In this paper, a two-dimensional (2D) autoregressive (AR) model is employed as a mitigation algorithm for interference in the Frequency Modulated Continuous Wave (FMCW) radars. The AR model, due to its simple structure, can perform super efficiently in multi-dimensional estimation problems and can be a suitable replacement for complex Neural Network (NN) based algorithms. This study addresses the advanced requirements of the 2DAR algorithm for mitigating interference in actual frames collected from real-world experiments. Three approaches to interference generation were incorporated to reproduce and study the most common and likely circumstances of mutual interference. A dynamic sampling direction selection framework is developed to address the unpredictable shapes of interfered segments within a frame. An iterative signal reconstruction algorithm is proposed to reconstruct the damaged areas using clean samples. Finally, the parallelizable processes were vectorized to make them implementable in the real world. The 2DAR mitigator’s performance was assessed using a diverse dataset of frames collected from real-world experiments, each containing unique target, noise, and interference attributes. The derived mitigator improved the signal in all experimental cases, down to the noise floor, and increased the Signal to Interference plus Noise Ratio (SINR) to almost 15 dB. Finally, the performance of different order models was compared in an identical hardware and software environment to provide a scaled indicator of the computation escalation in different model orders.

This study proposes the inflated modified Kumaraswamy (iMK) distribution, a flexible probability model defined on the unit interval [0,1]. It captures asymmetric behaviors while accommodating inflation at zero, one, or both boundaries, as commonly observed in normalized difference vegetation index (NDVI) data. Based on the iMK distribution, we develop a new regression model (iMKreg) suitable for double-bounded responses. From this model, we derive a detection tool for invasive plant species, particularly applicable to NDVI imagery. Model performance was evaluated using synthetic NDVI data, with further assessment of predictive accuracy and detection efficacy conducted on real-world measured NDVI image. The application to detecting black-grass (Alopecurus myosuroides) in wheat crops in southern Sweden shows that the iMKreg model outperforms both standard Gaussian-based linear regression and existing inflated Kumaraswamy regression models. 

This paper presents a change detection algorithm based on Shannon entropy and the Rayleigh distribution for both single-look and multi-look SAR images. While hypothesis testing has been widely used in SAR change detection, few studies have applied this approach to both types of data. To address this, we propose a method that utilizes Shannon entropy to detect changes between two samples. The performance of the algorithm was evaluated through Monte Carlo simulations using synthetic SAR data and further validated on real-world datasets, including single-look images from the CARABAS II dataset and multi-look data from the UAVSAR radar. The results demonstrate that the proposed method is effective in detecting changes. This paper highlights the versatility of the approach, which is capable of handling both single-look and multi-look SAR data, and reinforces the way for future research into alternative entropy measures and probability distributions in change detection tasks. 

This paper addresses the parameters that affect multiple interacting Frequency Modulated Continuous Wave radars, which can cause mutual interference. Multiple parallel chirp radars can shape ghost targets in the Range-Doppler plane as a unique case of mutual interference. In a real-world scenario, the carrier frequencies of the two radars are not synchronized. Therefore, the ghost will occupy a significant portion of the Range-Doppler plane, which can reduce the quality and clarity of the target signature and increase the false alarm in the detection process. By synchronizing two radars at the frame level, it has been tried to capture the ghost target's highest interfering condition and identify the radar circuit's influencing parameters on the chirp parallelization. This synchronizing FMCW radar system can minimize ghost targets and reduce mutual interference through a frequency-shifting algorithm that compresses the ghost targets. The result is an effective strategy for improving radar system precision and reliability in manageable environments with multiple interacting radar systems, which is crucial for future applications like autonomous vehicles and surveillance. 

Introduction Organ donation and transplantation can be lifesaving. Many donors are older adults. National Donor Registries can promote adherence to patients' rights and safeguard patient integrity if the question about organ donation arises. However, a majority have not entered the National Donor Registry in Sweden.

Objective Investigate how sociodemographic factors, health-related factors, and attitudes toward organ donation are associated with whether or not older adults in Sweden are registered in the National Donor Registry.

Methods A cross-sectional survey design was chosen to explore associations between individual characteristics and registration behavior among older adults. Data was collected through the Swedish National Study on Ageing and Care (SNAC)-IT survey in 2023, a sub-study to the SNAC. 436 participants 60 years old or older answered the survey. The survey included questions on sociodemographic and health-related factors and attitudes toward organ donation. Data was analyzed using descriptive statistics, Spearman's rank correlation, and the Chi-square test.

