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Renewable hydrogen obtained from renewable energy sources, especially when produced through water electrolysis, is gaining attention as a promising energy vector to deal with the challenges of climate change and the intermittent nature of renewable energy sources. In this context, this work analyzes a pilot plant that uses this technology, installed in the Itumbiara Hydropower Plant located between the states of Goiás and Minas Gerais, Brazil, from technical and economic perspectives. The plant utilizes an alkaline electrolyzer synergistically powered by solar photovoltaic and hydro sources. Cost data for 2019, when the equipment was purchased, and 2020–2023, when the plant began continuous operation, are considered. The economic analysis includes annualized capital, maintenance, and variable costs, which determines the levelized cost of hydrogen (LCOH). The results obtained for the pilot plant's LCOH were USD 13.00 per kilogram of H2, with an efficiency loss of 2.65% for the two-year period. Sensitivity analysis identified the capacity factor (CF) as the main determinant of the LCOH. Even though the analysis specifically applies to the Itumbiara Hydropower Plant, the CF can be extrapolated to larger plants as it directly influences hydrogen production regardless of plant size or capacity. [ABSTRACT FROM AUTHOR]

The focus on building energy efficiency using alternative materials in structures, especially concrete, and the main technical and environmental challenges therein, aligns with Sustainable Development Goals (SDG). This study proposes a review that analyzes structures made with recycled concrete, relating to the energy efficiency of buildings. A classification structure was proposed, addressing the following questions: (i) What are the various dimensions in which research into energy-efficient recycled concrete is concentrated? (ii) What are the themes and classes of research associated with these dimensions? (iii) What are the main shortcomings of current approaches, and what would be a good research agenda for the future development of energy-efficient recycled concrete? A bibliometric analysis was carried out, presenting geographical and cluster maps to understand different research trends and refine future research. This was followed by a bibliographic analysis, reviewing the most relevant studies from the last five years (2019–2024). The results showed some residual alternative materials (around 45 types from five different industries) used in the production of energy-efficient concrete. And, as a negative effect, as substitution rates increase, porosity is the property with the greatest impact on energy efficiency. The greater the number of pores and the greater their interconnection, the lower the material's thermal insulation. [ABSTRACT FROM AUTHOR]

Air pollution is one of the biggest problems affecting large urban areas. Better monitoring of regions suffering from this type of pollution is in the interest of public health. Although many cities employ sensors to monitor air pollution, a current concern is how to establish the ideal number of sensors to monitor a given geographical region. To address this concern, this research proposes a method to optimize the number of sensors in an air pollution monitoring network to cover a given region efficiently and precisely and uses the metropolitan region of São Paulo, Brazil, and CO sensors as an example. The model of Fragmentation into Groups via Routes is proposed to distribute sensors within micro-regions that display similar air pollution characteristics. A network of virtual sensors is created, and the output of each sensor is established using a method of spatial interpolation called IDW. To identify the optimum sensor configuration, a genetic algorithm is used to assess the topology with the lowest variance of data spread. A lesser number of sensor stations to be treated leads to faster responses to sudden changes in urban conditions. Therefore, municipality authorities can take quick measures to improve the population's wellness. [ABSTRACT FROM AUTHOR]

In real-world scenarios, deviations in the frequency response of instrumentation transformers can lead to distorted harmonic measurements, highlighting the critical role harmonic measurement plays in assessing power quality. The blind channel equalization technique offers a potential solution to improve the frequency response of a large number of instrumentation transformers already installed in substations. These transformers were designed to accurately measure only the fundamental phasor component. Therefore, in order to use them for harmonic phasor measurement, methodologies for reducing frequency distortion must be applied. In this work, we propose a novel approach to improve the frequency response of the instrument transformer using adaptive blind equalization. The blind technique can compensate for distortions caused by voltage and current transducers without requiring prior knowledge of input signals or circuit characteristics. The proposed methodology uses a Linear Prediction Filter to convert the colored noise present at the channel output into white noise. Furthermore, a genetic algorithm is used to find a pole to cancel possible zeroes present in the frequency response of some transducers. The main advantage of blind equalization with the genetic algorithm is its independence, operating without clear information about the channel or the input signal. Through extensive experimentation, we demonstrate the effectiveness of the proposed methodology in significantly reducing the absolute error in ratio and phase caused by current and voltage transformers. Simulated and laboratory experiments are presented in this paper. [ABSTRACT FROM AUTHOR]

Soybean sudden death syndrome (SDS) is a destructive disease that causes substantial yield losses in South and North America. Whereas four Fusarium species were identified as the causal agents, F. virguliforme is the primary SDS-causing pathogen in North America and it also contributes substantially to SDS in Argentina. In this study, we comparatively analyzed genome assemblies of four F. virguliforme strains and identified 29 informative microsatellite markers. Sixteen of the 29 markers were used to investigate the genetic diversity and population structure of this pathogen in a collection of 90 strains from Argentina and the USA. A total of 37 multilocus genotypes (MLGs) were identified, including 10 MLGs in Argentina and 26 in the USA. Only MLG2, the most dominant MLG, was found in both countries. Analyses with three different approaches showed that these MLGs could be grouped into three clusters. Cluster IA consisting of four MLGs exclusively from the USA has much higher genetic diversity than the other two clusters, suggesting that it may be the ancestral cluster although additional data are necessary to support this hypothesis. Clusters IB and II consisted of 13 and 21 MLGs, respectively. MLGs belonging to these two clusters were present in all four sampled states in Argentina and all five sampled states in the USA. [ABSTRACT FROM AUTHOR]

This study compares the performance of machine learning models for selecting COVID-19 and influenza tests during coexisting outbreaks in Brazil, avoiding the waste of resources in healthcare units. We used COVID-19 and influenza datasets from Brazil to train the Decision Tree (DT), Multilayer Perceptron (MLP), Gradient Boosting Machine (GBM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), K-Nearest Neighbors, Support Vector Machine (SVM), and Logistic Regression algorithms. Moreover, we tested the models using the 10-fold cross-validation method to increase confidence in the results. During the experiments, the GBM, DT, RF, XGBoost, and SVM models showed the best performances, with similar results. The high performance of tree-based models is relevant for the classification of COVID-19 and influenza because they are usually easier to interpret, positively impacting the decision-making of health professionals. [ABSTRACT FROM AUTHOR]

