Search Results (102)

Hybrid PV-TEG systems can harvest both solar electrical and thermoelectric power, but their operating point drifts with irradiance, temperature gradients, partial shading, and load changes—often yielding multi-peak P-V characteristics. Conventional MPPT (e.g., P&O) and fixed-structure integer-order PID struggle to remain fast, stable, and globally optimal in these conditions. To address fast, robust tracking in these conditions, we propose an adaptive fractional-order PID (FOPID) MPPT whose parameters (K_p, K_i, K_d, λ, μ) are auto-tuned by the red-billed blue magpie optimizer (RBBMO). RBBMO is used offline to set the controller’s search ranges and weighting; the adaptive law then refines the gains online from the measured ΔV, ΔI slope error to maximize the hybrid PV-TEG output. The method is validated in MATLAB R2024b/Simulink 2024b, on a boost-converter–interfaced PV-TEG using five testbeds: (i) start-up/search, (ii) stepwise irradiance, (iii) partial shading with multiple local peaks, (iv) load steps, and (v) field-measured irradiance/temperature from Shanxi Province for spring/summer/autumn/winter. Compared with AOS, PSO, MFO, SSA, GHO, RSA, AOA, and P&O, the proposed tracker is consistently the fastest and most energy-efficient: 0.06 s to reach 95% MPP and 0.12 s settling at start-up with 1950 W·s harvested (vs. 1910 W·s AOS, 1880 W·s PSO, 200 W·s P&O). Under stepwise irradiance, it delivers 0.95–0.98 kJ at t = 1 s and under partial shading, 1.95–2.00 kJ, both with ±1% steady ripple. Daily field energies reach 0.88 × 10⁻³, 2.95 × 10⁻³, 2.90 × 10⁻³, 1.55 × 10⁻³ kWh in spring–winter, outperforming the best baselines by 3–10% and P&O by 20–30%. Robustness tests show only 2.74% power derating across 0–40 °C and low variability (Δv^max typically ≤ 1–1.5%), confirming rapid, low-ripple tracking with superior energy yield. Finally, the RBBMO-tuned adaptive FOPID offers a superior efficiency–stability trade-off and robust GMPP tracking across all five cases, with modest computational overhead. Full article

Leveraging catalytic microreactors as compact yet powerful thermal sources represents a promising approach to enable rapid and reliable startup of small-scale solid oxide fuel cell (SOFC) systems. In the present study, the homogeneous–heterogeneous (HH) combustion behavior of a propane/air mixture in a Pt-catalyzed microreactor is investigated using two-dimensional computational fluid dynamic (CFD) simulations. The catalytic reaction kinetics model is integrated into the general module of ANSYSY Fluent via a user-defined function (UDF) interface. By varying the surface area factor, the ignition characteristics of the propane/air mixture under different catalyst activities are systematically explored. Numerical results reveal that the relative catalyst activity range of 0–2 represents a sensitive region for propane/air ignition characteristics, characterized by a 541 K decrease in ignition temperature and a 50% reduction in ignition delay time. Nevertheless, further increases in relative catalyst activity from 2 to 10, yield a much smaller reduction—64 K in ignition temperature and 6.7 s in ignition delay time—indicating a weakly responsive regime. The relative contribution of the heterogeneous reaction (HTR) to the total heat release decreases with higher feed temperatures but increases with enhanced catalyst activity. Regarding the temporal evolution of HTR contribution, the initiation of homogeneous ignition undermines the dominance of HTR contribution. Irrespective of catalytic activity levels, the relative contributions of the two reaction pathways subsequently undergo dynamic redistribution and ultimately stabilize, reaching an equilibrium state within approximately 10 s. These findings provide critical insights into the role of catalyst activity in propane/air mixture ignition and the interplay between homogeneous and heterogeneous reactions in microscale combustion systems. Full article

This paper presents a pioneering and novel analysis of the synergistic relationship between China’s leadership in electric vehicle (EV) adoption and the rapid advancement of autonomous driving (AD) technologies within the nation’s mobility ecosystem. Challenging the conventional view of electrification as a parallel trend, this study introduces a new perspective by demonstrating how EV infrastructure serves as a fundamental enabler of autonomy, providing the necessary high-voltage architectures for critical AD functions like real-time sensor fusion and over-the-air updates. In doing so, it addresses the central research question: How does large-scale electrification influence the architecture, deployment, and safety development of autonomous driving vehicles, particularly in the context of China’s intelligent mobility ecosystem? Through technical analysis and industry examples, the paper offers original contributions by illustrating how EV-driven platforms overcome the inherent limitations of internal combustion engine systems, enhancing autonomous execution and system reliability. Furthermore, this research provides novel insights into China’s unique public–private innovation ecosystem, highlighting the role of vertically integrated startups and cross-sector coordination in driving AD development. By analyzing these previously overlooked systemic interactions, the paper posits that China’s EV dominance strategically amplifies its autonomous vehicle ambitions, positioning the nation to lead the next generation of intelligent transportation systems. Full article

