Search Results (710)

The conversion of alkanes/alkenes into useful intermediates is highly important in the chemical industry. In this study, the physicochemical properties and catalytically active forms of bismuth molybdates (BiMo) were investigated using the selective oxidation of propene to acrolein as a model reaction. The catalysts were prepared by two methods, coprecipitation and spray-drying, with emphasis on spray-drying. The catalysts were characterized using X-ray diffraction, N₂ adsorption/desorption isotherms, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. The catalytic properties of the BiMo samples were studied in a conventional fixed-bed reactor operated under different reaction conditions. The one-dimensional (1D) pseudohomogeneous model was applied to describe the obtained experimental results. The experimental kinetic data were correlated with two complex kinetic models based on multiple reactions (parallel and serial reaction systems). The proposed models were verified by comparing computer simulation data with experimental laboratory results. This study aimed to extend the understanding of the relationship between catalyst composition/structure and catalyst activity/selectivity for different BiMo structures, and to propose kinetic models using two approaches based on parallel and series reactions, in line with efforts to improve the valorization of light olefins. Full article

This study presents an experimental and numerical investigation of reinforced concrete beams incorporating micro polypropylene, macro polypropylene, and steel fibers. Three concrete series of equal strength classes were prepared and tested to evaluate compressive strength, splitting tensile strength, flexural performance, and deformation behavior under short-term loading. Strain development in both concrete and reinforcement was measured using strain gauges and mechanical deformometers. In parallel with the experimental program, a nonlinear finite element model was developed using the DIANA FEAsoftware 10.5 to simulate the deformation behavior and strain development of the tested beams. The concrete material was represented using a total strain-based smeared crack model with rotating crack orientation, while the contribution of fiber reinforcement was incorporated through a CMOD-based post-cracking tensile constitutive law. The numerical results showed good agreement with the experimental load–deflection and strain measurements, confirming the suitability of the adopted modeling approach. These findings demonstrate that the combined experimental–numerical framework provides a reliable tool for assessing the deformation and cracking behavior of fiber-reinforced concrete beams. The experimental results indicate that fiber type and combination strongly influence the deformation behavior and mechanical performance of reinforced concrete beams, with hybrid systems incorporating steel fibers exhibiting enhanced flexural response, improved strain compatibility, and more ductile behavior compared to polypropylene-only reinforcement. The inclusion of steel fibers led to distributed cracking, delayed stiffness degradation, increases of up to approximately 6.3% in concrete strains and 10.3% in reinforcement strains, and a substantial improvement in compressive strength (up to approximately 28.8%), confirming the synergistic effect of hybrid fiber reinforcement. Full article

Accurate power load forecasting is crucial for ensuring the safety and economic operation of power systems. However, the complex, non-stationary, and heterogeneous nature of power load data presents significant challenges for traditional prediction methods, particularly in capturing instantaneous dynamics and effectively fusing multi-feature information. This paper proposes a novel framework—Ensemble Entropy with Adaptive Deep Fusion (EEADF)—for short-term multi-feature power load forecasting. The framework introduces an ensemble instantaneous entropy extraction module to compute and fuse multiple entropy types (approximate, sample, and permutation entropies) in real-time within sliding windows, creating a sensitive representation of system states. A task-adaptive hierarchical fusion mechanism is employed to balance computational efficiency and model expressivity. For time-series forecasting tasks with relatively structured patterns, feature concatenation fusion is used that directly combines LSTM sequence features with multimodal entropy features. For complex multimodal understanding tasks requiring nuanced cross-modal interactions, multi-head self-attention fusion is implemented that dynamically weights feature importance based on contextual relevance. A dual-branch deep learning model is constructed that processes both raw sequences (via LSTM) and extracted entropy features (via MLP) in parallel. Extensive experiments on a carefully designed simulated multimodal dataset demonstrate the framework’s robustness in recognizing diverse dynamic patterns, achieving MSE of 0.0125, MAE of 0.0794, and R² of 0.9932. Validation on the real-world ETDataset for power load forecasting confirms that the proposed method significantly outperforms baseline models (LSTM, TCN, transformer, and informer) and traditional entropy methods across standard evaluation metrics (MSE, MAE, RMSE, MAPE, and R²). Ablation studies further verify the critical roles of both the entropy features and the fusion mechanism. Full article

