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Dissemination of digital cognitive behavioural therapy is a promising approach for treating insomnia in the broad population. Current evidence supports the effectiveness of the digital format, but clinical findings are often limited by the choice of control group and lack of in-depth therapeutic measures. This study was designed to investigate the specific effects of digital cognitive behavioural therapy in comparison to a self-monitoring application. Participants meeting criteria for insomnia were randomly allocated (1:1) to 8 weeks of digital cognitive behavioural therapy or 8 weeks of digital sleep monitoring (control application). The primary outcome, insomnia severity, was assessed at baseline, 8- and 16-weeks post-randomisation. Secondary outcomes included the assessment of sleep via application-integrated sleep diaries and actigraphy. Linear-mixed models were fitted to assess between-group differences. Fifty-six participants (48 females, mean age: M = 45.55 ± 13.70 years) were randomised to either digital cognitive behavioural therapy (n = 29) or digital sleep monitoring (n = 27). At 8- and 16-weeks post-randomisation, large treatment effects (d = 0.87-1.08) indicated robust reductions (-3.70 and -2.97, respectively; p ≤ 0.003) in insomnia severity in the digital cognitive behavioural therapy arm, relative to digital sleep monitoring. Treatment effects in favour of digital cognitive behavioural therapy were also found for self-reported and actigraphy-derived sleep continuity variables, indicating that sleep improved throughout the 8-week intervention period. Our study reinforces the role of digital cognitive behavioural therapy in achieving clinical improvements for patients with insomnia, affirming previous findings and supporting the specific effects of cognitive behavioural therapy.

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The use of inertial and physiological sensors in a sport such as judo is scarce to date. The information provided by these sensors would allow practitioners to have a better understanding of sports performance, which is necessary for an accurate training prescription. The purpose of this study was to use inertial and physiological sensors in order to investigate the effect of a plyometric and high-intensity interval training (HIIT) training program on Special Judo Fitness Test (SJFT) performance and speed of execution of throws in young judokas. A total of 32 participants were divided into two groups: experimental and control. The intervention consisted of six sessions with a duration of 60 min for 3 weeks. Physiological sensors collected heart rate data to assess the Special Judo Fitness Test, and inertial sensors collected angular velocity. The results show a significant decrease in the SJFT index (Score pre: 22.27 ± 2.73; Score post: 19.65 ± 1.70; p ≤ 0.05; d = 0.61) and a significant increase in the angular velocity of the X-axis (Pre: 320.87 ± 51.15°/s; Post: 356.50 ± 40.47°/s; p ≤ 0.05; d = 0.45) and Y-axis (Pre: 259.40 ± 41.99°/s; Post: 288.02 ± 65.12°/s; p ≤ 0.05; d = 0.31) in the experimental group. In conclusion, this study demonstrates that using inertial and physiological sensors allowed us to analyze the effect that a high-intensity interval training program and plyometrics had on the performance of young judokas. Strength and conditioning coaches should consider these results because including plyometric training and HIIT in judokas' workout programming can be especially positive for eliciting increases in performance. However, future training interventions should investigate the training adaptations to longer interventions.

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Biocatalytic conversion of greenhouse gases such as carbon dioxide into commercial products is one of the promising key approaches to solve the problem of climate change. Microbial enzymes, including carbonic anhydrase, NAD-dependent formate dehydrogenase, ribulose bisphosphate carboxylase, and methane monooxygenase, have been exploited to convert atmospheric gases into industrial products. Carbonic anhydrases are Zn2+-dependent metalloenzymes that catalyze the reversible conversion of CO2 into bicarbonate. They are widespread in bacteria, algae, plants, and higher organisms. In higher organisms, they regulate the physiological pH and contribute to CO2 transport in the blood. In plants, algae, and photosynthetic bacteria carbonic anhydrases are involved in photosynthesis. Converting CO2 into bicarbonate by carbonic anhydrases can solidify gaseous CO2, thereby reducing global warming due to the burning of fossil fuels. This review discusses the three-dimensional structures of carbonic anhydrases, their physiological role in marine life, their catalytic mechanism, the types of inhibitors, and their medicine and industry applications.

