Search Results (215)

In the era of digital transformation, the operational reliability of hydraulic energy conversion systems is paramount for the overall efficiency of sustainable integrated energy infrastructures. This study evaluates the robustness of machine learning-based fault diagnosis for positive displacement pumps, which are critical components in energy-intensive industrial applications. The research addresses a key challenge: the instability of diagnostic features under varying operational regimes. Using vibration signals from units at three distinct wear levels, we evaluated multiple machine learning architectures, including SVM, KNN, and ensemble trees. Our findings reveal that traditional data-driven models suffer a performance degradation of over 21% when subjected to domain shifts caused by load variability. To mitigate this, we implemented a frequency-domain signal conditioning layer that aligns extracted descriptors with physically meaningful wear phenomena. This enhanced feature representation improved classification accuracy to 93.5% under variable load conditions. The results demonstrate that improving the robustness of diagnostic models is essential for reliable operation, maintenance planning, and energy efficiency of hydraulic energy conversion systems within modern industrial energy infrastructures. Full article

Magnetic sensors offer a physically grounded and non-invasive approach to probing biological processes that remain inaccessible to optical, electrochemical, and radio-frequency techniques in complex agricultural environments. In recent years, advances in both classical and quantum magnetic sensors have enabled the detection of bioelectromagnetic signals across plants, soils, animals, and aquatic systems, spanning spatial scales from ionic currents to organ-level electrophysiology and population-level dynamics, positioning magnetometry as an emerging modality within the broader biosensor landscape. This review surveys the evolution of magnetic sensing technologies for agricultural and animal systems, from robust classical sensors used in navigation and soil mapping to quantum-enabled platforms, including Optically Pumped Magnetometers (OPMs) and Nitrogen-Vacancy (NV) centers, capable of resolving pT to fT biomagnetic signals. We synthesize the characteristic amplitudes, frequency ranges, and physiological origins of agriculturally relevant magnetic signals, and critically assess how techniques originally developed for medical magnetoencephalography, magnetocardiography, and low-field magnetic resonance imaging (LF-MRI) are being translated into field-deployable agricultural applications. Beyond sensing hardware, we highlight the essential role of artificial intelligence in extracting weak biological signals from dominant environmental noise, enabling synthetic gradiometry, low-field image reconstruction, and scalable interpretation in unshielded settings. Finally, we discuss how the integration of magnetic biosensing with digital twins supports predictive, multiscale monitoring of plant, animal, and ecosystem health. Together, these developments position magnetometry as an enabling technology for next-generation biosensors in precision and sustainable agriculture. Full article

The phenomenon of pulse tailing, primarily caused by relaxation oscillations, presents a significant challenge to increasing the repetition rates of gain-switched semiconductor lasers. This paper proposes a novel approach to mitigate this issue by simultaneously regulating both the magnitude and pulse width of the pump current, enabling stable, tail-free pulse generation across a broad range of repetition frequencies. Numerical solutions to the carrier rate equations are first employed to investigate the origins of optical pulse tailing. By reducing the current injection duration from 200 ps to 50 ps, carrier injection is effectively truncated, suppressing relaxation oscillations. However, this reduction also leads to a decrease in peak optical pulse power, limiting the laser’s applicability. Increasing the injection current’s magnitude provides a solution. Consequently, a high-precision circuit design has been developed to digitally adjust both the magnitude with a precision of ~3 μA and the pulse width with a resolution of 5 ps. This configuration successfully generates 200 ps optical pulses with a single-pulse energy of 0.96 pJ at 1550 nm, over a repetition rate range from 10 kHz to 1 GHz. With this laser as the transmitter, RZ-OOK modulated signal transmission at a slot rate of 250 MHz has been realized. The proposed scheme offers a stable, reliable optical emission source, making it ideal for high-speed, high-capacity optical time-division multiplexing communication, time-resolved spectroscopy, and laser ranging and imaging applications. Full article

