Search Results (153)

This study investigated the effects of a single-night sleep extension protocol on physical performance and cognitive function in physically active university students across different times of day. Using a within-subjects, counterbalanced crossover design, 24 physically active university students (17 males, 7 females; age: 22.7 ± 1.6 years) completed performance assessments under normal-sleep and sleep-extension conditions. Participants’ sleep was monitored via wrist actigraphy, and a comprehensive assessment battery comprising vertical jumps, Y-Balance tests, medicine-ball throws, 5 m shuttle-run tests, reaction-time tests, and digit-cancellation tests was administered at baseline (8 PM), morning (8 AM), and afternoon (4 PM). Sleep extension increased total sleep time by approximately 55 min (531.3 ± 56.8 min vs. 476.5 ± 64.2 min; p < 0.001, d = 0.91). Significant improvements were observed in 5 m shuttle-run performance at 8 AM (best distance: 102.8 ± 11.9 m vs. 93.3 ± 8.5 m, p < 0.001, d = 0.93; fatigue index: 13.1 ± 8.3% vs. 21.2 ± 9.5%, p < 0.001, d = 0.90), squat-jump heights (28.2 ± 8.0 cm vs. 26.3 ± 7.2 cm, p = 0.005, d = 0.25), simple reaction time (252.8 ± 55.3 ms vs. 296.4 ± 75.2 ms, p < 0.001, d = 0.66), and digit-cancellation performance (67.6 ± 12.6 vs. 63.0 ± 10.0 targets, p = 0.006, d = 0.40). Sleep extension significantly enhances both physical and cognitive performance in physically active individuals, with effects more pronounced during morning hours, partially attenuating typical circadian performance decline and establishing sleep extension as an effective, non-pharmacological strategy for optimizing performance capabilities. Full article

COVID-19 school closures forced German high-school graduates (Abitur 2022 cohort) to prepare for their final examinations with lengthy learning times at home. Guided by transactional stress theory, we tested how personal resources—self-regulated learning (SRL) skills and academic self-efficacy—and contextual resources—perceived teacher support and teacher digital competence—jointly predicted perceived stress during exam preparation. A cross-sectional online survey (June–July 2022) yielded complete data from N = 2379 students (68% female; Mage = 18.3). Six latent constructs were measured with 23 items and showed adequate reliability (0.71 ≤ α/ω ≤ 0.89). A six-factor CFA fit the data acceptably (CFI = 0.909, RMSEA = 0.064). The structural equation model (CFI = 0.935, RMSEA = 0.064) explained 35% of the variance in stress and 23% of the variance in SRL—action. Academic self-efficacy (β = −0.31, p < 0.001), perceived support (β = −0.28, p < 0.001), teacher digital competence (β = −0.08, p < 0.001), COVID-19 learning disruptions (β = +0.13, p < 0.001), and gender (male = 0.32 SD lower stress, p < 0.001) had direct effects on stress. SRL—action’s direct path was small and non-significant (β = −0.02). Teacher digital competence also reduced stress indirectly through greater perceived support (standardized indirect β = −0.11, p < 0.001). The results highlight self-efficacy and perceived instructional support as the most potent buffers of pandemic-related stress, whereas cancelled lessons added strain. Boosting teachers’ digital pedagogical skills has a dual payoff—raising students’ sense of support and lowering their stress. Full article

Rail-based crop phenotypic platforms operating in open-field environments face challenges such as environmental variability and unstable data quality, highlighting the urgent need for intelligent, online data acquisition strategies. This study proposes a digital twin-based data acquisition strategy tailored to such platforms. A closed-loop architecture “comprising connection, computation, prediction, decision-making, and execution“ was developed to build DT-FieldPheno, a digital twin system that enables real-time synchronization between physical equipment and its virtual counterpart, along with dynamic device monitoring. Weather condition standards were defined based on multi-source sensor requirements, and a dual-layer weather risk assessment model was constructed using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation by integrating weather forecasts and real-time meteorological data to guide adaptive data acquisition scheduling. Field deployment over 27 consecutive days in a maize field demonstrated that DT-FieldPheno reduced the manual inspection workload by 50%. The system successfully identified and canceled two high-risk tasks under wind-speed threshold exceedance and optimized two others affected by gusts and rainfall, thereby avoiding ineffective operations. It also achieved sub-second responses to trajectory deviation and communication anomalies. The synchronized digital twin interface supported remote, real-time visual supervision. DT-FieldPheno provides a technological paradigm for advancing crop phenotypic platforms toward intelligent regulation, remote management, and multi-system integration. Future work will focus on expanding multi-domain sensing capabilities, enhancing model adaptability, and evaluating system energy consumption and computational overhead to support scalable field deployment. Full article