This study explores the use of a pre-acclimated Geobacter-enriched inoculum as a novel strategy to accelerate the start-up of biocathodes. Unlike conventional inoculation with broad-spectrum communities, the proposed inoculum combines a long-term electroactive consortium, previously adapted to anaerobic bioelectrochemical conditions, with digestate produced under controlled laboratory conditions. This prior acclimation ensures the presence of Geobacter strains already conditioned to electrode-associated growth, promoting rapid colonization and early electrochemical activity. Experiments were conducted in a dual-chamber electrochemical cell equipped with a three-electrode setup polarized at −1 V vs. Ag/AgCl. The enriched biocathode reached current densities exceeding 1.4 A/m² within 24 h, whereas the control exhibited significantly lower, less stable, and inconsistent performance. Unlike previously reported approaches based on broad-spectrum co-inoculation, this work presents a tailor-made inoculum in which the electroactive community is not only dominated by Geobacter, but also selectively preconditioned under functional bioelectrochemical conditions. This prior adaptation is a key differentiator that markedly enhances start-up efficiency. The results demonstrate that strategic enrichment with pre-acclimated Geobacter significantly accelerates start-up and improves electrochemical performance, offering a promising pathway toward more efficient and scalable bioelectrochemical systems for wastewater treatment and renewable energy generation. Full article

This paper presents a unified modeling framework for quantifying power and energy consumption in motor-driven systems operating under variable frequency control and soft starter conditions. By formulating normalized expressions for voltage, current, and power factor as functions of motor speed, the model enables accurate estimation of instantaneous and cumulative energy use using only measurable electrical quantities. The effect of soft starter operation during startup is incorporated through ramp-based profiles, while variable frequency control is modeled through dynamic speed modulation. Analytical results show that variable speed control can achieve energy savings of up to 36.1% for sinusoidal speed profiles and up to 42.9% when combined with soft starter operation, with the soft starter alone contributing a consistent 8.6% reduction independent of the power factor. To support energy optimization under uncertain demand scenarios, a two-stage stochastic optimization framework is developed for motor sizing and control assignment, and four physics-guided machine learning models—MLP, LSTM, GRU, and XGBoost—are benchmarked to forecast normalized energy ratios from key electrical parameters, enabling rapid and interpretable predictions. The proposed framework provides a scalable, interpretable, and practical tool for monitoring, diagnostics, and smart energy management of industrial motor-driven systems. Full article

The rapid adoption of green mobility solutions—such as electric-vehicle sharing and intelligent transportation systems—has accelerated the integration of Internet of Things (IoT) technologies, introducing complex security and performance challenges. While conceptual Identity and Access Management (IAM) frameworks exist, few are empirically validated for the scale, heterogeneity, and real-time demands of modern mobility ecosystems. This work presents a data-backed, container-native reference architecture for secure and resilient Authentication, Authorization, and Accounting (AAA) in green mobility environments. The framework integrates Keycloak within a Kubernetes-orchestrated infrastructure and applies Zero Trust and defense-in-depth principles. Effectiveness is demonstrated through rigorous benchmarking across latency, throughput, memory footprint, and automated fault recovery. Compared to a monolithic baseline, the proposed architecture achieves over 300% higher throughput, 90% faster startup times, and 75% lower idle memory usage while enabling full service restoration in under one minute. This work establishes a validated deployment blueprint for IAM in IoT-driven transportation systems, offering a practical foundation for a secure and scalable mobility infrastructure. Full article

