A common way of exposing functionality in contemporary systems is by providing a Web-API based on the REST API architectural guidelines. To describe REST APIs, the industry standard is currently OpenAPI-specifications. Test generation and fuzzing methods targeting OpenAPI-described REST APIs have been a very active research area in recent years. An open research challenge is to aid users in better understanding their API, in addition to finding faults and to cover all the code. In this paper, we address this challenge by proposing a set of behavioural properties, common to REST APIs, which are used to generate examples of behaviours that these APIs exhibit. These examples can be used both (i) to further the understanding of the API and (ii) as a source of automatic test cases. Our evaluation shows that our approach can generate examples deemed relevant for understanding the system and for a source of test generation by practitioners. In addition, we show that basing test generation on behavioural properties provides tests that are less dependent on the state of the system, while at the same time yielding a similar code coverage as state-of-the-art methods in REST API fuzzing in a given time limit.

Background Mammogram risk scores based on texture and density defined by different brightness thresholds are associated with breast cancer risk differently and could reveal distinct information about breast cancer risk. We aimed to investigate causal relationships between these intercorrelated mammogram risk scores to determine their relevance to breast cancer aetiology. Methods We used digitised mammograms for 371 monozygotic twin pairs, aged 40–70 years without a prior diagnosis of breast cancer at the time of mammography, from the Australian Mammographic Density Twins and Sisters Study. We generated normalised, age-adjusted, and standardised risk scores based on textures using the Cirrus algorithm and on three spatially independent dense areas defined by increasing brightness threshold: light areas, bright areas, and brightest areas. Causal inference was made using the Inference about Causation from Examination of FAmilial CONfounding (ICE FALCON) method. Results The mammogram risk scores were correlated within twin pairs and with each other ( r  = 0.22–0.81; all P  < 0.005). We estimated that 28–92% of the associations between the risk scores could be attributed to causal relationships between the scores, with the rest attributed to familial confounders shared by the scores. There was consistent evidence for positive causal effects: of Cirrus, light areas, and bright areas on the brightest areas (accounting for 34%, 55%, and 85% of the associations, respectively); and of light areas and bright areas on Cirrus (accounting for 37% and 28%, respectively). Conclusions In a mammogram, the lighter (less dense) areas have a causal effect on the brightest (highly dense) areas, including through a causal pathway via textural features. These causal relationships help us gain insight into the relative aetiological importance of different mammographic features in breast cancer. For example our findings are consistent with the brightest areas being more aetiologically important than lighter areas for screen-detected breast cancer; conversely, light areas being more aetiologically important for interval breast cancer. Additionally, specific textural features capture aetiologically independent breast cancer risk information from dense areas. These findings highlight the utility of ICE FALCON and family data in decomposing the associations between intercorrelated disease biomarkers into distinct biological pathways.

As the future electric power grid will be driven by distributed renewable energy sources, the deployment of grid-connected power converters will also grow to enable seamless grid and energy source interaction. To provide the reliable operation of these converters, the estimation of fundamental grid parameters is important. The most common estimation techniques are a phase-locked loops (PLL) and a frequency-locked loops (FLL). However, those techniques encounter challenges in conducting parameter estimation when the input signal is unbalanced due to DC-offset, harmonics, signal sags, and frequency and phase variations. This paper presents an enhanced FLL loop enriched with an additional loop for estimation and rejection of the DC-offset. Active and reactive power calculations in grid-connected microgrids by using the modified FLL loops with DC-offset rejection is a novel application introduced in this paper. Experimental verification has demonstrated that the enhanced FLL loop provides fast and reliable parameter estimation as well as stable and robust power calculations, even in the presence of a DC-offset.

In memristors and resistance switching devices, there is a region prior to switching which exhibits current transients with potentially useful dynamics. We refer to this region as the subthreshold region owing to it occurring prior to any switching threshold. These transients exhibit a characteristic peaked response with a fast rise in current followed by a slower decay. This behaviour has previously been used to quantify the mobilities of defects drifting within the active layer of the devices, but it has also been used in neuromorphic circuits to carry out edge detection, to implement homeostasis within artificial synapses and could have uses in replicating eligibility traces. We present an empirical SPICE model to reproduce these transients within circuit simulators. The model is compared with experimental datasets for a range of applied voltages and we present experimentally verified parameters for readers to use within their own simulations.

