed that the female non-injured players presented values in anxiety higher than those corresponding to the non-injured male players. Thus, sport injury affects mental health issues such as anxiety and stress, and in a different way regarding gender.

The Vu Gia - Thu Bon River Basin (VG-TB) is confronting numerous challenges related to water security (WS), particularly in the face of the impacts of climate change. These challenges manifest in salinity intrusion, altered rainfall patterns, and reduced water supply in downstream areas. To assess the current state of water security in the basin, the study employed statistical analysis methods, Process Analysis Method (PAM), SMART, and Analytic Hierarchy Process (AHP) to develop a comprehensive Assessment Framework for Water Security (AFWS) in the VG-TB. The AFWS encompasses five key dimensions, 17 indicators, and 28 variables, aligning with the UN-Water definition of water security and addressing the Sustainable Development Goal 6 (SDG6) criteria for global water sustainability. It also adheres to the Asian Development Bank's (ADB) approach to water security assessment as outlined in the Asian Water Development Outlook (AWDO) reports. This allows for the computation of a comprehensive Water Security Index (WSI) for specific regions, serving as a foundation for decision-making and policy formulation aimed at enhancing water security in the context of climate change and facilitating sustainable basin development in the future.

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

This paper presents a script that classify spatial patterns of residential urban growth using a morpho-structural approach. The script performs a combination of variography analysis and morphological closings over buildings possessing a residential function in 2002 and 2017 within a region located in southern France named Centre-Var. The different bounding regions then allow classifying new residential buildings into different categories according to their degrees of clustering/scattering and to their locations regarding existing urban areas. Preliminary results show that this protocol is able to provide useful insights regarding the degree of contribution of each new residential building to different patterns of urban growth (clustered infill, scattered infill, clustered edge-expansion, scattered edge-expansion, clustered leapfrog and scattered leapfrog). Open-access to the script and to the test region data is provided.

Background: Nowadays vital signs in clinics are electronically monitored excepted for urine output which still requires, manual recording, which is subject to human errors. We, in-vitro, assessed in this study the ability a novel electronic device to measure by a combination of two different methods, the real time flow-output, like urine output. Methods: Flow or urine output was measured by a combination technique of gravimetry using a dynamometer and capacitive sensing to improve precision. Data were obtained in 3 different situations, low, medium, and high flow. Results: Three different situations were tested: full or empty dead space and Pasteur’s chamber. The differences between the injected and measured volume varies from 0 to 5 ml representing a maximal error of 3 % regardless of the volume injected and the flow in ml/hour. The capacitive sensors behave as expected with a parallel curve of the expected and measured flow. Conclusion: We have shown in this in-vitro study that the combination of gravimetry and capacitive sensors analysis allow accurate measurement of volume and flow-output. Clinical trials should be conducted to confirm its usefulness in urine output measurement in the real life.

Turbidity is an important indicator of water quality in hydrology. More traditional ways to monitor turbidity can provide reliable results. However, they are prone to human error, have elevated costs, and lack real-time monitoring capacity. Addressing these hindrances, in this work we combine spectral bands and indices from Sentinel-2 with several machine learning paradigms, namely XGBoost, Random Forests, GMDH, Support Vector Regression, k-Nearest Neighbors and Least Absolute Shrinkage and Selection Operator to model turbidity, using data from twelve monitoring stations encompassing the Mississippi River, USA. Results show that considering the individual monitoring stations, the ML paradigms for turbidity modeling were satisfactory at locations with a larger range and standard deviation values, achieving a mean R2 value of 59.5%. Tree-based models were the best overall approach, often ranking as the best or second-best performing model. When all the samples from the monitoring stations were used, the XGBoost provided superior output for turbidity modeling, reaching an R2 equal to 75.7%. A comprehensive comparison with the literature found values showed that the models implemented using this study’s methodology could provide competitive results, deeming it as an alternative for turbidity modeling from remote sensing data.

Several observations have shown that patients with polycythemia have iron deficiency. Our objectives were to report the prevalence of iron deficiency, to evaluate the diagnostic performance of serum ferritin in polycythemia vera and to describe the mechanisms of this association. This is a retrospective descriptive and analytical study carried out in the internal medicine department of the Henri Mondor Hospital, Aurillac, France. The study involved 114 patients with polycythemia, followed in the department from January 1, 2010 to December 31, 2021. To evaluate the diagnostic performance, the JAK2 mutation was considered as the gold standard of diagnosis. Thirty-three patients had polycythemia vera and 76 patients had secondary polycythemia. The mean age of the patients was 61.79 years (±15.44) with a sex ratio of 4.43. The overall prevalence of iron deficiency was 21.05%. The prevalence was 53% in polycythemia vera group and 1.32% in secondary polycythemia group. The risk of iron deficiency was high in polycythemia vera (OR=115; 95% CI [14.4-918.2], p<0.000). The sensitivity and specificity of serum ferritin were 52.63% and 100% respectively. Assessment of iron deficiency should be part of the initial evaluation of polycythemia. The presence of iron deficiency is specific for polycythemia vera.