The underutilization of fruit waste in agroindustry—particularly star fruit—leads to leachate generation, emissions, and disposal costs, highlighting the need for circular alternatives that treat organic fractions while producing energy. This study evaluated the bioelectrochemical conversion of carambola (Averrhoa carambola) residues in single-chamber microbial fuel cells (MFCs). Three 1000 mL reactors were constructed using carbon anodes and zinc cathodes, operated for 35 days with continuous voltage recording and daily monitoring of pH, conductivity, and ORP. Polarization curves were obtained, and FTIR and SEM analyses were conducted to characterize substrate transformation and anode colonization. The anodic biofilm was also profiled using metagenomics. Measurements were performed using calibrated electrodes and a data logger with one minute intervals. The systems exhibited rapid startup and reached peak performance on day 22, with a voltage of 1.352 V, current of 3.489 mA, conductivity of 177.90 mS/cm, ORP of 202.01 mV, and pH of 4.89. The V–I curve indicated an internal resistance of 16.51 Ω, and the maximum power density reached 0.517 mW/cm². FTIR revealed a reduction in bands associated with carbohydrates and proteins, consistent with biodegradation, while SEM confirmed extensive biofilm formation and increased anode surface roughness. Metagenomic analysis showed dominance of Acetobacter (59.35%), with Bacteroides (12.93%) and lactobacilli contributing to fermentative and electrogenic synergies. Finally, the series connection of three MFCs generated 2.71 V, sufficient to power an LED, demonstrating the feasibility of low-power applications and the potential for system scalability. Full article

The rapid development of marine recirculating aquaculture systems (RASs) worldwide offers an efficient and sustainable approach to aquaculture. However, the slow start-up of the nitrification process under low-temperature conditions remains a significant challenge. This study evaluated multiple start-up strategies for moving bed biofilm reactors (MBBRs) operating at 13–15 °C. Among them, the salinity-gradient (SG) strategy exhibited the best performance, reducing the start-up time by 38 days compared to the control, with microbial richness (Chao1 index) reaching 396 and diversity (Shannon index) of 4.89. Inoculation with mature biofilm (MBI) also showed excellent results, shortening the start-up period by 26 days and achieving a stable total ammonia nitrogen (TAN) effluent concentration below 0.5 mg/L within 132 days. MBI exhibited the highest microbial richness (Chao1 index = 808) and diversity (Shannon index = 5.55), significantly higher than those of the control (Chao1 index = 279, Shannon index = 3.90) and other treatments. The hydraulic retention time-gradient (HRT) strategy contributed to performance improvement as well, with a 24-day reduction in start-up time and a Chao1 index of 663 and a Shannon index is 4.69. In contrast, nitrifying bacteria addition (NBA) and carrier adhesion layer modification (CALM) had limited effects on start-up efficiency or microbial diversity, with Chao1 indices of only 255 and 228, and Shannon indices were both 3.24, respectively. Overall, the results indicate that salinity acclimation, mature biofilm inoculation, and extended HRT are effective approaches for promoting microbial community adaptation and enhancing MBBR start-up under low-temperature marine conditions. Full article

Malicious actors often exploit persistence mechanisms, such as unauthorized modifications to Windows startup directories or registry keys, to achieve privilege escalation and maintain access on compromised systems. While information technology (IT) teams legitimately use these AutoStart Extension Points (ASEPs), adversaries frequently deploy malicious binaries with non-standard naming conventions or execute files from transient directories (e.g., Temp or Public folders). This study proposes a threat-hunting framework using a custom Elasticsearch Security Information and Event Management (SIEM) system to detect such persistence tactics. Two hypothesis-driven investigations were conducted: the first focused on identifying unauthorized ASEP registry key modifications during user logon events, while the second targeted malicious Dynamic Link Library (DLL) injections within temporary directories. By correlating Sysmon event logs (e.g., registry key creation/modification and process creation events), the researchers identified attack chains involving sequential registry edits and malicious file executions. Analysis confirmed that Sysmon Event ID 12 (registry object creation) and Event ID 7 (DLL loading) provided critical forensic evidence for detecting these tactics. The findings underscore the efficacy of real-time event correlation in SIEM systems in disrupting adversarial workflows, enabling rapid mitigation through the removal of malicious entries. This approach advances proactive defense strategies against privilege escalation and persistence, emphasizing the need for granular monitoring of registry and filesystem activities in enterprise environments. Full article