Electromagnetic interference (EMI) remains a difficult task in the design and operation of contemporary power electronic systems, especially in those applications where signal quality has a direct impact on the overall performance and efficiency. Conventional control schemes that have evolved to counteract the effects of EMI generally tend to have greater design complexity, greater error rates, poor control accuracy, and large amounts of harmonic distortion. In order to overcome these constraints, this paper introduces an intelligent and advanced control approach founded on the signal randomization principle. The suggested approach controls the switching activity of a DC–DC converter by dynamically tuned parameters like duty cycle, switching frequency, and signal modulation. A boost interleaved topology is utilized to maximize the current distribution and minimize ripple, and an innovative space vector-dithered sigma delta modulation (SV-DiSDM) scheme is proposed for cancelling harmonics via a digitalized control action. The used modulation scheme can effectively distribute the harmonic energy across a larger range of frequencies to largely eliminate EMI and boost the stability of the system. High-performance analysis is conducted by employing significant measures like total harmonic distortion (THD), switching frequency deviation, switching loss, and distortion product. Verification against conventional control models confirms the increased efficiency, less EMI, and greater signal integrity of the proposed method, and hence, it can be a viable alternative for EMI-aware power electronics applications. Full article

This paper presents a comprehensive review of GaAs HBT-based Doherty power amplifiers (DPAs) targeting 5G New Radio (NR) handset applications. Focusing on the critical challenges of linearity enhancement, back-off efficiency improvement, bandwidth extension under low-voltage (3.4 V) operation, and chip thermal management, the authors analyze state-of-the-art DPAs published in recent years. Key innovations including dynamic power division technique, third order intermodulation (IM3) cancellation technology, and compact output combiners are comparatively studied. Using 5G NR signals, the critical performance of the latest reported PA such as maximum linear power, back-off efficiency, bandwidth, and operating voltage are quantitatively investigated. The measurement results demonstrated that the best performance in recent DPAs achieved high linear power of 31 dBm with 34% PAE and 30 dBm with 31% PAE at the N78 and N77 bands, respectively. The corresponding adjacent channel leakage ratios (ACLRs) were lower than −36.5 dBc without digital pre-distortion (DPD). This review provides a comprehensive understanding of the latest advancements and future directions in highly efficient and linear DPA designs for 5G handset front-end modules. Full article

Background: The increasing demand for elective surgery makes optimizing preoperative assessment a priority. Value-based healthcare aims to provide the highest value for patients at the lowest possible cost through various mechanisms, including reorganizing care into integrated practice units (IPUs). However, few studies have analyzed the effectiveness of implementing virtually led IPUs for preoperative assessment. Methods: We performed a retrospective observational cohort study including patients undergoing elective surgery at a teaching hospital in Madrid, Spain from 1 January 2018 to 31 December 2023, analyzing changes in surgical complications, efficiency, and patient satisfaction between the pre-implementation (2018–2019) and post-implementation (2020–2023) periods. Anesthesiologists’ satisfaction with the virtual assessments was described. During the post-implementation period, preoperative assessment was reorganized as a virtually led IPU. At the IPU appointment, preoperative testing and physical (including airway) examinations were performed by a nurse anesthesiologist. The results were uploaded to the electronic health records, and asynchronous virtual anesthesiologist assessment using a store-and-forward approach was performed. Digital patient education was carried out over the Patient Portal mobile application. Results: A total of 40,233 surgical procedures were included, of which 31,259 were from the post-intervention period. During the post-intervention period, no increase in surgical complications was observed, while same-day cancellations decreased from 4.3% to 2.8% of the total procedures (p < 0.001). The overall process time did not increase, despite the rising number of surgical procedures per year. Patient satisfaction improved. The median time to complete anesthesiologist assessment was significantly lower for virtual assessment (4.5 versus 10 min (p < 0.001), signifying estimated time savings of 716 person-hours per year. Anesthesiologists agreed that virtual assessment was more efficient than in-person evaluation, and half of the participants agreed that virtual preoperative care improved their work–life balance and reduced burnout. Conclusions: A digitally enhanced value-based model of preoperative care can improve efficiency and satisfaction metrics, reducing unnecessary costs and potentially improving the quality of care. Full article

