Tribological Performance of Additive Manufactured PLA-Based Parts.

Polymer additive manufacturing has advanced from prototyping to producing essential parts with improved precision and versatility. Despite challenges like surface finish and wear resistance, new materials and metallic reinforcements in polymers have expanded its applications, enabling stronger, more durable parts for demanding industries like aerospace and structural engineering. This research investigates the tribological behaviour of FFF surfaces by integrating copper and aluminium reinforcement particles into a PLA (polylactic acid) matrix. Pin-on-disc tests were conducted to evaluate friction coefficients and wear rates. Statistical analysis was performed to study the correlation of the main process variables. The results confirmed that reinforced materials offer interesting characteristics despite their complex use, with the roughness of the fabricated parts increasing by more than 300%. This leads to an increase in the coefficient of friction, which is related to the variation in the material's mechanical properties, as the hardness increases by more than 75% for materials reinforced with Al. Despite this, their performance is more stable, and the volume of material lost due to wear is reduced by half. These results highlight the potential of reinforced polymers to improve the performance and durability of components manufactured through additive processes.

ELI: an IoT-aware big data pipeline with data curation and data quality.

The complexity of analysing data from IoT sensors requires the use of Big Data technologies, posing challenges such as data curation and data quality assessment. Not facing both aspects potentially can lead to erroneous decision-making (i.e., processing incorrectly treated data, introducing errors into processes, causing damage or increasing costs). This article presents ELI, an IoT-based Big Data pipeline for developing a data curation process and assessing the usability of data collected by IoT sensors in both offline and online scenarios. We propose the use of a pipeline that integrates data transformation and integration tools and a customisable decision model based on the Decision Model and Notation (DMN) to evaluate the data quality. Our study emphasises the importance of data curation and quality to integrate IoT information by identifying and discarding low-quality data that obstruct meaningful insights and introduce errors in decision making. We evaluated our approach in a smart farm scenario using agricultural humidity and temperature data collected from various types of sensors. Moreover, the proposed model exhibited consistent results in offline and online (stream data) scenarios. In addition, a performance evaluation has been developed, demonstrating its effectiveness. In summary, this article contributes to the development of a usable and effective IoT-based Big Data pipeline with data curation capabilities and assessing data usability in both online and offline scenarios. Additionally, it introduces customisable decision models for measuring data quality across multiple dimensions.

Study of Abrasive Water Jet Machining as a Texturing Operation for Thin Aluminium Alloy UNS A92024.

Surface modification of metallic alloys can create hydrophilic or hydrophobic surfaces that enhance the functional performance of the material. For example, hydrophilic surfaces have improved wettability, which improves mechanical anchorage in adhesive bonding operations. This wettability is directly related to the type of texture created on the surface and the roughness obtained after the surface modification process. This paper presents the use of abrasive water jetting as an optimal technology for the surface modification of metal alloys. A correct combination of high traverse speeds at low hydraulic pressures minimises the power of the water jet and allows for the removal of small layers of material. The erosive nature of the material removal mechanism creates a high surface roughness, which increases its surface activation. In this way, the influence of texturing with and without abrasive has been evaluated, reaching combinations where the absence of abrasive particles can produce surfaces of interest. In the results obtained, the influence of the most relevant texturing parameters between hydraulic pressure, traverse speed, abrasive flow and spacing has been determined. This has allowed a relationship to be established between these variables and surface quality in terms of Sa, Sz and Sk, as well as wettability.

Modelling urban cyclists' exposure to black carbon particles using high spatiotemporal data: A statistical approach.

This is a pioneering work in South America to model the exposure of cyclists to black carbon (BC) while riding in an urban area with high spatiotemporal variability of BC concentrations. We report on mobile BC concentrations sampled on 10 biking sessions in the city of Curitiba (Brazil), during rush hours of weekdays, covering four routes and totaling 178 km. Moreover, simultaneous BC measurements were conducted within a street canyon (street and rooftop levels) and at a site located 13 km from the city center. We used two statistical approaches to model the BC concentrations: multiple linear regression (MLR) and a machine-learning technique called random forests (RF). A pool of 25 candidate variables was created, including pollution measurements, traffic characteristics, street geometry and meteorology. The aggregated mean BC concentration within 30-m buffers along the four routes was 7.09 µg m-3, with large spatial variability (5th and 95th percentiles of 1.75 and 16.83 µg m-3, respectively). On average, the concentrations at the street canyon façade (5 m height) were lower than the mobile data but higher than the urban background levels. The MLR model explained a low percentage of variance (24%), but was within the values found in the literature for on-road BC mobile data. RF explained a larger variance (54%) with the additional advantage of having lower requirements for the target and predictor variables. The most impactful predictor for both models was the traffic rate of heavy-duty vehicles. Thus, to reduce the BC exposure of cyclists and residents living close to busy streets, we emphasize the importance of renewing and/or retrofitting the diesel-powered fleet, particularly public buses with old vehicle technologies. Urban planners could also use this valuable information to project bicycle lanes with greater separation from the circulation of heavy-duty diesel vehicles.

