Search Results (17,436)

The current manuscript presents an application of the Sumudu decomposition method (SDM) in efficiently tackling the systems of coupled nonlinear partial fractional differential equations. The technique combines the strengths of the Adomian decomposition method and the Sumudu transform, enabling the transformation of complex systems into rapidly converging series solutions. The efficacy of the technique is then portrayed on various nonlinear coupled fractional models, where approximate solutions are successfully obtained. Furthermore, the computational results indicate efficient numerical performance of the proposed approach for the cases considered. Certainly, the study’s results demonstrate that SDM is an effective and reliable technique for solving the examined class of fractional-order systems. Full article

With the rapid advancement of information technology, the energy consumption of data centers has become a critical issue. Accurate cooling load prediction is essential for optimizing cooling system operations and improving energy efficiency. However, conventional models often struggle to capture the complex nonlinearities and multi-variable coupling effects inherent in data centers. To address the limitations of existing models in terms of training efficiency and generalization performance, this study proposes a cooling load prediction model that integrates the light gradient boosting machine (LightGBM) algorithm with Bayesian optimization. The model was validated using data generated from an EnergyPlus simulation of a representative medium-scale data center. Comparative analysis demonstrates that the proposed model surpasses naive benchmarks (T-1, T-24, and T-168) and other machine learning models (SVR, XGBoost, and LSTM), achieving superior performance with a Root Mean Squared Error (RMSE) of 4.3234 kW, R² of 0.9999, and Mean Absolute Percentage Error (MAPE) of 0.07%. A noise robustness analysis further reveals that the model maintains excellent performance under realistic uncertainties, achieving an R² above 0.99 and an RPD exceeding 12 even at high noise levels (SNR = 20 dB). The total runtime and Relative Prediction Deviation (RPD) were 33.45 s and 86.2685, respectively, indicating an excellent balance between computational efficiency and robust predictive reliability. The key contribution of this research is the effective integration of LightGBM and Bayesian optimization to provide a highly accurate and efficient tool for data center cooling load prediction. This approach offers a scientific foundation for the intelligent control of cooling systems and energy efficiency optimization in data centers, with direct practical implications for building energy management. Full article

Hydrogels and nanocomposite−enhanced hydrogels, owing to their high−water content, excellent biocompatibility, and mechanical flexibility, have demonstrated broad application prospects in tissue engineering, drug delivery, and flexible electronics. With the continuous advancement of computational power, molecular dynamics (MD) simulations have increasingly become an important tool for characterizing nanocomposite materials and hydrogel systems. This approach enables the capture of structural evolution at the atomic/molecular scale and provides mechanistic insights into deformation behaviors and interaction mechanisms under external stimuli such as mechanical force, temperature, and electric fields. This review is organized around the central framework of “structural construction–interfacial regulation−responsive behavior–dynamic evolution”, and systematically summarizes the recent progress in the application of molecular dynamics and multiscale simulation methods to hydrogels and nanocomposite hydrogels. The systems discussed mainly include synthetic polymer-based hydrogels, natural polymer−based hydrogels, peptide/protein−based hydrogels, and nanocomposite hydrogels. Particular emphasis is placed on modeling strategies and force−field selection principles for describing atomic interactions in various nanocomposite hydrogel systems. In addition, the important applications of multiscale simulation strategies in elucidating the interfacial behavior of hydrogels and the mechanisms underlying their dynamic responses under nonequilibrium conditions are also discussed. Finally, future development trends are outlined, including multiscale coupled simulations, closed−loop correction between experiments and simulations, and data−driven modeling strategies for the precise design and performance prediction of complex hydrogel systems. Full article

Free space detection in assisted driving applications is essential to provide information to vehicles about traversable surfaces and potential obstacles to be avoided. The current trend in free space detection favors the use of deep learning techniques. However, Deep Neural Networks require extensive training that considers as many scenarios as possible, which makes it difficult to create a model that can be generalized to all types of surfaces. Additionally, their lack of explainability contrasts with the growing interest in geometrically grounded and safety-oriented design principles for autonomous vehicle systems. To address these limitations, we propose a geometric approach that incorporates coplanarity conditions and normal vector estimation, removing the dependence on datasets for different types of surfaces. Additionally, the stereoscopic images are clustered in superpixels. The use of images clustered in superpixels allows us to obtain shorter processing times, in addition to taking advantage of the spatial and color information provided by the superpixels to increase the robustness of the three-dimensional reconstruction of the scene. Experimental results show that the proposed superpixel-based approach achieves competitive performance compared to unsegmented dense stereo methods, while significantly reducing algorithmic complexity. These results demonstrate the viability of integrating superpixel clustering into stereo-based free space estimation frameworks. Full article