Background: During anatomy (topographic) chair, we prepared an anatomic piece of an older female pelvis (unknown causa mortis). Here, we present an anatomic piece prepared by medical students that helped us to better understand hypertrophic bladder pathophysiology. Methods: A traditional corpse dissection guided by an assistant professor. Results: Corpse dissection enhanced our personal experience during the medical course. It also helped us to better visualize how big a hypertrophic urinary bladder can become as the etiologic cause is not taken care. Conclusions: Despite virtual and 3D anatomy lessons, we believe corpse dissection must remain as a teaching strategy that can help to build a new generation of surgeons as well as honor the History of Medicine.

Among the many Lysosomal Storage Disorders (LSDs) that would benefit from the establish-ment of novel cell models, either patient-derived or genetically engineered, is Mucopolysaccha-ridosis type II (MPS II). In fact, even though a specific therapeutic approach does exist for this disorder (Enzyme Replacement Therapy, ERT, with recombinant human IDS) and haematopoiet-ic stem cell transplantation (HSCT) may also hold promise whenever the disorder is diagnosed soon enough, both approaches have their own drawbacks. Here we present our results on the establishment and characterization of two MPS II pa-tient-derived stem cell line(s) from deciduous baby teeth. To the best of our knowledge, this is the first time a stem cell population is isolated from LSD patient samples obtained from the oral cavity. Taking into account our results on the molecular and biochemical characterization of those cells and our first insights on their subcellular MPS phenotype, we consider these cells will be a valuable tool for both pathophysiological assessments and drugs screenings. Ultimately, we believe patient-derived dental pulp stem cells (DPSC) may represent a feasible alternative to induced pluripotent stem cells (iPSC) in many labs with standard cell culture con-ditions and limited (human and economic) resources.

Ankle sprains are exceedingly common injuries in both athletes and general population. They account for 10 to 30% of all sports injuries. Although the vast majority of lateral ankle ligamentous injuries respond successfully to conservative management, the absolute number of those that progress to chronic lateral ankle instability (CLAI) remains considerably important. This condition is characterized by persistent symptoms and may be associated with short-term and long-term complications and functional deficits. There is still a lack of ideal postoperative management of CLAI patients. Furthermore, an evidence-based rehabilitation phasing does not exist and most of the published studies regarding this subject suggest some protocols based on a wide variety of functional assessment scores and other modalities which are not accurate enough. Moreover, the literature that assesses the ability to return to work (RTW) and return to sport (RTS) in general population and athletes operated for CLAI shows most commonly aggregated results with global rates of RTW or RTS without describing a detailed timeline based on the readiness of patients to return to each level of activity. Although stress radiographs and MRI have been assessed as potential tools to improve postoperative management of CLAI patients, the first modality is limited by its low sensitivity to detect laxity and the second one by its static character and its inability to predict the healing process phase nor the mechanical properties of the repaired/reconstructed ligaments. Bioelectrical impedance, mechanical impedance and near-infrared spectroscopy are non-invasive methods of measurement that could be potential assessment tools to help surgeons improve the postoperative management of patients after CLAI surgery.

There is growing awareness that “Nature-based Solutions” (NbS) can help to protect us from climate change impacts whilst slowing further warming, supporting biodiversity and securing ecosystem services. However, the potential of NbS to provide the intended benefits has not been rigorously assessed. There are concerns over their reliability and cost-effectiveness compared to engineered alternatives, and their resilience to climate change. Trade-offs can arise if climate mitigation policy encourages NbS with low biodiversity value, such as afforestation with non-native monocultures. This can result in maladaptation, especially in a rapidly changing world where biodiversity-based resilience and multifunctional landscapes are key. Here we highlight the rise of NbS in climate policy—focussing on their potential for climate change adaptation as well as mitigation—and discuss barriers to their evidence-based implementation. We outline the major financial and governance challenges to implementing NbS at scale, highlighting avenues for further research. As climate policy turns increasingly towards greenhouse gas removal approaches such as afforestation, we stress the urgent need for natural and social scientists to engage with policymakers. They must ensure that NbS can achieve their potential to tackle both the climate and biodiversity crisis while also contributing to sustainable development. This will require systemic change in the way we conduct research and run our institutions.

Green investors have expressed concerns about the environment and sustainability due to the high energy consumption involved in cryptocurrency mining and transactions. This article investigates the safe haven characteristics of clean energy stock indexes in relation to 3 cryptocurrencies, taking into account their respective levels of "dirty" energy consumption from May 16, 2018, to May 15, 2023. By virtue of analyzing a tumultuous era in the global economy, the level of integration between clean energy stock indexes and cryptocurrencies will be inferred by using Gregory and Hansen's methodology. Furthermore, to assess the presence of a volatility spillover effect between clean energy stock indexes and “dirty-classified” cryptocurrencies, the ?-test of heteroscedasticity of two samples from Forbes and Rigobon will be employed. The empirical findings show that clean energy stock indexes may offer a viable safe haven for dirty energy cryptocurrencies. However, the precise associations differ depending on the cryptocurrency under examination. The implications of the study's results are significant for investment strategies, this knowledge can inform decision-making procedures and facilitate the adoption of sustainable investment practices. Investors and policymakers can gain a deeper understanding of the interplay between investments in renewable energy and the cryptocurrency market.

