Search Results (39)

High-resolution range profile (HRRP) reconstruction is essential for extracting range-direction scattering characteristics in wideband radar remote sensing, particularly in synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) imaging. Coded interrupted sampling (CIS) can improve radar low probability of intercept (LPI) performance by controlling signal transmission with a binary sequence. However, the reduced number of valid echo samples may degrade HRRP reconstruction, especially under low-duty-ratio and low signal-to-noise ratio (SNR) conditions. Conventional orthogonal matching pursuit (OMP) processes each frame independently and ignores the inter-frame continuity of scattering-center positions, which may lead to false selections and missed detections. To address this problem, this paper proposes a candidate-interval-assisted orthogonal matching pursuit (CI-OMP) algorithm based on multi-frame sequential priors. Stable scattering-center positions are extracted from historical reconstruction results and expanded into candidate intervals to guide atom matching in the current frame. Simulation results show that CI-OMP outperforms standard OMP in terms of normalized mean squared error (NMSE), tolerant support recovery rate (Tol-SRR), and peak-to-sidelobe ratio (PSLR). At a duty ratio of 0.20, CI-OMP reduces the NMSE by 1.71 dB and improves the PSLR by 7.56 dB compared with OMP. In addition, the candidate-interval strategy reduces the atom-search range by approximately 54–75% under different duty ratios and by approximately 50–83% under different SNRs, demonstrating improved search efficiency. These results demonstrate that CI-OMP improves the accuracy, robustness, and search efficiency of HRRP reconstruction for CIS radar echoes, particularly under low-duty-ratio and low-to-medium-SNR conditions. Full article

Water quality management in large-scale institutional infrastructures faces significant challenges due to the high latency of manual sampling and the energy–connectivity trade-offs in traditional IoT deployments. This paper proposes HydroSense AI, a robust three-tier IoT framework designed for real-time multi-parameter water quality monitoring and predictive analytics. The system integrates a heterogeneous sensing layer (pH, TDS, turbidity, and temperature) with a hybrid communication architecture, utilising Long Range (LoRa) technology for low-power transmission over long ranges (manufacturer-rated for line-of-sight distances of up to 16 km, and validated up to 2 km within a dense campus environment in this study), bridged via an ESP32-based gateway to the cloud. To address the critical issue of energy autonomy in remote sensing nodes, we implement a hardware-synchronised duty-cycling mechanism using a DS3231 Real-Time Clock (RTC), enabling precise deep-sleep scheduling and significantly extending battery operational life. Beyond data acquisition, the framework incorporates AI-driven trend-forecasting and anomaly-detection models to provide early warnings of water degradation through a Telegram-integrated alert system. Experimental validation over an extended deployment period demonstrates high measurement stability, with the forecasting model achieving a one-step (10-min) normalised RMSE of 0.0063 (equivalent to 0.033 pH units) for pH and 0.0298 (17.0 ppm) for TDS on a held-out test partition; a benchmark against persistence and ARIMA baselines is also provided. A complete measured energy decomposition of the deployed node is reported: hardware-synchronised duty cycling reduces the quiescent current to 18.2 μA, and with a 12 s acquisition window at 112 mA on a 10-min cycle, the mean current is 2.26 mA, corresponding to an estimated 46 days of unattended operation on a 2500 mAh cell. Critically, the acquisition window accounts for 99.2% of the per-cycle energy budget and the sleep interval for only 0.8%, so quiescent current—the figure of merit most often reported as evidence of low-power design—is shown not to be the binding constraint for sensor-dominated nodes of this class. The results indicate that the proposed hybrid architecture offers a 99.8% packet delivery ratio for sustainable water management. Full article

