In order to ensure sufficient recovery of the human body and brain, healthy sleep is indispensable. For this purpose, appropriate therapy should be initiated at an early stage in the case of sleep disorders. For some sleep disorders (e.g., insomnia), a sleep diary is essential for diagnosis and therapy monitoring. However, subjective measurement with a sleep diary has several disadvantages, requiring regular action from the user and leading to decreased comfort and potential data loss. To automate sleep monitoring and increase user comfort, one could consider replacing a sleep diary with an automatic measurement, such as a smartwatch, which would not disturb sleep. To obtain accurate results on the evaluation of the possibility of such a replacement, a field study was conducted with a total of 166 overnight recordings, followed by an analysis of the results. In this evaluation, objective sleep measurement with a Samsung Galaxy Watch 4 was compared to a subjective approach with a sleep diary, which is a standard method in sleep medicine. The focus was on comparing four relevant sleep characteristics: falling asleep time, waking up time, total sleep time (TST), and sleep efficiency (SE). After evaluating the results, it was concluded that a smartwatch could replace subjective measurement to determine falling asleep and waking up time, considering some level of inaccuracy. In the case of SE, substitution was also proved to be possible. However, some individual recordings showed a higher discrepancy in results between the two approaches. For its part, the evaluation of the TST measurement currently does not allow us to recommend substituting the measurement method for this sleep parameter. The appropriateness of replacing sleep diary measurement with a smartwatch depends on the acceptable levels of discrepancy. We propose four levels of similarity of results, defining ranges of absolute differences between objective and subjective measurements. By considering the values in the provided table and knowing the required accuracy, it is possible to determine the suitability of substitution in each individual case. The introduction of a “similarity level” parameter increases the adaptability and reusability of study findings in individual practical cases.

Measuring cardiorespiratory parameters in sleep, using non-contact sensors and the Ballistocardiography technique has received much attention due to the low-cost, unobtrusive, and non-invasive method. Designing a user-friendly, simple-to-use, and easy-to-deployment preserving less error-prone remains open and challenging due to the complex morphology of the signal. In this work, using four forcesensitive resistor sensors, we conducted a study by designing four distributions of sensors, in order to simplify the complexity of the system by identifying the region of interest for heartbeat and respiration measurement. The sensors are deployed under the mattress and attached to the bed frame without any interference with the subjects. The four distributions are combined in two linear horizontal, one linear vertical, and one square, covering the influencing region in cardiorespiratory activities. We recruited 4 subjects and acquired data in four regular sleeping positions, each for a duration of 80 seconds. The signal processing was performed using discrete wavelet transform bior 3.9 and smooth level of 4 as well as bandpass filtering. The results indicate that we have achieved the mean absolute error of 2.35 and 4.34 for respiration and heartbeat, respectively. The results recommend the efficiency of a triangleshaped structure of three sensors for measuring heartbeat and respiration parameters in all four regular sleeping positions.