Simple Summary: Vaccination against human papillomavirus (HPV) helps prevent cancer caused by this virus. Determining which viral genotypes should be included is key for developing successful vaccination strategies. Low-risk genotypes, especially HPV6 and HPV11, are associated with benign warts. However, some studies also report their presence in cancers. We reviewed the scientific literature to estimate the proportion of cancers that bear single or dual HPV6/11 infections. HPV6 and HPV11 have been reported in up to 5.5% of penile and 87.5% of laryngeal cancers; however, they have not been reported in vulvar, vaginal or oral cancers. Next, we compared the HPV6/11 genomes with HPV16, the most common high-risk HPV genotype, and observed that the similarities mainly involved the E7 gene, suggesting a limited ability to interfere with the differentiation of the host cells. These findings support the use of HPV vaccines that cover HPV6/11 not only for preventing genital warts but also for preventing specific types of cancers. High-risk human papillomavirus (HPV) is etiologically related to cervical cancer, other anogenital cancers and oropharyngeal carcinomas. Low-risk HPV, especially HPV6 and HPV11, cause genital warts and laryngeal papillomas. However, the accumulating data suggests that HPV6 and HPV11 may cause malignant lesions at non-cervical anatomic sites. This review aims to estimate the proportions of single and dual HPV6/11 infections in multiple cancers reported in the last 10 years in the Cochrane, Embasa and PubMed databases. Secondly, the genomes of HPV6/11 were compared with the most common high-risk genotype, HPV16, to determine the similarities and differences. A total of 11 articles were selected, including between one and 334 HPV+ cancer patients. The frequencies of single or dual HPV6/11 infections ranged between 0–5.5% for penile and 0–87.5% for laryngeal cancers and were null for vulvar, vaginal and oral cancers. The genomic similarities between HPV6/11 and HPV16 mainly involved the E7 gene, indicating a limited ability to block cell differentiation. The presence of single or dual HPV6/11 infections in variable proportions of penile and laryngeal cancers support the vaccination strategies that cover these genotypes, not only for preventing genital warts but also for cancer prevention. Other risk factors and co-carcinogens are likely to participate in epithelial carcinogenesis associated with low-risk HPV. [ABSTRACT FROM AUTHOR]

The escalating demand for natural resources within the construction industry is progressing upward. At the same time, however, there is a great concern regarding the depletion of these resources. This review paper emphasizes the significance of utilizing alternative aggregate materials in concrete. Particularly, it aims to explore replacing natural sand with stone dust. On the one hand, the depletion of primary sources of natural sand worldwide, combined with environmental and ecological concerns, drives the adoption of alternative aggregate materials for sustainable concrete construction. On the other hand, stone dust, a waste from the quarrying industry, offers a cost-effective and practical solution for producing concrete. This article presents a comprehensive literature review of the main trends in utilizing stone dust in recycled aggregates in the past decade and its influence on concrete properties. It addresses critical research questions regarding the physical and chemical properties of stone dust aggregates compared to natural sand; the impact of stone dust on the workability, mechanical, physical, and durability properties of recycled concrete; and the potential reduction of environmental impacts in terms of energy consumption and emissions through the replacement of natural sand with stone dust. Ultimately, this paper proposes future investigative work based on identified research gaps. [ABSTRACT FROM AUTHOR]

Discrimination and limited access to healthcare services in remote areas can affect vaccination coverage. Therefore, this study aimed to estimate vaccination coverage for children living in quilombola communities and rural settlements in the central region of Brazil during their first year of life and to analyze the factors associated with incomplete vaccination. An analytical cross-sectional study was conducted on children born between 2015 and 2017. The percentage of children who received all vaccines recommended by the National Immunization Program in Brazil by 11 months and 29 days was used to calculate immunization coverage. Children who received the following vaccines were considered as having a complete basic vaccination schedule: one dose of BCG; three doses of Hepatitis B, of Diphtheria-Tetanus-Pertussis (DPT), of Haemophilus influenzae type b (Hib), and of Poliovirus (Polio); two doses of Rotavirus, of 10-valent pneumococcal (PCV10), and of Serogroup C meningococcal conjugate (MenC); and one dose of Yellow Fever (YF). Measles-mumps-rubella (MMR) and other doses recommended at or after 12 months were not included. Consolidated logistic regression was used to identify factors associated with incomplete vaccination coverage. Overall vaccination coverage was 52.8% (95% CI: 45.5–59.9%) and ranged from 70.4% for the Yellow Fever vaccine to 78.3% for the Rotavirus vaccine, with no significant differences between the quilombola and settler groups. Notably, the likelihood of incomplete general vaccination coverage was higher among children who did not receive a visit from a healthcare professional. Urgent strategies are required to achieve and ensure health equity for this unique and traditionally distinct group with low vaccination coverage. [ABSTRACT FROM AUTHOR]

The industrial impacts on the environment need to be minimized to reduce climate change, which will benefit human beings. Industry 4.0, the new production paradigm, promises productivity gains for companies that manage to implement it, but it is also dependent on natural resources, impacting the environment. The aim of this study is to identify and analyze possible cleaner production strategies associated with Industry 4.0 to optimize manufacturing systems in Industry 4.0 implementation projects, in addition to reducing the environmental impacts of these companies. Through a literature search, cleaner production strategies associated with Industry 4.0 were identified and classified into ten dimensions (strategy, waste, recycling, life cycle, resources, energy, production, work, performance and environment) contributing to the theory. The possibilities of using Industry 4.0 technologies were analyzed to meet each dimension. The relevance of this study lies in presenting possibilities for using and developing technologies and applications to meet these dimensions of cleaner production and helping those involved in Industry 4.0 projects to implement it more stably, contributing to the theory and practice. [ABSTRACT FROM AUTHOR]

Phosphorus (P) is one of the essential macronutrients for plant growth, being a highly required resource to improve the productive performance of several crops, especially in highly weathered soils. However, a large part of the nutrients applied in the form of fertilizers becomes "inert" in the medium term and cannot be assimilated by plants. Rationalizing the use of phosphorus is a matter of extreme importance for environmental sustainability and socioeconomic development. Therefore, alternatives to the management of this nutrient are needed, and the use of P-solubilizing microorganisms is an option to optimize its use by crops, allowing the exploration of less available fractions of the nutrient in soils and reducing the demand for phosphate fertilizers. The objective of this study is to discuss the importance of phosphorus and how microorganisms can intermediate its sustainable use in agriculture. In this review study, we present several studies about the role of microorganisms as phosphorus mobilizers in the soil. We describe the importance of the nutrient for the plants and the main problems related to the unsustainable exploitation of its natural reserves and the use of chemical fertilizers. Mainly we highlight how microorganisms constitute a fundamental resource for the release of the inert portion of the nutrient, where we describe several mechanisms of solubilization and mineralization. We also discussed the benefits that the inoculation of P-solubilizing microorganisms provides to crops as well as practices of using them as bioinoculants. The use of microorganisms as inoculants is a viable resource for the future of sustainable agriculture, mainly because its application can significantly reduce the application of P and, consequently, reduce the exploitation of phosphorus and its reserves. In addition, new research must be conducted for the development of new technologies, prospecting new biological products, and improvement of management practices that allow for higher efficiency in the use of phosphorus in agriculture. [ABSTRACT FROM AUTHOR]

