Search Results (3,042)

The bone marrow tumor microenvironment (TME) is critical for acute myeloid leukemia (AML) progression, immune evasion, and treatment resistance. SRC, a non-receptor tyrosine kinase involved in multiple oncogenic pathways, has not been systematically characterized in AML in relation to prognosis and immune regulation. We integrated bulk transcriptomic and single-cell RNA-sequencing datasets from TCGA, BeatAML, and GEO. Immune-related targets were identified using xCell-based immune scoring and weighted gene co-expression network analysis (WGCNA), followed by protein–protein interaction analysis and multi-algorithm machine-learning screening. We then evaluated SRC expression patterns, prognostic associations, immune microenvironment features, predicted drug sensitivity, single-cell differentiation dynamics, intercellular communication, and in silico virtual knockout perturbation (scTenifoldKnk). SRC emerged as the most robust hub gene after integration of WGCNA, PPI analysis, machine-learning feature selection, and survival screening. SRC was significantly upregulated in AML compared with normal controls and was independently associated with poor overall survival (HR = 1.231, p = 0.037). High SRC expression was linked to adverse ELN/FAB features, increased immune checkpoint expression, enrichment of inflammatory and immunoregulatory pathways, and a higher proportion of primitive leukemia-associated cell states. Single-cell analyses further suggested that SRC was enriched in CD34⁺ progenitor compartments, associated with altered cell–cell communication, and accompanied by distinct mutation and pathway profiles. Drug-response prediction and in silico network perturbation analysis further supported the potential biological and translational relevance of SRC-centered programs. SRC is a prognostically relevant and immune-associated hub linked to AML microenvironment remodeling, and may serve as a candidate biomarker and potential therapeutic target that warrants further experimental validation. Full article

The aim of this work is to explore a load frequency control (LFC) strategy in micro hydro power plants (MHPPs). Using MATLAB/Simulink, we examined several variants of genetic algorithms (GAs), including Roulette, Tournament, and Uniform, which are utilized to optimize tuning proportional integral derivative (PID) parameters by addressing the problem of instability caused by load variations. The performances are compared with conventional PID methods and other advanced techniques like particle swarm optimization (PSO), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANN) algorithms for both single and dual-area MHPP systems. The results show that the GA-optimized PID controller with the roulette wheel achieves the fastest settling time of 0.3 s and the smallest undershoot of 0.015 pu in the single area. Also, optimizing GA demonstrates superior performance in the dual area, with the fastest settling times of 2.5 s for both Roulette and Uniform. In contrast, PSO is slower than GA, and conventional PID requires a much longer settling time of 19.8 s, a similar result occurring in the dual area. These findings confirm the effectiveness of the GA-optimized PID controller, especially the Roulette variant, as a reliable and fast solution for maintaining frequency stability in MHPPs. Full article

The global shift toward decentralized generation has established standalone energy supply systems as a vital solution for remote regions. However, the integration of intermittent renewable sources and the inherent lack of rotational inertia in power electronic interfaces create significant challenges for frequency stability. This study addresses these issues by introducing an original Two-Tier Fractional-Order PID (TTFOPID) controller designed for robust Load Frequency Control (LFC) in a hybrid system comprising solar, diesel, biodiesel, and battery energy storage (BESS). The research utilizes the Multi-Objective Imperialist Competitive Algorithm (MOICA), enhanced with an attractive and repulsive assimilation phase, to navigate the high-dimensional parameter space. A unique framework is established to simultaneously tune controller gains and high-level system parameters, specifically BESS sizing and droop settings. Results demonstrate that the MOICA-tuned TTFOPID provides superior performance, achieving a 72% improvement in the Integral of Time-Weighted Absolute Error (ITAE) compared to NSGA-II and a 56% improvement in the Integral of the Square of Control (ISC) compared to MOPSO. Furthermore, robustness analysis validates the controller’s stability against significant parametric variations. The study concludes that the integrated TTFOPID-MOICA approach provides a superior pathway for stabilizing autonomous energy supply systems while protecting hardware longevity through optimized control effort. Full article

