Search Results (29,491)

Additive Manufacturing (AM) has emerged as a transformative technology enabling the production of complex geometries and customized components with minimal material waste. Within this field, the processing of ceramic materials represents a rapidly expanding research area due to their exceptional mechanical, thermal, and chemical properties. This work presents a comprehensive review of additive manufacturing processes applied to ceramics, such as Vat Photopolimerization, Binder Jetting and Laser Powder Bed Fusion, emphasizing their technological principles and capabilities. Particular attention is given to material extrusion-based additive manufacturing (MEX-AM) for ceramics, detailing its process mechanisms, rheological requirements, feedstock formulations and post-processing treatments necessary to achieve high-density and defect-free components. Furthermore, the study develops a sustainability-oriented evaluation of the ceramic MEX-AM process, addressing its environmental, economic, and social dimensions. Based on this assessment, several methodological approaches and tools are proposed to enhance process sustainability, as well as its alignment with Circular Economy principles. The outcomes of this research provide an integrated perspective on the sustainable development of ceramic additive manufacturing, supporting future advancements in Circular Design, process optimization, and industrial implementation. Full article

Manganese is a critical raw material and there is currently a great interest in decarbonization in the metallurgical sector for its production. Hydrogen use in manganese and its alloys’ production is in principle possible for sustainable production; however, this requires a technological shift from traditional carbothermic processes to completely new processes; like the HAlMan process. To design a process, it is crucially important to optimize the process conditions (such as temperature) and minimize the quantity of hydrogen gas and the related energy consumptions. In the present work, energy and mass balances for a hydrogen-based reduction reactor were carried out employing thermodynamics software and analytical approaches from room temperatures to 900 °C. It was found that the quantity of hydrogen gas required for the pre-reduction of manganese ore can be significantly reduced via coupling the reduction reactor with a calciner and the hot charge of the calcined ore into the reduction reactor. Moreover, hot H₂-H₂O gas mixture from the reduction reactor outlet can be used for preheating the hydrogen feed of the reactor, and the calcination of the ore, while a portion or all its hydrogen can be recovered and looped. The integrated coupled calcination-reduction process was found to be operated with no external energy supply, or insignificant fuel use. Full article

Extreme weather conditions, specifically typhoons and strong gusts, create a highly transient environment for wind power data collection, leading to performance degradation that significantly impacts the safety and stability of the wind power system. To accurately predict wind power trends under these conditions, this paper proposes a prediction model integrating Singular Spectrum Analysis (SSA), Temporal Convolutional Network (TCN), Convolutional Neural Network (CNN), and a global average pooling layer, combined with inverse adaptive transfer learning. First, SSA is applied to reduce noise in the collected wind power operation data and extract key information. Subsequently, a prediction model is constructed based on TCN, CNN, and global average pooling. The model employs dilated causal convolutions to capture long-term dependencies and uses two-dimensional convolution kernels to extract local mutation features. Furthermore, a domain-adaptive transfer learning module is designed to adjust the model’s parameter weights via backward optimization based on the Maximum Mean Discrepancy (MMD) between the source and target domains. Experimental validation is conducted using real-world wind power operation data from a wind farm in Guangxi, containing 3000 samples sampled at 10 min intervals specifically during severe typhoon periods. Experimental results demonstrate that even with only 60% of the target data, the proposed method outperforms the traditional TCN neural network, reducing the Root Mean Square Error (RMSE) by 58.1% and improving the Coefficient of Determination (R²) by 32.7%, thereby verifying its effectiveness in data-scarce extreme scenarios. Full article

Lipopeptides (LPs) have evolved from naturally occurring compounds to key therapeutic agents against multidrug-resistant (MDR) bacterial infections. However, their expanding clinical use has triggered emerging resistance mechanisms, posing serious challenges to anti-infective therapy. This systematic review outlines the development of LP resistance and highlights innovative strategies to counteract it. To overcome these evolving barriers, the field has transitioned from traditional empirical optimization to multidimensional rational design. Moving beyond conventional structure–activity relationship (SAR)-guided chemical synthesis, current approaches integrate diverse innovative methodologies. Based on these advances, this review provides the first systematic summary of contemporary strategies for developing novel LPs, offering new perspectives and methodological support to combat resistant bacterial infections and accelerate the development of next-generation LP-based therapeutics. Full article