Aiming to address the complex coupling of transient impulses and steady-state components in vibration signals of scroll compressors in air conditioning systems, this study proposes a physically driven heterogeneous dual-path adaptive weighting network (PDW-Net). The approach constructs a physics-inspired weighting module based on kurtosis and energy criteria, enabling adaptive reconstruction of transient impulses and steady-state vibration components. Feature extraction and decision-level fusion are achieved through a heterogeneous dual-branch network comprising a Fast Fourier Transform (FFT)-based one-dimensional convolutional neural network (1D-CNN) and a Short-Time Fourier Transform (STFT)-based two-dimensional convolutional neural network (2D-CNN). In experimental validation covering four typical fault conditions—condenser failure, refrigerant deficiency, refrigerant overcharge, and main shaft wear—the PDW-Net achieved an average diagnostic accuracy of 97.87% (standard deviation: 2.60%), with 100% accuracy in identifying refrigerant deficiency and normal operating states, demonstrating significant superiority over existing mainstream methods. Ablation studies reveal that the adaptive weighting mechanism contributes most substantially to performance, as its removal results in a 34.24 percentage point drop in accuracy. Replacing the heterogeneous dual-branch structure with a homogeneous counterpart reduces accuracy by 16.18 percentage points, robustly validating the efficacy of the physics-guided and heterogeneous fusion design. Full article

The safety and stability of underground coal mining are largely determined by the structural features of coal seams and surrounding rocks. Geological heterogeneities such as faults, fracture zones, and lithological variations strongly influence the distribution of rock pressure and the occurrence of geodynamic hazards. This highlights the need for reliable geophysical methods capable of identifying such zones under mining conditions. Electrical prospecting represents a promising diagnostic approach, as it is highly sensitive to changes in the physical properties of rocks. Unlike conventional geological mapping, it enables the detection of hidden structures and weakened zones often invisible to direct observation. Advances in instrumentation and data processing have further expanded the applicability of electrical methods in complex environments. This study introduces a methodology of electrical prospecting observations for the diagnosis of coal seams. The analysis focuses on conductivity anomalies that reflect tectonic disturbances, fracture systems, and lithological heterogeneities. Field investigations demonstrated the sensitivity of the method to local environmental variations. Comparison with geological records confirmed the validity of the approach: the identified anomalous zones correlated well with documented tectonic features. The methodology showed a stable performance and revealed potential for integration into mine monitoring systems. It allows the identification of areas associated with elevated rock pressure and possible geodynamic activity, thereby contributing to safer underground operations. In the longer term, electrical prospecting may be applied to other coal deposits, including those with a high gas content and complex structure. The development of automated interpretation tools and machine learning algorithms could further increase processing efficiency and improve predictive reliability. Overall, the results confirm that electrical prospecting in mining environments can become an effective instrument for enhancing safety and building more accurate geological–geophysical models of coal seams. Full article

The growing integration of generative artificial intelligence in logistics demands efficient simulation modeling. This study evaluates generative large language models, Perplexity and ChatGPT, for discrete-event simulation in ExtendSim. It focuses on modeling a real, complex manufacturing system, yielding 9721 tons of output. The following three scenarios were assessed: autonomous model creation, output estimation from process descriptions and parameters, and copilot-guided manual building. LLMs cannot autonomously construct ExtendSim models due to the lack of APIs. Output estimation only matched benchmarks after iterative prompt refinement, achieving errors of 0.1% for Perplexity and 1.2% to 22.8% for ChatGPT. Estimation without substantial human intervention proved infeasible. Only the copilot approach appeared viable despite initial errors. It enabled a validated model with 9718 tons output after resolving 25 errors for Perplexity and 22 for ChatGPT through iterative refinement. Approximately 28% (Perplexity) or 32% (ChatGPT) of the errors were hallucinations. The copilot approach reduced development time from several days to 8–10 h. Human expertise remained essential for verifying model outputs and addressing hallucinations and logical flaws. Consequently, this approach may be less feasible for inexperienced users. The copilot paradigm offers practical acceleration for experienced users; however, its limitations underscore the need for API integration and retrieval-augmented generation enhancements. Full article

The coordination of multiple unmanned aerial vehicles traditionally relies on pre-defined control strategies and complex programming implementations, making adaptation to dynamic environments and tasks challenging. The purpose of this study is to explore intent-driven control supported by large language models to address these limitations. The codified objective is to develop a framework capable of interpreting high-level human intent and automatically translating it into executable control instructions for vehicle swarms. As a first approach to the methodology, we present a dual-layer intent-driven cooperative control framework that separates cognitive planning from real-time execution. The design tools include a hierarchical interface, standardized application programming interfaces, retrieval-augmented generation for incorporating domain knowledge, and multimodal prompt engineering to process natural-language instructions and sensor data into Python code. The main findings demonstrate that this framework achieves high code-generation accuracy in typical scenarios, enhances programming efficiency compared to manual methods, and enables adaptive optimization of cooperative strategies through the monitoring of emergent behaviors. In summary, this study contributes an intent-driven solution that simplifies the programming complexity of cooperative swarm control, lowering the technical barrier for deploying advanced autonomous aerial systems. Full article