The wide application of liquid hydrogen as a key energy carrier is severely limited by the reliability of high-pressure and low-temperature pumps. The traditional research on liquid hydrogen pumps relies on empirical analysis of isolated components, but fails to reveal the fundamental failure mechanism of these pumps. This review argues for a paradigm shift in the understanding and design of liquid hydrogen pumps. We systematically decomposed the failure of the liquid hydrogen pump into a thermodynamic-driven, cross-scale cascading process rather than the failure of isolated components. At the molecular level, the extreme thermal physical properties of liquid hydrogen (ultra-low latent heat and surface tension) can lead to widespread nucleation under slight thermal disturbances. At the mesoscopic scale, the initial perturbation is significantly amplified through the nonlinear dynamics of bubble clusters. This amplification is characterized by intense collapse and strong energy concentration due to the low density and low viscosity of liquid hydrogen. At the component level, this enhanced destructive energy will cause faults similar to phase transitions; namely, the liquid lubrication in the bearings will disappear, the seals will shift from viscous blockage to gas diffusion, and at the same time, the damage caused by low-temperature hydrogen cavitation and corrosion to the materials will also occur simultaneously. At the system level, the strong dynamic coupling among the subsystems has led to a nonlinear performance collapse. This cross-scale failure chain reveals the flaws in the classical cavitation theory, which is based on the assumptions of isothermal and inertia dominance. We have expounded the thermodynamic-dominated cavitation state in liquid hydrogen. This state is quantified by the Σ parameter and governs the multimodal behavior of low-temperature cavitation phenomena. To address this complexity, we have proposed a comprehensive framework that integrates multi-scale collaborative simulation and digital twin, combining molecular dynamics, CFD, system dynamics, and targeted experiments. This review proposes a candidate physical framework for addressing the reliability challenges of liquid hydrogen pumps. It also provides a clear roadmap for the next generation of inherently robust cryogenic fluid machinery, and offers a reference for the design of energy systems under other extreme conditions. Full article

To investigate the dynamic characteristics of key parameters in a piston gravity energy-storage system, an experimental system for novel piston gravity energy storage is designed and developed. Firstly, the structure and working principle of the piston gravity energy-storage system are analyzed. Adopting a modular modeling approach, the system is divided into four core modules, and the piston motion, vertical cylinder chamber pressure, hydraulic actuator, and turbine power models are established. Subsequently, a case study simulation is conducted on the piston gravity energy-storage system to model its dynamic characteristics during discharge conditions, analyzing the variation patterns of key parameters such as the chamber pressure, flow rate, and output power within the system. Finally, the experimental system integrates a digital controller with proportional–integral power regulation and an automatic mode switching logic to enable the constant power closed-loop control, with real-time acquisition of the chamber height, pressure, flow rate, and electrical parameters. The dynamic responses of various system parameters are analyzed. Experimental results indicate that under constant power charging and discharging conditions, the height of the upper chamber exhibits a linear trend, the pressure in the lower chamber is inversely proportional to the height of the upper chamber, and the flow rate remains stable with charging and discharging power. Neglecting energy losses of the pump and hydraulic turbine and only considering friction and hydraulic losses, the charge–discharge efficiency of the energy-storage experimental system is 65%. Full article

Integrated energy systems (IES) are widely recognized as a key pathway toward carbon neutrality, enabling the coupling and coordinated optimization of electricity, heat, gas, and cooling. This review provides a structured, technology-oriented overview of IES based on a unified five-subsystem framework (production, conversion, transmission, storage, and consumption). It systematically covers: (1) renewable energy utilization—solar, wind, and geothermal—supported by a global spatial distribution map and representative top-performing commercial products; (2) energy cascade utilization, where combined heat and power/combined cooling, heating and power (CHP/CCHP) raises overall efficiency from approximately 35–40% to 70–90%; (3) multi-form energy storage—electrical, electrochemical, chemical, thermal, and mechanical—distinguishing short-term balancing (e.g., lithium-ion (Li-ion), flywheels, supercapacitors, with 85–95% round-trip efficiency) from long-duration and seasonal applications (e.g., pumped hydro, hydrogen/power-to-gas (P2G), redox flow batteries); and (4) forecasting, collaborative optimization, and the bidirectional integration of IES with smart grids and grid modernization. A strategic strengths, weaknesses, opportunities, and threats–Political, Economic, Sociological, Technological, Legal, and Environmental (SWOT–PESTLE) analysis is further presented to position IES within the global energy transition. The review highlights that IES and grid innovation are mutually enabling, and that realizing the full carbon-neutrality potential of IES requires coordinated progress in standardization, digitalization, long-duration storage, and cross-sector policy alignment. Full article

