Search Results (4,701)

Catalytic transfer hydrogenation (CTH) provides a practical and sustainable approach for reducing unsaturated compounds, serving as an alternative to high-pressure H₂ in laboratory and fine chemical contexts. This broad reaction class includes asymmetric transfer hydrogenation (ATH), a key strategy in enantioselective synthesis due to its operational simplicity, high stereocontrol, and compatibility with sensitive functional groups. A central variable governing CTH efficiency is the role of bases, which may function as essential activators, co-hydrogen donors, or be entirely absent depending on the catalytic system. This review provides a comparison of base-assisted, base-free, and base-as-co-hydrogen-donor CTH methodologies across diverse metal catalysts and substrates. We highlight how bases such as triethylamine, K₂CO₃, and NaOH facilitate catalyst activation, modulate hydride formation, and tune reactivity and selectivity. The dual function of bases in formic-acid-driven systems is examined alongside synergistic effects observed with mixed-base additives. In contrast, base-free CTH platforms demonstrate how tailored ligand frameworks, metal-ligand cooperativity, and engineered surface basicity can eliminate the need for external additives while maintaining high activity. Through mechanistic analysis and cross-system comparison, this review identifies the key structural, electronic, and environmental factors that differentiate base-assisted from base-free pathways. Emerging trends—including greener hydrogen donors, advanced catalyst architectures, and additive-minimized protocols—are discussed to guide future development of sustainable CTH processes. Full article

Turbocharged diesel engines are widely used for the propulsion and as the generators for powering auxiliary systems in marine applications. Many works were published on the development of diagnosis tools for the engines using data from simulation models or from experiments on a sophisticated engine test bench. However, the simulation data varies a lot with actual operational data, and the available sensor data on the actual vessel is much less compared to the data from test benches. Therefore, it is necessary to develop anomaly prediction and fault diagnosis models from limited data available from the engines. In this paper, an artificial intelligence (AI)-based anomaly detection model and machine learning (ML)-based fault diagnosis model were developed using the actual data acquired from a diesel engine of a cargo vessel. Unlike the previous works, the study uses operational, thermodynamic, and vibration data for the anomaly detection and fault diagnosis. The paper provides the overall architecture of the proposed predictive maintenance system including details on the sensorization of assets, data acquisition, edge computation, and AI model for anomaly prediction and ML algorithm for fault diagnosis. Faults with varying severity levels were induced in the subcomponents of the engine to validate the accuracy of the anomaly detection and fault diagnosis models. The unsupervised stacked autoencoder AI model predicts the engine anomalies with 87.6% accuracy. The balanced accuracy of supervised fault diagnosis model using Support Vector Machine algorithm is 99.7%. The proposed models are vital in marching towards sustainable shipping and have potential to deploy across various applications. Full article

We present a differentiable, curriculum-based optimization workflow for the engineering-oriented design of large-aperture reflective optical systems. The method integrates physics-informed differentiable ray tracing with a progressive, two-stage optimization strategy that evolves from simple Ritchey–Chrétien (RC) foundations to complex four-mirror architectures. Without relying on pretrained models or large datasets, the workflow optimizes geometric and physical parameters under field-weighted RMS, focal length, and dynamic obscuration constraints while maintaining minimal perturbation to primary and secondary mirrors. Validated through Zemax-based analysis, the optimized systems achieve high imaging quality with improved RMS uniformity and stable convergence across varying aperture scales. This approach provides a practical and scalable pathway for the design and optimization of reflective optical instruments, offering strong robustness and adaptability for diverse imaging applications. Full article

Quantitative Feedback Theory (QFT) enables the control system to guarantee stability and performance in the presence of plant uncertainty, thus offering a quantitative and less conservative framework for designing robust yet practical controllers. The presented work investigates a single-stage constraint optimization-based approach for synthesizing controllers for the ship roll stabilization. The typical QFT loop shaping is a manual two-stage procedure that demands a proficient understanding of loop-shaping principles on Nichols charts. The proposed procedure simplifies the QFT synthesis process by introducing a single-stage method that allows for concurrent synthesis of both the QFT controller and pre-filter. The present work considers the synthesis of fractional order controllers (using the FOMCON toolbox). The proposed method also enables the designer to pre-specify the controller architecture at the beginning of the design procedure. A comparative analysis with the controllers obtained using the QFT toolbox, Ziegler–Nichols, ${\mathcal{H}}_{\infty }$, IMC, and MPC have also been presented in the work. The implementation has been carried out for the ship roll stabilization, which is one of the critical problems in marine engineering, as it directly impacts the vessel safety, operational efficiency, and passenger comfort, wherein excessive roll can lead to reduced propulsion efficiency. The obtained results highlight that the proposed controller performs better than the benchmark controllers, and Monte Carlo simulations have also been included to support the results. Full article

