The ability to robustly quantify the potential for strontium precipitation and scaling in both natural surface waters and water infrastructure systems is limited. In some regions, both surface and ground water supplies contain significant concentrations of naturally occurring radionuclides, such as strontium, that can accumulate in water, soils and sediments, media, and living tissues. Methods for quantifying and predicting the potential for these occurrences are not readily available nor have they been tested and calibrated to cold region aquatic environments. Through extensive literature review, it was determined that a modified calcium carbonate precipitation potential (CCPP) model offered a scientifically credible approach to filling that knowledge gap in both the science and engineering of strontium fate and transport in water. The results from previous field and laboratory experiments were compiled to not only elucidate the fate and transport of strontium in water systems, but also to calculate the logarithmic distribution coefficient, λ, for strontium under co-precipitation conditions. Lambda (λ) is both time- and water-quality sensitive and must be measured as water mixes from source to receiving environment to determine continuous loss of Sr from the water phase. The data were collected to develop the strontium precipitation potential model that can be used in surface water quality assessment. The tool was then applied to pre-existing, publicly available, and extensive datasets for several rivers in Saskatchewan, Canada, to validate the model and produce estimates for strontium precipitation potential in those rivers.

Introduction Resource-oriented interventions can be a low-cost option to improve care for patients with severe mental illnesses in low-resource settings. From 2018 to 2021 we conducted three randomized controlled trials testing resource-oriented interventions in Bosnia and Herzegovina (B&H), i.e. befriending through volunteers, multi-family groups, and improving patient-clinician meetings using the DIALOG+ intervention. All interventions were applied over 6 months and showed significant benefits for patients’ quality of life, social functioning, and symptom levels. In this study, we explore whether patient experiences point to common processes in these interventions. Methods In-depth semi-structured interviews were conducted with 15 patients from each intervention, resulting in a total sample of 45 patients. Patients were purposively selected at the end of the interventions including patients with different levels of engagement and different outcomes. Interviews explored the experiences of patients and were audio-recorded, transcribed, and analysed using the thematic analysis framework proposed by Braun and Clark. Results Three broad themes captured the overall experiences of patients receiving resource-oriented interventions: An increased confidence and agency in the treatment process; A new and unexpected experience in treatment; Concerns about the sustainability of the interventions. Conclusions The findings suggest that the three interventions – although focusing on different relationships of the patients – lead to similar beneficial experiences. In addition to being novel in the context of the mental health care system in B&H, they empower patients to take a more active and confident role in treatment. Whilst strengthening patients’ agency in their treatment may be seen as a value in itself, it may also help to achieve significantly improved treatment outcomes. This shows promise for the implementation of these interventions in other low-resource countries with similar settings.

ABSTRACT A working memory (WM) deficit is a reliable observation in people experiencing anxiety. Whether the level of anxiety is related to the severity of WM difficulties is still an open question. In the present experiment, we investigated this aspect by testing the WM performance of people with different levels of anxiety symptoms. Participants were grouped according to self-report anxiety into a control group with low anxiety scores and an experimental group with clinically relevant anxiety. The experimental group was then divided into a high anxiety group and a severe anxiety group. Participants performed a battery of WM tasks tagging different WM processes. The results showed that, compared to participants with low anxiety, participants with clinically relevant anxiety scores had reduced accuracy in all the WM tasks. Interestingly, participants with high and severe anxiety did not present any significant difference. Anxious participants showed difficulties also in cognitive domains other than WM. Hence, these results supply reliable evidence that people with clinically relevant anxiety scores present WM difficulties, irrespective of symptoms severity. The observation that anxiety compromises performance also in cognitive domains other than WM suggests that the deficit might affect fluid cognition.

ABSTRACT In this study, 3D-printed connectors to replace the typical L-shaped joints in the construction of a chair were developed, tested and numerically analysed. Different connectors were designed and manufactured with a fused deposition modelling (FDM) 3D printer using acrylonitrile butadiene styrene (ABS) with the aim to find a simple shaped connector which could be used to build chairs and withstand standard chair loading requirements. The strength and stiffness of the joints were tested and compared with traditional beech mortise-and-tenon joints. Numerical stress and strain analyses were performed with the finite element method for an orthotropic linear-elastic model. The experimental results showed that joints with 3D-printed connectors achieved lower strength than the traditional wooden mortise-and-tenon joints with similar dimensions. The results indicate that the effect of reinforcement of the connector were not recognised due to the small thickness and inadequate geometric position and arrangement of the reinforcement ABS material. The chair assembled with 3D-printed connectors could withstand the loads for seating, but failed the backrest test according to standard EN 1728:2002. The connectors need to be optimised and reinforced to withstand standard loads.

