Search Results (6,132)

Background and Objectives: The incidence of colorectal cancer (CRC) in developed Western countries is constantly growing. CRC represents the third most common cancer and the second leading cancer-related cause of death worldwide. Innate and adaptive immunity play a pivotal role in the tumor response, but many of these interactions are still not well understood. Granulysin (GNLY) is an effector, cytolytic molecule, present in human cytotoxic granules of different lymphocyte subpopulations, mainly in cytotoxic T cells and NK cells. Pore-forming proteins GNLY, perforin and granzymes play a key role in cell-mediated immune responses against tumors and infections. Materials and Methods: We aimed to analyze perforin and GNLY-mediated cytotoxicity in the peripheral blood of patients with CRC by flow cytometry. Simultaneously, the cells were labeled with monoclonal antibodies against perforin, GNLY and different surface antigens (CD3, CD4, CD8 and CD56). Phenotypes of lymphocyte subpopulation and expression of perforin and GNLY were analyzed using intracellular and surface immunofluorescence. Results: Total perforin and GNLY expressions in peripheral blood mononuclear cells (PBMC) were significantly lower than in the control group. Statistically significant differences were observed in the distribution of perforin and GNLY expression in different stages of tumors classified according to Dukes’, indicating that the percentage of total perforin and GNLY was significantly diminished in accordance with tumor progression. Perforin and GNLY expression were significantly reduced in NK and NKT cells, accompanied by reduced cytolytic potential in patients with CRC and a consequent reduction in their ability to eliminate tumors and infected cells. Conclusions: The determination of cytotoxic potential may provide a valuable assessment of a patient’s immune status and represent a novel therapeutic target. Patients with CRC exhibit markedly impaired perforin- and GNLY-mediated cytotoxicity that correlates with disease progression. Assessment and restoration of cytolytic potential may therefore serve as indicators of immune competence and promising therapeutic strategies to improve perioperative and oncologic outcomes. Full article

Multi-Robot Systems (MRS) demand optimal spatial resource configuration to ensure systemic efficiency in mission-critical applications. Conventional paradigms rely on rigid coverage-first principles, prioritizing exhaustive spatial scanning over rapid target discovery, thereby compromising systemic responsiveness. To bridge this gap, this study proposes the Attraction of Unknown area Centroid for Exploration (AUCE) architecture, a centralized framework designed to simultaneously optimize global exploration efficiency and early-stage target discovery rates. The control framework incorporates a dynamic region planning strategy that adaptively modulates the systemic search focus based on the specific field of view of autonomous agents, alongside an optimized S-shaped trajectory pattern to establish a rigorous balance between localized path simplicity and global coverage. A versatile profit function synthesizing constant and time-varying coefficient strategies explicitly regulates the systemic trade-off between accelerated early-stage target discovery and global path cost minimization. Quantitative simulations demonstrate that AUCE significantly outperforms established methods by mitigating redundant path costs and generating a distinct front-loading effect to accelerate target localization. Subsequent evaluations confirm the framework’s computational scalability in expanded swarms and its systemic adaptability when navigating static obstacles. Full article

Permanent magnet synchronous motor (PMSM)-driven position servo systems in high-speed flight vehicles face severe challenges from extreme thermal environments, which induce significant parameter variations up to 25% (e.g., motor torque constant) and complex multi-scale disturbances. This paper proposes a novel adaptive robust control strategy integrating three key components: (1) an ultra-local model formulation motivated by physically consistent thermal effect analysis of electromagnetic, mechanical, and tribological parameters; (2) a dual-layer disturbance observer architecture comprising a third-order finite-time convergent extended state observer (FTCESO) for fast-varying disturbances and a σ-modification adaptive estimator for slow-varying thermal drifts; and (3) a global nonlinear integral terminal sliding mode controller with a cycloidal reaching law. Stability analysis based on homogeneous system theory and Lyapunov methods establishes practical finite-time convergence with explicit bounds. The experimental results on a TMS320F28335-based servo platform demonstrate that the proposed method reduces the maximum position deviation by 83–94% compared to PID, LADRC, and conventional SMC controllers under the tested disturbance conditions, achieving settling time reductions exceeding 90%. Under combined thermal drift and external loading, the proposed approach limits the maximum tracking error to below 0.45° while maintaining a steady-state error under 0.08°. Full article