Results Registration in the Swedish National Donor Registry was low among older adults, with only 25.5% of participants listed despite 70.4% expressing a positive attitude toward organ donation. Among those who oppose organ donation only 6.9% were in the registry. Younger age, higher self-rated health, better health-related quality of life (EQ-5D VAS), being unmarried, being a previous smoker, and having a positive attitude toward donation were all significantly associated with being registered (p < 0.05). These findings demonstrate a gap between supportive beliefs and registration behavior among older adults.

Conclusion There is a gap between beliefs and actions regarding organ donation among older adults. While most have a positive attitude toward organ donation, they are not registered in the National Donor Registry. Additionally, the vast majority who oppose organ donation after death are not registered, raising the risk that their actual wishes may be disregarded due to presumed consent.

This paper proposes the MKSARMAX model for modeling and forecasting time series that can only take on values within a specified range, such as in the interval (0,1). The model is especially good for modeling double-bounded hydro-environmental time series since it accommodates bounded support and asymmetric distribution, making it advantageous compared to the traditional Gaussian-based time series model. The MKSARMAX models the conditional median of a modified Kumaraswamy distributed variable observed over time, by a dynamic structure considering stochastic seasonality and including autoregressive and moving average terms, exogenous regressors, and a link function. The conditional maximum likelihood method is employed to estimate the model parameters. Hypothesis tests and confidence intervals for the parameters of the proposed model are derived using the asymptotic theory of the conditional maximum likelihood estimators. Quantile residuals are defined for diagnostic analysis, and goodness-of-fit tests are subsequently implemented. Synthetic hydro-environmental time series are generated in a Monte Carlo simulation study to assess the finite sample performance of the inferences. Moreover, MKSARMAX outperforms beta SARMA, SARMAX, Holt-Winters, and KARMA models in most accuracy measures analyzed when applied to useful water volume datasets, presenting for the first-step forecast at least 98% lower MAE, RMSE, and MAPE values than competitors in the Caconde UV dataset, and 54% lower MAE, RMSE, and MAPE values than competitors in the Guarapiranga UV dataset. These findings suggest that the MKSARMAX model holds strong potential for water resource management. Its flexibility and accuracy in the early forecasting steps make it particularly valuable for predicting flood and drought periods.

This letter introduces a novel regression model structure for the inflated Rayleigh distribution, which effectively models high dynamic amplitude pixel values in synthetic aperture radar (SAR) images. The proposed model estimates the mean of inflated Rayleigh distribution signals by a structure that includes a set of regressors and a link function. The inflated Rayleigh distribution combines the Rayleigh and a degenerate distribution, assigning non-null probability specifically for observed values equal to zero. Null pixel values in amplitude SAR images can be randomly distributed within the image, especially in low-intensity areas; a model capable of incorporating these values is essential to avoid changes in image statistics. Extensive evaluations are conducted using simulated and real SAR images to validate the proposed model, specifically focusing on ground-type detection in high dynamic amplitude pixel values scenarios. The performance of the proposed inflated Rayleigh regression model is compared with traditional Gaussian-based regression models, excelling in terms of ground-type detection in a SAR image obtained from the ICEYE radar. 

Background: The growing presence of digital technologies in health care requires the health workforce to have proficiency in subjects such as informatics. This has implications in the education of nursing students, as their preparedness to use these technologies in clinical situations is something that course administrators need to consider. Thus, students’ attitudes toward technology could be investigated to assess their needs regarding this proficiency.

Objective: This study aims to investigate attitudes (enthusiasm and anxiety) toward technology among nursing students and to identify factors associated with those attitudes. Methods: Nursing students at 2 universities in Sweden and 1 university in Poland were invited to answer a questionnaire. Data about attitudes (anxiety and enthusiasm) toward technology, eHealth literacy, electronic device skills, and frequency of using electronic devices and sociodemographic data were collected. Descriptive statistics were used to characterize the data. The Spearman rank correlation coefficient and Mann-Whitney U test were used for statistical inferences.