This study investigates upper-reservoir dam-axis siting in pumped-storage hydropower projects, where cut–fill balance and construction cost are critical under complex terrain conditions. Existing approaches still rely heavily on manual interpretation or static GIS-based analysis and therefore do not adequately optimize dam-axis geometry or earthwork balance. To address this limitation, we propose an Improved Adaptive Variable Neighborhood Search (IAVNS) algorithm that integrates high-resolution digital elevation model (DEM) data within a two-layer adaptive framework. The inner layer performs staged planar and elevation adjustments through adaptive neighborhood operators, whereas the outer layer conducts fitness-guided subregion migration to strengthen global exploration. Experiments on the Qiannan pumped-storage project show that IAVNS obtains layouts with improved cut–fill balance. In the 30-run benchmark comparison, IAVNS achieved a mean CFR of 1.31, which is close to, although slightly above, the upper bound of the adopted earthwork-balance reference interval. In the separate 20-run case-study analysis, the average storage-volume deviation was 0.13%, with run-level deviations ranging from $-1.39\%$ to $1.16\%$. In benchmark comparisons, IAVNS improves solution quality by 22.8% relative to the Genetic Algorithm (GA) and by 16.5% relative to classical Variable Neighborhood Search (VNS), while reducing convergence time by 49.5% and 27.4%, respectively. Sensitivity analysis further suggests that the framework remains locally robust under practically reasonable parameter perturbations, and the module-level ablation study indicates that the observed performance gains arise mainly from the problem-tailored search mechanisms for dam-axis siting rather than from a generic combination of metaheuristic components. Taken together, the case-study results, repeated-run comparison, sensitivity analysis, and ablation study support the use of IAVNS as a geometry-oriented decision-support framework for preliminary dam-axis design in terrain-sensitive hydraulic engineering applications. Full article

Urban drainage systems (UDSs) are critical built assets increasingly challenged by short-duration extreme rainfall, aging infrastructure, and rising surcharge risk. Physics-based hydrodynamic models are widely used for system assessment, but their high computational cost limits real-time operational prediction. Existing data-driven prediction approaches improve computational efficiency, but often rely mainly on sensor inputs and provide limited asset-level interpretation. This study develops an explainable digital twin for real-time prediction of storm-driven water level response in a separate sewer network in the Yangtze River Delta, China. The framework integrates 5 min monitoring and SCADA data, including water level, flow, pump status, and rainfall, with GIS and as-built asset information, including pipe geometry, hydraulic capacity, catchment characteristics, and network connectivity. A hybrid TCN-LSTM model was developed to predict water level and surcharge risk probability at 15–60 min lead times. A surrogate-based SHAP module was used to explain model predictions at the node and subcatchment scales. Multi-source fusion reduced the RMSE by approximately 18% compared with sensor-only baselines. The SHAP results showed that the pipe capacity-related variables and upstream contributing area were the main drivers of surcharge onset. The framework provides interpretable, operationally relevant predictions to support the resilience-oriented management of urban drainage systems. Full article

The global network of pipelines constitutes a strategic backbone for the world economy, enabling safe and efficient transportation of energy products. These pipelines serve distinct functions in the energy supply chain: gas pipelines support emerging cleaner energy carriers; multi-product pipelines provide versatility in transporting refined liquid fuels; and oil pipelines remain dominant for crude oil delivery. Energy management across the pipeline value chain emphasizes efficiency optimization, cost reduction, and sustainability through real-time monitoring, data analytics, integrated systems, and technological innovations spanning operations, maintenance, and emission control. Despite their critical role, petroleum depots remain relatively understudied, particularly in developing and Sub-Saharan African contexts. This review synthesizes insights from over 100 studies on energy-efficient pumping, predictive control, digitalization, and socio-technical energy management in depots. Analysis of these studies highlights recurring operational and infrastructural issues that constrain energy efficiency in depots. The challenges include irregular truck-loading schedules, frequent pump cycling, aging equipment, power-supply instability, manual operator interventions, and policy-driven constraints. The reviewed studies demonstrate that anticipatory, multi-layer control strategies integrating short-horizon flow forecasting, hybrid model predictive control, and cyber–physical–human–institutional system representations outperform reactive approaches in mitigating energy losses and operational variability. Site-specific calibration and phased deployment emerge as pragmatic pathways for implementing advanced energy optimization under the constrained conditions typical of real-world petroleum depots. Full article