Given the growing need to enhance the accuracy of exploration robots, this study focuses on designing a teleoperated navigation system for a robot equipped with a continuous-track traction system. The goal was to improve navigation performance by developing mathematical models that describe the robot’s behavior, which were validated through experimental measurements. The system incorporates a digital twin based on ROS (Robot Operating System) to configure the nodes responsible for teleoperated navigation. A PID controller is implemented for each motor, with zero-pole cancellation to achieve first-order dynamics, and anti-windup to prevent integral error accumulation when the reference is not met. Finally, a physical implementation was carried out to validate the functionality of the proposed navigation system. The results demonstrated that the system ensured precise and stable navigation, highlighting the effectiveness of the proposed approach in dynamic environments. This work contributes to advancing robotic navigation in controlled environments and offers potential for improving teleoperation systems in more complex scenarios. Full article

Unmanned aerial vehicles (UAVs) are promising and powerful aerial platforms that can execute a variety of complex tasks. However, the increasing complexity of tasks and number of UAV nodes pose significant challenges for UAV sensing networks, such as limiting the spectral resources and increasing device complexity. A potential solution is to implement full-duplex (FD) technology in UAV sensor network transceivers. Although appropriate self-interference (SI) cancellation techniques have been employed in the digital domain, the amplitude of the signal of interest (SoI) is relatively small and can be obscured by SI, especially over longer distances. Moreover, the introduction of phase offsets when filtering measurement signals can lead to signal distortion, resulting in estimation errors in the measurement results. To address these issues, this paper presents a joint transmit (TX) and receive (RX) beamforming algorithm based on the penalty dual decomposition (PDD) algorithm, which considers the constraints of transmission power, reception power, and residual SI power. The simulation analyses demonstrate that with a limited number of antennas, the proposed joint TX-RX beamforming algorithm can effectively suppress SI by up to 140 dB, yielding high-precision measurements in UAV sensor networks without compromising the accuracy of the control signals. Compared with that of the traditional frequency-division duplex (FDD) mode, the measurement accuracy is not decreased; compared with those of the time-division duplex (TDD) mode, the distance and speed measurement accuracies of the UAVs are increased by 10 m and 1.5 m/s, respectively, in the FD mode because there is no interruption of the tracking loop and no continuous retracking in the FD mode. Full article

Continuous-time Sigma-Delta (CTSD) Analog-to-Digital Converter (ADC) is widely used in wireless receivers due to its built-in anti-aliasing and resistive input. In order to achieve a wide bandwidth while ensuring low power consumption, this paper proposes a CT 2-2 Multi-stAge Noise-sHaping (MASH) ADC for wireless communication. In order to reduce power consumption, the loop filter adopts a feedforward structure, and the operational amplifier uses complementary differential input pairs and feedforward compensation. The pseudo-random sequence injection and Least Mean Squares (LMS) algorithm are adopted to calibrate the digital noise cancelation filter to match the analog transfer function. The simulation results obtained in 40 nm CMOS show that the presented 2-2 CT MASH ADC achieves a 76.8 dB signal-to-noise-and-distortion ratio (SNDR) at a 50MHz bandwidth (BW) with a 1.6 GHz sampling rate and consumes 29.7 mW power under 1.2/0.9 V supply, corresponding to an excellent figure of merit (FoM) of 169.1 dB. Full article

This paper presents a low-power photoplethysmography (PPG) readout system designed for wearable health monitoring. The system employs a differential current mirror (DCM) to convert single-ended PPG currents into differential voltages, inherently suppressing DC components. A wide common-mode input range (WCMIR) SAR ADC processes the differential signals, ensuring accurate analog-to-digital conversion. The DCM eliminates the need for DC cancelation loops, simplifying the design and reducing power consumption. Implemented in a 0.18 µm CMOS process, the system occupies only 0.30 mm², making it suitable for multi-channel applications. The system achieves over 60 dB DC dynamic range and consumes only 9.6 µW, demonstrating its efficiency for portable devices. The simulation results validate its ability to process PPG signals across various conditions, offering a scalable solution for advanced biomedical sensing platforms. Full article