Recurring socio-temporal events, such as Ramadan in Middle Eastern cities, introduce pronounced non-stationarity in urban traffic demand. During these periods, daytime traffic volumes typically decline, while congestion becomes more severe in the evening around the Iftar (fast-breaking) period and persists into late-night hours, making conventional fixed-time signal plans less effective. An additional challenge is that demand is not only time-varying, but also unevenly distributed across competing movements: attempts to prioritize high-volume phases can inadvertently cause excessive delays—or even starvation—on lower-demand approaches. To address these issues, this study presents an adaptive, regime-aware traffic signal control framework that combines predictive modeling with constrained optimization. Short-term phase-level delays are forecast using Long Short-Term Memory (LSTM) models, and a Model Predictive Control (MPC) scheme then determines the green time allocation at each control cycle through a receding-horizon strategy. The optimization explicitly represents phase interactions by including constraints that prevent excessive delay in competing movements, thereby yielding a balanced and operationally realistic control policy. The approach is validated with one-minute-resolution TomTom delay data from a signalized intersection in Jeddah, Saudi Arabia, covering both Normal and Ramadan conditions. The LSTM models show stable predictive performance, achieving root mean square errors (RMSEs) of 19.8 s under Normal conditions and 17.1 s during Ramadan. In general, the results show that the proposed framework cuts total intersection delay by about 0.3% to 2.8% compared to standard control strategies. Even though these total-delay improvements are small, they come with big drops in delay for lower-demand phases (about 12–20%) and keep the delay increases for higher-demand phases under control. This shows that the method makes the whole process more efficient by fairly spreading out the delay instead of just making one phase better on its own. The results show that combining forecasting with constrained optimization is a strong and useful way to handle changing traffic demand. This is especially true during times of high demand when flexibility, stability, and fairness across movements are all important. Full article

The building façade serves as the primary interface between the built environment and external climate, marking the transition from static regulation to dynamic response in climate-adaptive design. While existing research predominantly addresses periodic climatic elements such as temperature and solar radiation, the highly stochastic wind environment and its potential for internal acoustic problems remain systematically unexplored. This study investigates the acoustic modulation mechanism of building façades under dynamic wind conditions through a simulation-based methodology. The primary aim is to demonstrate the use of active control to mitigate the influence of fluctuating wind on the internal acoustic environment of buildings with open windows or semi-open boundaries, focusing on the coupling between stochastic wind fields and architectural acoustics in humid subtropical climates. We propose a wind-responsive adaptive acoustic façade system employing fractal geometry and configurable delay strategies, and develop a high-fidelity simulation framework to quantify how façade geometry and activation logic regulate acoustic parameters under varying wind conditions (1–8 m/s). Results indicate that: (1) support vector regression-based mapping of wind speed to delay strategies maintains key sound-field parameters (Lateral Fraction (LF), Speech Clarity (C₅₀), and Early Decay Time to Reverberation Time ratio (EDT/RT₃₀)) within 10% fluctuation across wind regimes; (2) fractal configurations achieve balanced wide-band (125 Hz–8 kHz) performance, with SPL fluctuation <3 dB, spectral tilt (+0.3 dB), and reverberation time slope <0.3; (3) configurational switching between column (high LF) and row (high C₅₀) arrangements enables dynamic trade-off between spatial impression and speech clarity. This work establishes an integrated framework coupling wind dynamics, façade morphology, and acoustic modulation to regulate objective indoor acoustic parameters. Based on the simulated omnidirectional point-source model, the results show that key acoustic indicators remain stable across varying wind conditions, providing a theoretical and quantifiable basis for climate-responsive acoustic envelope design. Future work will include empirical prototype testing and listening tests to determine whether these simulated acoustic parameters translate into improved comfort and well-being for occupants. Full article