The deployment of 5G (Fifth-Generation) networks in industrial Internet of Things (IoT), intelligent transportation, and emergency communications introduces heterogeneous and dynamic network states, leading to frequent and diverse faults. Traditional fault detection methods typically emphasize either local temporal anomalies or global distributional characteristics, but rarely achieve an effective balance between the two. In this paper, we propose a parallel multi-branch convolutional neural network (CNN)–Transformer framework (MBCT) to improve fault diagnosis accuracy in 5G networks. Specifically, MBCT takes time-series network key performance indicator (KPI) data as input for training and performs feature extraction through three parallel branches: a CNN branch for local patterns and short-term fluctuations, a Transformer encoder branch for cross-layer and long-term dependencies, and a statistical branch for global features describing quality-of-experience (QoE) metrics. A gating mechanism and feature-weighted fusion are applied outside the branches to adjust inter-branch weights and intra-branch feature sensitivity. The fused representation is then nonlinearly mapped and fed into a classifier to generate the fault category. This paper evaluates the performance of the proposed model on both the publicly available TelecomTS multi-modal 5G network observability dataset and a self-collected SDR5GFD dataset based on software-defined radio (SDR). Experimental results demonstrate that the proposed model achieves superior performance in fault classification, achieving 87.7% accuracy on the TelecomTS dataset and 86.3% on the SDR5GFD dataset, outperforming the baseline models CNN, Transformer, and Random Forest. Moreover, the model contains approximately 0.57M parameters and requires about 0.3 MFLOPs per sample for inference, making it suitable for large-scale online fault diagnosis. Full article

Polyester yarn bundle geogrids are widely used materials in flexible retaining structures due to their high toughness and high-strength mechanical properties. To investigate the mechanical characteristics and the interfacial mechanical properties of these geogrids, a series of pull-out tests were conducted under different pull-out rates and filling water contents. Based on the test results, a DEM-FDM coupled numerical model for pull-out behavior was established to analyze the pull-out deformation behavior of the geogrids. Combined with the theoretical analysis of the load-bearing characteristics of the geogrids, the reinforcement mechanism of polyester yarn bundle geogrids was revealed. The results show that there exists a critical pull-out rate of 1 mm/min that maximizes the pull-out resistance; the interface friction angle decreases with an increase in pull-out rate, while the interface cohesion shows an opposite trend. The filling water content presents a more significant weakening effect on the soil–geogrid interface strength under low stress, resulting in a strain-softening type of pull-out curve. Unlike fine-ribbed plastic geogrids, the sliding frictional resistance of polyester yarn bundle geogrids accounts for 80% of the total pull-out resistance during the pull-out process. The mechanical interlocking force, which arises from the bulges on the mid-section of transverse ribs and the downward bending of longitudinal rib edges, is subject to dynamic changes in the course of the pull-out process. The geogrid exhibits overall shear failure under low normal stress (${\sigma }_n<$ 200 kPa) and penetration shear failure under high normal stress (${\sigma }_n\ge$ 200 kPa). In practical engineering installation, polyester yarn bundle geogrids should be placed as parallel as possible to maximize the frictional resistance with filled soil and should take care of the geogrid joints for enhanced durability of the geogrids. Full article

