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Wheeled multifunctional motorized crossing frames represent a new type of crossing equipment for high-voltage transmission line construction. The initial design is too conservative, having a large safety margin and high material redundancy. Therefore, it is necessary to study a lightweight design version. However, as the structure constitutes an assembly consisting of multiple components, it also exhibits relatively high complexity. In a lightweight design, optimizing multi-component and multi-size parameters can lead to structural interference and separation, seriously affecting the smooth progress of design optimization. Therefore, an optimization design method of a multi-parameter complex assembly structure is proposed to solve this problem. Firstly, the typical stress conditions of the wheeled multifunctional motorized crossing frame were analyzed using its structural model. Then, a finite element model of the beam was established in ANSYS 2021 R1 Workbench, and the mechanical characteristics were analyzed. The results show that the arm support is the key load-bearing component and has significant optimization potential. Subsequently, functional mapping relationships were established among the 14 dimension parameters of the arm support, reducing the number of design variables to six and successfully avoiding component separation or interference during optimization. Through global sensitivity analysis, the height, thickness, and length of the arm body were screened out as the core optimization parameters from six initial design variables. Then, 29 groups of sample points were generated via central composite design (CCD), and a response surface model reflecting the relationships among the arm body’s dimensional parameters, total mass, maximum stress, and maximum deformation was established using the Kriging method. Leave-one-out cross-validation (LOOCV) was performed, and the coefficients of determination (R²) for model fitting were all higher than 0.995, indicating extremely high prediction accuracy. Taking mass and deformation minimization as the optimization objectives, the MOGA algorithm was adopted to perform multi-objective optimization and determine the optimal engineering parameters. Simulation verification was conducted on the optimized arm support, and an eigenvalue buckling analysis was performed simultaneously to verify structural stability. Finally, the proposed optimization method was experimentally verified through mechanical performance tests of the full-scale prototype under symmetric and eccentric loads. The results show that the mass of the optimized arm support is reduced from 217.73 kg to 189.8 kg, with a weight reduction rate of 12.8%. Under an eccentric load of 70,000 N, the maximum deformation of the arm support is 8.9763 mm, the maximum equivalent stress is 314.86 MPa, and the buckling load factor is 6.08, all of which meet the requirements for structural stiffness, strength, and buckling stability. The maximum error between the experimental and finite element results is only 4.64%, verifying the accuracy and reliability of the proposed method. The proposed optimization methodology, validated on a wheeled multifunctional motorized crossing frame, serves as a transferable paradigm for the lightweight design of complex assemblies with coupled dimensional constraints, thereby offering a general reference for the structural optimization of multi-component transmission line equipment, construction machinery, and other multi-component engineering systems. Full article

Maritime ship target tracking plays an important role in applications such as maritime patrol and maritime surveillance. However, complex sea conditions, similar target appearances, and long-distance imaging often lead to target identity confusion and unstable trajectories. To address these issues, in this paper, a ship multi-object tracking algorithm, DeepSORT-OCR, that integrates hull number semantic features is proposed. Based on the YOLO detection framework and the DeepSORT tracking architecture, a CBAM-ResNet network is introduced to enhance the representation of ship appearance features. An Inner-SIoU metric is adopted to improve the geometric matching of slender ship targets, while an LSTM-Adaptive Kalman Filter is employed to model the nonlinear motion patterns of ships and improve trajectory prediction stability. In addition, a Hull Number Feature Extraction module is designed in order to recognize ship hull numbers using OCR and match them with a hull number database. The extracted hull number semantic features are dynamically fused with visual appearance features to strengthen identity constraints during target association. The experimental results show that the proposed method achieves an MOTA of 66.53% on the MOT16 dataset, representing an improvement of 5.13% over DeepSORT. On the self-constructed maritime ship dataset, the method achieves an MOTA of 70.89% and an MOTP of 80.84%. Furthermore, on the hull-number subset, the MOTA further increases to 77.18%, an improvement of 7.31% compared with DeepSORT, while the number of ID switches is significantly reduced. In addition, experiments conducted on pure real data, pure synthetic data, and cross-domain evaluation settings demonstrate the stability and strong generalization capability of the proposed algorithm under different data distributions. The proposed method effectively improves the stability and identity consistency of ship multi-object tracking in complex maritime environments. Full article