The increasing demand for high-performance power converters for electric vehicle (EV) applications places a significant emphasis on developing effective and robust control strategies for DC-DC converter operation. This paper deals with the development, simulation, and experimental validation of an adaptive Lyapunov-type Nonlinear Sliding Mode Control (L-SMC) strategy for a DC–DC boost converter, addressing significant uncertainties caused by large variations in system parameters (R and L) and ensuring the tracking of a voltage reference. The proposed control strategy employs the Lyapunov stability theory to build an adaptive law to update the parameters of the sliding surface so the system can achieve global asymptotic stability in the presence of uncertainty in inductance, capacitance, load resistance, and input voltage. The nonlinear sliding manifold is also considered, which contributes to a more robust and faster convergence in the controller. In addition, a logic optimization technique was implemented that minimizes switching (chattering) operations significantly, and as a result of this, increases ease of implementation. The proposed L-SMC is validated through both simulation and experimental tests under various conditions, including abrupt increases in input voltage and load disturbances. Simulation results demonstrate that, whether under nominal parameters (R = 320 Ω, L = 2.7 mH) or with parameter variations, the voltage overshoot in all cases remains below 0.5%, while the steady-state error stays under 0.4 V except during the startup, which is a transitional phase lasting a very short time. The current responds smoothly to voltage reference and parameter variations, with very insignificant chattering and overshoot. The current remains stable and constant, with a noticeable presence of a peak with each change in the reference voltage, accompanied by relatively small chattering. The simulation and experimental results demonstrate that adaptive L-SMC achieves accurate voltage regulation, a rapid transient response, and reduces chattering, and the simulation and experimental testing show that the proposed controller has a significantly lower steady-state error, which ensures precise and stable voltage regulation with time. Additionally, the system converges faster for the proposed controller at conversion and is stabilized quickly to the adaptation reference state after the drastic and dynamic change in either the input voltage or load, thus minimizing the settling time. The proposed control approach also contributes to saving energy for the application at hand, all in consideration of minimizing losses. Full article

The recirculating aquaculture system (RAS) has been rapidly adopted worldwide in recent years due to its high productivity, good stability, and good environmental controllability (and therefore friendliness to environment and ecology). Nevertheless, the effluent from seawater RAS contains a high level of ammonia nitrogen which is toxic to fish, so it is necessary to overcome the salinity conditions to achieve rapid and efficient nitrification for recycling. The moving bed biofilm reactor (MBBR) has been widely applied often by using PE fillers for efficient wastewater treatment. However, the start-up of MBBR in seawater environments has remained a challenge due to salinity stress and harsh inoculation conditions. This study investigated a new PE-filler surface modification method towards the enhanced start-up of mariculture MBBR by combining liquid-phase oxidation and maifanstone powder. The aim was to obtain a higher porous surface and roughness and a strong adsorption and alkalinity adjustment for the MBBR PE filler. The hydrophilic properties, surface morphology, and chemical structure of a raw polyethylene filler (an unmodified PE filler), liquid-phase oxidation modified filler (LO-PE), and liquid-phase oxidation combined with a coating of a maifanstone-powder-surface-modified filler (LO-SCPE) were first investigated and compared. The results showed that the contact angle was reduced to 45.5° after the optimal liquid-phase oxidation modification for LO-PE, 49.8% lower than that before modification, while SEM showed increased roughness and surface area by modification. Moreover, EDS presented the relative content of carbon (22.75%) and oxygen (42.36%) on the LO-SCPE surface with an O/C ratio of 186.10%, which is 177.7% higher than that of the unmodified filler. The start-up experiment on MBBRs treating simulated marine RAS wastewater (HRT = 24 h) showed that the start-up period was shortened by 10 days for LO-SCPE compared to the PE reactor, with better ammonia nitrogen removal observed for LO-SCPE (95.8%) than the PE reactor (91.7%). Meanwhile, the bacterial community composition showed that the LO-SCPE reactor had a more diverse and abundant AOB and NOB. The Nitrospira has a more significant impact on nitrification because it would directly oxidize NH₄⁺-N to NO₃⁻-N (comammox pathway) as mediated by AOB and NOB. Further, the LO-SCPE reactor showed a higher NH₄⁺-N removal rate (>99%), less NO₂⁻-N accumulation, and a shorter adaption period than the PE reactor. Eventually, the NH₄⁺-N concentrations of the three reactors (R1, R2, and R3) reached <0.1 mg/L within 3 days, and their NH₄⁺-N removal efficiencies achieved 99.53%, 99.61%, and 99.69%, respectively, under ammonia shock load. Hence, the LO-SCPE media have a higher marine wastewater treatment efficiency. Full article

Proton exchange membrane fuel cells (PEMFCs) are ideal for fuel cell vehicles due to their high specific power, rapid start-up, and low operating temperatures. However, their limited lifespan presents a challenge for large-scale deployment. Accurate assessment of remaining useful life (RUL) is essential for enhancing longevity. Automotive PEMFC systems are complex and nonlinear, making lifespan prediction difficult. Recent studies suggest deep learning approaches hold promise for this task. This study proposes a novel EMD-TCN-GN algorithm, which, for the first time, integrates empirical mode decomposition (EMD), temporal convolutional network (TCN), and group normalization (GN) by using EMD to adaptively decompose non-stationary signals (such as voltage fluctuations), the dilated convolution of TCN to capture long-term dependencies, and combining GN to group-calibrate intrinsic mode function (IMF) features to solve the problems of modal aliasing and training instability. Parametric analysis shows optimal accuracy with the grouping parameter set to 4. Experimental validation, with a voltage lifetime threshold at 96% (3.228 V), shows the predicted degradation closely aligns with actual results. The model predicts voltage threshold times at 809 h and 876 h, compared to actual values of 807 h and 872 h, with a temporal prediction error margin of 0.250–0.460%. These results demonstrate the model’s high prediction fidelity and support proactive health management of PEMFC systems. Full article