Results: In total, 646 students answered the questionnaire—342 (52.9%) from the Swedish sites and 304 (47.1%) from the Polish site. It was observed that the students’ technology enthusiasm (techEnthusiasm) was on the higher end of the Technophilia instrument (score range 1-5): 3.83 (SD 0.90), 3.62 (SD 0.94), and 4.04 (SD 0.78) for the whole sample, Swedish students, and Polish students, respectively. Technology anxiety (techAnxiety) was on the midrange of the Technophilia instrument: 2.48 (SD 0.96), 2.37 (SD 1), and 2.60 (SD 0.89) for the whole sample, Swedish students, and Polish students, respectively. Regarding techEnthusiasm among the nursing students, a negative correlation with age was found for the Swedish sample (P<.001; ρSwedish=−0.201) who were generally older than the Polish sample, and positive correlations with the eHealth Literacy Scale score (P<.001; ρall=0.265; ρSwedish=0.190; ρPolish=0.352) and with the perceived skill in using computer devices (P<.001; ρall=0.360; ρSwedish=0.341; ρPolish=0.309) were found for the Swedish, Polish, and total samples. Regarding techAnxiety among the nursing students, a positive correlation with age was found in the Swedish sample (P<.001; ρSwedish=0.184), and negative correlations with eHealth Literacy Scale score (P<.001; ρall=−0.196; ρSwedish=−0.262; ρPolish=−0.133) and with the perceived skill in using computer devices (P<.001; ρall=−0.209; ρSwedish=−0.347; ρPolish=−0.134) were found for the Swedish, Polish, and total samples and with the semester only for the Swedish sample (P<.001; ρSwedish=−0.124). Gender differences were found regarding techAnxiety in the Swedish sample, with women exhibiting a higher mean score than men (2.451, SD 1.014 and 1.987, SD 0.854, respectively).

Conclusions: This study highlights nursing students’ techEnthusiasm and techAnxiety, emphasizing correlations with various factors. With health care’s increasing reliance on technology, integrating health technology–related topics into education is crucial for future professionals to address health care challenges effectively.

©Ana Luiza Dallora, Ewa Kazimiera Andersson, Bruna Gregory Palm, Doris Bohman, Gunilla Björling, Ludmiła Marcinowicz, Louise Stjernberg, Peter Anderberg.

The combination of a large number of students and a diverse student population poses additional pedagogical challenges in higher education courses. The teacher's perception of student engagement becomes more challenging in a large group, and students may experience reduced motivation despite the teacher's efforts and pedagogical approach. This paper discusses the challenges of teaching approaches and students' motivation in large groups. For that, a survey at Blekinge Tekniska Hogskola (BTH) in Sweden was performed to evaluate and discuss learning improvement in science, technology, engineering, and mathematical (STEM) courses attended by Swedish and international students. The survey explores how teachers can encourage student motivation in large classes, and it was based on related works reporting teaching methods and common issues in teaching STEM courses. Based on the survey's result analysis, we can observe that the physical learning environment and teaching style do not play an essential role in the student's motivation. However, the teacher and student interaction influences their motivation. These findings provide insight into the diverse perceptions of students regarding the connections between teaching style, feedback, learning environment, and motivation, for example. The conclusions derived from the survey are expected to serve as guidelines for improving student performance in large STEM groups. © 2024 IEEE.

This work confronts the complex issue of cross-interference in Frequency Modulated Continuous Wave (FMCW) radars, a critical concern that has become more pronounced with the proliferation of automotive radar systems. The study intro-duces a two-dimensional autoregressive (AR) modeling technique for signal reconstruction in the time domain, tailored specifically for the textured nature of FMCW radar frames composed of fast- time (Range bin) and slow-time (Doppler bin) signals. According to the simulations conducted in this study, the proposed 2-D AR model (of order 3) exhibits superior performance compared to its 1-D counterpart (of order 5). This is evidenced by a slightly lower Mean Absolute Percentage Error (MAPE) during model training and a higher Signal-to-Interference-plus-Noise Ratio (SINR) for the reconstructed signal, suggesting that the 2-D model requires less frequent temporal sampling. The study further investigates different sampling strategies and evaluates the influence of model order on signal reconstruction. Based on these assessments, a third-order 2-D AR is recommended as a suitable trade-off model for interference mitigation of FMCW radars for the evaluated scenarios. This paper is structured as follows: Section I defines the interference problem in FM CW radars and the latest solutions to this problem are discussed. Sections II and III include the working principles of FMCW radar and theoretical backgrounds about multi-dimension auto-regressive modeling, respectively. Eventually, the mitigation techniques and numerical evaluations of the proposed approach are presented in Sections IV and V. © 2024 Warsaw University of Technology.