Chronic wound healing represents an impactful financial burden on healthcare systems. In this context, the use of natural products as an alternative therapy reduces costs and maintains effectiveness. Phytotherapeutic gels applied in photodynamic therapy (PDT) have been developed to act as topical healing medicines and antibiotics. The bioactive system is composed of Spirulina sp. (source of chlorophylls) and Copaifera reticulata oil microdroplets, both incorporated into a polymeric blend constituted by kappa-carrageenan (k-car) and F127 copolymer, constituting a system in which all components are bioactive agents. The flow behavior and viscoelasticity of the formulations were investigated. The photodynamic activity was accessed from studies of the inactivation of Staphylococcus aureus bacteria, the main pathogen of hospital relevance. Furthermore, in vivo studies were conducted using eighteen rabbits with dermatitis (grade III and IV) in both paws. The gels showed significant antibiotic potential in vitro, eliminating up to 100% of S. aureus colonies in the presence or absence of light. The k-car reduced 41% of the viable cells; however, its benefits were enhanced by adding chlorophyll and copaiba oil. The animals treated with the phytotherapeutic medicine showed a reduction in lesion size, with healing and re-epithelialization verified in the histological analyses. The animals submitted to PDT displayed noticeable improvement, indicating this therapy's viability for ulcerative and infected wounds. This behavior was not observed in the iodine control treatment, which worsened the animals' condition. Therefore, gel formulations were a viable alternative for future pharmaceutical applications, aiming at topical healing. [ABSTRACT FROM AUTHOR]

Computer vision techniques can monitor the rotational speed of rotating equipment or machines to understand their working conditions and prevent failures. Such techniques are highly precise, contactless, and potentially suitable for applications without massive setup changes. However, traditional vision sensors collect a significant amount of data to process and measure the rotation of high-speed systems, and they are susceptible to motion blur. This work proposes a new method for measuring rotational speed processing event-based data applied to high-speed systems using a neuromorphic sensor. This sensor produces event-based data and is designed to work with high temporal resolution and high dynamic range. The main advantages of the Event-based Angular Speed Measurement (EB-ASM) method are the high dynamic range, the absence of motion blurring, and the possibility of measuring multiple rotations simultaneously with a single device. The proposed method uses the time difference between spikes in a Kernel or Window selected in the sensor frame range. It is evaluated in two experimental scenarios by measuring a fan rotational speed and a Router Computer Numerical Control (CNC) spindle. The results compare measurements with a calibrated digital photo-tachometer. Based on the performed tests, the EB-ASM can measure the rotational speed with a mean absolute error of less than 0.2% for both scenarios. [ABSTRACT FROM AUTHOR]

Agriculture is a fundamental activity for human development. However, its sustainable practice is required to ensure the perpetuity of future resources. In this way, bacteria can benefit crops by making available nutrients and metabolites, mainly contributing to reducing the demand for chemical fertilizers. This study aimed to evaluate the biofertilizing capacity of Azospirillum brasilense, Bacillus megaterium, and Brevibacillus fluminis and their effects on improving the physiological and morphoanatomical properties of strawberry seedlings. In vitro tests were performed to evince their potential to supply nutrients (P and K) and produce siderophores and indole-acetic acid. In an inoculation experiment, these strains were inoculated in isolation and mixed in pairs and triples. This experiment was carried out in a greenhouse in a completely randomized design (CRD). The inoculated treatments were fertilized with 30% N and P demands; the uninoculated control received 30% and 100% of these demands. Leaf gas exchange, total chlorophylls, and crown diameter were evaluated during cultivation. After 138 days, leaf number, nutrient content, root length, root and shoot fresh and dry weight, and total seedlings were evaluated. The bacteria tested positive in all in vitro evaluations except for siderophore production. The strawberry responded positively to inoculation. The inoculation, either in isolation or in a mixture, improved stomatal conductance, leaf transpiration, internal CO2 concentration, leaf N and Mg contents, crown diameter, leaf area, and root elongation. We can conclude that the intermediation of microorganisms improves nutrient use efficiency and reduces the strawberry's fertilizer demand by up to 70%, leading to plant development and yields comparable to complete fertilization. [ABSTRACT FROM AUTHOR]

Several market sectors are attracted by the potential of unmanned aerial vehicles (UAVs), such as delivery, agriculture, and cinema, among others. UAVs are becoming part of Internet of Things (IoT) networks in the development of autonomous and scalable solutions. However, these vehicles are gradually becoming attractive targets for cyberattacks. This study proposes the development of an efficient platform based on the Message Queuing Telemetry Transport (MQTT) protocol for UAV control and Denial-of-Service (DoS) detection embedded in the UAV system. For the efficiency test, latency, network and memory consumption on the platform were measured, in addition to the correlation between payload and delay time. The results of efficiency tests were collected for the three levels of quality of service (QoS). A strong correlation greater than 90% was found between delay and data size for all QoS levels, showing almost a linear proportion. In DoS detection, the best results were a true positive rate (TPR) of 0.97 with 16 features from the AWID2 dataset using LightGBM with Bayesian optimization and data balancing. Unlike other studies, the built platform shows efficiency for UAV control and guarantees security in the communication with the broker and in the Wi-Fi UAV network. [ABSTRACT FROM AUTHOR]

Featured Application: A new algorithm that uses self-organizing maps and entropy calculus to generate a tree structure based on XAI software principles. Data classification is an automatic or semi-automatic process that, utilizing artificial intelligence algorithms, learns the variable and class relationships of a dataset for use a posteriori in situations where the class result is unknown. For many years, work on this topic has been aimed at increasing the hit rates of algorithms. However, when the problem is restricted to applications in healthcare, besides the concern with performance, it is also necessary to design algorithms whose results are understandable by the specialists responsible for making the decisions. Among the problems in the field of medicine, a current focus is related to COVID-19: AI algorithms may contribute to early diagnosis. Among the available COVID-19 data, the blood test is a typical procedure performed when the patient seeks the hospital, and its use in the diagnosis allows reducing the need for other diagnostic tests that can impact the detection time and add to costs. In this work, we propose using self-organizing map (SOM) to discover attributes in blood test examinations that are relevant for COVID-19 diagnosis. We applied SOM and an entropy calculation in the definition of a hierarchical, semi-supervised and explainable model named TESSOM (tree-based entropy-structured self-organizing maps), in which the main feature is enhancing the investigation of groups of cases with high levels of class overlap, as far as the diagnostic outcome is concerned. Framing the TESSOM algorithm in the context of explainable artificial intelligence (XAI) makes it possible to explain the results to an expert in a simplified way. It is demonstrated in the paper that the use of the TESSOM algorithm to identify attributes of blood tests can help with the identification of COVID-19 cases. It providing a performance increase in 1.489% in multiple scenarios when analyzing 2207 cases from three hospitals in the state of São Paulo, Brazil. This work is a starting point for researchers to identify relevant attributes of blood tests for COVID-19 and to support the diagnosis of other diseases. [ABSTRACT FROM AUTHOR]