With the increasing demand for high speed and high-precision motion control in CNC machine tools, permanent magnet linear synchronous motors (PMLSMs) have been widely adopted in feed drive systems due to their excellent dynamic performance and positioning accuracy. However, existing model predictive current control (MPCC) variants still face challenges regarding high computational overhead and strong dependency on accurate motor parameters, which limit their industrial applicability. To address these issues, this paper proposes an adaptive recursive MPCC for PMLSM drives based on the Cartesian distance minimization principle. An adaptive recursive prediction scheme that is inspired by the feedback structure of recurrent architectures is first introduced. By cyclically utilizing the previously sampled current to predict the next period’s state, the strategy effectively decouples the control law from inductance variations. The dependence on resistance is further mitigated by analyzing the correlation between the ideal current vector and voltage vector deviations. Second, the selection of the optimal voltage vector is transformed into a geometric problem: minimizing the Cartesian distance between the reference voltage and 19 candidate deviations within a proposed virtual voltage vector hexagon. To minimize the computational burden, the vector space is partitioned into eight regions, allowing the optimal candidate to be selected from only two pre-derived deviations. The experimental results demonstrate that the proposed method significantly outperforms existing MPCC benchmarks. Specifically, the execution time is reduced by 63.6%. Under severe parameter mismatch, the current THD is reduced from 14.82% to 6.35%, and the thrust ripple is improved from 12.06 N to 5.25 N, validating its superior robustness and efficiency. Full article

Servo electric cylinders have been widely adopted in high-performance linear drive applications such as aerospace systems, robotic servo systems, medical equipment, advanced manufacturing, precision testing, and high-end equipment due to their advantages, including high cleanliness, compact structure, high transmission efficiency, and ease of achieving precise control. However, under complex operating conditions, system performance is influenced not only by control strategies but also closely related to factors such as friction, clearance, transmission flexibility, structural vibrations, and modeling accuracy. This paper reviews mainstream control strategies and dynamic simulation methods for servo electric cylinders, providing structured analysis and systematic evaluation of representative research. In terms of control strategies, key approaches, including classical PID control, robust nonlinear control, intelligent and learning-based control, and active disturbance rejection control, are discussed, with comparative analysis of their characteristics and limitations in tracking accuracy, robustness, adaptability, and engineering feasibility. Regarding dynamic modeling and simulation, methods such as multibody dynamics, finite element analysis, rigid-flexible coupling, and multi-domain collaborative simulation are reviewed, examining their applicability in nonlinear mechanism characterization, local structural response assessment, and high-fidelity system modeling. Current research indicates that servo cylinder control is evolving from single-method improvements toward integrated and composite approaches, while dynamic modeling has progressed from low-order simplified analyses to system-level, multi-level, and high-fidelity descriptions. Existing studies still face challenges, including insufficient unified evaluation criteria, inadequate cross-method comparisons, and insufficient integration between control design and high-fidelity models. Future research should focus on enhancing control-model co-design, experimental validation under complex conditions, and system-level optimization oriented toward intelligent and high-reliability systems. Full article

Taste perception is known to be altered in microgravity, which can significantly impact astronauts’ food acceptance and overall dietary experience. This study examines the effects of microgravity and virtual reality (VR) on the sensory perception and overall liking of foods, specifically lemonade and vegetable soup, under controlled experimental conditions. The results indicate that overall liking for both products decreased significantly in microgravity, consistent with prior research on sensory suppression in space environments. However, VR demonstrated a compensatory effect, as overall liking scores in VR-enhanced microgravity stabilized and closely resembled those observed under normal gravity. This suggests that VR has the potential to mitigate the adverse effects of microgravity on taste perception, thereby improving food acceptability for astronauts. These findings underscore the necessity for further research into sensory modulation in altered-gravity environments, particularly for long-duration space missions. Future studies should explore VR-based interventions, adaptive food formulations, and multisensory integration strategies to optimize food palatability and acceptance in space. Full article