In order to investigate the effects of bentonite and glass fiber on the macroscopic mechanical properties and microscopic mechanisms of cement soil in dry environments, a series of laboratory tests were conducted in this study, including drying tests under controlled environments (30 °C, 50% humidity), unconfined compressive strength (UCS) tests, digital image processing technology, and scanning electron microscopy (SEM) analyses. The moisture evaporation law, surface crack development process, UCS variation, and microstructure evolution of cement soil with different mix proportions (bentonite content: 0–9%; glass fiber content: 0–0.5%) were systematically analyzed. The results show that bentonite can significantly enhance the water retention capacity of cement soil, reduce the water evaporation rate, and increase the unconfined compressive strength by filling internal pores to densify the microstructure. Glass fibers form a three-dimensional network structure in the matrix, exerting a bridging effect to inhibit crack initiation and propagation, and optimize the mechanical properties. The unconfined compressive strength increases significantly with an increase in bentonite content (3–9%), and the optimal fiber content for strength improvement is determined as 0.3%. The synergistic effect of bentonite and fibers optimizes the interfacial bonding force between fibers and the matrix, which remarkably improves the anti-cracking performance of cement soil. Specifically, when the bentonite content is 6–9% and the fiber content is 0.3–0.5%, the cement soil maintains complete integrity after drying, with no obvious cracks on the surface. SEM analysis reveals that the addition of bentonite and fibers inhibits the expansion and connection of internal voids, avoiding the cycle of “void enlargement–stress concentration–crack propagation”. This study provides a scientific basis for the engineering application of cement soil in a dry environment. Full article

We present a reproducible pipeline that converts color images into quantitative fluorescence maps by combining spectral measurement with a linear mixture model. The method is designed specifically for quantitative comparisons of Glo Germ™ on images of hands taken under different experimental conditions with controlled illumination. The emission spectrum of Glo Germ is measured using a spectral photometer and normalized to obtain its spectral power density function. This spectrum is projected into CIE XYZ coordinates and incorporated into a linear mixture model in which each pixel contains contributions from white light, UV-illuminated skin reflectance, and fluorophore emission. Component magnitudes are estimated with non-negative least squares, yielding a grayscale image whose intensity is a monotonic proxy for local fluorophore density. Spatial integration provides an image-level summary proportional to total detected material. Compared with single-channel proxies, the observer suppresses background structure, improves contrast, and remains radiometrically interpretable. Because the method depends only on measurable spectra and linear transforms, it can be reproduced across cameras and extended to other fluorophores. Full article

This study presents an optimized and comparative investigation of four intelligent energy management strategies—Proportional–Integral–Derivative (PID), Fuzzy Logic Control (FLC), Equivalent Consumption Minimization Strategy (ECMS), and Artificial Neural Network (ANN)—applied to a photovoltaic–fuel cell–battery hybrid electric vehicle (PV–FC–HEV). A high-fidelity MATLAB/Simulink model integrates a 6 kW proton-exchange membrane fuel cell (PEMFC), a 500 W photovoltaic subsystem with MPPT, and a lithium-ion battery (LiB) pack. While 1000 W/m² represents Standard Test Conditions (STC), the level of 400 W/m² was specifically selected to simulate average cloudy conditions common in urban driving environments, rather than standard NOCT (800 W/m²), to test the EMS’s robustness under significantly reduced PV support and stressed battery conditions (initial SOC = 30%). While surface contamination and the resulting performance degradation significantly impact real-world results, this study assumes a clean surface to establish an idealized performance baseline for the control algorithms. However, the authors acknowledge that contaminant accumulation is a key factor; future work will incorporate a degradation factor (e.g., a 10–15% efficiency penalty) to evaluate the reliability of these EMS strategies under actual operating conditions. ECMS achieved the lowest hydrogen consumption, saving up to 10 L compared with PID, while ANN maintained the most stable state of charge (SOC > 80%), minimizing deep discharge cycles and improving operational stability. FLC provided balanced operation under fluctuating irradiance. Overall, ANN offered the most harmonized energy flow and dynamic stability, whereas ECMS maximized fuel economy. The findings provide practical guidance for designing sustainable and intelligent control systems in next-generation hybrid electric vehicles. Full article