From a practical point of view, a turbine load cycle (TLC) is defined as the time a turbine in a power plant remains in operation. TLC is used by many electric power plants as a stop indicator for turbine maintenance. In traditional operations, a maximum time for the operation of a turbine is usually estimated and, based on the TLC, the remaining operating time until the equipment is subjected to new maintenance is determined. Today, however, a better process is possible, as there are many turbines with sensors that carry out the telemetry of the operation, and machine learning (ML) models can use this data to support decision making, predicting the optimal time for equipment to stop, from the actual need for maintenance. This is predictive maintenance, and it is widely used in Industry 4.0 contexts. However, knowing which data must be collected by the sensors (the variables), and their impact on the training of an ML algorithm, is a challenge to be explored on a case-by-case basis. In this work, we propose a framework for mapping sensors related to a turbine in a hydroelectric power plant and the selection of variables involved in the load cycle to: (i) investigate whether the data allow identification of the future moment of maintenance, which is done by exploring and comparing four ML algorithms; (ii) discover which are the most important variables (MIV) for each algorithm in predicting the need for maintenance in a given time horizon; (iii) combine the MIV of each algorithm through weighting criteria, identifying the most relevant variables of the studied data set; (iv) develop a methodology to label the data in such a way that the problem of forecasting a future need for maintenance becomes a problem of binary classification (need for maintenance: yes or no) in a time horizon. The resulting framework was applied to a real problem, and the results obtained pointed to rates of maintenance identification with very high accuracies, in the order of 98%. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) is a worldwide public health problem, usually diagnosed in the late stages of the disease. To alleviate such issue, investment in early prediction is necessary. The purpose of this study is to assist the early prediction of CKD, addressing problems related to imbalanced and limited-size datasets. We used data from medical records of Brazilians with or without a diagnosis of CKD, containing the following attributes: hypertension, diabetes mellitus, creatinine, urea, albuminuria, age, gender, and glomerular filtration rate. We present an oversampling approach based on manual and automated augmentation. We experimented with the synthetic minority oversampling technique (SMOTE), Borderline-SMOTE, and Borderline-SMOTE SVM. We implemented models based on the algorithms: decision tree (DT), random forest, and multi-class AdaBoosted DTs. We also applied the overall local accuracy and local class accuracy methods for dynamic classifier selection; and the k-nearest oracles-union, k-nearest oracles-eliminate, and META-DES for dynamic ensemble selection. We analyzed the models' performances using the hold-out validation, multiple stratified cross-validation (CV), and nested CV. The DT model presented the highest accuracy score (98.99%) using the manual augmentation and SMOTE. Our approach can assist in designing systems for the early prediction of CKD using imbalanced and limited-size datasets. [ABSTRACT FROM AUTHOR]

The oral administration of the anti-inflammatory indomethacin (INDO) causes severe gastrointestinal side effects, which are intensified in chronic inflammatory conditions when a continuous treatment is mandatory. The development of hybrid delivery systems associates the benefits of different (nano) carriers in a single system, designed to improve the efficacy and/or minimize the toxicity of drugs. This work describes the preparation of hybrid nanobeads composed of nanostructured lipid carriers (NLC) loading INDO (2%; w/v) and chitosan, coated by xanthan. NLC formulations were monitored in a long-term stability study (25 °C). After one year, they showed suitable physicochemical properties (size < 250 nm, polydispersity < 0.2, zeta potential of −30 mV and spherical morphology) and an INDO encapsulation efficiency of 99%. The hybrid (lipid-biopolymers) nanobeads exhibited excellent compatibility between the biomaterials, as revealed by structural and thermodynamic properties, monodisperse size distribution, desirable in vitro water uptake and prolonged in vitro INDO release (26 h). The in vivo safety of hybrid nanobeads was confirmed by the chicken embryo (CE) toxicity test, considering the embryos viability, weights of CE and annexes and changes in the biochemical markers. The results point out a safe gastro-resistant pharmaceutical form for further efficacy assays. [ABSTRACT FROM AUTHOR]

Safety and effectiveness are crucial quality attributes for insulin infusion pump systems. Therefore, regulatory agencies require the quality evaluation and approval of such systems before the market to decrease the risk of harm, motivating the usage of a formal Model-Based Approach (MBA) to improve quality. Nevertheless, using a formal MBA increases costs and development time because it requires expert knowledge and thorough analyses of behaviors. We aim to assist the quality evaluation of such systems in a cost-effective and time-efficient manner, providing re-usable project artifacts by applying our proposed approach (named MBA with CPN— M B A / C P N ). We defined a Coloured Petri nets MBA and a case study on a commercial insulin infusion pump system to verify and validate a reference model (as a component of M B A / C P N ), describing quality assessment scenarios. We also conducted an empirical evaluation to verify the productivity and reusability of modelers when using the reference model. Such a model is relevant to reason about behaviors and quality evaluation of such concurrent and complex systems. During the empirical evaluation, using the reference model, 66.7% of the 12 interviewed modelers stated no effort, while 8.3% stated low effort, 16.7% medium effort, and 8.3% considerable effort. Based on the modelers' knowledge, we implemented a web-based application to assist them in re-using our proposed approach, enabling simulation-based training. Although a reduced number of modelers experimented with our approach, such an evaluation provided insights to improve the M B A / C P N . Given the empirical evaluation and the case study results, M B A / C P N showed to be relevant to assess the quality of insulin infusion pump systems. [ABSTRACT FROM AUTHOR]

The overlapping problem occurs when a region of the dimensional data space is shared in a similar proportion by different classes. It has an impact on a classifier's performance due to the difficulty in correctly separating the classes. Further, an imbalanced dataset consists of a situation in which one class has more instances than another, and this is another aspect that impacts a classifier's performance. In general, these two problems are treated separately. On the other hand, Prototype Selection (PS) approaches are employed as strategies for selecting appropriate instances from a dataset by filtering redundant and noise data, which can cause misclassification performance. In this paper, we introduce Filtering-based Instance Selection (FIS), using as a base the Self-Organizing Maps Neural Network (SOM) and information entropy. In this sense, SOM is trained with a dataset, and, then, the instances of the training set are mapped to the nearest prototype (SOM neurons). An analysis with entropy is conducted in each prototype region. From a threshold, we propose three decision methods: filtering the majority class (H-FIS (High Filter IS)), the minority class (L-FIS (Low Filter IS)), and both classes (B-FIS). The experiments using artificial and real dataset showed that the methods proposed in combination with 1NN improved the accuracy, F-Score, and G-mean values when compared with the 1NN classifier without the filter methods. The FIS approach is also compatible with the approaches mentioned in the relevant literature. [ABSTRACT FROM AUTHOR]