This paper describes the influence of the regeneration treatments on 4-chloro-2-methyl-phenoxyacetic acid (MCPA)-loaded activated carbons (ACs) on adsorption-desorption cycles with two commercial ACs, Merck and Norit 1240 X ( Norit 1240 X was used in a granular and powder form). The ACs were saturated with MCPA and then submitted to four regeneration cycles, by washing with ethanol, NaOH solutions and washed with NaOH solutions followed by a thermal treatment. The ACs regenerated with ethanol showed a better performance concerning the successive adsorption-desorption cycles. On the first cycle, the MCPA desorbed, from washing with ethanol, from all ACs was higher than 99%. After a fourth adsorption-desorption cycle, washing regeneration combined with thermal treatment allowed excellent regeneration results. The amount of MCPA adsorbed on Norit 1240 X AC was higher than the amount adsorbed in the first cycle. MCPA adsorption kinetic data were analyzed by applying pseudo-first-order, pseudo-second-order, and Weber-Morris models. The pseudo-second-order fit better fit to the data and the Weber-Morris representation allows confirming that on Norit 1240 X, in a granular form, the pore diffusion was the limiting factor concerning the MCPA adsorption.

The quantitative and qualitative assessment of gold grains from samples of glacial till is a well-established method for exploring gold deposits hidden under glaciated cover. This method, widely used by the industry and which produced numerous successes in locating gold deposits in glaciated terrain, is still based on artisanal gravity separation techniques and visual identification. However, being artisanal, it is limited by inconsistent recoveries and the difficulty to visually identify the predominantly occurring small gold grains. These limitations hinder its capability to deceipher subtle or complex signal. To improve detection limits through recovery of small gold grains, a new approach has recently been introduced in the industry (commercially referred as “ARTGold” procedure) using an optimized miniature sluice box coupled with an automated scanning electron microscopy routine. The capabilities of this improved method are highlighted by comparing till surveys conducted around the Borden gold deposit (Ontario, Canada) using the conventional and improved methods at both local and regional scales. Relative to the conventional approach, the improved method recovered almost one order of magnitude more gold grains from samples (regional and down-ice mineralization), dominantly in small size fractions. Increasing the counts in low-abundance regional samples enables better discrimination between background signals and significant dispersions. The method offers an alternative to improve characterization of gold dispersal in glaciated terrain and the related gold deposit footprints.

This paper deals with a non-contact method to identify the aerodynamic propeller parameters of the Parrot AR.Drone quadrotor. The experimental set consists in a camera recording the vehicle flights, the audio signal is extracted and is used a spectrogram analysis to estimates the propeller velocity. First, the aerial vehicle takes off and starts a hovering maneuver. The experiment is repeated with different additional masses attached to its rigid body. If the weight over the UAV increases/decreases, then the propeller must rotates faster/slower to produce a higher/lower thrust, and consequently, the sound frequency increases/decreases. Finally, this proposition is validated experimentally, and the estimated velocity is used to identify the quadrotor thrust parameters.

In this article we presented an application of the quantum cosmological model in teleparallel gravity. Working with a vacuum solution, the gravitational energy density is quantized with the Weyl procedure and we obtain a discrete expression for the gravitational energy. As an immediate consequence the empty space exhibits an expansion for an early universe.

The Brazilian livestock is predominantly extensive, with approximately 90% of the production being sustained on pasture, which occupies around 20% of the territory. In the current climate change scenario and where cropland is becoming a limited resource, there is a growing need for a more efficient land use and occupation. It is estimated that more than half of the Brazilian pastures have some level of degradation; however there is still no mapping of the quality of pastures on a national scale. In this study, we mapped and evaluated the spatio-temporal dynamics of pasture quality in Brazil, between 2010 and 2018, considering three classes of degradation: Absent (D0), Intermediate (D1), and Severe (D2). There was no variation in the total area occupied by pastures in the evaluated period, in spite of the accentuated spatial dynamics, with a retraction in the center-south and expansion to the north, over areas of ​​native vegetation. The percentage of non-degraded pastures increased ~12%, due to the recovery of degraded areas and the emergence of new pasture areas as a result of the prevailing spatial dynamics. However, about 44 Mha of the pasture area is currently severely degraded. The dynamics in pasture quality were not homogeneous in property size classes. We observed that in the approximately 2.68 million properties with livestock activity, the proportion with quality gains was twice as low in small properties compared to large ones, and the proportion with losses was three times greater, showing an increase in inequality between properties with more and less resources (large and small, respectively). The areas occupied by pastures in Brazil present an unique opportunity to increase livestock production and make available areas for agriculture, without the need for new deforestation in the coming decades.

