Differences in chemical composition and antioxidant activity of three propolis samples collected in the same apiary

Financial support provided by FCT (PD/BD/128276/2017), under the Doctoral Programme Agrichains - PD/00122/2012

Active learning in the detection of anomalies in cryptocurrency transactions

The cryptocurrency market has grown significantly, and this quick growth has given rise to scams. It is necessary to put fraud detection mechanisms in place. The challenge of inadequate labeling is addressed in this work, which is a barrier to the training of high-performance supervised classifiers. It aims to lessen the necessity for laborious and time-consuming manual labeling. Some unlabeled data points have labels that are more pertinent and informative for the supervised model to learn from. The viability of utilizing unsupervised anomaly detection algorithms and active learning strategies to build an iterative process of acquiring labeled transactions in a cold start scenario, where there are no initial-labeled transactions, is being investigated. Investigating anomaly detection capabilities for a subset of data that maximizes supervised models’ learning potential is the goal. The anomaly detection algorithms under performed, according to the results. The findings underscore the need that anomaly detection algorithms be reserved for situations involving cold starts. As a result, using active learning techniques would produce better outcomes and supervised machine learning model performance.FCT - Fundação para a Ciência e a Tecnologia(UIDB/00319/2020

Diabetic ketoacidosis in pregnancy

Diabetic ketoacidosis in pregnancy is a rare but potential life-threatening condition for the mother and the fetus. It tends to occur latter in pregnancy and is more common in patients with pregestational diabetes. Obstetricians should be aware of the events that can trigger diabetic ketoacidosis in pregnancy. Prompt recognition and aggressive treatment of this condition are essential in order to reduce perinatal mortality and morbidity. The authors present a case of a pregnant woman with type 1 diabetes with a poor surveillance of pregnancy and noncompliance to treatment that develops severe diabetic ketoacidosis at 34 weeks of gestation

Characterization of initial stages of diabetic macular edema

This study is aimed at characterizing the type of retinal edema in the initial stages of retinopathy in type 2 diabetes. In this retrospective cross-sectional study, spectral domain optical coherence tomography (OCT) layer by layer analysis of the retina in association with OCT-Leakage, an algorithm to detect sites of low optical reflectivity, were used to examine eyes with minimal, mild, and moderate diabetic retinopathy (DR). A total of 142 eyes from 142 patients (28% women) aged 52–88 years were imaged. Macular edema, either subclinical (SCME) or central-involved macular edema (CIME), was present in 43% of eyes in group 10–20, 41% of eyes in group 35, and 38% of eyes in group 43–47. The inner nuclear layer (INL) was the layer showing higher and most frequent increases in retinal thickness (79%). The edema was predominantly intracellular in group 10–20 (65%) and extracellular in groups 35 (77%) and 43–47 (69%). Eyes from diabetic patients in the initial stages of DR with different Early Treatment Diabetic Retinopathy Study gradings show similar prevalence of SCME and CIME, independent of the severity of the retinopathy. Retinal edema is located mainly in the INL and appears to be mostly extracellular except in the earliest stages of diabetic retinal disease where intracellular edema predominates.info:eu-repo/semantics/publishedVersio

A conversion model for OCTA vessel density metrics in diabetic eyes: AngioVue vs Angioplex

To understand measurements variability between 2 different OCTA devices and to develop a conversion model that translate vascular metrics into a standardized and comparable value in patients with different stages of DR.info:eu-repo/semantics/publishedVersio

A penalty scheme for the Tchebycheff scalarization method to optimize the single screw extrusion