The increasing deployment of off-grid photovoltaic–battery energy storage systems (PV–BESSs) has intensified operational demands on battery energy storage, particularly when second-life lithium-ion batteries are employed. Due to aging-induced increases in internal resistance and reduced thermal margins, second-life batteries are more vulnerable to high-current operation at a low state-of-charge (SOC), which aggravates heat generation and accelerates degradation. In this study, an SOC-dependent soft current limiting strategy is proposed that reshapes the discharge current reference under low-SOC conditions while maintaining fixed SOC limits, thereby targeting current-domain protection rather than SOC-boundary adaptation for reliable off-grid operation. The proposed method introduces two SOC thresholds to gradually derate the allowable discharge current, preventing abrupt current changes near the lower SOC bound. A unified MATLAB/Simulink-based framework is developed for a 24 h representative off-grid PV–BESS scenario using a second-order equivalent circuit model coupled with a lumped thermal model. Simulation results show that the proposed current shaping reduces low-SOC current stress and associated Joule heating, leading to moderated temperature rise, while only slightly affecting the unmet load under the tested conditions. These findings indicate that SOC-dependent current shaping can provide a control-oriented means to reduce low-SOC electro-thermal stress in second-life batteries within the studied off-grid PV–BESS framework. Full article

Petroleum hydrocarbons frequently contaminate arid oilfield soils, but remediation is challenging because these soils typically contain little organic matter, retain little moisture, and are exposed to high temperatures, that hinder natural attenuation. This study evaluated indigenous bioaugmentation of an aged crude oil-contaminated sandy soil from the Burgan oilfield in Kuwait, in contrast to exogenous commercial microbial products and to natural attenuation. In a 140-day bench-scale tray study, aged crude oil–contaminated soil from the Burgan oilfield (initial TPH 2.49–4.78%, dry wt.) was treated with an enriched indigenous consortium, a commercial consortium, or no inoculum under controlled moisture, nutrient, and aeration conditions. TPH was quantified as hexane-extractable material, and degradation kinetics were evaluated using a first-order model. A statistical comparison of replicate-derived decay constants (k) was conducted using one-way ANOVA and subsequent post hoc testing. Among the replicated treatments, the indigenous consortium showed the strongest performance. In the low-TPH indigenous group, TPH removal reached 63.8 ± 3.1% and fell below 1% by day 140; at higher starting TPH, removal remained substantial but slower. Commercial inoculation was less effective and more variable, while uninoculated controls showed minimal decline. The decay constant for the indigenous (0.0053–0.0075 day⁻¹) was much higher (p < 0.001) than those in commercial (0.0025 day⁻¹) and natural attenuation (0.0005 day⁻¹). Furthermore, the model fit was robust for indigenous treatments (R² = 0.89–0.91) but weaker for commercial and uninoculated controls. The study findings demonstrate that bioaugmentation utilizing well-adapted indigenous consortia offers a statistically validated and kinetically predictable strategy for TPH remediation in desert soils. Full article

Physical coupling, nonlinearity and uncertainty degrade the dynamic performance of electro-hydraulic servo systems, particularly under conditions involving input delays, leading to reduced trajectory tracking accuracy or even system instability. These factors often fail to meet the high-precision requirements of engineering applications. To effectively address these difficulties, this paper proposes a novel adaptive control protocol for networked electro-hydraulic servo systems. For the minimal communication delay problem of networked electro-hydraulic servo systems, Laplace transform algorithm together with Padé approximation is adopted in this study to remove the delay term from the mathematical system model. Moreover, the matched modeling parametric uncertainty of systems is estimated and compensated by the neural network adaptive method to improve the dynamical performance of the system during the steady state. The controller is designed on the basis of recursive backstepping strategy and the finite-time stability theorem, which can handle system nonlinearity and guarantee transient response. The validity of the proposed theoretical results is proved by Lyapunov stability and the feasibility and superiority are verified via physical simulation. Full article

Chroococcidiopsis sp. was isolated from the endolithic habitat of the Atacama Desert (northern Chile), one of the most challenging-to-life polyextreme environments on Earth. The photosynthetic machinery of microorganisms inhabiting this environment is supposed to be highly adapted to cope with the intense solar radiation of the area. Thus, PAR-red light ranging from 100 to 900 µmol photon·m⁻²·s⁻¹ has been investigated as a strategy to enhance culture productivity and stimulate the synthesis of bioactive molecules in Chroococcidiopsis sp. A control culture was maintained under white light at 100 µmol photon·m⁻²·s⁻¹. The results revealed that red light was utilized more efficiently than white light of similar irradiance, and its modulation enhanced both growth and photosynthetic activity of the cyanobacterium. Furthermore, Chroococcidiopsis sp. appeared to activate mechanisms to mitigate photooxidative stress produced by excess light energy. Specifically, increasing light irradiance induced photoacclimation responses, characterized by a decrease in chlorophyll content and a concomitant increase in carotenoid accumulation, likely aimed at reducing photon flux transduced to photosynthesis. Additionally, scytonemin synthesis was enhanced under high irradiances, contributing to dissipating excess light energy. Overall, this study demonstrates that modulation of red-light irradiance effectively improves the growth of Chroococcidiopsis sp. while promoting the accumulation of antioxidant compounds—primarily carotenoids and, to a lesser extent, scytonemin. Full article