Paracoccidioidomycosis (PCM), caused by the Paracoccidioides species, is a systemic disease endemic in several Latin American countries, mainly in Brazil, Colombia, Argentina, and Venezuela. Current treatment approaches are challenging as they require prolonged durations of antifungal drugs that have potential toxicities, and despite antifungals, relapses are common. Hence, new therapeutic approaches, such as vaccines, are being investigated. The therapeutic vaccine consisting of peptide P10 associated with lipid cationic DODAB (P10+DODAB) is effective in murine models of PCM. However, the specific immune mechanisms required for the protective response has not been fully elucidated. The present work aims at evaluating the participation of neutrophils in the immune response induced by P10+DODAB. We found that the vaccine reduced both the influx of pulmonary neutrophils and the fungal load in comparison to infected animals that did not receive this treatment. The parenchymal architecture of the lungs of P10+DODAB-treated animals was largely preserved with only a few granulomas present, and tissue cytokine analysis showed a Th1 cytokine profile with augmented levels of IL-12, IFN-γ and TNF-α, and low levels of IL-4. When neutrophils were depleted 24 h prior to each treatment, the effectiveness of the P10+DODAB vaccine was completely lost as the fungal burdens remained high and histological examination showed a marked inflammation and fungal dissemination with a dysregulated cytokine response. In conclusion, these findings indicate that neutrophils are vital to ensure the triggering of an effective immune response to P10+DODAB. [ABSTRACT FROM AUTHOR]

The successful and efficient production of parts with specific features by Wire + Arc Additive Manufacturing (WAAM) strongly depends on the selection of proper and typically interrelated deposition parameters. This task might be particularly challenging in the making of thin walls, which might be highly impacted by processing conditions and heat accumulation. In this context, this study aims at expanding the work envelope and optimizing the parametric conditions in WAAM with relative density and surface aspects of the preforms as quality constraints. The experimental approach was based on the deposition of thin Al5Mg walls by the CMT process on its standard welding setup and with an active cooling technique to enhance the deposition robustness. Internal voids were estimated by Archimedes’ method. The surface quality of the walls was assessed through the visual aspect and the surface waviness by cross-section analysis. All the conditions presented relative density higher than 98%. The upgrade of the standard welding hardware toWAAM purposes through the addition of a supplementary shielding gas nozzle to the torch and the intensity of the heat sinking from the part significantly expanded the process work envelope, with its applicability being successfully demonstrated with multi-objective optimization. To sum up, a decision-making procedure is presented towards achieving intended preform quality. [ABSTRACT FROM AUTHOR]

The technological growth of the last decades has brought many improvements in daily life, but also concerns on how to deal with electronic waste. Electrical and electronic equipment waste is the fastest-growing rate in the industrialized world. One of the elements of electronic equipment is the printed circuit board (PCB) and almost every electronic equipment has a PCB inside it. While waste PCB (WPCB) recycling may result in the recovery of potentially precious materials and the reuse of some components, it is a challenging task because its composition diversity requires a cautious pre-processing stage to achieve optimal recycling outcomes. Our research focused on proposing a method to evaluate the economic feasibility of recycling integrated circuits (ICs) from WPCB. The proposed method can help decide whether to dismantle a separate WPCB before the physical or mechanical recycling process and consists of estimating the IC area from a WPCB, calculating the IC's weight using surface density, and estimating how much metal can be recovered by recycling those ICs. To estimate the IC area in a WPCB, we used a state-of-the-art object detection deep learning model (YOLO) and the PCB DSLR image dataset to detect the WPCB's ICs. Regarding IC detection, the best result was obtained with the partitioned analysis of each image through a sliding window, thus creating new images of smaller dimensions, reaching 86.77% mAP. As a final result, we estimate that the Deep PCB Dataset has a total of 1079.18 g of ICs, from which it would be possible to recover at least 909.94 g of metals and silicon elements from all WPCBs' ICs. Since there is a high variability in the compositions of WPCBs, it is possible to calculate the gross income for each WPCB and use it as a decision criterion for the type of pre-processing. [ABSTRACT FROM AUTHOR]

The objective of this paper was to examine the dynamic impedance estimation of electrical systems from online measurements. The paper makes several considerations and highlights the challenges to obtain a precise estimation. Transducer equalization and harmonic synchrophasor estimation (HSpE) are reviewed and discussed. The use of online and adaptive equalization for transducers proves to be a viable solution for improving voltage transducer's (VT's) and current transducer's (CT's) frequency response. Additionally, the use of oversampling algorithms can mitigate the effects of noise in the HSpE. Furthermore, methods for harmonic impedance estimation are discussed. The independent component analysis ICA-based dynamic impedance estimation is proposed and results presented, which yields excellent agreement. Finally, harmonic modeling and simulation of injected harmonic currents are used to observe resonances through the amplification and attenuations and, consequently, the opportunity to confirm the system self and transfer impedances of a test system. Dynamic impedance estimation will continue to be a great challenge for the power systems engineer as the system complexity increases with the massive insertion of power electronic inverters and the associated required filtering. Real-time signal processing will be an effective tool to determine the dynamic self or transfer impedance. [ABSTRACT FROM AUTHOR]

Background: Paracoccidioidomycosis (PCM) is a chronic disease that causes sequelae and requires prolonged treatment; therefore, new therapeutic approaches are necessary. In view of this, three peptides from Paracoccidioides brasiliensis 14-3-3 protein were selected based on its immunogenicity and therapeutic potential. Methods: The in vitro antifungal activity and cytotoxicity of the 14-3-3 peptides were evaluated. The influence of the peptides in immunological and survival aspects was evaluated in vivo, using Galleria mellonella and the expression of antimicrobial peptide genes in Caenorhabditis elegans. Results: None of the peptides were toxic to HaCaT (skin keratinocyte), MRC-5 (lung fibroblast), and A549 (pneumocyte) cell lines, and only P1 exhibited antifungal activity against Paracoccidioides spp. The peptides could induce an immune response in G. mellonella. Moreover, the peptides caused a delay in the death of Paracoccidioides spp. infected larvae. Regarding C. elegans, the three peptides were able to increase the expression of the antimicrobial peptides. These peptides had essential effects on different aspects of Paracoccidioides spp. infection showing potential for a therapeutic vaccine. Future studies using mammalian methods are necessary to validate our findings. [ABSTRACT FROM AUTHOR]