The miniaturisation of electronic devices has intensified the demand for compact, high-performance lithium-ion batteries. This review synthesises recent progress in microscale battery development, focusing on microfabrication techniques, nanostructured materials, porosity-engineered architectures, and strategies for reducing non-active components. It explores both top–down and bottom–up fabrication methods, the integration of nanomaterials, the role of gradient electrode architectures in enhancing ion transport and energy density, along with strategies to reduce non-active components, such as separators and current collectors, to maximise volumetric efficiency. Advances in top–down and bottom–up fabrication methods, including photolithography, laser structuring, screen printing, spray coating, mechanical structuring, and 3D printing, enable precise control over electrode geometry and enhance ion transport and material utilisation. Nanostructured anodes, cathodes, electrolytes, and separators further improve conductivity, mechanical stability, and cycling performance. Gradient porosity designs optimise ion distribution in thick electrodes, while innovations in ultra-thin separators and lightweight current collectors support higher energy density. Remaining challenges relate to scalability, mechanical robustness, and long-term stability, especially in fully integrated micro-battery architectures. Future development will rely on hybrid fabrication methods, advanced material compatibility, and data-driven optimisation to bridge laboratory innovations with practical applications. By integrating microfabrication and nanoscale engineering, next-generation LIBs can deliver high energy density and long operational lifetimes for miniaturised and flexible electronic systems. Full article

To meet the real-time, multi-task processing demands of spaceborne synthetic aperture radar (SAR) systems under limited onboard resources, this paper presents a configurable field-programmable gate array (FPGA) architecture that supports both water body and oil spill detection. First, an efficient computing engine partitioning method at coarse and fine granularities is proposed. The operations of the water body and oil spill detection algorithms are clustered and analyzed at two levels, and both general-purpose and specialized computing engines are designed to minimize resource usage. Second, a high-reuse storage optimization strategy is introduced. Based on the data buffering cycle, a shared on-chip memory is designed to minimize storage resource consumption. Building upon these foundations, a software and hardware co-programmable efficient processing system is developed, successfully mapping both detection algorithms onto the FPGA. Finally, the effectiveness of the proposed architecture is confirmed through experimentation, and processing performance is analyzed. Processing times for a 16K × 16K water body scene and a 16K × 16K oil spill scene are 15 s and 13 s, respectively, at a clock frequency of 100 MHz, meeting the real-time multi-task processing requirements of on-board operations. Full article

State-of-charge (SoC) balancing across multiple battery energy storage systems (BESS) is a central challenge in renewable-rich mini-grids. Heterogeneous battery capacities, differing states of health, stochastic renewable generation, and variable loads create a high-dimensional uncertain control problem. Conventional droop-based SoC balancing strategies are decentralized and computationally light but fundamentally reactive and limited, whereas model predictive control (MPC) is insightful but computationally intensive and prone to modeling errors. This paper proposes a Hierarchical Predictive–Adaptive Control (HPAC) framework for SoC balancing in mini-grids using deep reinforcement learning. The framework consists of two synergistic layers operating on different time scales. A long-horizon Predictive Engine, implemented as a federated Transformer network, provides multi-horizon probabilistic forecasts of net load, enabling multiple mini-grids to collaboratively train a high-capacity model without sharing raw data. A fast-timescale Adaptive Controller, implemented as a Soft Actor-Critic (SAC) agent, uses these forecasts to make real-time charge/discharge decisions for each BESS unit. The forecasts are used both to augment the agent’s state representation and to dynamically shape a multi-objective reward function that balances SoC, economic performance, degradation-aware operation, and voltage stability. The paper formulates SoC balancing as a Markov decision process, details the SAC-based control architecture, and presents a comprehensive evaluation using a MATLAB-(R2025a)-based digital-twin simulation environment. A rigorous benchmarking study compares HPAC against fourteen representative controllers spanning rule-based, MPC, and various DRL paradigms. Sensitivity analysis on reward weight selection and ablation studies isolating the contributions of forecasting and dynamic reward shaping are conducted. Stress-test scenarios, including high-volatility net-load conditions and communication impairments, demonstrate the robustness of the approach. Results show that HPAC achieves near-minimal operating cost with essentially zero SoC variance and the lowest voltage variance among all compared controllers, while maintaining moderate energy throughput that implicitly preserves battery lifetime. Finally, the paper discusses a pathway from simulation to hardware-in-the-loop testing and a cloud-edge deployment architecture for practical, real-time deployment in real-world mini-grids. Full article