The disorderly charging of a large number of electric vehicles (EVs) intensifies the operational pressure on the distribution network and negatively impacts the users’ charging experience. This paper proposes an orderly-charging optimization strategy based on the Deep Deterministic Policy Gradient (DDPG) algorithm. First, a comprehensive EV charging behavior model is developed, incorporating regional functional characteristics, vehicle categories, and user behavioral diversity to more accurately reflect real-world charging patterns. Second, a closed-loop control architecture is designed, integrating charging load forecasting, dynamic energy storage regulation, and real-time power allocation. Finally, the DDPG algorithm is applied to enable intelligent dynamic power allocation, which effectively flattens peak–valley load disparities and minimizes user charging costs. The simulation results demonstrate that the proposed strategy significantly enhances distribution network performance and user satisfaction. Specifically, the strategy reduces peak load by 17.08% and achieves a total cost saving of USD 511.49 (17.08%). By considering real-world zones and diverse EV types, this strategy provides substantial engineering value for practical implementation in multi-zone charging systems. Full article

The integration of reconfigurable intelligent surfaces (RISs) with flying ad hoc networks (FANETs) offers new opportunities to enhance performance in aerial communications. This paper proposes a novel FANET architecture in which each unmanned aerial vehicle (UAV) or drone is equipped with an RIS comprising M passive elements, enabling dynamic manipulation of the wireless propagation environment. We address the joint power allocation and RIS configuration problem to maximize the sum spectral efficiency, subject to constraints on maximum transmit power and unit-modulus phase shifts. The formulated optimization problem is non-convex due to coupled variables and interference. We develop an alternating optimization-based joint power and phase shift (AO-JPPS) algorithm that decomposes the problem into two subproblems: power allocation via successive convex approximation and phase optimization via Riemannian manifold optimization. A key contribution is addressing the RIS coupling effect, where the configuration of each RIS simultaneously influences multiple communication links. Complexity analysis reveals polynomial-time scalability, while derived performance bounds provide theoretical insights. Numerical simulations demonstrate that our approach achieves significant spectral efficiency gains over conventional FANETs, establishing the effectiveness of RIS-assisted drone networks for future wireless applications. Full article

Accurate registration of optical and synthetic aperture radar (SAR) images is a fundamental prerequisite for multi-source remote sensing data fusion and analysis. However, due to the substantial differences in imaging mechanisms, optical–SAR image pairs often exhibit significant radiometric discrepancies and spatially varying geometric inconsistencies, which severely limit the robustness of traditional feature or region-based registration methods in cross-modal scenarios. To address these challenges, this paper proposes an end-to-end Optical–SAR Registration Network (OSR-Net) based on multi-constraint joint optimization. The proposed framework explicitly decouples cross-modal feature alignment and geometric correction, enabling robust registration under large appearance variation. Specifically, a multi-modal feature extraction module constructs a shared high-level representation, while a multi-scale channel attention mechanism adaptively enhances cross-modal feature consistency. A multi-scale affine transformation prediction module provides a coarse-to-fine geometric initialization, which stabilizes parameter estimation under complex imaging conditions. Furthermore, an improved spatial transformer network is introduced to perform structure-preserving geometric refinement, mitigating spatial distortion induced by modality discrepancies. In addition, a multi-constraint loss formulation is designed to jointly enforce geometric accuracy, structural consistency, and physical plausibility. By employing a dynamic weighting strategy, the optimization process progressively shifts from global alignment to local structural refinement, effectively preventing degenerate solutions and improving robustness. Extensive experiments on public optical–SAR datasets demonstrate that the proposed method achieves accurate and stable registration across diverse scenes, providing a reliable geometric foundation for subsequent multi-source remote sensing data fusion. Full article

Glioblastoma (GB) is one of the most aggressive and invasive cancers. Current treatment protocols for GB include surgical resection, radiotherapy, and chemotherapy with temozolomide. However, despite these treatments, physicians still struggle to effectively image, diagnose, and treat GB. As such, patients frequently experience recurrence of GB, demanding innovative strategies for early detection and effective therapy. Bioconjugated quantum dots (QDs) have emerged as powerful nanoplatforms for precision imaging and targeted drug delivery due to their unique optical properties, tunable size, and surface versatility. Due to their extremely small size, QDs can cross the blood–brain barrier and be used for precision imaging of GB. This review explores the integration of QDs with click chemistry for robust bioconjugation, focusing on artificial intelligence (AI) to advance GB therapy, mechanistic insights into cellular uptake and signaling, and strategies for mitigating toxicity. Click chemistry enables site-specific and stable conjugation of targeting ligands, peptides, and therapeutic agents to QDs, enhancing selectivity and functionalization. Algorithms driven by AI may facilitate predictive modeling, image reconstruction, and personalized treatment planning, optimizing QD design and therapeutic outcomes. We discuss molecular mechanisms underlying interactions of QDs with GB, including receptor-mediated endocytosis and intracellular trafficking, which influence biodistribution and therapeutic efficacy. Use of QDs in photodynamic therapy, which uses reactive oxygen species to induce apoptotic cell death in GB cells, is an innovative therapy that is covered in this review. Finally, this review addresses concerns associated with the toxicity of metal-based QDs and highlights how QDs can be coupled with AI to develop new methods for precision imaging for detecting and treating GB for induction of apoptosis. By converging nanotechnology and computational intelligence, bioconjugated QDs represent a transformative platform for paving a safer path to smarter and more effective clinical interventions of GB. Full article