Neuropathological studies have shown that multiple sclerosis (MS) lesions are heterogeneous in terms of myelin/axon damage and repair as well as iron content. However, it remains a challenge to identify specific chronic lesion types, especially remyelinated lesions, in vivo in patients with MS.

Hepatitis C is an inflammatory condition of the liver caused by the hepatitis C virus. Diagnosis of the disease itself is difficult because the incubation period is long, often the disease is initially without some characteristic symptoms, but also due to a lack of laboratory methods. Artificial intelligence is increasingly being used nowadays to make it easier and faster to assess the illness. As hepatitis C is a rising healthcare burden it is of utmost importance to construct effective and reliable screening methods. As AI has already proven useful for diagnosis of a variety of conditions based on clinical parameters, this study focuses on the application of artificial neural network (ANN) for hepatitis C diagnosis. In this study, a database of 1000 respondents divided into two groups was used to develop the ANN: healthy (n = 200) and sick (n = 800). Monitoring parameters were: albumin, alkaline phosphatase, alanine aminotransferase, aspartate aminotransferase, bilirubin, acetylcholinesterase and anti-HCV antibodies. The overall accuracy of the developed ANN was 97,78%, which indicates that the potential of artificial intelligence in diagnosing hepatitis C is enormous, and in the future, attention should be paid to the development of new systems with as much data as possible.

The application of spectral analysis methods to the heart rate (HR) signal is challenging due to the nature of the signal itself, which is non-uniform. Methods for non-uniform signals can be applied directly, whilst the methods designed for uniform signals can be used after the signal is adequately preprocessed beforehand. Preprocessing consists of interpolation and resampling. In this paper, we have implemented a tool for explorative evaluation of various spectral analysis methods applied to HR signal. The tool is based on heat maps used for visualization of frequency metrics for the ECG signals selected from the MIT-BIH Arrhythmia Database. Evaluated methods are the Lomb-Scargle method for nonuniform signal analysis and Welch's method which is applied in conjunction with different interpolation approaches. A set of frequency-domain metrics are evaluated with the proposed tool for exploratory analysis. The evaluation indicates that the Lomb-Scargle method produces a loss of information in certain frequency bands. Furthermore, Welch method better demonstrates the difference in spectral power metrics for frequency bands of interest, irrespective of the type of interpolation used.

Ghrelin, a stomach-produced hormone, is well-recognized for its role in promoting feeding, controlling energy homeostasis, and glucoregulation. Ghrelin’s function to ensure survival extends beyond that: its release parallels that of corticosterone, and ghrelin administration and fasting have an anxiolytic and antidepressant effect. This clearly suggests a role in stress and anxiety. However, most studies of ghrelin’s effects on anxiety have been conducted exclusively on male rodents. Here, we hypothesize that female rats are wired for higher ghrelin sensitivity compared to males. To test this, we systematically compared components of the ghrelin axis between male and female Sprague Dawley rats. Next, we evaluated whether anxiety-like behavior and feeding response to endogenous or exogenous ghrelin are sex divergent. In line with our hypothesis, we show that female rats have higher serum levels of ghrelin and lower levels of the endogenous antagonist LEAP-2, compared to males. Furthermore, circulating ghrelin levels were partly dependent on estradiol; ovariectomy drastically reduced circulating ghrelin levels, which were partly restored by estradiol replacement. In contrast, orchiectomy did not affect circulating plasma ghrelin. Additionally, females expressed higher levels of the endogenous ghrelin receptor GHSR1A in brain areas involved in feeding and anxiety: the lateral hypothalamus, hippocampus, and amygdala. Moreover, overnight fasting increased GHSR1A expression in the amygdala of females, but not males. To evaluate the behavioral consequences of these molecular differences, male and female rats were tested in the elevated plus maze (EPM), open field (OF), and acoustic startle response (ASR) after three complementary ghrelin manipulations: increased endogenous ghrelin levels through overnight fasting, systemic administration of ghrelin, or blockade of fasting-induced ghrelin signaling with a GHSR1A antagonist. Here, females exhibited a stronger anxiolytic response to fasting and ghrelin in the ASR, in line with our findings of sex differences in the ghrelin axis. Most importantly, after GHSR1A antagonist treatment, females but not males displayed an anxiogenic response in the ASR, and a more pronounced anxiogenesis in the EPM and OF compared to males. Collectively, female rats are wired for higher sensitivity to fasting-induced anxiolytic ghrelin signaling. Further, the sex differences in the ghrelin axis are modulated, at least partly, by gonadal steroids, specifically estradiol. Overall, ghrelin plays a more prominent role in the regulation of anxiety-like behavior of female rats.