In the framework of Agriculture 4.0, the modernization and predictive maintenance of legacy heavy machinery are essential for ensuring food security and operational efficiency. This study presents a non-invasive automated diagnostic system for classifying the operational status of mechanical diesel injection pumps in agricultural tractors through vibration analysis and machine learning. A rigorous experimental setup was conducted on an International 523 tractor to acquire vibration signals under controlled fuel pressure conditions ranging from 1 to 4 bar, with 2 bar established as the optimal nominal pressure. The signal processing methodology employed a hybrid feature extraction approach, integrating spectral components from the Fast Fourier Transform (FFT) with time-domain statistical variables. After evaluating 33 classification algorithms, a Support Vector Machine (SVM) model demonstrated superior performance, achieving a training accuracy of 96.7% and Area Under the Curve (AUC) values exceeding 0.90 across all classes. Notably, the model achieved perfect identification (AUC = 1.0) of critical low-pressure faults (1 bar), which significantly compromise engine start-up and combustion efficiency. Validation with an independent dataset confirmed the robustness of the system, maintaining a 95% accuracy rate. These findings validate the proposed approach as a reliable, low-cost solution for condition monitoring, facilitating the integration of conventional tractors into digital maintenance ecosystems. Full article

Post-processing remains a major bottleneck in the fabrication of microfluidic devices using Digital Light Processing Stereolithography (DLP-SLA) 3D printing, where unpolymerized resin trapped within internal structures must be removed without damaging delicate features such as thin membranes, valves, and pumps. Manual flushing is slow, inconsistent, and prone to structural failure, especially as device complexity and port counts increase. Here, we present the first fully automated flushing system for DLP-SLA microfluidic devices, enabled by a standardized chip-to-chip (C2C) interconnect architecture and an electronically controlled pneumatic routing platform. A reusable 32-port flushing interface chip provides alignment, sealing, and modular coupling to arbitrary device chips through integrated microgaskets, while a network of electronic pressure controllers, differential pressure sensors, and multi-port rotary valves enable precise, programmable application of pressure, vacuum, and solvent conditions. We introduce a fluidic-circuit model of the system that relates applied pressure to the pressure drop across device structures and experimentally validate this model using channels with varying fluidic resistances. Using this platform, we demonstrate robust flushing of both passive (straight and serpentine channels) and active (valves, pumps) microfluidic elements, as well as application-specific devices including mixers and concentration-gradient generators. Our system eliminates manual handling, improves valve membrane survival, and provides repeatable flushing across a broad range of device geometries. This work establishes a scalable foundation for automated post-processing in 3D-printed microfluidics and significantly advances the practicality of DLP-SLA fabrication for complex, multi-layered microfluidic devices. Full article

Air-to-air heat pumps are a key technology for improving energy efficiency and reducing carbon emissions in residential buildings, yet their optimal control remains challenging under real-world conditions. This study evaluates the performance of a hybrid physical–LSTM model for controlling an air-to-air heat pump in a residential building in Zadar, Croatia. The hybrid framework integrates a first-order energy balance model of the building envelope with LSTM-based temperature correction using adaptive weighting. The physical model was calibrated and validated against 52,128 real IoT measurements collected during the 2024/2025 heating season, achieving high accuracy (RMSE ≈ 0.076 °C). Rolling one-day and continuous multi-day closed-loop simulations (up to 15 days) show that the hybrid model yields slightly lower RMSE in long-term runs compared to the pure physical model. However, this apparent statistical improvement is accompanied by systematic underestimation of indoor temperature and significantly higher simulated energy consumption. The results indicate that the observed effect originates from an implicit virtual heat flux introduced by the LSTM correction, which affects thermodynamic consistency in closed-loop operation. The findings highlight that short-term error metrics such as RMSE alone are insufficient for evaluating hybrid models intended for model predictive control (MPC). The main contribution of this study is the explicit demonstration and quantification of an implicit virtual heat flux generated by the LSTM correction in closed-loop multi-day operation, which leads to misleading statistical improvements while causing significant thermodynamic inconsistency and energy overconsumption. In 15-day continuous simulations the hybrid model (ω = 0.05–0.10) caused an indoor temperature underestimation of 1.25–1.31 °C and increased simulated electricity consumption by more than 300% (316 kWh vs. 72 kWh) compared to the physical model. These results have direct implications for the development of reliable digital twins and model predictive control strategies in residential HVAC systems. Full article