The synergistic development of digitalization and green transition has become a key driver for promoting China’s high-quality economic development. To elucidate the impact and mechanism of digital–green policy synergy on urban carbon emissions, this paper utilizes the intersection of the “National Big Data Comprehensive Pilot Zones” (BDPZ) and “Low-Carbon City Pilot” (LCCP) programs as a quasi-natural experiment. Based on panel data from 300 prefecture-level cities in China from 2005 to 2023, a multi-period DID model is constructed for empirical research. The empirical results indicate the following: (1) The synergy between digital and green policies significantly curbs urban carbon emissions, and this conclusion remains robust after parallel trend tests and a series of robustness checks. (2) Compared with single digital or green policies, the digital–green synergy exhibits a significantly superior carbon reduction effect. (3) Mechanism analysis reveals that digital–green synergy promotes low-carbon transition primarily through three pathways: driving green technology innovation, promoting the agglomeration of scientific and technological talent, and optimizing the allocation efficiency of capital factors. (4) Heterogeneity analysis reveals stronger emission reduction effects in non-resource-based, eastern, and developed cities, highlighting how structural rigidities and the digital divide constrain the policy’s effectiveness. We suggest strengthening policy integration and adopting differentiated strategies to break path dependence and achieve “Dual Carbon” goals. Full article

Unmanned aerial vehicles open new possibilities for developing technologies that support more sustainable and efficient agriculture. This paper presents a non-invasive method for repelling rodents from crop fields using ultrasound. The proposed system is implemented as a spherical-cap ultrasound loudspeaker array consisting of eight transducers, mounted on a drone that overflies the field while emitting sound in the 20–70 kHz range. The hardware design includes both the loudspeaker array and a custom printed circuit board hosting power amplifiers and a signal generator tailored to drive multiple ultrasonic transducers. In parallel, a genetic algorithm is used to compute flight paths that maximize coverage and increase the probability of driving rodents away from the protected area. As part of the validation phase, artificial intelligence models for rodent detection using a thermal camera are developed to provide quantitative feedback on system performance. The complete prototype is evaluated through a series of experiments conducted both in controlled laboratory conditions and in the field. Field trials highlight which parts of the concept are already effective and identify open challenges that need to be addressed in future work to move from a research prototype toward a deployable product. Full article

Solar-aided power generation offers a pathway to reduce the carbon dioxide emissions from existing coal-fired plants. This study addresses the gap in comparing different solar integration modes by conducting a thermo-economic analysis of a 600 MW coal-fired system retrofitted with a solar tower. Four integration modes were designed and rigorously compared, encompassing series and parallel configurations at either the high-exergy reheater or the lower-exergy economizer. A detailed thermodynamic model was developed to simulate its off-design and annual performance. The results showed that integration at the primary reheater outperformed the economizer integration. Specifically, the parallel configuration at the primary reheater (Mode II) achieved the highest annual solar-to-electricity efficiency of 18.43% at a thermodynamically optimal heliostat field area of 125,025.6 m². Economic analysis revealed a trade-off, with the minimum levelized cost of energy (LCOE) of −0.00929 USD/kWh for Mode II occurring at the economically optimal area of 321,494 m² due to greater coal and emission savings. Sensitivity analysis across two other locations confirmed that the annual solar-to-electricity efficiency and LCOE are directly influenced by solar resource quality, but the thermodynamically optimal and economically optimal heliostat field area remain consistent. This work demonstrates that parallel integration with the primary reheater presents a favorable and practical configuration, balancing high solar-to-electricity conversion efficiency with favorable economics for hybrid solar–coal power plants. Full article

Hardware-in-the-loop (HIL) simulation is an essential tool for rapid and cost-effective development and validation of power-electronic systems. The primary objective of this work is to validate and fine-tune a PLECS-based HIL model of a single dual active bridge (DAB) DC-DC converter, thereby laying the foundation for building more complex models (e.g., multiple converters connected in series or parallel). To this end, the converter is experimentally characterized, and the HIL model is validated across a wide range of operating conditions by varying the PWM phase-shift angle, voltage gain, switching frequency, and leakage inductance. Power transfer and efficiency are analyzed to quantify the influence of these parameters on converter performance. These experimental trends provide insight into the optimal modulation range and the dominant loss mechanisms of the DAB under single phase shift (SPS) control. A detailed comparison between HIL simulations and hardware measurements, based on transferred power and efficiency, shows close agreement across all the tested operating points. These results confirm the accuracy and robustness of the proposed HIL model, demonstrate the suitability of the PLECS platform for DAB development and control validation, and support its use as a scalable basis for more complex multi-converter studies, reducing design time and prototyping risk. Full article