Conventional multispectral photodetectors typically rely on multiple electrical terminals to discriminate different wavelengths, which inevitably increases structural complexity. Here, we break this paradigm by demonstrating a dual-color visible–infrared photodetector based on a simple two-terminal Au/MoS₂/Te heterostructure. The device operates through a bias-switching mechanism: reversing the voltage polarity selectively activates either the MoS₂/Au Schottky junction for visible-light detection (520 nm) or the Te/MoS₂ heterojunction for infrared detection (1550 nm). This bias-controlled wavelength selectivity is unambiguously verified by scanning photocurrent mapping. Beyond dual-color discrimination, an adaptive convolutional neural network is employed to decode the nonlinear current–voltage characteristics and enable precise spectral identification, achieving a reconstruction error of approximately 4.5%. Furthermore, high-fidelity dual-color imaging is demonstrated at room temperature. These results establish a hardware–algorithm co-design strategy based on a minimalist two-terminal architecture, providing a viable route toward compact and intelligent spectral-sensing systems. Full article

Distributed generation (DG) exhibits inherent volatility and intermittency, and its grid-integration expansion presents formidable challenges to microgrid regulation and control. Conventional control strategies often neglect the uncertainties associated with renewable energy generation and the coordinated management of flexible resources. This paper proposes a multi-time-scale optimal control strategy for microgrids that explicitly accounts for uncertainty. The strategy integrates a collaborative scheduling framework for assets, including electric vehicles (EVs) and energy storage systems, alongside a stochastic optimization model for microgrids that comprehensively incorporates uncertainties from wind and solar power generation, EV operations, and load forecasting errors. The improved Archimedean chaotic adaptive whale optimization algorithm is utilized to solve the optimal scheduling model, while the Latin hypercube sampling (LHS) technique is employed to address uncertainty-related problems in the optimization process. Case study results demonstrate that, in comparison with traditional optimal scheduling strategies, the proposed approach more effectively mitigates uncertainties in real-world operations, reduces microgrid operational risks, achieves a significant reduction in scheduling costs, and concurrently fulfills the dual objectives of microgrid economic efficiency and operational security. Full article

Accurate wind speed forecasting is critical for reliable wind-power integration, yet it remains challenging due to the strongly non-stationary and inherently multi-scale nature of atmospheric processes. While deep learning models—such as LSTM, GRU, and Transformer architectures—achieve competitive short- and medium-term performance, they frequently suffer from spectral bias, hyperparameter sensitivity, and reduced generalization under heterogeneous operating regimes. To address these limitations, we propose a multi-scale spectral–recurrent framework, termed RFF-RNN, which integrates multi-band Random Fourier Feature (RFF) encodings with parameterizable recurrent backbones. A key innovation of our approach is the deliberate relaxation of strict shift-invariance constraints; by jointly optimizing spectral frequencies, phase biases, and bandwidth scales alongside the neural weights, the framework dynamically shapes a fully data-driven spectral embedding. To ensure robust adaptation, we employ a two-stage optimization strategy combining gradient-based inner-loop learning with outer-loop Bayesian hyperparameter tuning. Our extensive evaluations on a controlled synthetic benchmark and six geographically diverse real-world wind datasets (spanning the USA, China, and the Netherlands) demonstrate the superiority of the proposed framework. Statistical validation via the Friedman test confirms that RFF-enhanced models—particularly RFF-GRU and RFF-LSTM—systematically outperform standard recurrent networks and state-of-the-art Transformer architectures (Autoformer and FEDformer). The proposed approach yields significantly lower error metrics (MAE and RMSE) and higher explained variance ($R^2$), while exhibiting remarkable resilience against error accumulation at extended forecasting horizons. Full article