Paracoccidioidomycosis (PCM) is a granulomatous systemic mycosis caused by the thermo-dimorphic fungi of the genus Paracoccidioides. Melanin production by fungi can affect their pathogenesis and virulence. This study evaluates the production of melanin by different isolates of genus Paracoccidioides and examines how the presence of this polymer affects yeast cell phagocytosis, as well as laccase enzyme production. The results obtained showed that the isolates of genus Paracoccidioides: P. lutzii (Pb01, Pb66, ED01, Pb1578, and Pb8334), P. restrepiensis (PS3-Pb60855), P. brasiliensis (S1-Pb18), and P. americana (PS2-Pbcão) produce melanin in the presence of L-3,4-dihydroxyphenylalanine (L-DOPA). Phagocytosis assays were carried out with peritoneal macrophages from C57Bl/6 mice that were challenged with Pb18, Pb60855, and Pb01. We observed that melanin interferes with phagocytosis in the presence or absence of complement or heat-inactivated serum. This article confirms that different species of the genus Paracoccidioides produce melanin in different magnitudes and that the polymer functions as a virulence factor. [ABSTRACT FROM AUTHOR]

Vaccination is one of the greatest public health achievements in the past century, protecting and improving the quality of life of the population worldwide. However, a safe and effective vaccine for therapeutic or prophylactic treatment of fungal infections is not yet available. The lack of a vaccine for fungi is a problem of increasing importance as the incidence of diverse species, including Paracoccidioides, Aspergillus, Candida, Sporothrix, and Coccidioides, has increased in recent decades and new drug-resistant pathogenic fungi are emerging. In fact, our antifungal armamentarium too frequently fails to effectively control or cure mycoses, leading to high rates of mortality and morbidity. With this in mind, many groups are working towards identifying effective and safe vaccines for fungal pathogens, with a particular focus of generating vaccines that will work in individuals with compromised immunity who bear the major burden of infections from these microbes. In this review, we detail advances in the development of vaccines for pathogenic fungi, and highlight new methodologies using immunoproteomic techniques and bioinformatic tools that have led to new vaccine formulations, like peptide-based vaccines. [ABSTRACT FROM AUTHOR]

Paracoccidioidomycosis (PCM) is a granulomatous fungal disease caused by the dimorphic fungal species of Paracoccidioides, which mainly affects the lungs. Modern strategies for the treatment and/or prevention of PCM are based on a Th1-type immune response, which is important for controlling the disease. One of the most studied candidates for a vaccine is the P10 peptide, derived from the 43 kDa glycoprotein of Paracoccidioides brasiliensis. In order to improve its immune modulatory effect, the P10 peptide was associated with a chitosan-conjugated nanoparticle. The nanoparticles presented 220 nm medium size, poly dispersion index (PDI) below 0.5, zeta potential of +20 mV and encapsulation efficiency around 90%. The nanoparticles’ non-toxicity was verified by hemolytic test and cell viability using murine macrophages. The nanoparticles were stable and presented physicochemical characteristics desirable for biological applications, reducing the fungal load and the usual standard concentration of the peptide from 4 to 20 times. [ABSTRACT FROM AUTHOR]

The purpose of the study was to propose a strategy to assess the potential reduction of the production cost during wire+arc additive manufacturing (WAAM) based on the combination of wire feed speed (related to deposition rate) and travel speed (related to deposition time). A series of experiments, using a multilayer-non-oscillated single pass wall made of an Al-Mg alloy, was conducted. The quality of the wall was assessed through the lateral surface waviness and top layer undulation. The concepts of SurfaceWaviness and Buy-to-Apply indices were introduced. Initially, the range of travel speed (TS) that provided layers with acceptable quality was determined for a given wire feed speed (WFS), corresponding to a constant current. Then, the effect of the increase of production capacity of the process (though current raising, yet maintaining the ratio WFS/TS constant) on the wall quality for a given condition within the TS range was assessed. The results showed that the useful range of TS prevents too rough a waving surface below the lower limit and top surface undulation over the higher limit. However, inside the range, there is little quality variation for the case under study. Finally, simulations of deposition time were developed to demonstrate the weight of the TS on the final deposition time and wall quality as a function of a target wall width. This respective weight showed the existence of a complex and unpredictable, yet determined, power of a combination of TS, target wall geometry, and dead time between subsequent layers. It was verified to be possible to find optimized TS as a function of different target geometries. [ABSTRACT FROM AUTHOR]

Simple Summary: DNA damage induces genome instability, which may elicit cancer development. Defects in the DNA repair machinery further enhance cancer predisposition, but can also be exploited as a therapeutic target. Indeed, targeted agents against specific components of DNA repair, such as PARP inhibitors, are employed in various tumor types, while others, such as ATR, CHK1 or WEE1 inhibitors, are in clinical development. Even though these molecules have proven to be effective in different settings, they display several on- and off-target toxicities, shared by the whole pharmacological class or are drug specific. Among these effects, hematological and gastrointestinal toxicities are the most common, while others are less frequent but potentially life-threatening (e.g., myelodysplastic syndromes). Particular caution is needed in the case of combinatorial therapeutic approaches, which are currently being developed in clinical trials. In any case, it is necessary to recognize and properly manage adverse events of these drugs. This review provides a comprehensive overview on the safety profile of DDR-targeting agents, including indications for their management in clinical practice. Targeting the inherent vulnerability of cancer cells with an impaired DNA Damage Repair (DDR) machinery, Poly-ADP-Ribose-Polymerase (PARP) inhibitors have yielded significant results in several tumor types, eventually entering clinical practice for the treatment of ovarian, breast, pancreatic and prostate cancer. More recently, inhibitors of other key components of DNA repair, such as ATR, CHK1 and WEE1, have been developed and are currently under investigation in clinical trials. The inhibition of DDR inevitably induces on-target and off-target adverse events. Hematological and gastrointestinal toxicities as well as fatigue are common with all DDR-targeting agents, while other adverse events are drug specific, such as hypertension with niraparib and transaminase elevation with rucaparib. Cases of pneumonitis and secondary hematological malignancies have been reported with PARP inhibitors and, despite being overly rare, they deserve particular attention due to their severity. Safety also represents a crucial issue for the development of combination regimens incorporating DDR-targeting agents with other treatments, such as chemotherapy, anti-angiogenics or immunotherapy. As such, overlapping and cumulative toxicities should be considered, especially when more than two classes of drugs are combined. Here, we review the safety profile of DDR-targeting agents when used as single agents or in combination and we provide principles of toxicity management. [ABSTRACT FROM AUTHOR]