The increasing demand for high-power electric vehicle charging systems with Vehicle-to-Grid (V2G) capability highlights the need for modular, scalable power converters. This paper proposes a hierarchical control strategy for a high-power, multi-port electric vehicle charging station. The system, based on a Series-Parallel Modular Multilevel Converter (SP-MMC) with isolated modules, is managed by a coordinated control strategy that integrates proportional-integral-resonant regulators, nearest-level control with voltage sorting, and single-phase-shifted modulation. The proposed system enables simultaneous, independent regulation of multiple bidirectional, isolated direct current ports while maintaining grid-side power quality and internal variables of the SP-MMC. The proposed control is validated using real-time Model-In-the-Loop (MIL) simulations that include sequential port activation, bidirectional power flow, and charging operation. MIL results demonstrate stable operation with controlled DC-link voltage ripple, accurate per-port current tracking, and near-unity grid power factor under multi-port operation. Full article

The microstructure of polyurethane (PU) grouting material is the key determinant of its macroscopic dielectric properties. In this study, based on its microscopic fractal characteristics and combined with effective medium theory and the Menger sponge structure, an n-stage fractal dielectric model was constructed. This model correlates the material’s dielectric response with its fractal dimension and porosity. The fractal dimensions of PU specimens with densities ranging from 0.29734 g/cm³ to 0.41817 g/cm³ were calculated using the box-counting method. Within this density range, the fractal dimension of the PU specimens showed no significant variation, with a calculated value of approximately 2.7355. By approximating the microscopic unit as an n-stage fractal cube based on the Menger sponge structure and incorporating series-parallel dielectric models, an analytical expression for the dielectric constant was derived. A comparison with experimental data shows that the model’s predictions are in good agreement with the measured values, with a mean relative error (MRE) of only 4%. Full article

Background/Objectives: Chemical and metabolic kinetics have historically been derived from mass balance differential equations expressed in terms of amounts, and this framework was later extended to pharmacokinetics by converting amount-based equations to concentration-based clearance relationships. That conversion is valid for fixed-volume in vitro experiments, but may be unreliable in vivo, where input, distribution, and elimination can occur in different volumes of distribution. The objective of this study is to present an alternate, mechanistically agnostic framework for deriving pharmacokinetic relationships by adapting Kirchhoff’s Laws to treat pharmacokinetic systems as networks of parallel and in-series rate-defining processes, and to identify where differential equation approaches fail in vivo. Methods: Clearance and rate constant equations were derived using the adapted Kirchhoff’s Laws by summing parallel rate-defining processes and summing inverses for in-series processes, explicitly incorporating organ blood flow, net transporter, and delivery site effects. The resulting expressions were compared with differential equation hepatic disposition elimination models (well-stirred, parallel tube, dispersion) and the Extended Clearance Concept (ECC). Mean residence time concepts were used to extend the framework to oral input, and the full approach was applied to a case study of a hypothetical drug (KL25A). Results: The adapted Kirchhoff-based approach reproduced standard pharmacokinetic analyses without mechanistic organ assumptions and yielded model-independent hepatic and renal clearance equations that include blood flow, net transport, and delivery kinetics. Inconsistencies with the traditional differential-based derivations were highlighted, including the interpretation of pharmacokinetics associated with slow absorption site clearance, as illustrated by KL25A. Conclusions: For linear drug metabolism and pharmacokinetics, clearance and rate constant relationships can be derived by summing parallel and in-series rate-defining processes, without differential equations. Differential equation methods may misestimate in vivo clearance and bioavailability when drug input is slow or when volumes of distribution differ across processes. The adapted Kirchhoff framework offers a simpler, model-independent basis for interpreting clinical data. Full article