To further explore the energy-saving potential of power-split hybrid electric vehicles, this paper addresses issues in traditional Radial Basis Function (RBF) neural network-based vehicle speed prediction methods, which rely solely on time-varying information from historical speed sequences of the host vehicle, leading to problems such as idle overestimation, large local prediction errors, and low prediction accuracy across different time horizons. An improved RBF neural network-based vehicle speed prediction method that integrates multi-sensor information is proposed. This method identifies the driver’s driving intention through a fuzzy inference system, extracts historical speed sequences within a fixed time window in a rolling manner, and integrates inter-vehicle motion characteristic parameters obtained through fusion of millimeter-wave radar and camera data. These multi-dimensional influencing factors are used as inputs to the RBF neural network for vehicle speed prediction. Based on this, an energy management optimization model for the vehicle is established, with the goal of optimizing fuel economy. The model predictive control (MPC) strategy is employed, and the Dynamic Programming (DP) algorithm is used to solve for the real-time optimal torque distribution among various power sources within a limited time horizon. Finally, simulation validation is conducted on the MATLAB/Simulink platform under the CHTC-B driving cycle, CCBC driving cycle, and actual road driving cycle. The results show that, compared with the traditional method adopting Radial Basis Function (RBF) neural network-based vehicle speed prediction and rule-based energy management, the proposed method improves the vehicle’s fuel economy by 4.11%. Full article

Olive pomace biochar, obtained through the pyrolysis of lignocellulosic biomass, has emerged as a sustainable and multifunctional additive for polymer composites. Its physicochemical properties, including porosity, surface area, and electrical conductivity, can be tailored by controlling feedstock type and pyrolysis conditions. Although mechanical reinforcement and thermal stability improvements are well documented, the influence of biochar on surface-related properties such as wettability and contact angle remains insufficiently explored for environmentally relevant composite systems. In this study, epoxy-based composites containing biochar synthesized at 750 °C were evaluated in terms of their water interaction behavior by monitoring the evaporation dynamics of ultra-pure water droplets (10 μL, 0.055 mS/cm conductivity) at eight time intervals between 20 and 580 s using high-resolution digital microscopy. Image enhancement and segmentation were performed prior to Discrete Cosine Transform (DCT) analysis to describe droplet geometry in the frequency domain. Time-dependent variations in the standard deviations of DCT coefficients were optimized using the Bat Algorithm, resulting in mathematical models capable of accurately representing droplet evolution and surface–fluid interactions. The primary novelty of this study lies in the development of a hybrid experimental–computational framework that integrates droplet-based wettability measurements with signal-domain analysis and metaheuristic optimization. Unlike conventional studies focusing solely on material characterization, this approach establishes quantitative relationships between surface behavior and numerical descriptors derived from DCT and the Bat Algorithm. The proposed methodology provides a data-driven tool for predicting wettability trends in biochar-reinforced composites and supports the development of moisture-resistant materials for coatings, packaging, and thermal insulation applications within the context of sustainable composite design. Full article

This work contributes to advancing sustainable energy and waste management strategies by investigating the thermochemical conversion of food-based biomass through pyrolysis, highlighting the role of artificial intelligence (AI) in enhancing process modelling accuracy and optimization efficiency. The main objective is to explore the potential of underutilized biomass resources like spent coffee grounds (SCGs) and DSs (date seeds) for sustainable hydrogen production. Specifically, it aims to optimize the pyrolysis process while evaluating the performance of these resources both individually and as blends. Proximate, ultimate, fibre, TGA/DTG, kinetic, thermodynamic, and Py-Micro-GC analyses were conducted for pure DS, SCG, and blends (75% DS-25% SCG, 50%DS-50%SCG, 25%DS–75%SCG). Blend 3 offered superior hydrogen yield potential but had the highest activation energy (Ea: 313.24 kJ/mol), while Blend 1 exhibited the best activation energy value (Ea: 161.75 kJ/mol). The kinetic modelling based on isoconversional methods (KAS, FWO, and Friedman) identified KAS as the most accurate. These approaches work together to provide a detailed understanding of the pyrolysis process with a particular emphasis on the integration of artificial intelligence (AI). An LSTM model trained with lignocellulosic data predicted TGA curves with exceptional accuracy (R²: 0.9996–0.9998). Full article