Temporal monitoring of bioprocesses is inherently complex because process variables do not evolve independently over time, and their interpretation changes as the reaction progresses. In many existing abnormality detection methods, sensor signals are analyzed at isolated time points or temporal characteristics are only weakly reflected through model structures. As a result, such approaches struggle to explain or detect abnormal behavior that emerges differently across reaction states. This study proposes TPP-TimeNet, a time-aware artificial intelligence framework developed to improve abnormality detection in bioprocess monitoring. Unlike conventional methods, the proposed framework explicitly incorporates reaction time as contextual information. Multivariate process signals are reorganized into sliding windows that reflect reaction-state transitions rather than uniform time segmentation. Temporal behavior inside each window is captured using a sequential encoding model, and reaction-state information is subsequently integrated to form state-dependent representations. Through this design, the model can distinguish between temporal patterns that are similar in shape but occur at different points in the reaction timeline. This capability leads to improved sensitivity to abnormal events that may otherwise remain undetected. Abnormality is evaluated at the window level using a probabilistic scoring scheme with a fixed threshold, enabling consistent and reproducible decision-making. The performance of TPP-TimeNet was evaluated using publicly available process control datasets from Kaggle. The datasets were reinterpreted in a bioprocess context by mapping variables such as temperature, pH, and pressure. Experimental results show that the proposed method outperforms traditional machine learning models as well as deep learning approaches that focus only on temporal features, achieving higher accuracy, sensitivity, and F1-score. These findings suggest that incorporating explicit reaction-state awareness is essential for effective abnormality detection in bioprocess monitoring systems. Full article

The hybrid parallel operation of the grid-following (GFL) converter and the grid-forming (GFM) converter has become a typical scenario in distribution networks. The vastly different control philosophies and dynamics between the two give rise to complex small-signal stability issues, especially under weak grids. Traditional methods primarily rely on equivalent models or impedance-based approaches at fixed operating points, which struggle to reveal the system instability mechanisms when the capacity ratio between the two types of converters changes. This paper establishes a three-port dynamic average model for a grid-connected system with heterogeneous GFL-GFM converters. Using the participation factor analysis method, the system’s dominant modes are identified, and the key parameters influencing oscillations at different frequencies, as well as their formation processes, are revealed. Furthermore, a stability analysis method for variable capacity ratios is proposed. This method re-performs modal analysis based on the varying capacities of the GFM and GFL converters, revealing the dominant factors and influencing mechanisms of system instability during capacity transitions. Finally, a simulation model is built in PSCAD/EMTDC to verify the correctness of the proposed three-port model and the theoretical analysis results. Full article

To address the challenges of coordinated optimization in building integrated energy systems (IES) under the dual-carbon targets—characterized by strong multi-energy coupling, significant uncertainty in renewable generation, and stringent safety constraints—a novel safe deep reinforcement learning algorithm, Safe-DDPG, is proposed. Traditional deep reinforcement learning methods often suffer from high constraint-violation risk and limited policy reliability due to coupled objectives in building IES optimization. To overcome these limitations, a dual-channel critic architecture is designed to independently evaluate and decouple economic and safety objectives. In addition, a dynamic safety–penalty mechanism based on logarithmic barrier functions is introduced, together with an adaptive exploration strategy, enabling dynamic balancing between economic cost and constraint satisfaction according to system states during training. Experimental results demonstrate that, compared with mainstream algorithms, Safe-DDPG achieves substantial improvements across multiple key performance indicators: safety violations are reduced by up to 96.7%, average daily operating costs decrease by 18.5%, and cumulative rewards increase by more than 30%. Ablation studies further confirm the effectiveness and necessity of each core component. Two DRL methods from reference papers are reproduced, and their performance is compared with the proposed method in the existing experimental results, showing that the proposed method has significant advantages in reward value and economic cost. This work provides a safe, reliable, and efficient reinforcement-learning-based approach for optimization and scheduling of building energy systems under complex operational constraints. Full article