The combination of reinforcement learning and deep learning has shown some remarkable results in many scientific fields. Deep reinforcement learning algorithms are particularly good at understanding and modeling adaptive decision-making in dynamic environments. In recent years, this concept has been successfully applied to smart grids. In this paper, we provide a brief introduction to the concepts of reinforcement and deep reinforcement learning to the power system engineers and present research progress and prospects in the field. Additionally, we identify smart grid engineering domains that need extensive pattern-based modeling as being particularly suitable for deep reinforcement learning.

The detonation properties of nonideal explosives are highly dependent on charge diameter and existence and properties of confinement. In this study, the effect of different confinements on the detonation velocity of ANFO explosives was experimentally determined along with the results of the plate dent test. ANFO explosive was selected as one of the most commonly used nonideal explosives. Following the measurement results, we found that the detonation velocity increased with increasing wall thickness, and the velocity increase was different for different confinement materials. A strong correlation existed between the ratio of the mass of confiner and explosive (M/C) and the detonation velocity (R = 0.995), and between (M/C) and the depth of the dent (δ) (R = 0.975). The data presented in this paper represent preliminary findings in developing a confinement model required for reliable numerical modeling of nonideal explosives.

The broader use of devices powered by rechargeable batteries, especially constrained embedded devices, makes the efficient Battery Management System (BMS) increasingly more important. The estimation accuracy of the amount of remaining charge in the battery is critical as it affects the device’s operation and reliability. For that reason, the estimation of state-of-charge (SoC) is considered one of the main functionalities of a BMS. However, SoC estimation remains a complex task that depends on a range of internal and external factors. Most traditional SoC estimation methods are either computationally complex, require special laboratory equipment or additional configuration efforts. In addition, most methods require continuous measurement of battery parameters, which, in turn, renders these methods not applicable to the class of constrained embedded devices. This paper aims to extend the Coulomb counting method to the class of duty-cycled energy-constrained devices by designing an algorithm that combines voltage-based evaluation and pre-recorded task power profiles to estimate the SoC. In addition, a setup for identifying the battery parameters and algorithm validation setup were also developed and described in the paper.

Ambient conditions, especially temperature and humidity, have a huge impact on the performance of an air quality sensor. In this paper, four correction models were built to compensate the impact of ambient conditions. Linear regression and machine learning algorithms were used for building the models. Correction models were trained by using three types of measurement data. Raw measurement data was used in the first case. Secondly, measurement data was corrected and a significant improvement was shown. Lastly, measurements of various ambient conditions were used as well. Using corrected and extended measurement data brought a great improvement in accuracy of the models. A neural network correction model proved to be the most efficient in all cases. Compensating the impact of ambient conditions on the performance of an air quality sensor by using correction models was efficient and this method could be used in the air quality monitoring applications. This is of particular importance for usage of low-cost sensors in the air quality monitoring.

In this paper approach for the experimental determination of the grounding system impulse impedance under the presence of the high-frequency electromagnetic interference is presented. The considered approach is based on the application of the discrete wavelet transform on the measured signals. Validation of the considered approach has been conducted in several experiments using a vertical grounding electrode. The experimental investigation has been performed using different impulse current peak values and different front rise times. On all measured current and voltage waveforms, high-frequency interferences were registered.

This paper treats the problem of 3D outdoor environment mapping using images acquired by Unmanned Aerial Vehicle (UAV). The main focus is on the generation of 3D model for large scale environments. In order to perform 3D model reconstruction and mapping from 2D aerial images we employed a Structure from Motion (SfM) based approach. The obtained results using this approach for different scenarios, the rubble field and village, are presented. The generated UAV 3D point cloud data are compared with the ground truth using the least square method, where the ground truth represents a reference model with high accuracy geodetic precision. The comparison of the 3D environment models with the rubble field and village scenarios and the ground truth data is also given.