The polymer single screw extruder optimal design has been involving the optimization of six objectives. Multi-objective optimization methods, in particular those based on the weighted Tchebycheff Scalarization (wTS) function, have provided reasonable solutions in a way that good trade-offs between conflicting objectives are identified. In this work, a new penalty term is added to the wTS function aiming to guide the solution toward the Pareto front. The corresponding formulation works similarly to the penalty-based boundary intersection function. The goal of the proposed penalty parameter scheme is to balance convergence and diversity. Since six objectives are simultaneously optimized, the penalty scheme provides large as well as small penalty parameter values to enlarge the improving region. The results show that the set of solutions obtained by the penalty-based wTS algorithm can reasonably well cover the Pareto front.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie SkłodowskaCurie grant agreement No. 734205 – H2020-MSCA-RISE-2016

Multi-objective polymer single screw extruder optimization

The optimal design of a polymer single screw extruder may involve various conflicting performance objectives and depends on operating and geometrical parameters. In this work, five bi-objective problems are analysed where one of the most relevant performance objectives, the mass output, is considered in all problems. For simplicity purposes, only the operating parameters are optimized being the geometrical parameters assumed to be fixed. The optimal solution set of each problem is compared with the corresponding two-dimensional projection of the Pareto front obtained when the six objectives are optimized simultaneously. A weighted Tchebycheff scalarization algorithm is implemented based on the simulated annealing method to obtain the Pareto front of the multi-objective optimisation problems.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie SkłodowskaCurie grant agreement No. 734205 – H2020-MSCA-RISE-2016

Developing a Compost Quality Index (CQI) based on the electrochemical quantification of Cd (HA) reactivity

The present work demonstrates the use of Cd2+ as a reactivity probe of the fulvic acids (FAs), humic acids (HAs) and dissolved organic matter (DOM) compost extracts. Significant differences were observed between the extracts, with the HA extract showing the highest reactivity. Comparing the different composts, the largest reactivity variation was again observed for HA then FA and finally DOM extracts. The Cd2+ binding extent was used to calculate the quality of composts and compared with a reference of uncomposted organic fertiliser (FLW), leading to the definition of an operational scale of compost quality. The parameter equivalent mass of fertiliser (mEF) was used for this scale sorted the seven composts from 0.353 to 1.09 kg FLW, for compost of sewage sludge (CSS) and vermicompost of domestic waste (CVDW), respectively. The significance of this parameter was verified through a correlation analysis between binding extent and the effect of compost application on lettuce crop growth in a field trial. The results demonstrate the potentiality of FA and HA extracts as markers of compost bioactivity and the use of Cd2+ as a reactivity probe.This work was financially supported by the Interreg VA Spain–Portugal Programme (EU) through the project Res2ValHum (0366_RES2VALHUM_1_P). A.C. Silva acknowledges receipt of a PhD grant (UMINHO/BD/40/2016) financed by the Operational Programme Norte 2020 (through the Project “NORTE-08-5369-FSE-000033”). J. Antelo and S. Fiol are also grateful for the financial support provided by Xunta de Galicia—Consellería de Educación e Ordenación Universitaria de Galicia (Consolidation of Competitive Groups of Research; GI-1245, ED431C 2022/40)

A Machine Learning App for Monitoring Physical Therapy at Home

Shoulder rehabilitation is a process that requires physical therapy sessions to recover the mobility of the affected limbs. However, these sessions are often limited by the availability and cost of specialized technicians, as well as the patient’s travel to the session locations. This paper presents a novel smartphone-based approach using a pose estimation algorithm to evaluate the quality of the movements and provide feedback, allowing patients to perform autonomous recovery sessions. This paper reviews the state of the art in wearable devices and camera-based systems for human body detection and rehabilitation support and describes the system developed, which uses MediaPipe to extract the coordinates of 33 key points on the patient’s body and compares them with reference videos made by professional physiotherapists using cosine similarity and dynamic time warping. This paper also presents a clinical study that uses QTM, an optoelectronic system for motion capture, to validate the methods used by the smartphone application. The results show that there are statistically significant differences between the three methods for different exercises, highlighting the importance of selecting an appropriate method for specific exercises. This paper discusses the implications and limitations of the findings and suggests directions for future research.info:eu-repo/semantics/publishedVersio