Background: Recent technology improvements have enabled desorption electrospray ionisation (DESI) mass spectrometry imaging to achieve down to 5 µm (pixel) image resolution. However, operating at this resolution introduces challenges, particularly regarding increased total analysis time and the need for sufficient instrument sensitivity to detect analytes from very small tissue areas. Methods: High mass and image resolution DESI imaging was performed on rat brain tissue using a Xevo™ MRT benchtop mass spectrometer equipped with a multi-reflecting time-of-flight mass analyser and a DESI XS source. Data acquisition was conducted at speeds of up to 100 Hz. Sensitivity was assessed using a dilution series of five Active Pharmaceutical Ingredients (APIs) spotted onto porcine liver tissue. Signal detection limits were evaluated using extracted ion chromatograms (XICs) with signal-to-noise (S/N) calculations against blank samples. Additionally, enhanced duty cycle (EDC) was applied to evaluate improvements in analyte signal intensity across specific mass ranges in both positive and negative ionisation modes. Results: At acquisition speeds of up to 100 Hz, excellent data quality was achieved, with signal intensity remaining suitable for analytical applications. All five tested APIs were detectable at concentrations of 25 pg/mm². Three of the five compounds were further detected at concentrations as low as 2.5 pg/mm², with signal-to-noise ratios greater than 5. The application of EDC resulted in a significant increase in analyte signal intensity within the targeted mass ranges, particularly for small molecule endogenous metabolites and lipids, in both ionisation modes. Furthermore, the system demonstrated substantially improved spectral quality, achieving mass resolution up to 100,000 FWHM. This enabled the resolution of previously indistinguishable analytes with significantly improved mass accuracy compared to systems operating at approximately 30,000 FWHM. Conclusions: The Xevo™ MRT mass spectrometer with DESI XS source enables high-resolution DESI imaging at speeds up to 100 Hz without compromising data quality or sensitivity. The system demonstrates excellent detection limits for pharmaceutical compounds and improved performance through enhanced duty cycle operation. Overall, the combination of high spatial resolution, increased mass resolution, and improved spectral quality allows for more accurate analyte differentiation, representing a significant advancement over lower-resolution systems. Full article

The real driving emission (RDE) test is going to be a necessary and effective evaluation method in the next-stage heavy-duty vehicle (HDV) emission standards, the rulemaking of which is under way worldwide (e.g., EPA 2027, Euro 7 and China 7). In this work, a time-based method (TBM) was proposed for future HDV RDE calculation. In TBM, cold-start and hot-run emissions are evaluated separately with moving average windows, yet no type-approval test results are needed so that it can also be used as a remote monitoring algorithm. This study analyzes the emissions of NO_x. The value of 0.1 times maximum engine power is utilized to determine the cold-start window, while a 2-bin window structure is adopted for hot-run analysis. In order to further illustrate and validate this method, 16,629.4 h of remote monitoring data with a sampling rate of 1 Hz from 36 China 6 HDVs and 4 different months were analyzed for driving and NO_x emission characteristics with TBM. The average duration of the 21,466 trips analyzed in this work was found to be 0.68 h, and the average ratio of trip work to WHTC (world harmonized transient driving cycle) work was around 1.38, indicating that lower duration and work requirements are needed in future RDE test. Moreover, the average cold-start length was approximately 912.4 s (15.2 min), and long cold starts could be found in cases with low ambient temperatures, low driving speeds and frequent stops. As for hot-run analysis, the proportion of Bin 1 (low-load windows) and Bin 2 (high-load windows) is directly related to the driving scenarios. The calculation results of TBM are comparable to the 2-bin method in EPA 2027. In addition, the optimization of NO_x emissions under cold start and idle conditions are challenging for future HDV updates. Full article

Resource-constrained IoT systems face a fundamental conflict between cryptographic security and energy efficiency, particularly in critical infrastructure monitoring requiring long-term autonomous operation. This study presents a hybrid framework integrating signal-adaptive compression with hardware-accelerated authenticated encryption to resolve this trade-off. The Dynamic Payload Compression with Selective Encryption framework classifies sensor data into three SNR regimes and applies adaptive compression strategies: 24.15-fold compression for low-SNR backgrounds, 1.77-fold for transitional states, and no compression for high-SNR leak detection events. Experimental validation using 2714 acoustic sensor samples demonstrates 5.91-fold average payload reduction with 100% detection accuracy. The integration with STM32L5 hardware AES acceleration reduces power–data correlation from 0.820 to 0.041, increasing differential power analysis attack complexity from 500 to over 221,000 required traces. Compression-induced timing variance provides additional side-channel masking, burying cryptographic signals beneath a 0.00009 signal-to-noise ratio. Projected on 19,200 mAh lithium thionyl chloride batteries, the system achieves 14-year operational lifetime under realistic duty cycles, exceeding industrial requirements for critical infrastructure protection while maintaining robust security against physical attacks. Full article