This study evaluates the effects of non-invasive home telemonitoring methods of managing congestive heart failure (CHF) patients with particular focus on complexity of intervention, patient characteristics, patient severity, and key enabling technologies (KETs) adopted. Our goal was to capture all possible aspects of previously documented outcomes and provide updated and clearer evidence on mixed effects on common themes. Randomized controlled trials (RCTs) published between 1 January 2012 and 6 June 2019, focusing on home telemonitoring of patients with only CHF or CHF coexisting with other chronic disease(s), were retrieved from online resources (PubMed, Embase, MEDLINE(R), Your journals@Ovid, Elsevier, and ClinicalTrials.gov). The snowball sampling method and forward citation tracking on Google Scholar were also adopted to identify additional relevant studies. Retrieved studies were in a language known by the authors (i.e., English, Spanish or Italian). Quality assessment of individual studies for shortcomings in design, management, evaluation, and reporting was done using the Cochrane risk of bias (RoB) tool. Variables of interest were synthesized as differences in relative risk (RR), or as weighted mean differences (WMD). Outcomes were assigned as primary or secondary based on a principal judgement of clinical importance, and secondarily on highest recurrent counts in included studies. In all, 28 RCTs involving 10,258 patients were included in the qualitative synthesis, out of which 24 were used for the quantitative synthesis. These studies focused on non-invasive telemonitoring practices for home monitoring of CHF patients, through the deployment of different kinds of electronic/mobile devices, with most having wireless communication capabilities. Moreover, studies focusing on implantable monitoring devices in terms of inputs, data and patient performance were also included. Brain natriuretic peptide (BNP) ((WMD = -27.75; 95% CI (-53.36, -2.14); p-value = 0.034), rehospitalization/hospitalization for heart failure (RR = 0.88; 95% CI (0.79, 0.98); p-value = 0.015), cardiovascular death/heart failure hospitalization (RR = 0.70; 95% CI (0.51, 0.97); p-value = 0.03), and six minute walk test (6MWT) (WMD = 25.61; 95% CI (9.22, 41.99); p-value = 0.002) significantly improved in the telemonitoring group, while the number of visits to a nurse (WMD = 1.42; 95% CI (0.33, 2.52); p-value = 0.011) increased considerably compared to usual care. Although there were limitations to the evidence provided in this review such as wide variations in certain variables (e.g., sample populations, RoB assessment, telemonitoring tools, follow-up periods), issues with allocation concealment and blinding of participants and personnel, and paucity of data for synthesizing particular outcomes of interest, overall, telemonitoring seems to offer much better results in the treatment of CHF patients compared to usual care. This systematic review and meta-analysis has been retrospectively registered in the Open Science Framework (OSF) repository with https://doi.org/10.17605/OSF.IO/NDXCP. All data related to this study, including the electronic supplementary data, can be found at this link: osf.io/57q3h.

Workplace stress or professional stress is a specific type of stress that is highly prevalent among police officers. Police officers are exposed to high levels of stress and its negative impact ontheir physical and mental health, as well as their social lives. The aim of this research is to determine the attitudes regarding the connection between physical fitness and stress prevention among police officers. The sample consists of 516 employees from police departments in the Central Bosnia Canton. The sample is structured with 312 male participants and 204 female participants. Both descriptive and analytical methods were applied in this research, as the descriptive method was used to describe the distribution of the studied phenomenon, while the analytical part followed the logic of the research. Analyzing all the results, it can be concluded that there is a high level of satisfaction with the management of work processes among police officers and with stress reduction in the workplace. The conclusion arises about the necessity of increasing the number of hours of police training, primarily for basic and investigative police work, in stress prevention among police officers. The results of comparative analysis indicate that there is no statistically significant difference among participants based on gender. The results show that the age of the participants significantly influences their attitudes towards overall satisfaction with management quality. Theresults suggest that participants who have been employed the longest and make the most use of the existing infrastructure express more positive attitudes.Key words:police, stress, physical fitness, burnout, prevention.

Modern business systems have the expectations and requirements of users and stakeholders for safer and better services that are constantly growing. The increasing use of information technology in business increases the threats and vulnerabilities to which information resources are exposed, which causes an increase in information risks. Many business institutions must constantly monitor their activities to establish an organized and sustainable information security management system and services. The requirements of the international standard ISO/IEC 27001 and the generally accepted COBIT management framework are important for the application of such a system. The paper presents a model of a sustainable information security management system (ISMS) at universities.

Unsupervised learning (UL) is widely used in the wireless resource allocation problems due to its lower computational complexity and better performance compared with traditional optimization algorithms. Since wireless resource allocation problems usually have several constraints, primal-dual learning based UL framework are widely adopted. However, the primal-dual learning approach has the problem of oscillation around the constraint threshold while training and there may be serious constraint violations when deployment. In addition, although the output of the neural network can also be restricted to the feasible region by the penalty function method, the optimality of such training methods cannot be guaranteed. In this article, we combine the primal dual learning method with the penalty function method and propose a regularized unsupervised learning (RUL) framework to enhance the robustness of the primal-dual learning based UL framework. In the proposed RUL framework, we use regularization techniques to improve the robustness of primal-dual learning by reducing the risk of constraint violations while training. A quadratic penalty term is introduced into the Lagrangian function of the wireless optimization problem where the constraints can be equivalent to equality constraints to form its augmented Lagrangian function. In the simulation, we give a simple point to point power optimization problem as an example to show that the proposed RUL can improve the robustness of constraint convergence, and can also accelerate training speed.