The aerobic deterioration of silage nutrients is inevitable in tropical countries, causing negative consequences in animal production systems. Aiming to minimize the losses, the effects of Lactobacillus buchneri strains on fermentation profile and aerobic stability of corn silages were evaluated. The experiment was conducted under a completely randomized design with 13 treatments and three replicates. The treatments were noninoculated, commercial L. buchneri (CI), and 11 wild strains of L. buchneri: LB-56.1, LB-56.2, LB-56.4, LB-56.7, LB-56.8, LB-56.9, LB-56.21, LB-56.22, LB-56.25, LB-56.26, and LB-56.27. The treatments could be divided into three different groups according to silage pH and acetic acid concentration. Silages inoculated with LB-56.1, LB-56.4, and LB-56.9 presented higher pH, whereas intermediate values were observed for LB-56.2, LB-56.7, and LB-56.8. The highest acetic acid production was observed for LB-56.1 and LB-56.7. On the other hand, lowest concentrations were found for CI, LB-56.22, LB-56.25, LB-56.26, and LB-56.27. Higher amounts of NH3–N were observed for LB-56.8, LB-56.21, LB-56.22, and LB-56.27 silages than others. Silage inoculation with CI, LB-56.1, LB-56.2, LB-56.4, LB-56.8, LB-56.9, and LB-56.25 strains had higher aerobic stability than others (59.7 vs. 41.2 h). The L. buchneri strains LB-56.1, LB-56.2, LB-56.4, LB-56.8, LB-56.9, and LB-56.25 provided potential features to improve the aerobic stability of corn silage. [ABSTRACT FROM AUTHOR]

Advanced materials are widely used in many industries. They play an important role especially in the aeronautic and automotive sectors where weight reduction is required in order to reduce fuel consumption. Composite materials have a high strength to weight ratio and are applied in airplane construction. Nevertheless, sometimes it is not viable to replace all metal parts by composite ones due to the cost factor. In this sense, hybrid structures are highly welcome. In order to ensure the safety of these hybrid components during their entire life cycle, non-destructive testing evaluation (NDT&E) methods are used and sometimes they are the only option. In this study, we use infrared thermography (IRT) to inspect an aluminum-composite hybrid structure with a 3D shape. The sample has a composite part with a small metal inlay (EN AW-6082) overmolded with a thermoplastic layer. The inlay is bended to reach the desired 3D geometry. This sample was design to be used for the connection between an A- or B-pillar and a car roof made of carbon fiber reinforced polymer (CFRP). A dual-band infrared camera is used in order to capture images in two different spectral ranges. In addition, two data processing techniques for infrared images are applied to enhance the images: principal component thermography (PCT) and partial least squares thermography (PLST). Then, a signal-to-noise ratio analysis is performed with three randomly chosen previous known defects to assess the quality of the images and detected defects. Results showed that principal component thermography has a slight advantage over partial least squares thermography in our specific experiments. Specifically, for the long-wave infrared band, PCT presented, among the defects analyzed, PCT presented a mean value 12.5% higher while the standard deviation was almost three times lower than PLST. In parallel to the non-detructive analysis, a numerical finite element model was formulated in ANSYS® to analyze the total deformations to which the metal-composite-hybrid structure is subjected during a possible use. Results obtained with the numerical model indicate that the interface region between composite and metal parts is where the highest degree of deformation occur, which indicates possible regions where defects and failures may occur in real use cases. [ABSTRACT FROM AUTHOR]

Endothelial dysfunction in obesity plays a key role in the development of cardiovascular diseases, and it is characterized by increased vascular tonus and oxidative stress. Thus, this study aimed to investigate the vasodilatory and antioxidant activities of Mandevilla moricandiana ethyl acetate fraction and subfractions. Vascular effects were investigated on aorta isolated from control and monosodium glutamate (MSG) induced-obese Wistar rats, and antioxidant activity was assessed by 2,2-diphenyl-1-picrylhydrazyl (DPPH) and oxygen radical absorbance capacity (ORAC) methods. The ethyl acetate fraction (MMEAF) induced a concentration-dependent vasodilation on aortic rings through the NO pathway, with the involvement of histamine H1 and estrogen ERα receptors and showed potent antioxidant activity. In aorta of MSG obese rats, maximal relaxation to acetylcholine was increased in the presence of MMEAF (3 µg/mL), indicating that MMEAF ameliorated obesity-induced endothelial dysfunction. Quercetin and kaempferol aglycones and their correspondent glycosides, as well as caffeoylquinic acid derivatives, A-type procyanidin trimer, ursolic and oleanolic triterpenoid acids were identified in subfractions from MMEAF and seem to be the metabolites responsible for the vascular and antioxidant activities of this fraction. [ABSTRACT FROM AUTHOR]

Simple Summary: The Neotropical brown stink bug Euschistus heros (Fabricius) (Hemiptera: Pentatomidae) is currently one of the major soybean pests in Brazil, causing considerable and often irreversible damage to the crop. The main control strategy for regulating brown stink bug populations has been the use of insecticides, especially those belonging to the neonicotinoid class; however, the spraying of these insecticides does not achieve the desired control efficiency in some locations. Therefore, to improve the programs for detecting and monitoring E. heros resistance to insecticides, a new ingestion bioassay method by liquid diet encapsulation was evaluated in comparison with traditional bioassay methods. The new bioassay method of ingestion by encapsulation is very promising for detecting and monitoring resistance of E. heros populations to neonicotinoid insecticides. The novel ingestion bioassay method was developed for detecting and monitoring resistance of Euschistus heros by encapsulating an artificial liquid diet using Parafilm®. This methodology was compared with the tarsal contact (vial test) and topical application methods for thiamethoxam, imidacloprid, and lambda-cyhalothrin. The best bioassay method for the neonicotinoid insecticides thiamethoxam and imidacloprid was ingestion. For pyrethroid insecticide lambda-cyhalothrin, the best result was obtained by topical application. Using the best bioassay method for each insecticide, the susceptibility to these insecticides was monitored in 30 populations of E. heros collected from soybean crops in Brazil from 2018 to 2020. High variations in susceptibility to thiamethoxam (resistance ratios, 1.6–22 times), imidacloprid (resistance ratios, 1.6–22 times), and lambda-cyhalothrin (resistance ratios, 5–40 times) were detected among the evaluated E. heros populations. In order to monitor the susceptibility of E. heros to insecticides, diagnostic concentrations were defined based on the LC99 of the susceptible reference population: 5.65 µL of a.i./mL for thiamethoxam, 12.45 µL of a.i./mL for imidacloprid, and 0.20 µg of a.i./insect for lambda-cyhalothrin. Subsequently, we select an E. heros strain resistant to neonicotinoid insecticides and another to lambda-cyhalothrin. The resistance ratios obtained after seven selection cycles were 66, 41 and 44 times for thiamethoxam, imidacloprid and lambda-cyhalothrin, respectively. [ABSTRACT FROM AUTHOR]