Post-combustion amine scrubbing using monoethanolamine (MEA) remains a leading carbon capture technology, yet its deployment is constrained by high regeneration energy requirements and the computational expense of rigorous process simulation. This study presents an integrated framework coupling high-fidelity rate-based process simulation with explainable machine learning to systematically characterize a ten-dimensional operating space for MEA-based CO₂ absorption. Latin hypercube sampling generated 10,000 steady-state cases, and five regression architectures were benchmarked under identical protocols. A neural network achieved the highest accuracy (R² = 0.9729, RMSE = 1.43%), while XGBoost was selected as the operational surrogate due to its robust computational efficiency (1.5 ms inference latency) and native compatibility with exact Shapley value decomposition. SHAP analysis identified liquid-to-gas ratio as the dominant efficiency determinant, contributing 46.6% of total predictive importance, followed by inlet temperature and MEA concentration, with these three parameters collectively explaining 85% of efficiency variation and establishing a compact control hierarchy suitable for reduced-order control architectures. Bivariate interaction analysis located a high-efficiency operating region, while sensitivity analysis confirmed the strong influence of inlet temperature across the operating envelope. Pareto optimization via NSGA-II generated tiered operational guidelines spanning the 85% to 98% capture efficiency range, quantifying a 39% specific regeneration duty penalty (3.1 to 4.3 MJ/kg CO₂) for pursuing maximum versus baseline capture targets. The framework demonstrates how explainable machine learning converts opaque process simulations into actionable engineering knowledge, providing a transparent and computationally efficient basis for design optimization and digital twin deployment in post-combustion carbon capture systems. Full article

This paper proposes an event-triggered extension of duty-ratio-based model predictive direct speed control (DR-MPDSC) for permanent magnet synchronous motor (PMSM) drives in electric vehicle (EV) applications. The main contribution is the development of an event-triggered execution framework specifically tailored to DR-MPDSC, in which control updates are performed only when the speed tracking error violates a prescribed condition, rather than at every sampling instant. Unlike conventional MPDSC and time-triggered DR-MPDSC schemes, the proposed strategy achieves a significant reduction in control execution frequency while preserving fast dynamic response and closed-loop stability. An optimized duty-ratio formulation is employed to regulate the effective application duration of the selected voltage vector within each sampling interval, resulting in reduced electromagnetic torque ripple and improved stator current quality. An extended Kalman filter (EKF) is integrated to estimate rotor speed and load torque, enabling disturbance-aware predictive speed control without mechanical torque sensing. Furthermore, a unified field-weakening strategy is incorporated to ensure wide-speed-range operation under constant power constraints, which is essential for EV traction systems. Simulation and experimental results demonstrate that the proposed event-triggered DR-MPDSC achieves steady-state speed errors below 0.5%, limits electromagnetic torque ripple to approximately 2.5%, and reduces stator current total harmonic distortion (THD) to 3.84%, compared with 5.8% obtained using conventional MPDSC. Moreover, the event-triggered mechanism reduces control update executions by up to 87.73% without degrading transient performance or field-weakening capability. These results confirm the effectiveness and practical viability of the proposed control strategy for high-performance PMSM drives in EV applications. Full article

This paper proposes an enhanced Model Predictive Direct Speed Control (MPDSC) framework for Permanent Magnet Synchronous Motor (PMSM) drives, integrating duty ratio optimization and load torque disturbance compensation to significantly improve both transient and steady-state performance. Traditional finite-control-set MPC strategies, which apply a single voltage vector per sampling interval, often suffer from steady-state ripples, elevated total harmonic distortion (THD), and high computational complexity due to exhaustive switching evaluations. The proposed approach addresses these limitations through a novel dual-stage cost function structure: the first cost function optimizes dynamic response via predictive control of speed error, while the second adaptively minimizes torque ripple and harmonic distortion by adjusting the active–zero voltage vector duty ratio without the need for manual weight tuning. Robustness against time-varying disturbances is further enhanced by integrating a real-time load torque observer into the control loop. The scheme is validated through both MATLAB/Simulink R2020a simulations and real-time experimental testing on a dSPACE 1202 rapid control prototyping platform across small- and large-scale PMSM configurations. Experimental results confirm that the proposed controller achieves a transient speed deviation of just 0.004%, a steady-state ripple of 0.01 rpm, and torque ripple as low as 0.0124 Nm, with THD reduced to approximately 5.5%. The duty ratio-based predictive modulation ensures faster settling time, improved current quality, and greater immunity to load torque disturbances compared to recent duty-ratio MPC implementations. These findings highlight the proposed DR-MPDSC as a computationally efficient and experimentally validated solution for next-generation PMSM drive systems in automotive and industrial domains. Full article

