Search Results (8,837)

Iso-cetane serves as an ideal component representing branched-chain alkanes in surrogate fuels for diesel. However, the predictive accuracy of existing detailed chemical kinetic models for iso-cetane requires improvement. In this study, focusing on the reaction processes of iso-cetane and its key intermediates, we first updated the thermodynamic data of iso-cetane and some of its intermediates, systematically analyzed the effects of various reactions on ignition delay time (IDT), and made targeted modifications to the relevant reaction rate constants. The reaction types involved include fuel cracking reactions of iso-cetane, hydrogen abstraction reactions, cracking reactions of fuel radicals, as well as the oxidation of fuel radicals, isomerization of alkylperoxy radicals (RO₂ )  concerted elimination reactions, formation of cyclic ethers, and the formation and decomposition of ketohydroperoxides (KHP). Additionally, reactions related to the formation and consumption of p-alkyl-dihydroperoxides (P(OOOH)₂) were supplemented. Based on the above work, we developed a detailed chemical kinetic model for iso-cetane, comprising 4541 species and 18,359 elementary reactions. Through systematic validation against experimental data on ignition delay time and concentration variations of key species during oxidation, the improved predictive performance of the proposed model was demonstrated. Furthermore, using sensitivity analysis and reaction pathway analysis for the ignition process, we revealed that the formation of the low-temperature negative temperature coefficient (NTC) region for iso-cetane is intrinsically associated with the competition between chain-branching and chain-propagating pathways. Full article

Dye-sensitized solar cells (DSSCs) remain attractive as low-cost photovoltaic devices; however, their practical efficiency is still constrained by electron-transport losses, interfacial recombination, and incomplete light harvesting in conventional titanium dioxide (TiO₂) photoanodes. The effects of TiO₂ film thickness, multi-walled carbon nanotube (MWCNT) incorporation, and multilayer oxide interface engineering on DSSC performance were examined. Degussa P25-TiO₂ photoanodes were first optimized with respect to thickness, after which controlled MWCNT loadings and sequential compact sol–gel TiO₂ and tin dioxide (SnO₂) sublayers were introduced. The optimum pristine P25-TiO₂ photoanode thickness was 9.11 μm, yielding an open-circuit voltage of 0.74 ± 0.01 V, a short-circuit current density of 14.10 ± 0.40 mA/cm², a fill factor of 56.24 ± 1.00%, and a power-conversion efficiency of 5.93 ± 0.20%. The incorporation of 0.025 wt% MWCNTs increased the efficiency to 6.04 ± 0.20%, corresponding to an absolute gain of 0.11 percentage points. The best performance was obtained with the sol–gel SnO₂/sol–gel TiO₂/P25-CNT multilayer photoanode, which delivered 0.74 ± 0.02 V, 16.22 ± 0.40 mA/cm², 57.59 ± 1.00%, and 6.89 ± 0.30%, respectively. FE-SEM, EIS, XRD, Heated Ultrasonic Cleaner and UV–visible analyses indicate that the multilayer architecture preserves porosity, enhances light harvesting, and suppresses interfacial recombination, while the CNT network facilitates charge transport. Full article

Prefabricated steel structures have been increasingly adopted in modern construction due to their high efficiency, sustainability, and industrialized production. However, their construction quality and efficiency are often compromised by accumulated geometric deviations during fabrication, transportation, assembly, and welding, while traditional construction control and welding processes remain highly dependent on manual measurements and empirical operations. To address these challenges, this study proposes a digital construction framework for prefabricated steel structures, integrating high-precision three-dimensional (3D) laser scanning, Building Information Modeling (BIM), and intelligent welding technologies. First, high-precision 3D laser scanning is employed to capture the as-built geometric information of prefabricated steel components, generating dense point cloud data for construction-stage deviation detection and quantitative comparison with BIM-based design models. Based on deviation analysis, a digital construction control strategy is established to support real-time feedback, error compensation, and assembly adjustment. An engineering case study involving a complex prefabricated steel structure is conducted to validate the proposed framework. The results demonstrate that the integrated digital construction and intelligent welding approach significantly improves assembly accuracy, weld positioning precision, and construction efficiency, while reducing manual intervention and error accumulation. Overall, this study contributes to the body of knowledge by proposing a unified closed-loop digital construction paradigm that integrates geometric perception, deviation-driven decision-making, and intelligent welding execution, thereby bridging the gap between construction control and robotic fabrication in prefabricated steel structures. Full article