Methane (CH₄) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global climate. Mitigating CH₄ emissions from oil and gas production sites has recently become a target to reduce overall greenhouse gas emissions; however, monitoring the efficacy of mitigation strategies depends on accurate quantification of CH₄ emissions at the facility-level. Near-field quantification of methane (CH₄) emissions from oil and gas (O&G) facilities remains challenging due to the effects of atmospheric variability and sensor configuration on atmospheric dispersion models. This study evaluates the performance of two atmospheric dispersion models, the Gaussian plume (GP) and backward Lagrangian stochastic (bLS), by comparing calculated CH₄ emissions to controlled single-point emissions between 0.4 and 5.2 kg CH₄ h⁻¹. Emissions were calculated by both models using 121 individual sets of measurements comprising five-minute averaged downwind methane mixing ratios and matching meteorological data. The comparison shows that the bLS approach achieved a higher proportion of emission estimates within a factor of two (FAC2) of the known emission rates compared to the GP approach. The emissions calculated by the bLS model also had a lower multiplicative error and reduced bias relative to GP. Other error-based metrics further confirmed the bLS model performed better, as it yielded lower RMSE and MAE than GP. Statistical analysis of the emission data shows that the lateral and vertical alignment of the source and the sensor plays a critical role in emission estimations, as measurements made closer to the plume centerline and at a distance between 40 and 80 m downwind yielded the best FAC2 agreement. High wind meander degraded the ability of both approaches to generate representative emissions, particularly with the GP approach, as it violates the modeling approach’s assumption of steady-state emissions. Data suggest emissions calculated by the bLS model are comprehensively in better agreement, but the computational demands of the modeling approach and integration into fenceline systems limit real-time applicability. While these results provide insight into model performance under controlled near-field conditions, their applicability to more complex or heterogeneous oil and gas production environments (e.g., the regions Marcellus or Unita Basins) remains limited and uncertain. 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

To meet the high-fidelity testing environment requirements for autonomous driving system development, driving simulators are gradually evolving from tools that “only provide scenes and interaction interfaces” into integrated verification platforms for autonomous driving capabilities. These simulators, in particular, need to feature testable and scalable decision-making modules. However, the autonomous driving functions in existing driving simulators mostly rely on rule-based or simplified model approaches, which are inadequate for depicting the complex interactions in real-world traffic and fail to meet the personalized decision-making needs under various driving styles. To address these challenges, this paper designs and implements a trajectory data-driven personalized autonomous driving decision system, using drone aerial imagery as the core data source to provide realistic background traffic flow and human-like decision-making capabilities. The proposed system can be interpreted as an integrated decision–planning–control framework deployed within a high-fidelity driving simulation platform. It consists of a driving style classification module based on drone trajectory data, a personalized decision module integrating inverse reinforcement learning and dynamic game theory, and a planning and control module. First, a natural driving database is built using 4997 real vehicle trajectories, and prior features of different driving styles are extracted through trajectory feature engineering and an improved K-means++ method. Based on this, a personalized decision-making framework that combines dynamic game theory and maximum entropy inverse reinforcement learning is proposed, aiming to learn the preference weights of different driving styles in terms of safety, comfort, and efficiency. Furthermore, the Dueling Network Architecture (DuDQN) is used to generate human-like lane-changing strategies. Subsequently, a real-time closed-loop execution of personalized decisions in the simulation platform is achieved through fifth-order polynomial trajectory planning, lateral Linear Quadratic Regulator (LQR) control, and longitudinal cascade Proportional–Integral–Derivative (PID) control. Experimental results show that the personalized decision model trained with drone data can realistically reproduce vehicle decision-making behaviors in natural traffic flows within the simulation environment and generate autonomous driving strategies that are highly consistent with different driving styles. This significantly enhances the humanization and personalization capabilities of the autonomous driving module in the driving simulator. Full article