Prognostics and Health Management (PHM) is a vital approach which aims to predict the failure of engineering systems at an early stage and optimize maintenance strategies. It operates through continuous system monitoring, anomaly detection, fault detection, and Remaining Useful Life (RUL) estimation. Accurate RUL prediction for aircraft engines is critical for enhancing operational safety and minimizing maintenance costs. Traditional methods are largely dependent on handcrafted features and domain-specific knowledge. They often fail to capture the nonlinear and high-dimensional degradation dynamics of real-world systems. In this study, we propose an enhanced deep learning architecture combining Long Short-Term Memory (LSTM) and Bidirectional LSTM networks with a new Matrix-Statistics-Aware Attention (LSTM-MSAA) method. Unlike conventional attention methods, our proposed method incorporates auxiliary scalar features, such as the Frobenius norm, spectral norm, and soft rank, into the attention score computation. This hybrid model provides a more informative representation of engine state transitions. The model is evaluated on both legacy and newly released C-MAPSS datasets from NASA’s Prognostics Data Repository. Experimental results reveal a reduction in RMSE compared to baseline models, validating the effectiveness of our attention fusion strategy in capturing intricate degradation behaviors and improving predictive performance. Full article

The capacity of traditional distribution networks is limited. After large-scale distributed power sources are connected, it is difficult to consume them at the same voltage level, which can lead to transformer reverse overloading and voltage limit violations. Although the unified power flow controller (UPFC) excels in flexible power flow regulation and power quality optimization, existing research on it is mostly focused on the transmission grid, focusing on device topology, power flow control, etc. Fault protection is also targeted at high-voltage and ultra-high-voltage domains and only covers a single overvoltage or overcurrent fault. Research on the protection of the unified power flow controller in a distribution network (D-UPFC) remains scarce. A key challenge is the absence of fault protection schemes that are compatible with the unified power flow controller in a distribution network, which cannot meet the requirements of the distribution network for monitoring and protecting multiple fault types, rapid response, and equipment economy. This paper first designs a protection device centered on the distribution thyristor bypass switch (D-TBS), completes the thyristor selection and transient energy extraction, optimizes the overvoltage protection loop parameter, then builds a three-level coordinated protection architecture, and, finally, verifies through functional and system tests. The results show that the thyristor control unit trigger is reliable and the total overvoltage response delay is 1.08 μs. In the case of a three-phase short-circuit fault in a 600 kVA/10 kV system, the distribution thyristor bypass switch can rapidly reduce the secondary voltage of the series transformer, suppress transient overcurrent, achieve isolation protection of the main equipment, provide a reliable guarantee for the engineering application of the distribution network unified power flow controller, and expand its distribution network application scenarios. Full article

The integration of Building Information Modeling (BIM) and Digital Twin (DT) has emerged as an innovative tool in the architecture, engineering, and construction (AEC) domain. To successfully utilize BIM and DT, it is crucial to update the 3D model in a timely and accurate manner. However, limitations remain when handling massive point clouds to reconstruct complex indoor structures with varying ceiling and floor heights. This study proposes a semi-automatic 3D model reconstruction method. First, point clouds are aligned with 3D Cartesian axes and the spatial extent of the indoor space is measured. Subsequently, the point clouds are projected onto each coordinate plane to hierarchically extract structural elements of a building component, such as boundary lines, rectangles, and cuboids. Boolean operations are then applied to the cuboids to reconstruct a 3D wireframe model. Additionally, wall points are segmented to identify openings like doors and windows. For validation, the method was applied to three typical building components with Manhattan-world structures: an office, a hallway, and a stairway. The reconstructed models were evaluated using reference points, resulting in positional accuracies of 0.033 m, 0.034 m, and 0.030 m, respectively. Finally, the resulting wireframe model served as a reference to build an as-built BIM model. Full article

Temperature-responsive hydrogels are sophisticated stimuli-responsive biomaterials that undergo rapid, reversible sol–gel phase transitions in response to subtle thermal stimuli, most notably around physiological temperature. This inherent thermosensitivity enables non-invasive, precise spatiotemporal control of material properties and bioactive payload release, rendering them highly promising for advanced biomedical applications. This review critically surveys recent advances in the design, synthesis, and translational potential of thermo-responsive hydrogels, emphasizing nanoscale and hybrid architectures optimized for superior tunability and biological performance. Foundational systems remain dominated by poly(N-isopropylacrylamide) (PNIPAAm), which exhibits a sharp lower critical solution temperature near 32 °C, alongside Pluronic/Poloxamer triblock copolymers and thermosensitive cellulose derivatives. Contemporary developments increasingly exploit biohybrid and nanocomposite strategies that incorporate natural polymers such as chitosan, gelatin, or hyaluronic acid with synthetic thermo-responsive segments, yielding materials with markedly enhanced mechanical robustness, biocompatibility, and physiologically relevant transition behavior. Cross-linking methodologies—encompassing covalent chemical approaches, dynamic physical interactions, and radiation-induced polymerization are rigorously assessed for their effects on network topology, swelling/deswelling kinetics, pore structure, and degradation characteristics. Prominent applications include on-demand drug and gene delivery, injectable in situ gelling systems, three-dimensional matrices for cell encapsulation and organoid culture, tissue engineering scaffolds, self-healing wound dressings, and responsive biosensing platforms. The integration of multi-stimuli orthogonality, nanotechnology, and artificial intelligence-guided materials discovery is anticipated to deliver fully programmable, patient-specific hydrogels, establishing them as pivotal enabling technologies in precision and regenerative medicine. Full article