The formation and stability of river ice covers in regulated waterways are critical for uninterrupted hydro-electric operations. This study investigates the modelling of ice cover development in the Beauharnois Canal along the St. Lawrence River with the presence and absence of ice booms. Ice booms are deployed in this canal to promote the rapid formation of a stable ice cover during freezing events, minimizing disruptions to dam operations. Remote sensing data were used to assess the spatial extent and temporal evolution of an ice cover and to calibrate the river ice model RIVICE. The model was applied to simulate ice formation for the 2019–2020 ice season, first for the canal with a series of three ice booms and then rerun under a scenario without booms. Comparative analysis reveals that the presence of ice booms facilitates the development of a relatively thinner and more uniform ice cover. In contrast, the absence of booms leads to thicker ice accumulations and increased risk of ice jamming, which could impact water management and hydroelectric generation operations. Computational efficiencies of the RIVICE model were also sought. RIVICE was originally compiled with a Fortran 77 compiler, which restricted modern optimization techniques. Recompiling with NVFortran significantly improved performance through advanced instruction scheduling, cache management, and automatic loop analysis, even without explicit optimization flags. Enabling optimization further accelerated execution, albeit marginally, reducing redundant operations and memory traffic while preserving numerical integrity. Tests across varying ice cross-sectional spacings confirmed that NVFortran reduced runtimes by roughly an order of magnitude compared to the original model. A test GPU (Graphics Processing Unit) version was able to run the data interpolation routines on the GPU, but frequent data transfers between the CPU (Central Processing Unit) and GPU caused by shared memory blocks and fixed-size arrays made it slower than the original CPU version. Achieving efficient GPU execution would require substantial code restructuring to eliminate global states, adopt persistent data regions, and parallelize at higher level loops, or alternatively, rewriting in a GPU-friendly language to fully exploit modern architectures. Full article

This article presents a database focused on measuring the experimental performance of a pilot parabolic trough collector (PTC) combined with the meteorological conditions corresponding to the installation site. Water was chosen as the fluid to recirculate through the PTC circuit. The data were recorded between August and September, assuming that global radiation was adequate for use in the concentration process. The database comprises seven experimental tests, which contain variables such as time, inlet temperature, outlet temperature, ambient temperature, global radiation, diffuse radiation, wind direction, wind speed, and volumetric flow rate. Based on the data obtained from this pilot PTC system, it is possible to provide relevant information for the installation and construction of large-scale solar collectors. Furthermore, the climatic conditions considered allow key factors in the design of multiple collectors to be determined, such as the type of arrangement (series or parallel) and manufacturing materials. In addition, the data collected in this study are key to validating future theoretical models of the PTC. Finally, considering the real operating conditions of a PTC in conjunction with meteorological variables could also be useful for predicting the system’s thermal performance using artificial intelligence-based models. Full article

High rock slopes are extensively distributed in areas of major engineering constructions, such as transportation infrastructure, hydraulic projects, and mining operations. The stability and failure evolution mechanism during their multi-stage excavation process have consistently been a crucial research topic in geotechnical engineering. In this paper, a series of two-dimensional rock slope models, incorporating various combinations of slope height and slope angle, were established utilizing the Discrete Element Method (DEM) software PFC2D. This systematic investigation delves into the meso-mechanical response of the slopes during multi-stage excavation. The Parallel Bond Model (PBM) was employed to simulate the contact and fracture behavior between particles. Parameter calibration was performed to ensure that the simulation results align with the actual mechanical properties of the rock mass. The research primarily focuses on analyzing the evolution of displacement, the failure modes, and the changing characteristics of the force chain structure under different geometric conditions. The results indicate that as both the slope height and slope angle increase, the inter-particle deformation of the slope intensifies significantly, and the shear band progressively extends deeper into the slope mass. The failure mode transitions from shallow localized sliding to deep-seated overall failure. Prior to instability, the force chain system exhibits an evolutionary pattern characterized by “bundling–reconfiguration–fracturing,” serving as a critical indicator for characterizing the micro-scale failure mechanism of the slope body. Full article