The aerosol optical depth is an important indicator of aerosol particle properties and associated radiative impacts. AOD determination is therefore very important to achieve relevant climate modeling. Most remote sensing techniques to retrieve aerosol optical depth are applicable to daytime given the high level of light available. The night represents half of the time but in such conditions only a few remote sensing techniques are available. Among these techniques, the most reliable are moon photometers and star photometers. In this paper, we attempt to fill gaps in the aerosol detection performed with the aforementioned techniques using night sky brightness measurements during moonless nights with the novel CoSQM: a portable, low cost and open-source multispectral photometer. In this paper, we present an innovative method for estimating the aerosol optical depth by using an empirical relationship between the zenith night sky brightness measured at night with the CoSQM and the aerosol optical depth retrieved at daytime from the AErosol Robotic NETwork. Such a method is especially suited to light-polluted regions with light pollution sources located within a few kilometers of the observation site. A coherent day-to-night aerosol optical depth and Ångström Exponent evolution in a set of 354 days and nights from August 2019 to February 2021 was verified at the location of Santa Cruz de Tenerife on the island of Tenerife, Spain. The preliminary uncertainty of this technique was evaluated using the variance under stable day-to-night conditions, set at 0.02 for aerosol optical depth and 0.75 for Ångström Exponent. These results indicate the set of CoSQM and the proposed methodology appear to be a promising tool to add new information on the aerosol optical properties at night, which could be of key importance to improve climate predictions.

Fire is a common disturbance in the Brazilian savanna (Cerrado), wherein high-frequency fires drive the vegetation structure, composition, function, and dynamics of savanna ecosystems. Under climate change pressure, further understanding of fire-vegetation relationships and interactions can provide new approaches for establishing strategies for integrated fire management and promote savanna vegetation recovery post-fire. To understand how 15 years of yearly manipulated burning affects the vertical and horizontal structure of the vegetation, the species composition and diversity metrics (species richness, Shannon's diversity, and Pielou's evenness), and the aboveground carbon stocks, we surveyed all woody plant species with a diameter greater than three centimeters in 15 plots of a typical Brazilian savanna (cerrado stricto sensu) at an experimental research station in central Brazil (Cerrado biome). Fifteen plots (five per treatment) were differently affected by fire events over a decade, comprising three treatments: (i) annual fire, (ii) legacy fire (> 11 years since the last fire event), and control (not burned in the past 30 years). Non-metric multidimensional scaling (NMDS) analysis indicated a significant effect of fire on the species composition among treatments and that some species benefited from fire, such as Erythroxylum suberosum, whereas others propagated better without fire, such as Camisala montana and Dalbergia miscolobium. Over a decade of annual fire events led to decreases in Shannon's diversity, species richness, and stem density, which were significantly lower in the annual fire treatment than in the control and legacy fire treatments. Stem density by diameter and height size class (except for the 1-2 m class and above 8 m) was higher in the control than in the annual fire treatment, but the number of dead trees did not differ between the control and annual fire treatments. Our results also showed that fire was a factor in changes in the evaluated parameters, such as annual fire treatment, which reduced the amount of biomass and, therefore, the carbon stock. This study suggests that, if burned yearly, typical Brazilian savannas can become less biodiverse in terms of woody plant species and negatively affect their fire resilience. Therefore, fire management practices should focus on determining the frequency with which these ecosystems may benefit the most from fire disturbances.

The term Concept has been a prominent part of investigations in psychology and neurobiology where, mostly, it is mathematically or theoretically represented. The Concepts are also studied computationally through their symbolic, distributed and hybrid representations. The majority of these approaches focused on addressing concrete concepts notion, but the view of the abstract concept is rarely explored. Moreover, most computational approaches have a predefined structure or configurations. The proposed method, Regulated Activation Network (RAN), has an evolving topology and learns representations of Abstract Concepts by exploiting the geometrical view of Concepts, without supervision. In the article, the IRIS data was used to demonstrate: the RAN's modeling; flexibility in concept identifier choice; and deep hierarchy generation. Data from IoT's Human Activity Recognition problem is used to show automatic identification of alike classes as abstract concepts. The evaluation of RAN with 8 UCI benchmarks and the comparisons with 5 Machine Learning models establishes the RANs credibility as a classifier. The classification operation also proved the RAN's hypothesis of abstract concept representation. The experiments demonstrate the RANs ability to simulate psychological processes (like concept creation and learning) and carry out effective classification irrespective of training data size.

CYP1B1 loss-of-function (LoF) is the main known genetic alteration present in recessive primary congenital glaucoma (PCG), an infrequent disease characterized by delayed embryonic development of the ocular iridocorneal angle and caused by poorly understood molecular mechanisms. To model CYP1B1 LoF underlying PCG, we developed a cyp1b1 knockout (KO) zebrafish line using CRISPR/Cas9 genome editing. This line carries the c.535_667del frameshift mutation that results in a 72% mRNA reduction with residual mRNA predicted to produce an inactive truncated protein (p.(His179Glyfs*6)). Craniofacial defects and jaw maldevelopment were observed in 23% of somatic mosaic F0 crispant larvae (144 hpf). These early phenotypes were not detected in KO F3 larvae (144 hpf) but 27% of adult fishes (4 months) showed uni or bilateral craniofacial alterations, indicating the existence of incomplete penetrance and variable expressivity. These phenotypes increased to 86% in the adult offspring of inbred progenitors with craniofacial defects. No glaucoma-related phenotypes were observed in the cyp1b1 mutants. Transcriptomic analyses of the offspring (7dpf) of KO cyp1b1 progenitors with adult-onset craniofacial defects revealed that differentially expressed genes were functionally enriched in groups related with extracellular matrix and cell adhesion, cell growth and proliferation, lipid metabolism (retinoids, steroids, and fatty acids, and oxidation-reduction processes which included several cytochrome P450 genes) and inflammation. In summary, this study shows the complexity of phenotypes and molecular pathways associated with cyp1b1 LoF, with species-dependency, and provides evidence for dysregulation of extracellular matrix gene expression as one of the mechanisms underlaying pathogenicity associated with cyp1b1 disruption.