[Frequency of CagA-positive Helicobacter pylori strains in 160 patients subjected to endoscopy]. / Prevalencia de cepas cagA-positivo en la región de Coquimbo, determinada mediante nested-qPCR en muestras fecales.

BACKGROUND: Helicobacter pylori is the most significant pathogen associated with gastric diseases, including gastric cancer. Infected patients with strains that are CagA-positive generally have worse outcomes than those infected with CagA-negative strains. Patients infected with CagA-positive strains have a higher risk for developing gastric cancer. AIM: To determine the prevalence of CagA-positive H. pylori strains in fecal samples of patients from the Coquimbo Region of Chile, using a non-invasive, nested-qPCR method. MATERIAL AND METHODS: We evaluated 160 patients with gastrointestinal symptoms subjected to an upper gastrointestinal endoscopy. DNA was extracted from fecal samples and tested for the presence of H. pylori using nested-qPCR for the ureC gene, and subsequently compared with the results of histology-Giemsa stain from the patients' endoscopic biopsies. When H. pylori was found, the presence of CagA-positive strains was determined via nested-qPCR. RESULTS: The histology-Giemsa stain was positive for H. pylori infection in 123 patients (76.9%), while the analysis of fecal samples detected H. pylori in 129 patients (80.6%). The sensitivity and specificity of nested-qPCR to detect the bacterium was 96.7 and 73.0% respectively. Among patients with the infection, 25% had CagA-positive strains. CONCLUSIONS: In this sample of patients, there is a low prevalence of CagA-positive H. pylori strains.

Evaluation of the Functional Performance in Turned Workpieces: Methodology and Application to UNS A92024-T3.

Turning of light alloys as aluminum-based UNS A92024-T3 is broadly implemented in the manufacture of critical aircraft parts, so ensuring a good functional performance of these pieces is essential. Moreover, operational conditions of these pieces include saline environments where corrosion processes are present. In this paper, a methodology for the evaluation of the functional performance in turned pieces is proposed. Specimens affected and not affected by corrosion are compared. In addition, performance in service through tensile stress tests of these parts is considered. The results show that turning improves the functional performance of UNS A92024-T3 alloy and that corrosion can enhance the mechanical properties of this alloy.

Prevalencia de cepas cagA-positivo en la región de Coquimbo, determinada mediante nested-qPCR en muestras fecales / Frequency of CagA-positive Helicobacter pylori strains in 160 patients subjected to endoscopy

Background: Helicobacter pylori is the most significant pathogen associated with gastric diseases, including gastric cancer. Infected patients with strains that are CagA-positive generally have worse outcomes than those infected with CagA-negative strains. Patients infected with CagA-positive strains have a higher risk for developing gastric cancer. Aim: To determine the prevalence of CagA-positive H. pylori strains in fecal samples of patients from the Coquimbo Region of Chile, using a non-invasive, nested-qPCR method. Material and Methods: We evaluated 160 patients with gastrointestinal symptoms subjected to an upper gastrointestinal endoscopy. DNA was extracted from fecal samples and tested for the presence of H. pylori using nested-qPCR for the ureC gene, and subsequently compared with the results of histology-Giemsa stain from the patients' endoscopic biopsies. When H. pylori was found, the presence of CagA-positive strains was determined via nested-qPCR. Results: The histology-Giemsa stain was positive for H. pylori infection in 123 patients (76.9%), while the analysis of fecal samples detected H. pylori in 129 patients (80.6%). The sensitivity and specificity of nested-qPCR to detect the bacterium was 96.7 and 73.0% respectively. Among patients with the infection, 25% had CagA-positive strains. Conclusions: In this sample of patients, there is a low prevalence of CagA-positive H. pylori strains.