This paper proposes a digital predictive peak current control (PPCC) strategy for superbuck converters. The proposed strategy incorporates a current predictor to calculate the output current and a peak current controller to calculate the required duty ratio for the next switching cycle. The duty ratio is precalculated ahead of a switching cycle, which creates a switching cycle for signal samplings and digital calculations. At the end of the next switching cycle, the output current peak value is regulated to match the reference value. The proposed strategy regulates the output current peak value to the reference value within two switching cycles. This increases the current loop bandwidth to π/T rad/s, which optimizes the transient performance. Moreover, a new damping parameter design method based on the damping ratio is given. Furthermore, a simplified version is proposed to facilitate digital realization. This version directly calculates the required duty ratio, which significantly reduces digital calculations. Finally, the experimental results demonstrate the effectiveness of the proposed control strategy in improving the transient performance of the superbuck converter. Full article

The model predictive-torque-control strategy of a permanent magnet synchronous motor (PMSM) has many advantages such as a fast dynamic response and the ease of implementation. However, when the permanent magnet motor has a large number of pole pairs or operates at high-speed, due to constraints such as the inverter switching frequency, sampling time, and algorithm execution time, the motor carrier ratio (the ratio of control frequency to operating frequency) becomes relatively low. The discrete model derived from and based on the forward Euler method has a large model error when the carrier ratio decreases, which leads to voltage vector misjudgment and inaccurate duty cycle calculation, thus leading to the decline of control performance. Meanwhile, the shortcomings of the traditional model predictive-torque-control strategy limit the steady-state performance. In response to the above issues, this paper proposes an improved model predictive-torque-control strategy suitable for low-carrier-ratio conditions. The strategy consists of an improved discrete model that considers rotor-angle-position variations and a model prediction algorithm. It also analyzes the sensitivity of model predictive control to parameter changes and designs an online parameter optimization algorithm. Compared with the traditional forward Euler method, the improved discrete model proposed in this paper has obvious advantages under low-carrier-ratio conditions; at the same time, the parameter optimization process enhances the parameter robustness of the model prediction algorithm. Moreover, the proposed model predictive-torque-control strategy has high torque tracking accuracy. The experimental results verify the feasibility and effectiveness of the proposed strategy. Full article

Background: Modeling tools should be tested against real-world outcomes to confirm their predictive ability compared to random chance. Insights is an analytical tool within the biomathematical modeling software SAFTE-FAST that identifies work patterns that consistently result in elevated fatigue risk. This study investigated the ability of Insights to correctly identify duties with an associated fatigue report using previously collected flight schedule and report data. Methods: Planned and completed flight roster schedules were analyzed using SAFTE-FAST Insights after the rosters had been flown. Fatigue reports were independently linked to planned and completed schedules at the duty level. Odds ratio (OR) analysis investigated the ability of Insights to predict which duties would be linked to a fatigue report. Differences in duties were compared using a one-way analysis of variance (ANOVA) and a two-sample t-test. Results: There were 157 fatigue reports out of 78,061 planned duties and 235 fatigue reports out of 82,612 completed duties. Insights had 3.04 odds of correctly identifying fatigue reports in planned duties but 0.41 odds for completed duties. Discussion: Insights showed good odds of correctly identifying a fatigue report duty using planned schedules but poor odds of identifying a fatigue report duty from completed schedules. Completed duties started later in the day and were shorter in duration than planned duties. Day-of-operations schedule changes may have reduced the fatigue risk in response to the fatigue reports. Full article

Vehicles’ exhaust particulate matter (PM) emissions have significantly decreased over the years. On the other hand, non-exhaust emissions, i.e., particle emissions from brakes and tires, have increased due to the increase in the vehicle fleet, traffic congestion, and the distance traveled. As a result, regulatory bodies are investigating the possibility of mitigating non-exhaust emissions. The Euro 7 proposal introduces specific emission limits for both brakes and tires for the first time in a regulation worldwide. The methodology for brake particle emissions sampling and measurement builds on the work of the Particle Measurement Programme (PMP) informal working group of the United Nations Economic Commission for Europe (UNECE). The recently adopted Global Technical Regulation (GTR) on brakes from light-duty vehicles up to 3.5 t prescribes the technical details. In this paper, we present the technical specifications for the measurements of PM. We also evaluate the penetrations for two cases with two setups for minimum and maximum particle losses. This study, using aerosol engineering calculations, estimates the maximum expected differences between the two setups, both of which are compliant with the GTR. This study also discusses the mass ratios of PM_2.5 and PM₁₀ as a function of the mass median diameters. Full article