Thanks to several Vitis vinifera backcrosses with an initial V. vinifera L. × V. rotundifolia (previously Muscadinia rotundifolia) interspecific cross, the MrRUN1/MrRPV1 locus (resistance to downy and powdery mildews) was introgressed in genotypes phenotypically close to V. vinifera varieties. To check the consequences of introgressing parts of the V. rotundifolia genome on gene expression during fruit development, we conducted a comparative RNA-seq study on single berries from different V. vinifera cultivars and V. vinifera × V. rotundifolia hybrids, including ‘G5’ and two derivative microvine lines, ‘MV102’ (resistant) and ‘MV32’ (susceptible) segregating for the MrRUN1/RPV1 locus. RNA-Seq profiles were analyzed on a comprehensive set of single berries from the end of the herbaceous plateau to the ripe stage. Pair-end reads were aligned both on V. vinifera PN40024.v4 reference genome, V. rotundifolia cv ‘Trayshed’ and cv ‘Carlos’, and to the few resistance genes from the original V. rotundifolia cv ‘52’ parent available at NCBI. Weighted Gene Co-expression Network Analysis (WGCNA) led to classifying the differentially expressed genes into 15 modules either preferentially correlated with resistance or berry phenology and composition. Resistance positively correlated transcripts predominantly mapped on the 4-5 Mb distal region of V. rotundifolia chromosome 12 beginning with the MrRUN1/MrRPV1 locus, while the negatively correlated ones mapped on the orthologous V. vinifera region, showing this large extremity of LG12 remained recalcitrant to internal recombination during the successive backcrosses. Some constitutively expressed V. rotundifolia genes were also observed at lower density outside this region. Genes overexpressed in developing berries from resistant accessions, either introgressed from V. rotundifolia, or triggered by these in the vinifera genome, spanned various functional groups, encompassing calcium signal transduction, hormone signaling, transcription factors, plant–pathogen-associated interactions, disease resistance proteins, ROS and phenylpropanoid biosynthesis. This transcriptomic insight provides a foundation for understanding the disease resistance inherent in these hybrid cultivars and suggests a constitutive expression of NIR NBS LRR triggering calcium signaling. Moreover, these results illustrate the magnitude of transcriptomic changes caused by the introgressed V. rotundifolia background in backcrossed hybrids, on a large number of functions largely exceeding the ones constitutively expressed in single resistant gene transformants.

IntroductionThe use of protective masks, especially medical masks, increased dramatically during the COVID-19 crisis. Medical masks are made of synthetic materials, mainly polypropylene, and a majority of them are produced in China and imported to the European market. The urgency of the need has so far prevailed over environmental considerations.ObjectiveAssess the environmental impact of different strategies for the use of facemaskMethod Different strategies for the use of medical and community masks are being investigated for their environmental impact in this study. 8 scenarios, differentiating the typologies of masks and the modes of reuse are compared using several environmental impact indicators, mainly the Global Warming Potential (GWP100), and the plastic leakage (PL). This study attempts to provide clear recommendations that consider both the environmental impact and the protective effectiveness of face masks used in the community.Results The environmental impact of single-use masks is the most unfavorable, with a GWP of 0.4 -1.3 kgCO₂ eq., depending on the transport scenario, and a PL of 1.8 g, for a one month protection against COVID-19. The use of home-made cotton masks and prolonged use of medical masks through wait-and-reuse are the scenarios with the lowest impact.ConclusionThe use of medical masks with a wait and reuse strategy seems to be the most appropriate when considering both environmental impact and effectiveness. Our results also highlight the need to develop procedures and the legal/operational framework to extend the use of protective equipment during a pandemic.

This paper presents new perspectives of photonic technologies for capsule endoscopy. It is firstly presented a review about conventional endoscopy (upper endoscopy and colonoscopy), followed by capsule endoscopy (CE), as well as their techniques, advantages, and drawbacks. The technologies for CEs presented in this paper have the potential of integration on the existing endoscopic systems commercially available. Such technologies include narrow band imaging (NBI), photodynamic therapy (PDT), confocal laser endomicroscopy (CLE), Optical coherence tomography (OCT) and Spectroscopy in order to improve the performance of the gastrointestinal (GI) tract examination. In the context of NBI, two optical filters were designed and fabricated for integration into endoscopic capsules, allowing the visualization of light centered at the 415 nm and 540 nm. These optical filters are based on the principle of Fabry-Perot and were made of thin-films of titanium dioxide (TiO2) and silicon dioxide (SiO2). Moreover, strategies and aid solutions for the adaptation of ECs for PDT are also discussed.