In the semi-arid Batna Belezma region of northeastern Algeria, groundwater is a vital resource for agriculture and drinking water. However, the climate leads to intense evaporation, which affects its quality. This study aims to identify the key hydrogeochemical processes that control groundwater composition in the Merouana Basin and to evaluate the predictive performance of machine learning (ML) models. A total of 30 groundwater samples were analyzed using multivariate statistical techniques, including Principal Component Analysis (PCA), and were modeled using PHREEQC to assess mineral saturation states. Additionally, ML-based regression models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB),were employed to predict groundwater chemistry. The results indicate that the dominant ion distribution follows the following trend: Ca²⁺ > Mg²⁺ > Na⁺ and HCO₃⁻ > SO₄²⁻ > Cl⁻. Alkaline earth metals (Ca²⁺ and Mg²⁺) constitute the major fraction of total dissolved cations, reflecting carbonate equilibrium and dolomite dissolution processes. In contrast, Na⁺ represents a smaller proportion of the cationic load; however, its hydro-agronomic significance is substantial due to its influence on sodium adsorption ratio (SAR) and soil permeability. The PHREEQC modeling showed that calcite and dolomite precipitation promote evaporite dissolution, while most samples remain undersaturated with respect to gypsum. The PCA results reveal high positive loadings of Mg²⁺, Cl⁻, SO₄²⁻, HCO₃⁻, and EC, suggesting that ion exchange and seawater mixing are the primary controlling processes, with carbonate weathering playing a secondary role. To enhance predictive assessment, several supervised machine learning models were tested. Among them, the Random Forest model achieved the highest predictive performance (R² = 0.96) with low RMSE and MAE values, confirming its robustness and reliability. The results indicate that silicate weathering and mineral dissolution are the primary mechanisms governing groundwater chemistry. The integration of multivariate statistics and machine learning provides a comprehensive understanding of groundwater evolution and offers a reliable predictive framework for sustainable water resource management in semi-arid environments. Geochemical model performance showed a high global accuracy (GPI = 0.91), confirming a strong agreement between observed and simulated chemical data. However, the HH value (0.81) indicates some discrepancies, particularly for specific ions or extreme conditions. Full article

Eutrophication caused by excessive nitrogen and phosphorus input remains the most severe environmental threat to the Baltic Sea. While nutrient sources in general are widely studied and regulated, the relative importance of maritime nutrient inputs and their regulatory treatment remain insufficiently integrated into land-based nutrient assessments. This study applies a load-based source apportionment approach and quantifies the maritime- and port-related nutrient inputs to a Baltic Sea coastal system, in relation to other nutrient contributors (riverine, municipal, and industrial sources). Additionally, the stringency of the regulatory frameworks governing each source is assessed using a qualitative regulatory classification scale and compared to the proportion of each nutrient source. The results show that riverine inputs dominate total nutrient loading, accounting for over 90% of both nitrogen and phosphorus. Maritime sources contribute only a small share overall. However, fertilizer cargo handling constitutes the largest nitrogen point source, while ship wastewater inputs are negligible. In contrast, ship wastewater is subject to the strictest regulatory controls, whereas fertilizer handling operates under permits lacking explicit nutrient discharge limits. The findings reveal a governance mismatch between nutrient pressures and regulatory focus and highlight the need to better align nutrient management priorities with actual environmental pressures in semi-enclosed seas. Full article

The enzymatic saccharification of cellulose remains a key step in biomass conversion processes, often influenced by enzyme stability, distribution, and accessibility at solid–liquid interfaces. Immobilization of cellulolytic enzymes on nanostructured supports has been proposed as a strategy to modulate catalytic behavior; however, the relationship between enzyme loading and catalytic response remains insufficiently understood. In this study, CuO-based nanobiocatalysts were prepared through controlled cellulase immobilization and systematically evaluated under defined experimental conditions. Structural and physicochemical characterization was performed using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and integrated thermal analysis (TGA–DTG–DSC), enabling a comparative assessment of the analyzed systems. SEM analysis showed that the average particle diameter increased from 39.5 ± 14.8 nm (CuO nanoparticles) to 95.6 ± 21.8 nm (NPI10), 106.6 ± 27.7 nm (NPI15), and 113.5 ± 23.1 nm (NPI20), indicating progressive variations in particle organization with increasing enzyme loading. Catalytic performance was evaluated through enzymatic hydrolysis of cellulose filter paper as a model substrate, with products quantified by HPLC at a representative reaction time. The system prepared at lower enzyme loading (NPI10) exhibited product formation comparable to that of the free enzyme, with apparent average glucose formation values of 1.054 and 1.047 mg·mL⁻¹·h⁻¹, respectively. In contrast, higher immobilization levels were associated with reduced catalytic output. Across all systems, glucose was the predominant product, with negligible accumulation of intermediate oligomers under the evaluated conditions. These results indicate that increasing enzyme loading does not correspond to proportional increases in product formation and highlight the influence of enzyme distribution and accessibility within the system. The combined structural and catalytic observations provide a controlled framework for evaluating how immobilization conditions influence system behavior in nanobiocatalytic systems. Full article