In the era of digital commerce, understanding consumer opinions has become crucial for businesses aiming to tailor their products and services effectively. This study investigates acoustic quality diagnostics of the latest generation of AirPods. From this perspective, the work examines consumer sentiment using text mining and sentiment analysis techniques applied to product reviews, focusing on Amazon’s AirPods reviews. Using the naïve Bayes classifier, a probabilistic machine learning approach grounded in Bayes’ theorem, this research analyzes textual data to classify consumer reviews as positive or negative. Data were collected via web scraping, following ethical guidelines, and preprocessed to ensure quality and relevance. Textual features were transformed using term frequency-inverse document frequency (TF-IDF) to create input vectors for the classifier. The results reveal that naïve Bayes provides satisfactory performance in categorizing sentiment, with metrics such as accuracy, sensitivity, specificity, and F1-score offering insight into the model’s effectiveness. Key findings highlight the divergence in consumer perception across ratings, identifying sentiment drivers such as noise cancellation quality and product integration. These insights underline the potential of sentiment analysis in enabling companies to address consumer concerns, improve offerings, and optimize business strategies. The study concludes that such methodologies are indispensable for leveraging consumer feedback in the rapidly evolving digital marketplace. Full article

This entry explores the financial impact of the COVID-19 pandemic on the football industry, highlighting the challenges, adaptations, and long-term implications for clubs across all levels. It examines the industry’s financial fragility, particularly for clubs reliant on matchday revenue, while showcasing adaptive strategies such as digital engagement, government support, and revenue diversification that sustained operations during the crisis. The pandemic exposed structural vulnerabilities within football, from elite clubs to grassroots teams, through revenue shortfalls caused by closed stadiums, cancelled matches, and reduced sponsorships. This study provides a comprehensive analysis of the pandemic’s effects on revenue streams, fixed costs, player contracts, and stakeholder roles, offering insights into strategies that promote financial resilience. Case studies illustrate how elite, semi-professional, and grassroots clubs responded to financial and operational challenges, emphasising the importance of diversified income sources, proactive financial planning, and community support. By identifying lessons from the pandemic, the entry underscores the critical need for sustainable practises and resilient models to prepare the football industry for future disruptions. Full article

Signal conditioning circuits, in particle energy spectrum determination systems, introduce shaping characteristics that affect pulse integrity. This study explores algorithms to compensate for these effects, focusing on digital signal processing for pole-zero cancellation (PZC) and unfolding techniques. The PZC algorithm successfully corrects baseline shift and pulse amplitude loss, providing significant improvements in signal fidelity. Although a digital PZC applied in streaming for high event rates was previously not feasible, this work proposes its implementation on FPGA, combining it with the unfolding method to enable online compensation and enhanced performance under various experimental conditions. Full article

This work presents a nano-watt digital output temperature sensor featuring a supply-insensitive, self-biased current source. Second-order temperature dependencies of the MOS diode are canceled to produce a stable reference and a linear temperature-sensitive voltage. The sensor integrates a sensing unit, voltage-controlled differential ring oscillators, and a low-power frequency-to-digital converter, utilizing a resistor-less design to minimize power and area. The delay element in the ring oscillator reduces stage count, improving noise performance and compactness. Fabricated in 65 nm CMOS, the sensor occupies 0.02 mm² and consumes 60 nW at 25 °C and 0.8 V. Measurements show an inaccuracy of +1.5/−1.6 °C from −20 °C to 120 °C after two-point calibration, with a resolution of 0.2 °C (rms) and a resolution FoM of 0.022 nJ·K⁻². Consuming 0.55 nJ per conversion with a 9.2 ms conversion time, the sensor was tested in a battery-less wireless sensor node, demonstrating its suitability for wireless sensing systems. Full article

The existence of multiple self-interference (SI) signal components, particularly the nonlinear ones, seriously constrains the performance of self-interference cancellation (SIC) methods. To decrease the complexity of SIC methods in full-duplex devices, this article proposes a power analysis method for SI signal components in a full-duplex transceiver. The proposed method comprises a separate analysis algorithm and a system-level power model. Initially, the algorithm is conducted to obtain the spectrum of the linear and nonlinear components in the power amplifier (PA) output signal. Once the linear-to-nonlinear power ratio (LNPR) has been obtained, a system-level power model is constructed by taking both the transmitter noise and analog-to-digital converter (ADC) quantization noise into account. The proposed power model allows for the allocation of SIC method performance in multiple domains during the design of full-duplex transceivers at the top level, thereby reducing the overall system complexity. The simulation results demonstrate that in a full-duplex transceiver with only antenna isolation, the power of the SI signal component is susceptible to alterations due to the operating waveform and transmission power. Finally, the accuracy of the power analysis method is verified through measurement and Simulink. Full article