If we accept that human body is a dissipative structure, then the recovery of the body should be considered as a redirection of the enegy flows. The recovery of vertebral cartilage through redirecting of inner dissipative flow requires the understanding of how is this case the fact of reversibility can be proven. We proposed the approach, that according to the collected data, satisfies all the scientific requirements.

Supplier selection/replacement strategies and optimized purchasing policies play a key role in efficient supply chain management in today’s dynamic market. Here we study supplier replacement in a one-level assembly system (OLAS) producing one type of finished product. To assemble the product, we need to provide multi-type components, but assembly will be interrupted if any single component is missing, and incoming units will get hoarded until the missing component arrives. The assembly process can be interrupted by various sources of uncertainty, including delays in component deliveries. There is consequently a non-negligible risk that the assembly process may get stopped any moment. This brings inventory-related costs, which should be minimized. Here we consider discrete lead-time distributions to mimic industry-world reality. We present a model that takes into account not only optimal assignment of component order release dates but also replacement of a critical supplier. For a given unit, we model several alternative suppliers with alternative pricing and lead-time uncertainties, and we evaluate the impact on the total assembly system. For a more general case where several suppliers may be replaced, we propose a genetic algorithm.

Gas flaring is a disposal process widely used in the oil extraction and processing industry. It consists in the burning of unwanted gas at the tip of a stack and due to its thermal characteristic and the thermal emission it is possible to observe and to quantify it from space. Spaceborne observations allows us to collect information across regions and hence to provide a base for estimation of emissions on global scale. We have successfully adapted the Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire algorithm for the detection and characterisation of persistent hot spots, including gas flares, to the Sea and Land Surface Temperature Radiometer (SLSTR) observations on-board the Sentinel- satellites. A hot event at temperatures typical of a gas flare will produce a local maximum in the night-time readings of the shortwave and mid-infrared (SWIR and MIR) channels of SLSTR. The SWIR band centered at 1.61 mmis closest to the expected spectral radiancemaximumand serves as the primary detection band. The hot source is characterised in terms of temperature and area by fitting the sum of two Planck curves, one for the hot source and another for the background, to the radiances from all the available SWIR, MIR and thermal infra-red channels of SLSTR. The flaring radiative power is calculated from the gas flare temperature and area. Our algorithm differs from the original VIIRS Nightfire algorithm in three key aspects: (1) It uses a granule-based contextual thresholding to detect hot pixels, being independent of the number of hot sources present and their intensity. (2) It analyses entire clusters of hot source detections instead of individual pixels. This is arguably a more comprehensive use of the available information. (3) The co-registration errors between hot source clusters in the different spectral bands are calculated and corrected. This also contributes to the SLSTR instrument validation. Cross-comparisons of the new gas flare characterisation with temporally close observations by the higher resolution German FireBIRD TET-1 small satellite and with the Nightfire product based on VIIRS on-board the Suomi-NPP satellite show general agreement for an individual flaring site in Siberia and for several flaring regions around the world. Small systematic differences to VIIRS Nightfire are nevertheless apparent. Based on the hot spot characterisation, gas flares can be identified and flared gas volumes and pollutant emissions can be calculated with previously published methods.

Diabetes mellitus (DM) poses a significant challenge to global health, with its prevalence projected to rise dramatically by 2045. This narrative review explores the bidirectional relationship between periodontitis (PD) and type 1 diabetes mellitus (T1DM), focusing on cellular and molecular mechanisms derived by the interplay between oral microbiota and the host immune response. A comprehensive search of literature published between 2008 and 2023 was conducted to elucidate the association between these two diseases. Preclinical and clinical evidence suggests a bidirectional relationship, with individuals with T1DM exhibiting heightened susceptibility to periodontitis, and vice versa. The review includes recent findings from human clinical studies, revealing variations in oral microbiota composition in T1DM patients, including increases in certain pathogenic species such as Porphyromonas gingivalis, Prevotella intermedia, and Aggregatibacter actinomycetemcomitans, along with shifts in microbial diversity and abundance. Molecular mechanisms underlying this association involve oxidative stress and dysregulated host immune responses, mediated by inflammatory cytokines such as IL-6, IL-8, and MMPs. Furthermore, disruptions in bone turnover markers, such as RANKL and OPG, contribute to periodontal complications in T1DM patients. While preventive measures to manage periodontal complications in T1DM patients may improve overall health outcomes, further research is needed to understand the intricate interactions between oral microbiota, host response, periodontal disease, and systemic health in this population.