In the process of extracting and transporting waxy crude oil, pipeline blockages resulting from wax deposition significantly impede production efficiency and lead to substantial economic losses. Ethylene vinyl acetate copolymer (EVA) is a widely used chemical wax inhibitor; however, its performance is influenced by multiple factors, including its molecular structure, concentration, and the carbon number distribution of the wax system. A systematic elucidation of its mechanism of action and associated phase changes is therefore necessary. In this study, differential scanning calorimetry (DSC) was employed to systematically investigate the thermal behavior of a wax system with a broad carbon number distribution (C5–C50). The objectives were to analyze the influence of EVA concentration, vinyl acetate (VA) content, and molecular weight on the phase transition parameters, to elucidate the wax inhibition mechanism, and to construct a phase prediction model based on the Flory–Huggins theory. The results demonstrate that the wax appearance temperature (WAT), phase transition temperature, and phase transition enthalpy of the wax systems increase monotonically with carbon number. Furthermore, the addition of EVA was found to significantly reduce both the WAT and the amount of wax precipitated. The optimal structural parameters were identified as a VA content of 10%, a number average molecular weight of 20,000, and an optimal concentration of 800 ppm. The medium-carbon wax system (C16–C30) was found to be the most sensitive to the EVA response. The established phase model exhibited high predictive accuracy, with a mean relative error of less than 4%, a root mean square error (RMSE) of 0.32%, and a coefficient of determination (R²) of 0.987, thereby providing preliminary insights and a practical tool for optimizing EVA wax inhibitor formulations under simplified conditions and guiding their potential engineering applications. Full article

Translating preclinical findings into effective clinical cancer immunotherapies remains a major challenge, mainly because conventional in vitro and animal models often fail to capture the complexity, dynamics, and species-specific features of human immune responses. Organ-on-a-chip (OoC) technologies that combine engineered tissue architectures with precisely controlled microfluidic transport provide human-relevant microphysiological platforms for mechanistic studies of immune–tumor interactions and evaluation of therapeutic efficacy and immunotoxicity under defined microenvironmental conditions. However, immune responses involve time-dependent and interconnected processes, including immune cell trafficking, cytokine programs, metabolic shifts, and cytolysis, that are not adequately resolved by static or endpoint assays. Engineering immune-competent OoC systems therefore requires coordinated design of platform architectures, immune cell incorporation strategies, and integrated measurement workflows capable of capturing dynamic and state-dependent responses. In this review, we summarize engineering strategies for building immune-competent OoC platforms for cancer immunotherapy, focusing on platform architectures, immune cell incorporation methods, and fit-for-purpose assay workflows. Emphasis is placed on embedded sensing modalities (e.g., cytokine, oxygen, and impedance readouts) that provide valuable kinetic and state-variable data. Finally, we discuss key translational challenges, including reproducibility, standardization, and benchmarking, and outline near-term priorities to accelerate the adoption of immune-competent OoC systems in immunotherapy research and development. Full article

Existing studies conduct general cost analyses for prefabricated components, yet structural heterogeneity results in distinct cost drivers. Most studies concentrate on the technical performance of prefabricated staircases, with insufficient investigation into dedicated cost-estimation methods. This study establishes a hybrid prediction framework integrating GAN-based data augmentation and SHAP-empowered Multilayer Perceptron (SHAP-MLP) modeling, using prefabricated straight staircases as empirical objects for multidimensional analysis. Total cost is classified into production, transportation, and on-site installation phases, followed by systematic screening of 33 influencing factors for predictive modeling. The Analytic Hierarchy Process (AHP), with a 1–9 scale, is adopted to quantify indicator weights and prioritize features. Triple verification (multi-expert consistency test, group opinion coordination test, and sensitivity analysis) removes five weakly correlated parameters to form a preliminary indicator system. Based on 240 original engineering data samples, the GAN generates 60 high-fidelity synthetic samples. Distribution consistency between synthetic and original data is validated via the Kolmogorov–Smirnov (KS) test, p-value verification, and kernel density estimation (KDE). SHAP interpretability analysis identifies four core determinants: prefabrication rate, total staircase area, standardization level, and number of floors. Eight low-impact parameters are excluded to optimize model input, leaving 20 validated indicators. The GAN-SHAP-MLP model maintains superior performance in testing, with a test-set RMSE of 49.538, representing improvements of 41.3%, 22.5%, and 25.7% over LSTM (89.33), CNN (67.59), and standard MLP (70.56), respectively. The difference between its test-set and overall R² is only 0.69%, significantly lower than 2.06% for LSTM and 5.47% for MLP. Empirical validation with real engineering cases from four different regions further confirms the model’s high prediction accuracy, with a minimum error of only 1.49%. The integration of data augmentation and interpretable deep learning provides a high-precision, interpretable cost prediction tool for prefabricated straight staircases, promoting methodological progress in construction economics. Full article