Background: Extracorporeal membrane oxygenation (ECMO) is commonly used for temporary support in patients with severe cardiogenic shock and may serve as a bridge to heart transplantation. In recent years, outcomes have improved with better timing, patient management and advances in ECMO technology. Case presentation: We describe the case of a 61-year-old man who developed refractory cardiogenic shock after an extensive acute myocardial infarction complicated by recurrent ventricular arrhythmias. After an initial period of stabilization following complex percutaneous coronary intervention, the patient suddenly deteriorated with acute pulmonary edema and severe hypoxemia. A peripheral femoro-femoral veno-arterial ECMO with distal limb perfusion was promptly implanted using the ECMOLIFE system and the Landing Advance system (Eurosets s.r.l., Medolla, MO, Italy) to stabilize the patient and enable continuous monitoring. Due to severe left ventricular distension, surgical left ventricular venting was performed through a minimally invasive approach. ECMO support allowed rapid hemodynamic stabilization without major complications. During ECMO support, the patient remained stable and after less than 48 h a suitable donor heart became available. The patient was safely transferred to a transplant center while on ECMO and successfully underwent heart transplantation. Conclusions: This case shows that early ECMO implantation, combined with appropriate ventricular unloading and careful management with an advanced monitoring system, can be an optimal support as a bridge to heart transplantation. Limiting the duration of ECMO support and ensuring timely referral to a transplant center may improve outcomes in patients with refractory cardiogenic shock. Full article

Remaining Useful Life (RUL) prediction is a key component of prognostics and health management for modern industrial systems. While deep learning methods have significantly improved prediction accuracy, many existing approaches rely on large neural networks that are difficult to deploy on resource-constrained edge devices. This study presents a deployment-oriented evaluation of compact neural networks for RUL prediction using the NASA C-MAPSS turbofan engine benchmark. Two lightweight hybrid architectures, CNN–GRU and CNN–TCN, were developed with approximately 28k–32k parameters to represent realistic models for CPU-based edge inference. A systematic experimental analysis was conducted across all four C-MAPSS subsets (FD001–FD004), which represent increasing levels of operational and fault complexity. In addition to baseline performance, two post-training compression techniques (i.e., global unstructured magnitude pruning and dynamic INT8 quantization) were evaluated. To assess real deployment behavior, inference latency was measured on both a high-performance Intel x86 workstation and a resource-constrained ARM platform. Results show that CNN–GRU generally achieves higher predictive accuracy, whereas CNN–TCN provides more consistent and lower inference latency due to its convolution-only temporal modeling. Unstructured pruning can yield modest improvements in prediction accuracy, suggesting a regularization effect, but it does not reliably reduce model size or latency on standard CPUs due to the overhead associated with pruning masks. Dynamic quantization substantially reduces model size (particularly for CNN–GRU) while preserving predictive accuracy; however, it increases runtime latency because of additional quantization and dequantization operations. These findings demonstrate that compression techniques commonly used for large models do not necessarily translate into deployment benefits for already compact RUL architectures and highlight the importance of hardware-aware evaluation when designing edge prognostics systems. Full article

The rapid evolution of the Open Radio Access Network (O-RAN) architecture and the exponential growth in specification complexity create significant barriers for researchers translating 5G standards into practical implementations. Existing evaluation frameworks for large language models, such as ORAN-Bench-13K, focus predominantly on the theoretical comprehension of regulatory documents while neglecting the critical aspect of software execution. This disparity results in a profound semantic gap, defined here as the structural and conceptual misalignment between abstract normative requirements and their concrete realization in the source code of open platforms like srsRAN. To bridge this divide and enable advanced cognitive reasoning, this paper presents a Hybrid Retrieval-Augmented Generation (RAG) framework designed to unify two heterogeneous knowledge domains: the O-RAN/3GPP specification corpus and the srsRAN C++ codebase. The proposed architecture leverages a hierarchical Parent–Child Chunking strategy to preserve the structural integrity of complex code and normative protocols. Additionally, it introduces a probabilistic Semantic Query Routing mechanism that dynamically selects the relevant context domain based on query intent. This routing actively mitigates semantic interference—a phenomenon where merging conflicting cross-domain terminology introduces informational noise, which our baseline tests showed degrades response accuracy by 4.7%. Empirical evaluation demonstrates that the hybrid approach successfully overcomes this, achieving an overall accuracy of 76.70% and outperforming the standard RAG baseline of 72.00%. Furthermore, system performance analysis reveals that effective context filtering reduces the average response generation latency to 3.47 s, compared to 3.73 s for traditional RAG methods, rendering the framework highly suitable for real-time telecommunications engineering tasks. Full article

Fine particulate matter (PM_2.5) poses a significant global environmental health threat and is closely associated with diseases across multiple organ systems. This review systematically summarizes the toxic effects and underlying mechanisms of PM_2.5 in the respiratory, cardiovascular, nervous, immune, endocrine, digestive, and genitourinary systems. Key pathogenic processes involve shared pathways such as oxidative stress, inflammatory responses, endoplasmic reticulum stress, autophagy, and apoptosis, along with the activation of system-specific signaling networks. The complex composition and notable spatiotemporal variability of PM_2.5 present challenges for assessing its health risks and clarifying its mechanisms. Moving forward, integrating multi-omics and molecular epidemiology approaches will be essential to unravel its multi-system pathogenic networks and support the development of effective intervention strategies. Full article