: Polymer single screw extrusion is a major industrial processing technique used to obtain plastics products. To assure high outputs, tight dimensional tolerances, and excellent product performance, extruder screws may show different design characteristics. Barrier screws, which contain a second flight in the compression zone, have become quite popular as they promote and stabilize polymer melting. Therefore, it is important to design efficiently extruder screws and decide whether a conventional screw will do the job efficiently, or a barrier-screw should be considered instead. This work uses multi-objective evolutionary algorithms to design conventional and barrier screws (Maillefer screws will be studied) with optimized geometry. Processing of two polymers, Low Density Polyethylene and Polypropylene, is analyzed. A methodology based on the use of Artificial Intelligence (AI) techniques, namely data mining, decision making and evolutionary algorithms, is presented and utilized to obtain results with practical significance, based on relevant performance measures (objectives) used in the optimization. For the various case studies selected, Maillefer screws were generally advantageous for processing LDPE, while for PP the use of both types of screws would be feasible.

The Atmospheric Dynamics Mission ADM-Aeolus was successfully launched in August 2018 by the European Space Agency (ESA). The Aeolus mission carried a single instrument, the first-ever Doppler Wind Lidar (DWL) in space, called Atmospheric LAser Doppler INstrument (ALADIN). Aeolus circled the Earth in a polar sun-synchronous orbit at about 320 km altitude with a repeat cycle of 7 days, providing vertical profiles of horizontal line-of-sight (HLOS) winds on a global scale. Exceeding scientific expectations, Aeolus, initially designed for a 3-year lifetime, provided global coverage of wind profiles for almost five years. In this long-term validation study, we assessed the accuracy of the Level 2B (L2B) Rayleigh-clear and Mie-cloudy wind products by collecting radiosonde data from three stations in the Brazilian Amazon: Cruzeiro do Sul (7.62° S, 72.67° W), Porto Velho (8.76° S, 63.91° W), and Rio Branco (10.00° S, 67.80° W). Statistical validation was conducted by comparing Aeolus L2B wind products and radiosonde data, launched daily at 12:00 UTC from October 2018 to March 2023. Considering all data collected during the mission period, Pearson’s coefficient (R) of 0.73 was observed in Rayleigh-clear and 0.85 in Mie-cloudy, revealing a strong correlation between Aeolus and radiosonde winds. No significant biases were detected, with values of -0.14 m/s for Rayleigh-clear and -0.40 m/s for Mie-cloud, fulfilling the mission requirement of having absolute biases below 0.7 m/s. However, when analyzed annually, in 2022, the bias for Rayleigh-clear was -0.95 m/s, exceeding the mission requirements.

In cold-climate regions, road salt is used as a de-icer for winter road maintenance. The applied road salt melts ice and snow on roads, being washed off through storm sewer systems into nearby urban streams, harming the freshwater ecosystem. Addressing the gap in the knowledge regarding the use of deep learning approaches for urban stream water quality forecasting, the present work discusses our implementation of a “Graph Neural Network” - “Sample and Aggregate” (GNN-SAGE) model for forecasting chloride concentrations in the Credit River in Ontario, Canada. The proposed GNN-SAGE is compared to other models, including a Deep Neural Network based transformer (DNN-Transformer) and a benchmarking persistence model for 6 hours forecasting horizon. Ac-cording to the results, the GNN-SAGE model surpasses other models in providing accurate predic-tions of chloride concentrations within the assessed prediction window. Also, a SHAP analysis provides insight into the variables that influence the model’s forecasting, showing the impact of the spatiotemporal neighbouring data from the network and the seasonality variables on the model’s result. The GNN-SAGE model shows potential for use in real-time forecasting of water quality in urban streams, aiding in the development of regulatory policies to protect the vulnerable freshwater ecosystems in urban areas.

The ingestion of ethanol presents a significant global public health challenge. Chronic alcohol consumption is associated with the development of conditions such as cancer, cirrhosis, hepatic diseases, cardiovascular diseases, and diabetes, as well as neurological impairments that may predispose individuals to dementia, schizophrenia, and behavioral disorders such as violence and suicide. Alcohol-related fatalities constitute 5.3% of worldwide deaths annually, totaling approximately 3 million. Notably, alcohol consumption is implicated in 25% of cirrhosis-related deaths and 4.1% of cancer-related deaths. Taurine (2-aminoethanesulfonic acid) is a non-protein β-amino acid essential for cellular homeostasis, possessing antioxidant, anti-inflammatory, and cytoprotective properties crucial for life maintenance. This study aimed to evaluate the effects of taurine administration on hippocampal neurogenesis, neuronal preservation or reverse damage in rats exposed to forced ethanol consumption. Results demonstrated the neuroprotective effects of taurine on ethanol-treated rats, promoting tissue regeneration and suggesting its pivotal role in this process. However, this aminoacid failed to induce neurogenesis in the tissues of healthy rats, implying that its activity may be contingent upon post-injury stimuli.