Variable cross-section cement–soil pipe piles are an innovative soft ground improvement technology. They are tubular, special-shaped cement–soil mixing piles characterized by a tapered profile along the pile shaft (larger diameter at the top and smaller at the bottom) and an internal soil core. They offer advantages including reduced material consumption, lower engineering cost, and shorter construction duration. However, the systematic theoretical understanding of their bearing performance remains insufficient. In this study, the bearing mechanism and influencing parameters of variable cross-section pipe piles were systematically investigated via full-scale field tests, numerical simulations, and laboratory model tests. An exponential decay constitutive model considering the strain-softening behavior of cement–soil was developed and implemented through secondary development in the ABAQUS platform for parametric analysis. Laboratory model tests were further conducted to advance the understanding of the bearing mechanism of variable cross-section pipe piles. The results show that the ultimate bearing capacity of the proposed variable cross-section cement–soil pipe pile is approximately 189% higher than that of the conventional ones. The expanded outer diameter and expanded height are the dominant factors affecting the bearing capacity, while the inner diameter and pile length have a comparatively minimal influence: increasing the expanded outer diameter from 0.6 m to 1.2 m and the expanded height from 0 m to 5 m increased the ultimate bearing capacity from 445 kN to 868 kN and 936 kN, respectively. The effective pile length is determined to be 6 m, and the recommended minimum wall thickness of the pipe pile is 1/4 of the inner diameter. Laboratory tests further demonstrated an abrupt change in axial force at the variable section. The findings provide reliable theoretical support for the engineering design and field application of cement–soil variable cross-section pipe piles. Full article

The oilfield gathering and transportation system (OGTS) accounts for a substantial share of total energy consumption, demonstrating considerable potential for energy-saving optimization. Previous research primarily focused on the independent optimization of gathering pipeline networks or processing stations, with little attention given to their integrated optimization. This study investigates the OGTS of a domestic oilfield block. It develops artificial neural network (ANN) surrogate models for both pipelines and processing station equipment to accurately capture system operating states. An optimization model linking the pipeline network and processing station is established, and a differential evolution algorithm is applied to enhance computational efficiency. The results indicate that optimal predictive performance was achieved when the first hidden layer of the backpropagation neural network (BPNN) contained 50 neurons with a rectified linear unit (ReLU) activation function, with the surrogate models achieving coefficient of determination (R²) values exceeding 0.85. Under a 30 min optimization cycle, total system energy consumption decreased by 2.28%, with computation completed in under 3 min, while daily average optimization led to a 1.55% reduction. These findings demonstrate that the proposed integrated optimization framework offers both a robust methodological foundation and practical engineering guidance for coordinated, low-carbon, and energy-efficient operation of oilfields. Full article

Rapid urbanization has intensified jobs–housing separation and increased commuting distances in megacities, posing challenges for sustainable urban development. Existing studies often examine commuting behavior at a single spatial scale or focus on either residential or employment locations. Using mobile phone signaling data, this study derives network-based commuting distances within the suburban ring of Shanghai and integrates multiple built environment indicators. A multiscale framework is developed using six spatial units, ranging from 2 to 4 km grids to street-level zones, to assess spatial scale effects and support the selection of an appropriate analytical unit. The 3.5 km grid was selected for subsequent analysis as a balance between spatial detail and statistical stability. Within this framework, Multiscale Geographically Weighted Regression (MGWR) examines the spatial heterogeneity and scale effects of built environment factors from both residential and employment perspectives. The results show: (1) The choice of spatial unit significantly affects model performance, with the 3.5 km grid providing a suitable balance between spatial detail and statistical stability. (2) Built environment indicators exhibit clear multiscale effects, with different variables operating at global and local spatial scales. (3) Residential and employment locations show significant asymmetric effects, as enterprise density is associated with shorter commuting distances at residential locations but longer distances at employment centers. These findings indicate the joint role of multiscale spatial structure and dual-end built environments, supporting spatially differentiated planning and transport policies. Full article