This study presents a leader–follower flight control architecture for a small-scale UAV swarm, demonstrated using a three-UAV system built on heterogeneous autopilots, GPS positioning, Raspberry Pi 3B+ units, and Wi-Fi communication. The follower UAVs autonomously maintain predefined formations while tracking the leader’s trajectory. During flight, each Raspberry Pi establishes inter-UAV communication via a Wi-Fi network using the UDP protocol, enabling real-time data exchange and attitude adjustments. An outer-loop proportional–integral control design implemented on the Raspberry Pi generates corrective commands to the corresponding autopilot to reduce the followers’ position errors. Under the tested conditions, the framework achieves formation tracking with horizontal and vertical errors of approximately 60 and 20 cm, respectively, providing initial experimental validation in a small-scale setting. In addition, a simulation environment based on pre-recorded UAV and environmental data with 3D visualization is developed to support behavior prediction, performance evaluation, and control tuning prior to real-world deployment, although its applicability beyond the tested scenarios remains to be established. Full article

In three-phase PWM rectifiers, abrupt load changes and parameter variations challenge DC-bus voltage regulation and degrade the performance of conventional controllers. To ensure robust regulation under nonlinear and time-varying conditions, this study proposes a type-3 fuzzy logic controller (T3-FLC) for DC-bus voltage regulation. The T3-FLC enhances the conventional type-1 framework by employing a three-dimensional membership structure that captures both vertical and horizontal uncertainties in the fuzzy inference process. This structure improves adaptability and stability in the face of system disturbances. The proposed controller was compared with a conventional proportional-integral (PI) controller and a type-1 fuzzy logic controller (T1-FLC) under different operating conditions: constant reference, reference tracking, load variation, regenerative operation, and grid disturbances. Under reference tracking mode, it settles within approximately 12 ms for the largest reference step, with the overshoot kept below 0.3%, whereas the T1-FLC and PI controllers require noticeably longer settling times and exhibit higher overshoot. In regenerative operation, the T3-FLC maintains tight DC-bus regulation with recovery times of 10–12 ms and an overshoot of about 2.7%, outperforming the benchmark controllers. Power quality analysis further shows that the proposed controller maintains low input-current distortion, with THD approximately 5–13%, and a near-unity power factor across all scenarios. These results confirm the T3-FLC as an effective control strategy for power converters. Full article

A dynamic analysis of a compressor system is presented to characterize its behavior and establish a mathematical framework for identifying stable and unstable operating regions. The study is grounded in the nonlinear Moore–Greitzer model, which describes compressor dynamics in terms of mass flow and pressure rise as functions of rotor speed. To predict the onset of surge and system instability, advanced nonlinear techniques are employed, including the Jacobian matrix, linear parameter-varying (LPV) modeling, Bendixson’s criterion, and phase plane analysis. These tools enable the identification of both stable and unstable regions, as well as the limit cycle associated with surge phenomena. All of these analyses of the compressor are innovative. Accurate prediction of compressor surge and instability is essential for defining and designing effective control strategies, as surge can damage the compressor, interrupt downstream flow, and inherently represents an unstable operating condition. However, analysis alone is insufficient for practical compressor operation. Therefore, three active control methods are considered: Proportional–Integral–Derivative (PID), Linear Quadratic Regulator (LQR), and Model Predictive Control (MPC). The comparative analysis reveals that insufficient consideration of varying system conditions in LQR design may lead to inferior performance relative to MPC and PID control, particularly under changing disturbances. In contrast, MPC and PID exhibit stronger robustness to disturbance variations and provide effective disturbance rejection. In the proposed approach, MPC simulations are conducted to evaluate controller performance. Due to disturbances in the closed-loop model, the LQR controller demonstrates reduced robustness compared to PID and MPC. Under surge-related disturbances, the minimum input mass flow by both PID and MPC controllers is 0.495 (very close to setpoint), and both controllers exhibit an overshoot of 33% and a rise time of 3 s. Full article