This study introduced and evaluated a new Carbon Nanotube (CNT) sheet-based method for battery temperature management, aimed at enhancing the performance of Li-ion batteries in subzero environments. This method addressed critical challenges such as startup failures, capacity loss, and the poor performance of the Li-ion battery in extreme cold conditions, particularly for industrial applications like forklifts operating at temperatures as low as −30 °C. Without CNT heating, the battery performance dropped significantly in low-temperature environments. At −20 °C, the battery delivered only 63.4% of its capacity, with minimal self-heating. At −30 °C, it failed almost entirely, shutting down after just 45 s. In contrast, CNT heating greatly enhanced performance. The CNT sheet quickly warmed the battery to 0 °C—within 97 s at −20 °C and 141 s at −30 °C—allowing it to recover up to 90% of its capacity. These improvements resulted in enhanced capacity and energy output compared to batteries without CNT heating, which suffered from severe performance losses, including a negligible capacity and energy output under −30 °C. It can be concluded that the CNT sheet-based approach provides superior thermal conductivity, rapid heating, and exceptional energy conversion efficiency, enabling extended battery life and enhanced operational reliability in subzero environments. Its scalability and affordability position it as a transformative innovation for industrial applications reliant on efficient battery performance in extreme cold environments. Full article

Anaerobic digestion (AD) has gained broad interest as a sustainable organic waste management and resource recovery method. However, the complexity of the AD process could pose serious risks in real-scale applications. One of the most critical phases in the operation of AD systems is the start-up phase, including the seeding strategy of the digesters. This study aims to assess the effect of digestate post-treatment before seeding on the start-up of thermophilic AD systems. Two anaerobic digesters (R1 and R2) were started using two different thermophilic inocula and were kept operational for 17 weeks under identical conditions. Lab digesters were seeded with digestates sampled from a thermophilic full-scale reactor (R2) and a post-treatment mesophilic tank (R1). The start-up strategies exhibited satisfactory stability and high productivity, achieving mean weekly methane-based biodegradability rates of 61 and 64% of the feed’s theoretical biomethane potential (BMP), respectively, in R1 and R2. However, R2 showed greater resilience to high and sudden organic loads applications, making it more suitable for rapid and aggressive start-ups. These results are expected to assist full-scale anaerobic digester operators in selecting an appropriate inoculum based on the characteristics of its source. Full article

Rice combine harvester conveyor troughs and their bolted connections are susceptible to vibration-induced failure due to operational and environmental excitations. Addressing the challenge of predicting the state of the combine harvester’s conveyor trough bolted structure prior to vibration-induced failure, this study addresses this by investigating signal analysis, system design, and condition identification for these critical components. Firstly, multi-point vibration signals from the conveyor trough were acquired and analyzed in the time-frequency domain. The analysis pinpointed the X-direction at the trough-frame connection (Point 5) as the most responsive location, with RMS peaking at 6.650 during header start-up (vs. 0.849 idle). Significant responses were also noted at Point 3 (Y-dir, 4.628) and Point 6 (X-dir, 3.896) under certain conditions (where Z-direction responses were minimal), identifying critical points that form the basis for condition assessment. Secondly, a vibration acquisition system was developed using a high-performance AD7606 ADC and A39C wireless technology. It features 16-bit resolution (0.00076 mm/s theoretical sensitivity), 8-channel synchronous sampling up to 200 kSPS, and rapid (0.8 s) wireless data transmission. This system meets the demands for high-frequency, high-precision monitoring of the bolted structure. Finally, after comparing machine learning algorithms, Support Vector Machine was chosen for its superior performance. Using a one-vs.-one strategy and data from critical points, an operational condition identification model was developed. Validation with field data confirmed high accuracy (96.9–99.7%) for principal states and low misclassification rates (<5%). This allows for precise identification of the bolted structure’s working status. The research presented in this study offers effective methodologies and technical underpinning for the condition monitoring of critical structural components in rice combine harvesters. Full article