Introduction: Hypertension is a major and modifiable risk factor for cardiovascular diseases. Essential, primary, or idiopathic hypertension accounts for 90–95% of all cases. Identifying novel biomarkers specific to essential hypertension may help in understanding pathophysiological pathways and developing personalized treatments. We tested whether the integration of circulating microRNAs (miRNAs) and clinical risk factors via machine learning modeling may provide useful information and novel tools for essential hypertension diagnosis and management. Materials and methods: In total, 174 participants were enrolled in the present observational case–control study, among which, there were 89 patients with essential hypertension and 85 controls. A discovery phase was conducted using small RNA sequencing in whole blood samples obtained from age- and sex-matched hypertension patients (n = 30) and controls (n = 30). A validation phase using RT-qPCR involved the remaining 114 participants. For machine learning, 170 participants with complete data were used to generate and evaluate the classification model. Results: Small RNA sequencing identified seven miRNAs downregulated in hypertensive patients as compared with controls in the discovery group, of which six were confirmed with RT-qPCR. In the validation group, miR-210-3p/361-3p/362-5p/378a-5p/501-5p were also downregulated in hypertensive patients. A machine learning support vector machine (SVM) model including clinical risk factors (sex, BMI, alcohol use, current smoker, and hypertension family history), miR-361-3p, and miR-501-5p was able to classify hypertension patients in a test dataset with an AUC of 0.90, a balanced accuracy of 0.87, a sensitivity of 0.83, and a specificity of 0.91. While five miRNAs exhibited substantial downregulation in hypertension patients, only miR-361-3p and miR-501-5p, alongside clinical risk factors, were consistently chosen in at least eight out of ten sub-training sets within the SVM model. Conclusions: This study highlights the potential significance of miRNA-based biomarkers in deepening our understanding of hypertension’s pathophysiology and in personalizing treatment strategies. The strong performance of the SVM model highlights its potential as a valuable asset for diagnosing and managing essential hypertension. The model remains to be extensively validated in independent patient cohorts before evaluating its added value in a clinical setting.

The Brief Psychiatric Rating Scale (BPRS) is a useful tool for measuring the severity of psychopathological symptoms among patients with psychosis. Many studies, predominantly in Western countries, have investigated its factor structure. This study has the following aims: (a) to further explore the factor structure of the BPRS-Expanded version (BPRS-E, 24 items) among outpatients with psychotic disorders in Southeast European countries; (b) to confirm the identified model; and (c) to investigate the goodness-of-fit of the three competing BPRS-E factor models derived from previous studies. The exploratory factor analysis (EFA) produced a solution with 21 items grouped into five factors, thus supporting the existence of a fifth factor, i.e., Disorganization. A follow-up confirmatory factor analysis (CFA) revealed a 19-item model (with two items removed) that fit the data well. In addition, the stability of two out of three competing factor models was confirmed. Finally, the BPRS-E model with 5 factors developed in this cross-national study was found to include a greater number of items compared to competing models.

Stroke is one of the leading causes of death and disability. To address this challenge, microwave imaging has been proposed as a portable medical imaging modality. However, accurate stroke classification using microwave signals is still an open challenge. In addition, identified features of microwave signals used for stroke classification need to be linked back to the original data. This work attempts to address these issues by proposing a wavelet convolutional neural network (CNN), which combines multiresolution analysis and CNN to learn distinctive patterns in the scalogram for accurate classification. A game theoretic approach is used to explain the model and indicate distinctive features for discriminating stroke types. The proposed algorithm is tested in simulation and experiments. Different types of noise and manufacturing tolerances are modeled using data collected from healthy human trials and added to the simulation data to bridge the gap between the simulation and real-life data. The achieved classification accuracy using the proposed method ranges from 81.7% for 3D simulations to 95.7% for lab experiments using simple head phantoms. Obtained explanations using the method indicate the relevance of wavelet coefficients on frequencies 0.95-1.45 GHz and the time slot of 1.3 to 1.7 ns for distinguishing ischemic from hemorrhagic strokes.

The awareness of the importance of physical literacy (PL) is globally increasing; however, knowledge of the applicability of PL measurement tools in southeastern Europe is limited. The aim of this study was to evaluate the reliability and validity of translated versions of the CAPL-2 and PLAYself questionnaires in 9- to 11-year-old elementary school children from Croatia, Bosnia and Herzegovina, and Montenegro. The participants were 303 children (141 girls; all 9 to 11 years of age) from Croatia (n = 71), Bosnia and Herzegovina (n = 162), and Montenegro (n = 70), enrolled in regular elementary school. The participants were tested throughout a test–retest procedure using two PL evaluation tools, i.e., the Canadian Assessment of Physical Literacy (shorter version, CAPL-2) and the Physical Literacy Assessment of Youth (PLAYself) questionnaires. With an intraclass correlation (ICC) of 0.70–0.80 for specific questionnaire subscales and 0.84 for the total score, PLAYself was found to be reliable. With Kappa values of 0.11–0.23 and a percentage of absolute agreement of less than 62%, CAPL-2 appeared to be less reliable. Factors related to sport participation were significantly positively associated with the PLAYself score, indicating its proper validity. In conclusion, we suggest the usage of the PLAYself questionnaire in further studies examining PL in children of a similar age in the region. Future studies in other age groups and languages are also warranted.