Decarbonising greenhouse food production requires improvements in thermal management, energy efficiency, and system integration. Greenhouse energy demand is shaped by coupled heat and mass transfer processes, particularly envelope performance, ventilation, and latent heat associated with humidity control. This article synthesises recent advances in greenhouse microclimate control with emphasis on heat transfer, low-carbon heating and cooling, thermal storage, renewable and waste heat integration, and advanced modelling and control approaches. The review shows that humidity control and latent load management are primary drivers of winter energy use, as moisture removal through ventilation and dehumidification directly increases the sensible heating required to maintain indoor temperature setpoints. When assessed using realistic psychrometric relationships, ventilation and dehumidification can dominate peak heating demand and seasonal consumption. The performance of heat pumps, storage systems, semi-closed greenhouse concepts, and renewable heat pathways depends on how thermal loads are defined, how system boundaries are set, and how technologies are integrated in operation. Digital twins, predictive control, and hybrid physics-data models are increasingly used to manage variability in weather, energy prices, and infrastructure constraints. Greenhouse decarbonisation cannot be treated as a simple substitution of energy sources. System performance depends on coordinated design and operation, including heat recovery, moisture removal, and integration of supply technologies. Semi-closed and heat recovery-based configurations can reduce the ventilation–heating penalty and lower primary energy demand compared with vent-to-dry approaches. Long-term market projections suggest that the commercial greenhouse sector could expand substantially by 2050 under plausible growth scenarios, reflecting increased capital investment rather than a proportional rise in global food output. Net-zero greenhouse production is achievable through combined improvements in thermal management, electrification, and renewable energy integration. However, large-scale deployment depends on consistent modelling assumptions, credible economic assessment, and alignment with heat and CO₂ supply infrastructure. The transition is therefore shaped by system integration and planning as much as by individual technologies. Full article

Severe congenital Protein C deficiency (SCPCD) is a rare autosomal recessive thrombophilia that typically presents in the neonatal period with early-onset life-threatening thrombotic complications. We report the case of a female infant who presented at birth with digital ischemic necrosis and laboratory evidence of consumptive coagulopathy consistent with neonatal purpura fulminans. Severe Protein C deficiency was confirmed by markedly reduced Protein C activity (<0.03 IU/mL) and compound heterozygous variants in the PROC gene. After initial stabilization and intermittent intravenous Protein C replacement, pharmacokinetic assessment showed marked peak–trough variability. Continuous subcutaneous infusion of Protein C concentrate was therefore initiated using a programmable insulin pump in combination with oral anticoagulation. This strategy achieved stable Protein C activity levels, allowed progressive reduction of the weight-adjusted replacement dose, and enabled removal of the central venous catheter. Continuous subcutaneous infusion of Protein C concentrate via an insulin pump, combined with oral anticoagulation, may represent a feasible long-term therapeutic option in selected patients with SCPCD. Full article

The HVAC/R sector in Europe is undergoing significant transformation driven by climate policy, technological innovation, and increasing digitalization of industrial services. This study examines the sustainability and digital transformation of the Slovak business-to-business (B2B) HVAC/R market in the context of EU F-gas regulation and emerging workforce constraints. The research applies a qualitative–interpretive design supported by structured secondary-data analysis, a review of European and Slovak regulatory frameworks, comparative benchmarking against selected European markets, and exploratory semi-structured interviews with industry professionals. The analysis indicates that regulatory pressure associated with the phase-down of fluorinated greenhouse gases, rising demand for energy-efficient systems, and the growing role of digital communication channels are reshaping procurement behaviour and market competition. At the same time, the sector faces structural barriers, particularly the limited availability of certified technicians and uneven digital adoption among small and medium-sized enterprises. The findings suggest that firms integrating transparent sustainability communication, environmental performance indicators, and digital engagement strategies can strengthen their competitive positioning within the evolving European HVAC/R ecosystem. Full article