To solve slot temperature accumulation in high thrust density permanent magnet linear synchronous motors (PMLSMs), this paper proposes an additive manufacturing (AM)-based variable cross-section winding design for coating-cooled tapered PMLSMs. Integrating the magnetic circuit features of tapered PMLSMs and AM windings’ technical merits, the motor’s operating mechanism and electromagnetic distribution are analyzed. With the coating cooling structure as the thermal management foundation, simulation reveals the motor’s temperature distribution under water cooling, defining core slot thermal management requirements. A novel cross-section winding design is then presented: first, a lumped-parameter thermal network model quantifies the coupling between the winding cross-sectional area and slot heat source distribution; second, a greedy algorithm optimizes the winding cross-section globally to reduce the slot hot-spot temperature and suppress temperature rise. Validated by a fabricated tapered PMLSM stator prototype and static temperature-rise experiments, the results confirm that winding cross-section reconstruction optimizes heat distribution effectively, offering a new approach for temperature rise suppression in high thrust density PMLSMs. Full article

Against the backdrop of global climate change and China’s “dual-carbon” goals, carbon emissions from the construction phase of transportation infrastructure, particularly the rapidly expanding railway network, have garnered significant attention. However, systematic research and general evaluation models targeting the factors influencing carbon emissions during the railway bridge construction phase remain insufficient. To address this gap, this study presents a novel low-carbon evaluation model that integrates the analytic network process (ANP) and the fuzzy comprehensive evaluation (FCE) method. First, a carbon accounting model covering four stages—material production, transportation, construction, and maintenance—is established based on life cycle assessment (LCA) theory, providing a data foundation. Second, an innovative low-carbon evaluation index system for railway bridges, comprising 5 criterion layers and 23 indicator layers, is constructed. The ANP method is employed to calculate weights, effectively capturing the interdependencies among indicators, while the FCE method handles assessment ambiguities, forming a comprehensive evaluation framework. A case study of the bridge demonstrates the model’s effectiveness, yielding an evaluation score of 82.38 (“excellent” grade), which is consistent with expert judgement. The ranking of indicator weights from the model is highly consistent with the actual carbon emission inventory ranking (Spearman coefficient of 0.714). Key indicators—C21 (use of high-performance materials), C22 (concrete consumption), and C25 (transportation energy consumption)—collectively account for approximately 60% of the total impact, accurately identifying the major emission sources. This research not only verifies the model’s efficacy in pinpointing critical carbon sources but also provides a scientific theoretical basis and practical tool for low-carbon decision-making and optimization in the planning and design stages of railway bridge projects. Full article

This study developed a design framework for porous mixtures using a 100% sustainable non-bituminous epoxy–polyurethane binder system. Conventional design protocols for porous asphalt mixtures exhibit limitations in accurately controlling void content and mixture composition. This study proposed a novel design framework for porous mixtures containing 100% sustainable binder based on statistical analysis and theoretical calculations. The relationships among target air voids, binder content, and aggregate gradation were systematically analyzed, and calculation formulas for coarse aggregate, fine aggregate, and mineral filler contents were derived. A mix design framework was further established by applying the void-filling theory, where the combined volume of binder, fine aggregate, and filler equals the void volume of the coarse aggregate skeleton, thereby ensuring precise control of the target void ratio. Additionally, mixing procedures were investigated with emphasis on feeding sequence, compaction method, and mixing temperature. Results indicated that the optimized feeding sequence significantly improved binder distribution; specimens compacted using the Marshall double-sided compaction method achieved a density of 89.60%. Rheological analysis revealed that at 30 °C, the viscosities of sustainable binder and polyurethane filler were 1280 mPa·s and 6825 mPa·s, respectively, suggesting optimal mixture uniformity. The proposed methodology and process parameters provide essential technical guidance for engineering applications of porous mixtures containing 100% sustainable binder. Full article