Nowadays, dried edible flowers have become one of the eating habits of a healthy lifestyle. The most common way to consume dried flowers is via infused water (tisane). A number of studies on dried edible flowers have reported antioxidant activities mainly due to their phenolic compounds. This work has developed a new extraction method using ultrasound technology to determine phenolic compounds in 15 widely consumed edible flowers. Several extraction factors including pulse duty cycle (0.2, 0.6, 1.0 s⁻¹), temperature (10, 40, 70 °C), solvent-to-sample ratio (10:1, 20:1, 30:1 mL of solvent g ⁻¹ of sample), and solvent composition (0, 25, 50% methanol in water) have been optimized based on a Box–Behnken design coupled with response surface methodology. UPLC-PDA has been employed to quantify 12 major phenolic compounds (2,4,6-trihydroxy benzoic acid, protocatechuic acid, protocatechuic aldehyde, p-hydroxybenzoic acid, caffeic acid, vanillic acid, epicatechin, p-coumaric acid, ferulic acid, quercetin-3-rutinose, iso-ferulic acid, and quercetin-3-glucoside) in the extracts. The optimum extraction conditions for a 1 g sample were 30 mL of solvent (28% methanol in water) at 42 °C with 1.0 s⁻¹ of pulse duty cycle. Based on the kinetic study, the optimal extraction time was 10 min. The method was validated with high precision (CVs of repeatability and intermediate precision were lower than 7%) and high accuracy (recovery higher than 90%). Additionally, the proposed ultrasound-assisted extraction was successfully applied in the determination of phenolic compounds in 15 dried edible flowers. Full article

Limited studies have explored different extraction techniques that improve cannabis extraction with scale-up potential. Ultrasound-assisted and microwave-assisted extraction were evaluated to maximize the yield and concentration of cannabinoids and terpenes. A central composite rotatable design was used to optimize independent factors (sample-to-solvent ratio, extraction time, extraction temperature, and duty cycle). The optimal conditions for ultrasound- and microwave-assisted extraction were the sample-to-solvent ratios of 1:15 and 1:14.4, respectively, for 30 min at 60 °C. Ultrasound-assisted extraction yielded 14.4% and 14.2% more oil and terpenes, respectively, compared with microwave-assisted extracts. Ultrasound-assisted extraction increased cannabinoid concentration from 13.2–39.2%. Considering reference ground samples, tetrahydrocannabinolic acid increased from 17.9 (g 100 g dry matter⁻¹) to 28.5 and 20 with extraction efficiencies of 159.2% and 111.4% for ultrasound-assisted and microwave-assisted extraction, respectively. Principal component analyses indicate that the first two principal components accounted for 96.6% of the total variance (PC1 = 93.2% and PC2 = 3.4%) for ultrasound-assisted extraction and 92.4% of the total variance (PC1 = 85.4% and PC2 = 7%) for microwave-assisted extraction. Sample-to-solvent ratios significantly (p < 0.05) influenced the secondary metabolite profiles and yields for ultrasound-assisted extracts, but not microwave-assisted extracts. Full article

With a stratified multi-stage sampling approach, 1361 male factory workers in the Longhua district of the Shenzhen Municipality of China were selected to investigate the multifaceted determinants of sexual intercourse with non-regular female sex partners (NRP) and female sex workers (FSW) among them. The results showed that 24.5% and 21.2% of participants had sexual intercourse with NRP and FSW in the past 6 months, respectively. More specifically, at the individual level, perceived higher job stress and maladaptive coping styles were linked with a higher likelihood of having sexual intercourse with NRP and FSW (adjusted odds ratios [AOR] ranged from 1.06 to 1.17). At the interpersonal level, those who had higher exposure to information related to sexual intercourse with NRP or FSW were more likely to have sex with these female sex partners (AOR: 1.08 & 1.11). At the social structural level, perceived social norms supporting multiple sex partnerships were linked with a higher likelihood of having sexual intercourse with NRP and FSW (AOR: 1.10 & 1.11). No interaction effects were found between the variables at different levels. Providing pre-employment training to clarify roles and job duties, introducing adaptive coping strategies, and addressing misconceptions of social norms are useful strategies to reduce sexual intercourse with NRP or FSW. Full article