Background: Compared to conventional 2D mammography, digital breast tomosynthesis (DBT) offers greater breast lesion detection rates. Ring-like hypodense artifacts surrounding dense lesions are a common byproduct of DBT. This study’s purpose was to assess whether minuscule changes spanning this halo—termed the “broken halo sign”—could improve lesion classification. Methods: This retrospective study was approved by the local ethics review board. After screening 288 consecutive patients, DBT studies of 191 female participants referred for routine mammography with a subsequent histologically verified finding of the breast were assessed. Examined variables included patient age, histological diagnosis, architectural distortion, maximum size, maximum halo depth, conspicuous margins, irregular shape and broken halo sign. Results: While a higher halo strength was indicative of malignancy in general (p = 0.031), the broken halo sign was strongly associated with malignancy (p < 0.0001, odds ratio (OR) 6.33), alongside architectural distortion (p = 0.012, OR 3.49) and a diffuse margin (p = 0.006, OR 5.49). This was especially true for denser breasts (ACR C/D), where the broken halo sign was the only factor predicting malignancy (p = 0.03, 5.22 OR). Conclusion: DBT-associated halo artifacts warrant thorough investigation in newly found breast lesions as they are associated with malignant tumors. The “broken halo sign”—the presence of small lines of variable diameter spanning the peritumoral areas of hypodensity—is a strong indicator of malignancy, especially in dense breasts, where architectural distortion may be obfuscated due to the surrounding tissue.

Rising shares of renewable generation raises uncertainty and thus the number of possible power flow scenarios in the power system, which in turn increases the possibility for unforeseen contingencies, such as power line or generator failures and their combinations. Therefore, operators cannot longer rely only on operational experience to deal with every contingency. Our proposed method involves identifying the most effective countermeasures to minimize the impact of contingencies on the power system. We take into account various options, such as load shedding, adjusting phase-shifting transformer angles, and injecting active power using fast elements. The proposed approach considers primary control of generators and its limitations in order to compensate for power imbalances in the system. The problem is formulated as a mixed-integer linear optimization problem, employing DC power flow equations. The applicability of the approach is evaluated on the IEEE 39-bus system, and the scalability of the approach is shown on five systems with up to 6470 buses.

The theory of planned behavior (TPB) is a widely used framework for predicting behavior. Considering that technology supported sexual behavior (e.g., sexting) is widespread among emerging adults, it is logical to assume that it is driven by existing social norms, accepted standards of behavior, and the opinions of others, all of which are core constructs of TPB. Therefore, the purpose of this study is to examine whether intention to sext can be predicted by the constructs of the theory of planned behavior. A quantitative cross-sectional study was conducted with 314 emerging adults aged 18–29 years who completed a self-report questionnaire online. The modified Theory of Planned Behavior Questionnaire for the sexting context, which measures attitudes toward sexting, subjective norms, and perceived behavioral control, and sexting intention, was used. The results of the path analyses mainly confirmed the model proposed by the Theory of planned behavior. Of the three TPB core factors, only subjective norm did not significantly have effect on sexting intention. Our results show that attitude, normative beliefs, and control directly predict sexting intention, and control beliefs indirectly predict sexting intention via perceived behavioral control, partially confirming the sequential relationship proposed by the Theory of planned behavior.

The essential oil (EO) of Artemisia plants contains a large number of bioactive compounds that are widely used. The aim of this study was to analyse the chemical composition of EOs of six Artemisia plants collected in Croatia and to test their cholinesterase inhibitory potential. GC–MS analysis of the EO of A. absinthium showed that the dominant compounds are cis-sabinyl acetate and cis-epoxy-ocimene; in EO of A. abrotanum, it is borneol; in the EO of A. annua, they are artemisia ketone, camphor and 1,8-cineole; in the EO of A. arborescens, they are camphor and chamazulene; in the EO of A. verlotiorum, they are cis-thujone, 1,8-cineole and trans-thujone; and in the EO of A. vulgaris, they are trans-thujone and trans-epoxy-ocimene. The EO of the five studied Artemisia species from Croatia is rich in monoterpenoid compounds (1,8-cineole, artemisia ketone, cis-thujone, trans-thujone, cis-epoxy-ocimene, camphor, borneol and cis-sabinyl acetate). The EO of A. arborescens is also rich in chamazulene. The results also showed that the tested EOs have moderate cholinesterase inhibition potential, especially the EOs of A. annua, A. vulgaris and A. abrotanum. This is the first analysis of the chemical composition of the EOs of four Artemisia plants and the first analysis of cholinesterase potential for plants collected in Croatia.