Technology has been promoting a great transformation in farming. The introduction of robotics; the use of sensors in the field; and the advances in computer vision; allow new systems to be developed to assist processes, such as phenotyping, of crop's life cycle monitoring. This work presents, which we believe to be the first time, a system capable of generating 3D models of non-rigid corn plants, which can be used as a tool in the phenotyping process. The system is composed by two modules: an terrestrial acquisition module and a processing module. The terrestrial acquisition module is composed by a robot, equipped with an RGB-D camera and three sets of temperature, humidity, and luminosity sensors, that collects data in the field. The processing module conducts the non-rigid 3D plants reconstruction and merges the sensor data into these models. The work presented here also shows a novel technique for background removal in depth images, as well as efficient techniques for processing these images and the sensor data. Experiments have shown that from the models generated and the data collected, plant structural measurements can be performed accurately and the plant's environment can be mapped, allowing the plant's health to be evaluated and providing greater crop efficiency. [ABSTRACT FROM AUTHOR]

This paper presents an application of a framework for Big Data Analytical Process and Mapping—BAProM—consisting of four modules: Process Mapping, Data Management, Data Analysis, and Predictive Modeling. The framework was conceived as a decision support tool for industrial business, encompassing the whole big data analytical process. The first module incorporates in big data analytical a mapping of processes and variables, which is not common in such processes. This is a proposal that proved to be adequate in the practical application that was developed. Next, an analytical "workbench" was implemented for data management and exploratory analysis (Modules 2 and 3) and, finally, in Module 4, the implementation of artificial intelligence algorithm support predictive processes. The modules are adaptable to different types of industry and problems and can be applied independently. The paper presents a real-world application seeking as final objective the implementation of a predictive maintenance decision support tool in a hydroelectric power plant. The process mapping in the plant identified four subsystems and 100 variables. With the support of the analytical workbench, all variables have been properly analyzed. All underwent a cleaning process and many had to be transformed, before being subjected to exploratory analysis. A predictive model, based on a decision tree (DT), was implemented for predictive maintenance of equipment, identifying critical variables that define the imminence of an equipment failure. This DT model was combined with a time series forecasting model, based on artificial neural networks, to project those critical variables for a future time. The real-world application showed the practical feasibility of the framework, particularly the effectiveness of the analytical workbench, for pre-processing and exploratory analysis, as well as the combined predictive model, proving effectiveness by providing information on future events leading to equipment failures. [ABSTRACT FROM AUTHOR]

Simple Summary: The European mole is a burrowing mammal which is widely distributed across Britain and much of continental Europe. Its presence is readily confirmed by the presence of molehills, which contain the spoil heaps left behind as the mole digs its underground tunnels. Despite being easy to record, there are very few studies of moles in an urban environment. We asked how area of greenspace (largely parks, recreation areas, nature reserves and playing fields), distance to the nearest patch, human disturbance, how long the green patch had been isolated for, and degree of urban construction around the patch affected mole presence and abundance. We found that patch size affected mole presence, with a minimum greenspace of approximately 10 ha required. Where moles were found, larger patches had more signs of moles and surprisingly, mole abundance was also associated with the degree of urban construction around the greenspace. This result shows how urban planning can affect the presence of unusual species, such as the European mole. The European mole Talpa europaea is common across much of Britain. It has a unique fossorial lifestyle, and evidence of its presence is readily identified through the presence of characteristic molehills. Although molehills are often a common sight in urban greenspaces, moles are remarkably understudied, with very few studies to date exploring the urban ecology of moles. Here, we investigate if factors such as greenspace (largely urban parks and playing fields) area, intensity of management, distance to nearest patch, amount of time the patch had been isolated from other green patches, and the amount of urbanization (constructed surfaces) surrounding the patch, influence the distribution and abundance of urban moles. Mole signs (hills and surface runs) were counted in all discrete urban greenspaces (excluding domestic gardens and one private golf course) within an 89.5 km2 area in the UK town of Reading. We found that 17 out of 59 surveyed sites contained moles, with their presence being recorded in greenspaces with a minimum patch area of approximately 0.1 km2 (10 ha). Where present, the abundance of mole territories in the greenspaces was associated with both the area of greenspace and degree of urbanization within 150 m of the patch boundary. While the former was not surprising, the latter outcome may be a consequence of sites with an increased risk of flooding being home to fewer moles, and the surrounding area is also less likely to be built upon. This case study highlights how choices made in designing urban green infrastructure will determine which species survive in urban areas long into the future. [ABSTRACT FROM AUTHOR]

The objective of this work was to evaluate the phytochemical composition and the antibacterial and antibiotic-modulating activities of the essential oils of Eugenia brasiliensis Lam (OEEb) and Piper mosenii C. DC (OEPm) singly or in association with blue LED (Light-emitting diode) light. The antibacterial and antibiotic-modulatory activities of the essential oils on the activity of aminoglycosides were evaluated to determine the minimum inhibitory concentration (MIC, μg/mL) in the presence or absence of exposure to blue LED light. The chemical analysis showed α-pinene and bicyclogermacrene as major constituents of OEPm, whereas α-muurolol was the main compound of OEEb. Both OEEb and OEPm showed MIC ≥ 512 μg/mL against the strains under study. However, the association of these oils with the blue LED light enhanced the action of the aminoglycosides amikacin and gentamicin. In conclusion, the association of aminoglycosides with the blue LED light and essential oils was effective against resistant bacteria. [ABSTRACT FROM AUTHOR]

Industry is constantly seeking ways to avoid corrective maintenance so as to reduce costs. Performing regular scheduled maintenance can help to mitigate this problem, but not necessarily in the most efficient way. In the context of condition-based maintenance, the main contributions of this work were to propose a methodology to treat and transform the collected data from a vibration system that simulated a motor and to build a dataset to train and test an Artificial Neural Network capable of predicting the future condition of the equipment, pointing out when a failure can happen. To achieve this goal, a device model was built to simulate typical motor vibrations, consisting of a computer cooler fan and several magnets. Measurements were made using an accelerometer, and the data were collected and processed to produce a structured dataset. The neural network training with this dataset converged quickly and stably, while the tests performed, k-fold cross-validation and model generalization, presented excellent performance. The same tests were performed with other machine learning techniques, to demonstrate the effectiveness of neural networks mainly in their generalizability. The results of the work confirm that it is possible to use neural networks to perform predictive tasks in relation to the conditions of industrial equipment. This is an important area of study that helps to support the growth of smart industries. [ABSTRACT FROM AUTHOR]