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Wireless sensors networks (WSNs) are characterized by flexibility and scalability in any environment. These networks are increasingly used in agricultural and industrial environments and have a dual role in data collection from sensors and transmission to a monitoring system, as well as enabling the management of the monitored environment. Environment management depends on trust in the data collected from the surrounding environment, including the time of data creation. This paper proposes a trust model for monitoring humidity and moisture in agricultural and industrial environments. The proposed model uses a digital signature and public key infrastructure (PKI) to establish trust in the data source, i.e., the trust in the sensor. Trust in data generation is essential for real-time environmental monitoring and subsequent analyzes, thus timestamp technology is implemented here to further ensure that gathered data are not created or changed after the assigned time. Model validation is performed using the Castalia network simulator by testing energy consumption at the receiver and sender nodes and the delay incurred by creating or validating a trust token. In addition, validation is also performed using the Ascertia TSA Crusher application for the time consumed to obtain a timestamp from the free TSA. The results show that by applying different digital signs and timestamps, the trust entity of the WSN improved significantly with an increase in power consumption of the sender node by up to 9.3% and receiver node by up to 126.3% for a higher number of nodes, along with a packet delay of up to 15.6% and an average total time consumed up to 1.186 s to obtain the timestamp from the best chosen TSA, which was as expected.

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Automatized scalable healthcare support solutions allow real-time 24/7 health monitoring of patients, prioritizing medical treatment according to health conditions, reducing medical appointments in clinics and hospitals, and enabling easy exchange of information among healthcare professionals. With recent health safety guidelines due to the COVID-19 pandemic, protecting the elderly has become imperative. However, state-of-the-art health wearable device platforms present limitations in hardware, parameter estimation algorithms, and software architecture. This paper proposes a complete framework for health systems composed of multi-sensor wearable health devices (MWHD), high-resolution parameter estimation, and real-time monitoring applications. The framework is appropriate for real-time monitoring of elderly patients' health without physical contact with healthcare professionals, maintaining safety standards. The hardware includes sensors for monitoring steps, pulse oximetry, heart rate (HR), and temperature using low-power wireless communication. In terms of parameter estimation, the embedded circuit uses high-resolution signal processing algorithms that result in an improved measure of the HR. The proposed high-resolution signal processing-based approach outperforms state-of-the-art HR estimation measurements using the photoplethysmography (PPG) sensor.

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With the Internet of Things (IoT), the number of monitoring applications deployed is considerably increasing, whatever the field considered: smart city, smart agriculture, environment monitoring, air pollution monitoring, to name a few. The LoRaWAN (Long Range Wide Area Network)architecture with its long range communication, its robustness to interference and its reduced energy consumption is an excellent candidate to support such applications. However, if the number of end devices is high, the reliability of LoRaWAN, measured by the Packet Delivery Ratio (PDR), becomes unacceptable due to an excessive number of collisions. In this paper, we propose two different families of solutions ensuring collision-free transmissions. The first family is TDMA (Time-Division Multiple Access)-based. All clusters transmit in sequence and up to six end devices with different spreading factors belonging to the same cluster are allowed to transmit in parallel. The second family is FDMA (Frequency Divsion Multiple Access)-based. All clusters transmit in parallel, each cluster on its own frequency. Within each cluster, all end devices transmit in sequence. Their performance are compared in terms of PDR, energy consumption by end device and maximum number of end devices supported. Simulation results corroborate the theoretical results and show the high efficiency of the solutions proposed.

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Cloud computing and the Internet of Things are the two hot points in the Internet application field. The application of the two new technologies is in hot discussion and research, but quite less on the field of medical monitoring and managing application. Thus, in this paper, we study and analyze the application of cloud computing and the Internet of Things on the medical field. And we manage to make a combination of the two techniques in the medical monitoring and managing field. The model architecture for remote monitoring cloud platform of healthcare information (RMCPHI) was established firstly. Then the RMCPHI architecture was analyzed. Finally an efficient PSOSAA algorithm was proposed for the medical monitoring and managing application of cloud computing. Simulation results showed that our proposed scheme can improve the efficiency about 50%.

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