Resistance to imatinib remains a therapeutic challenge, largely driven by point mutations within the kinase domain of the BCR-ABL, among which the T315I substitution constitutes the most clinically significant barrier. Ponatinib effectively inhibits this mutant form but is limited by dose-dependent cardiovascular toxicity, prompting efforts to develop safer and more selective agents. Recent advances highlight aminopyrimidine-derived scaffolds and their evolution into thienopyrimidines, oxadiazoles, and pyrazines with improved activity against BCR-ABL^T315I. Further progress has been achieved with benzothiazole–picolinamide hybrids incorporating a urea-based pharmacophore, which benefit from strategic hinge-region substitutions and phenyl linkers that enhance potency. Parallel research into dual-mechanism inhibitors, including Aurora and p38 kinase modulators, demonstrates additional opportunities for overcoming resistance. Combination strategies, such as vorinostat with ponatinib, provide complementary therapeutic avenues. Natural-product-inspired approaches utilizing fungal metabolites provided structurally diverse scaffolds that could engage sterically constrained mutant kinases. Hybrid molecules derived from approved TKIs, including GNF-7, olverembatinib, and HG-7-85-01, exemplify rational design trends that balance efficacy with improved safety. Molecular modeling continues to deepen understanding of ligand engagement within the T315I-mutated active site, supporting the development of next-generation inhibitors. In this review, we summarized recent progress in the design, optimization, and biological evaluation of small molecules targeting the BCR-ABL^T315I mutation. Full article

This study establishes a systematic and reproducible workflow for topology optimization (TO) of indexable face milling cutter bodies with integrated internal coolant channels, designed for Additive Manufacturing (AM) of metallic parts. Grounded in Design for Additive Manufacturing (DfAM) principles, the workflow combines displacement-based TO and computational fluid dynamics analysis to generate simulation-driven tool geometries tailored to the constraints of AM. By leveraging iterative design knowledge, the proposed methodology enhances the scalability and repeatability of the design process, reducing development time and supporting rapid adaptation across various tool geometries. AM is explicitly exploited to integrate support-free internal coolant channels directed toward the insert cutting edge, thereby achieving a 20% mass reduction relative to the initial milling tool designs, and improving material usage efficiency at the design stage. The workflow yields numerically optimized geometries that maintain simulated global stiffness under the considered loading conditions and exhibit coolant flow distributions that effectively target the exposed cutting edges. These simulation results demonstrate the feasibility of an AM oriented, workflow-based approach for the numerical design of milling tools with internal cooling, mass reduction and provide a focused basis for subsequent experimental validation and comparison with conventionally manufactured counterparts. Full article

The growing global population has increased the demand for reliable security systems, especially in areas with limited or unstable energy infrastructure. Renewable energy sources, particularly solar panels, offer an effective solution to ensure continuous operation of cameras and sensors on security poles in such regions. This study analyzes data from a solar-powered security pole and develops Maximum Power Point Tracking (MPPT) algorithms to improve system efficiency. The original design, which relied solely on a buck converter, lacked flexibility. To address this, a buck–boost converter capable of operating in both buck and boost modes was designed, and the proposed algorithms were implemented and tested on this converter. Classical MPPT techniques, including Perturb and Observe (P&O) and Incremental Conductance (IC), were evaluated for their performance. Additionally, under partial shading conditions, metaheuristic approaches such as Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) were examined and compared. The performance of all algorithms was assessed in terms of energy efficiency and system adaptability. This study aims to contribute to renewable energy-based solutions by developing flexible and high-performance energy management systems for applications with limited energy access, such as security poles in rural areas. Full article

The efficiency optimization of encryption and decryption algorithms in cloud environments is addressed in this study, where the processing speed of encryption and decryption is enhanced through the application of multi-threaded parallel technology. In view of the high-concurrency and distributed storage characteristics of cloud platforms, a multi-threaded concurrency mechanism is adopted for the direct processing of data streams. Compared with the traditional serial processing mode, four distinct encryption algorithms, namely AES, DES, SM4 and Ascon, are employed, and different data units are processed concurrently by means of multithreaded technology. Based on multi-dimensional performance evaluation indicators (including throughput, memory footprint and security level), comparative analyses are carried out to optimize the design scheme; accordingly, multi-threaded collaborative encryption is realized to improve the overall operation efficiency. Experimental results indicate that, in comparison with the traditional serial encryption method, the encryption and decryption latency of the algorithm is reduced by around 50%, which significantly lowers the time overhead associated with encryption and decryption processes. Simultaneously, the throughput of AES and DES algorithms is observed to be doubled, which leads to a remarkable improvement in communication efficiency. Moreover, under the premise that the original secure communication capability is guaranteed, system resource overhead is effectively reduced by SM4 and Ascon algorithms. On this basis, a quantitative reference basis is provided for cloud platforms to develop targeted encryption strategies tailored to diverse business demands. In conclusion, the proposed approach is of profound significance for advancing the synergistic optimization of security and performance in cloud-native data communication scenarios. Full article