Reliable high-precision positioning in railway tunnels is essential for intelligent train operation and safety monitoring, yet GNSS signals are severely degraded by blockage and multipath. This paper proposes a deployment-oriented numerical framework to optimize pseudolite layouts in tunnels by explicitly modeling visibility obstruction and controlling worst-case geometry along the train trajectory. A high-fidelity 3D tunnel–train model is established, in which line-of-sight (LoS) availability is screened under vehicle occlusion and trajectory-level geometric quality is evaluated accordingly. Instead of optimizing only the average PDOP, the proposed framework minimizes the trajectory 90th-percentile PDOP (qPDOP) to suppress tail-risk geometric degradation, while interpreting PDOP as an error amplification factor that directly affects positioning reliability under measurement noise and local multipath. The core contribution is a Voronoi-partition-constrained improved genetic algorithm (IGA) for tunnel pseudolite deployment. Voronoi partitioning enforces segment-wise coverage by requiring at least one pseudolite in each partition cell and avoids clustering-induced blind zones. Meanwhile, the IGA incorporates improved search and constraint-handling mechanisms to satisfy practical engineering requirements, including feasible installation regions, minimum spacing, mounting-face balance (ceiling/side walls), communication range, and continuous satellite visibility. Comparative simulations and ablation studies demonstrate that the proposed method achieves more uniform coverage and significantly improves full-trajectory geometric stability, reducing high-quantile PDOP and mitigating local spikes in occlusion-sensitive sections under cost-constrained sparse deployments. The proposed framework provides a practical and flexible toolchain for designing positioning-oriented pseudolite infrastructures in underground transportation environments. Full article

The growing interconnection of critical engineering infrastructure through IoT introduces unprecedented exposure to cyber threats. Emerging quantum computing capabilities pose a transformative risk to classical cryptographic primitives such as Rivest–Shamir–Adleman and Elliptic-Curve Cryptography, which underpin secure communication and device authentication in industrial control systems, power grids, transportation networks, and healthcare infrastructure. This paper investigates quantum-resistant encryption, often termed post-quantum cryptography (PQC), as a sustainable security paradigm for IoT communication within critical systems. By analyzing lattice-based, code-based, multivariate, and hash-based schemes, the study evaluates trade-offs between computational cost, memory footprint, and latency constraints intrinsic to resource-limited IoT nodes. A hybrid architectural framework integrating the National Institute of Standards and Technology-standardized algorithms (e.g., Cryptographic Suite for Algebraic Lattices—Kyber, Dilithium) with lightweight symmetric primitives (e.g., Ascon, GIFT block cipher in Combined Feedback mode) is proposed for secure data transmission across heterogeneous IoT layers. Experimental simulations benchmark key-exchange throughput, ciphertext expansion, and resilience against quantum-adversarial models, demonstrating up to 65% reduction in handshake latency compared to baseline lattice implementations under constrained conditions. The paper concludes with policy and engineering recommendations for the adoption of quantum-resistant IoT protocols in energy, transportation, and industrial automation sectors, highlighting alignment with global PQC migration roadmaps and IEC 62443 cybersecurity standards. Full article

The Variable Cycle Engine (VCE) is a key enabling technology for addressing the economic and environmental challenges of next-generation supersonic civil aircraft. This paper presents a multidisciplinary design analysis and optimization (MDAO) approach to quantitatively assess the potential benefits of Variable Cycle Engines (VCE) in the conceptual design of supersonic civil aircraft. In this approach, component-level models of a conventional Mixed-Flow Turbofan (MFTF) and a double-bypass VCE with a Core Driven Fan Stage (CDFS) are integrated into the MDAO process. Employing a multi-point optimization strategy, the engine design parameters and off-design control schedules are first determined. Subsequently, for each given engine design (MFTF and CDFS VCE), the airframe geometry parameters are optimized to minimize the aircraft Maximum Take-off Weight (MTOW). The application of this approach is illustrated through a case study of a medium-sized supersonic civil transport. The results indicate that, under the assumption of identical weights for the VCE and the MFTF, the design with the VCE reduces the MTOW by 2.8%, block fuel consumption by 5.7%, and total mission Nitrogen Oxides (NOx) emissions by 24.2% compared to the design with the MFTF. Additionally, lateral noise and flyover noise during the take-off phase are decreased by 2.2 EPNdB and 1.9 EPNdB, respectively. To account for the potential weight increase caused by the structural complexity of the VCE, a parametric weight sensitivity analysis is conducted. Results show that the VCE retains its advantages in MTOW, fuel efficiency, noise, and emissions for weight penalty factors up to 1.15. Full article