Millimeter wave (mmWave) systems need beam management to establish and maintain reliable links. This complex and time-consuming process seriously affects communication efficiency. Benefiting from data-driven technology in deep learning, the beam can be predicted from the waveform without coordination between transceivers. By passively listening enough waveforms that are transmitted from the base station (BS) to other receivers, the BS can predict which beam is suitable for transmitting in the downlink. However, training such a waveform learning neural network usually requires a large number of labeled training samples. This is a huge challenge, because it is difficult for the receiver to get the precise signal parameters from the transmitter in advance in the non-cooperative mmWave system. As a result, the limited samples may cause overfitting and seriously restrict the performance. Although the data augmentation technology can improve the performance under limited samples, existing data augmentation methods are mostly based on strong prior knowledge which cannot further exploit the potential characteristics of data in the real environment. This paper proposes a mixed regularization training method for training the beam prediction neural network under limited training samples. Specifically, data augmentation is implemented in the data pre-processing procedure with prior knowledge and then the signal splicing strategy is proposed in the training procedure. In order to mine the time correlation characteristics of signals, the cyclic time shift (CTS) based data augmentation method is proposed in the data augmentation step. The simulation results show that our proposed deep regularized waveform learning method needs less training samples under the same performance. Moreover, the proposed method can achieve best performance compared with existing data augmentation methods.

As technology is the driver of the economy, it is necessary to follow emerging technological trends and to create appropriate conditions for its adoption and implementation as a human-centred technology. In this regard, rules and standards for the Internet of Things (IoT) and Artificial Intelligence (AI) should be established to best use the benefits of technology and to prevent or minimize the consequences of technology misuse. The fifth industrial revolution (Industry 5.0) has already begun, although Industry 4.0 is still developing. Consequently, the original attention has shifted from IoT to AI, with the IoT debate now being a prerequisite for the AI debate. As AI is transforming our lives, a growing number of countries have considered or already adopted national AI strategies. However, in many developing countries, national AI strategies and initiatives for establishing AI and IoT regulation and legislation frameworks yet need to be discussed. The subject of this article is the research of existing initiatives related to establishing the IoT and AI regulatory and legislative framework in the EU and its applicability in developing countries.

: The understanding of the transboundary impact of Climate Change on hydropower is not well-established in the literature, where few studies take a system perspective to understand the relative roles of different technological solutions for coordinated water and energy management. This study contributes to addressing this gap by introducing an open-source, long-term, technologically-detailed water and energy resources cost-minimisation model for the Drin River Basin, built in OSeMOSYS. The analysis shows that climate change results in a 15-52% annual decline in hydro generation from the basin by mid-century. Albania needs to triple its investments in solar and wind to mitigate the risk of climate change. Changing the operational rules of hydropower plants has a minor impact on the electricity supply. However, it can spare significant storage volume for flood control.

Gravity inversions have contributed greatly to our knowledge of the interior of planetary bodies and the processes that shaped them. However, previous global gravity inversion methods neglect the inference of mantle density anomalies when using techniques to decrease the non‐uniqueness of the inversion. In this work, we present a novel global gravity inversion algorithm, named THeBOOGIe, suited to inferring global‐scale density anomalies within the crust and mantle of planetary bodies. The algorithm embraces the nonuniqueness inherent in gravity inversions by not prescribing at the outset a density interface or depth range of interest. Instead, the method combines a Bayesian approach with a flexible incorporation of prior geological or geophysical information to infer density anomalies at any depth. A validation test using synthetic lunar‐like gravity data shows that THeBOOGIe can constrain the lateral location of crustal density anomalies but tends to overestimate their thicknesses. Importantly, THeBOOGIe can detect deep mantle density anomalies and quantify the level of confidence in the inferred density models. Our results show that THeBOOGIe can provide complementary information to one‐dimensional seismic models of the interior of the terrestrial planets and the Moon by constraining density anomalies that are not spherically symmetric. Additionally, THeBOOGIe is specially suited to constraining the interior of partially differentiated bodies where these large‐scale density anomalies are more likely to exist. Finally, thanks to the flexible use of priors, THeBOOGIe is an essential tool to understand the interior of planetary bodies lacking additional constraints.