Although operons are a fundamental feature of prokaryotic genomes, their organization is non-random. The specific influence of operon architecture on gene expression, however, remains poorly characterized. In this study, we systematically analyzed the effects of operon length and gene position on expression levels in Escherichia coli and Bacillus subtilis. We found that promoter-proximal (5′ end) genes were expressed at higher levels and that expression of a given gene could be enhanced by increasing the overall length of the operon. To leverage these principles for metabolic engineering, we developed a Head-to-Tail PCR (HTPCR) method for the rapid assembly of synthetic operons with permuted gene orders. Application of this method enabled the construction of a synthetic rib operon that increased riboflavin yield by 35.38%. Collectively, these findings provide a theoretical framework and a practical methodology for designing efficient synthetic operons to enhance the production of target compounds. Full article

Cracks serve as a critical indicator of tunnel structural degradation. Manual inspections are difficult to meet engineering requirements due to their time-consuming and labor-intensive nature, high subjectivity, and significant error rates, while traditional image processing methods exhibit poor performance under complex backgrounds and irregular crack morphologies. To address these limitations, this study developed a high-quality dataset of tunnel crack images and proposed an improved lightweight semantic segmentation network, LiteSqueezeSeg, to enable precise crack identification and quantification. The model was systematically trained and optimized using a dataset comprising 10,000 high-resolution images. Experimental results demonstrate that the proposed model achieves an overall accuracy of 95.15% in crack detection. Validation on real-world tunnel surface images indicates that the method effectively suppresses background noise interference and enables high-precision quantification of crack length, average width, and maximum width, with all relative errors maintained within 5%. Furthermore, an integrated intelligent detection system was developed based on the MATLAB (R2023b) platform, facilitating automated crack feature extraction and standardized defect grading. This system supports routine tunnel maintenance and safety assessment, substantially enhancing both inspection efficiency and evaluation accuracy. Through synergistic innovations in lightweight network architecture, accurate quantitative analysis, and standardized assessment protocols, this research establishes a comprehensive technical framework for tunnel crack detection and structural health evaluation, offering an efficient and reliable intelligent solution for tunnel condition monitoring. Full article

This study develops a spatial-analytical framework that integrates commercial hierarchy theory with cultural sustainability principles to support the sustainable development of traditional cultural marketplaces. Using kernel density estimation and Ripley’s K function analysis of 160 bazaars and 83,127 POI data points in Ürümqi, we established a hierarchical business district classification system incorporating both cultural-demographic factors and commercial indicators. Our findings reveal that culturally attuned spatial planning generates synergistic effects between heritage conservation and contemporary development needs. The research contributes to sustainable urban theory by extending Central Place Theory through cultural dimensions while providing practical design strategies validated through 15 case studies. This framework offers urban planners an implementable revitalization approach that maintains cultural authenticity while achieving a balance between commercial vitality and social cohesion, thereby presenting an effective pathway for sustainable urban development. Full article

Choline is a central metabolite that connects membrane turnover, neurotransmission, and one-carbon metabolism, and its reliable measurement across diverse biological matrices remains a significant analytical challenge. This review brings together biological context, electrochemical mechanisms, and device engineering to define realistic performance targets for choline sensors in blood, cerebrospinal fluid, extracellular space, and milk. We examine enzymatic sensor architectures ranging from peroxide-based detection to mediated electron transfer via ferrocene derivatives, quinones, and osmium redox polymers and assess how applied potential, oxygen availability, and film structure shape electron-transfer pathways. Evidence for direct electron transfer with choline oxidase is critically evaluated, with emphasis on the essential controls needed to distinguish true flavin-based communication from peroxide-related artefacts. We also examine bienzymatic formats that allow operation at low or negative bias and discuss strategies for matrix-matched validation, selectivity, drift control, and resistance to fouling. To support reliable translation, we outline reporting standards that include matrix-specific concentration ranges, reference electrode notation, mediator characteristics, selectivity panels, and access to raw electrochemical traces. By connecting biological requirements to mechanistic pathways and practical design considerations, this review provides a coherent framework for developing choline sensors that deliver stable, reproducible performance in real samples. Full article