Avatar therapy (AT) is a novel virtual reality-based psychotherapy that has been developed to treat auditory verbal hallucinations (AVH) in treatment-resistant schizophrenia patients. Various psychotherapeutic components, such as emotions and sense of presence, could contribute to clinical outcomes. However, the interplay between sense of presence, emotions and clinical response has seldom been investigated. This study aimed to explore the relations between sense of presence, emotions, and clinical outcomes in AT. To conduct this investigation, data from previous and ongoing AT trials were used. Sense of presence and emotions were assessed using standardized questionnaires. AVH were evaluated using the Psychotic Symptom Rating Scales. While sense of presence was positively associated with positive emotions such as control and serenity, no significant associations were found for negative emotions. Moreover, a higher level of sense of presence was associated with a bigger decrease in AVH. Overall, positive emotions seem to be associated with sense of presence in AT. Sense of presence also seems to be involved in the therapeutic outcome, thereby suggesting that this could be an important component related to clinical response. More studies are needed to confirm these trends, which could be generalized to other virtual reality-based psychotherapies.

The quality of yarn is essential in the control of the fabrics processes. There is some commercial equipment that measures the quality of yarn based on sensors, of different types, used for collecting data about some yarn characteristic parameters. The existing equipment, for the above-mentioned purpose, is characterized by its high size and cost, and for allowing the analysis of only few yarn quality parameters. Thus, this paper presents the development and results obtained with the design of a mechatronic prototype integrating a computer vision system that allows, among other parameters, the analysis and classification, in real time, of the hairs of the yarn using artificial intelligence techniques. The system also determines other characteristics inherent to the yarn quality analysis, such as: linear mass, diameter, volume, twist orientation, twist step, average mass deviation, coefficient of variation, hairiness coefficient, average hairiness deviation and standard hairiness deviation, as well as performs spectral analysis. A comparison of the obtained results with the designed system and a commercial equipment was performed validating the undertaken methodology.

Nature-based solutions (NbS) are increasingly recognised for their potential to address both the climate and biodiversity crises. These outcomes are interdependent, and both rely on the capacity of NbS to support and enhance the health of an ecosystem: its biodiversity, the condition of its abiotic and biotic elements, and its capacity to function normally despite environmental change. However, while understanding of ecosystem health outcomes of nature-based interventions for climate change mitigation is growing, the outcomes of those implemented for adaptation remain poorly understood with evidence scattered across multiple disciplines. To address this, we conducted a systematic review of the outcomes of 109 nature-based interventions for climate change adaptation using 33 indicators of ecosystem health across eight broad categories (e.g. diversity, biomass, ecosystem functioning and population dynamics). We showed that 88% of interventions with positive outcomes for climate change adaptation also reported measurable benefits for ecosystem health. We also showed that interventions were associated with a 67% average increase in local species richness. All eight studies that reported benefits in terms of both climate change mitigation and adaptation also supported ecosystem health, leading to a triple win. However, there were also trade-offs, mainly for forest management and creation of novel ecosystems such as monoculture plantations of non-native species. Our review highlights two major limitations of research to date. First, only a limited selection of metrics are used to assess ecosystem health and these rarely include key aspects such as functional diversity and habitat connectivity. Second, taxonomic coverage is poor: 67% of outcomes assessed only plants and 57% did not distinguish between native and non-native species. Future research addressing these issues will allow the design and adaptive management of NbS to support healthy and resilient ecosystems, and thereby enhance their effectiveness for meeting both climate and biodiversity targets.

This study investigates the relationship between energy metals and precious metals to assess their suitability as safe haven assets in clean energy investment portfolios. It focuses on the impact of events in 2020 and 2022, characterized by substantial investments in clean energy. The research reveals that, except for Nickel Futures (NICKELc1), energy metals are positively linked with clean energy indexes. This suggests that they can serve as a secure investment option for green investors looking to diversify their portfolios. As a result, the study dismisses the initial question, indicating that energy metals can indeed be considered safe havens within the context of clean energy investments. Additionally, the research reinforces prior findings that precious metals like gold, silver, and platinum possess safe haven characteristics in relation to specific clean energy stock indexes. These results have significant implications, particularly given the increasing investments in clean energy stocks and the recurring uncertainties in the market. In summary, this study supports the idea that both energy metals and precious metals can play valuable roles in clean energy portfolios, providing stability during turbulent times in the market.

Positioning is an essential aspect of robot navigation, and visual odometry an important technique for continuous updating the internal information about robot position, especially indoors without GPS. Visual odometry is using one or more cameras to find visual clues and estimate robot movements in 3D relatively. Recent progress has been made, especially with fully integrated systems such as the RealSense T265 from Intel, which is the focus of this article. We compare between each other three visual odometry systems and one wheel odometry, on a ground robot. We do so in 8 scenarios, varying the speed, the number of visual features, and with or without humans walking in the field of view. We continuously measure the position error in translation and rotation thanks to a ground truth positioning system. Our result show that all odometry systems are challenged, but in different ways. In average, ORB-SLAM2 has the poorer results, while the RealSense T265 and the Zed Mini have comparable performance. In conclusion, a single odometry system might still not be sufficient, so using multiple instances and sensor fusion approaches are necessary while waiting for additional research and further improved products.

The current focus on afforestation in climate policy runs the risk of compromising both longterm carbon storage and human adaptation. It also works against efforts to stem the tide of biodiversity loss. We outline why an emphasis on diverse, intact natural ecosystems—as opposed to tree plantations with fast-growing exotic species—will help nations deliver the goals of the Paris Agreement and much more.