The hydrogen fuel cell electric vehicles (FCEVs) are becoming a worldwide recognized eco-friendly choice which produces no tailpipe emissions while providing better energy efficiency than traditional internal combustion engine vehicles. The review delivers an in-depth evaluation of FCEVs through their assessment which focuses on their transportation and power generation functions. The research investigates hydrogen production methods together with storage and distribution systems and vehicle integration practices and performance enhancement techniques. The paper highlights major technical challenges such as high production costs, limited refueling infrastructure, storage inefficiencies, and fuel cell durability. The research uses battery electric and hybrid vehicle comparisons to assess FCEV market competitiveness. The life-cycle environmental impact assessment proves that using clean hydrogen sources and sustainable end-of-life strategies is essential for achieving FCEV operational capabilities. The review examines new electrochemistry materials science and hybridization solutions which have become essential methods for creating better efficiency and durability while decreasing costs. The study shows how policy regulations and collaborative programs fast-track hydrogen adoption through their impact on future hydrogen grid integration and renewable hydrogen production and circular economy methods. The review shows how experts from different fields reached their achievements while still facing challenges to improve FCEVs as fundamental components of environmentally friendly transportation systems and clean energy networks. Full article

A ternary CdS–MoS₂–rGO photocathode was developed to enhance visible light-driven hydrogen evolution through interfacial heterostructure engineering. The composite was fabricated via a solution-based deposition method followed by thermal conversion, resulting in crystalline CdS and MoS₂ phases that were uniformly integrated within a conductive reduced graphene oxide (rGO) framework. Structural and surface analyses (XRD and XPS) confirmed the coexistence of Cd²⁺, Mo⁴⁺, and S²⁻ chemical states without detectable secondary phases. Photoelectrochemical measurements revealed that the ternary architecture significantly improves charge separation efficiency and interfacial charge-transfer kinetics compared to binary and single-component films. The CdS–MoS₂–rGO photocathode exhibited the highest photocurrent density, reduced charge-transfer resistance, and favorable Tafel slope under visible-light irradiation (0.25 sun, neutral electrolyte). Gas chromatography measurements verified that these electrochemical enhancements translate into increased hydrogen production rates, following the trend: CdS–MoS₂–rGO > CdS–rGO > MoS₂–rGO >> rGO. Applied bias photon-to-current efficiency (ABPE) analysis further confirmed improved photon utilization efficiency in the ternary system. The enhanced performance is attributed to synergistic integration of CdS (light harvesting), rGO (rapid electron transport), and MoS₂ (catalytic edge sites), which suppresses recombination and accelerates proton reduction kinetics. These findings demonstrate that rational multi-component heterostructure design is an effective strategy for improving hydrogen evolution rate under mild operating conditions. Full article

As a vital part of urban public transportation system, subway passenger flow prediction plays a crucial role in alleviating traffic congestion, improving transportation infrastructure, and optimizing travel experience. Existing subway passenger flow prediction mainly focuses on short-term predictions of inbound/outbound passenger flow and origin-destination (O-D) demand. Subway section passenger flow prediction can provide a more direct reflection of passenger fluctuations across different line segments, and offer robust support for management and resource allocation. We propose a subway section passenger flow generation model and a prediction method based on LTiT (LSTM-TSSA-iTransformer). This model is based on the overall architecture of the iTransformer encoder, and an LSTM (Long Short-Term Memory) network is employed to capture the temporal characteristics of subway section passenger flow. This is combined with the TSSA (Token Statistics Self-Attention) to adaptively weight the information at key time points. Efficient performance of the model was evaluated by comparing its predictions with other models, including SARIMA (Seasonal Auto-Regressive integrated moving average), BP neural networks, LightGBM (Light Gradient Boosting Machine) and LSTM (Long Short-Term Memory). Experimental results show that the proposed model outperforms traditional baseline models in evaluation metrics such as R², MAE, MSE, and MAPE. Finally, we further investigate the selection of input window length and prediction step size, and perform robustness analysis under different noise conditions. Full article