Background: Liquid biopsy using circulating tumor DNA (ctDNA) is rapidly reshaping gastrointestinal (GI) oncology. The highest-impact applications are molecular residual disease (mRD) detection after curative-intent therapy and early recognition of progression or resistance during systemic treatment. Methods: We performed a structured, clinically oriented narrative synthesis by using explicit search, eligibility, evidence prioritization, and clinical interpretation rules, integrating landmark prospective cohorts, randomized ctDNA-guided strategy trials where available, meta-analyses, key methodological research (e.g., pre-analytics, assay design, and clonal hematopoiesis (CH)/clonal hematopoiesis of indeterminate potential (CHIP)), and selected trial registries. Results: In resected colorectal cancer (CRC), postoperative ctDNA positivity is among the strongest known biomarkers of recurrence risk; large prospective studies demonstrate clear separation of disease-free survival (DFS)/overall survival (OS) between mRD+ and mRD− patients. In stage II colon cancer, randomized data (DYNAMIC) show that a ctDNA-guided strategy reduces adjuvant chemotherapy exposure without compromising long-term outcomes. In metastatic CRC, ctDNA supports early response monitoring and resistance tracking; ctDNA-selected anti-EGFR rechallenge provides a model of biomarker-driven actionability (CHRONOS). In gastroesophageal cancers, longitudinal ctDNA dynamics correlate with relapse risk and treatment efficacy, and in esophageal squamous cell carcinoma, ctDNA after neoadjuvant chemoradiotherapy informs residual disease risk and adjuvant stratification. In pancreatic ductal adenocarcinoma and hepatobiliary malignancies, sensitivity is constrained by low shedding and background cell-free DNA (cfDNA), yet ctDNA positivity remains clinically meaningful, and emerging data in resected extrahepatic cholangiocarcinoma (STAMP-linked analyses) show that ctDNA dynamics during adjuvant therapy predict recurrence. Conclusions: ctDNA is a clinically validated biomarker for mRD in CRC, whereas in other GI cancers, it remains a promising but methodologically heterogeneous tool whose clinical utility is tumor- and context-dependent. The next phase requires interventional trials demonstrating outcome improvement, harmonized sampling and reporting standards, and rigorous control of confounders (notably CH/CHIP). Full article

With the rapid development in new media and social platforms on the internet, some social hotspots or sensitive events can easily ferment and spread in the online space, attracting the attention or concentrated discussion of young students. Network cluster behavior is a collective behavior in which a large number of netizens collectively express and gather opinions around social hot issues of common concern, creating online public opinion. The study explored the influence of group psychology on the process of college students participating in online cluster behavior. A survey was conducted involving 2137 college students from over 10 universities in Zhejiang Province, Jiangsu Province, and other regions. The data were analyzed using correlation analysis and moderated mediation model testing. This study found that group psychological factors, such as emotional infection, depersonalization, the spiral of silence, relative deprivation, group polarization, and action mobilization, positively predicted network cluster behavior. The action mobilization of opinion leaders mediated the relationship between emotional infection and network cluster behavior. Group polarization mediated the relationship between the spiral of silence and network cluster behavior. Additionally, group efficacy moderated the latter part of the mediation process between group polarization and network cluster behavior. Full article

Photovoltaic (PV) power forecasting is strongly influenced by volatility, randomness, and changing meteorological conditions, while conventional point forecasting provides limited uncertainty information for engineering use. This study proposes a hybrid interval forecasting framework for PV prediction. Similar-day clustering first segments weather data into distinct scenarios (sunny, cloudy and overcast) to reduce noise and redundant information within sequences, enhancing stability and thereby providing a more refined feature space for deep learning. A BiLSTM–Transformer model is then used as the core forecaster, taking multiple meteorological variables as multi-feature time-series inputs. BiLSTM captures bidirectional temporal dependencies, and the Transformer enhances long-range feature extraction via attention. To improve robustness and stability, the Alpha Evolution (AE) algorithm is applied for hyperparameter optimization, balancing global exploration and local refinement. For probabilistic forecasting, Adaptive Bandwidth Kernel Density Estimation (ABKDE) is employed to construct prediction intervals, where the local bandwidth is determined by minimizing a local error function to adapt to data density and error distribution. Case studies utilizing a full-year, 5 min high-resolution dataset from the DKASC station demonstrate that the proposed AE-BiLSTM–Transformer achieves highly accurate point forecasts across diverse weather conditions, reducing the RMSE by 81.85%, 76.99%, and 72.26% under sunny, cloudy, and overcast scenarios, respectively, compared to the baseline LSTM. ABKDE further produces reliable and compact intervals; at the 90% confidence level on sunny days, it achieves PICP = 0.921 with PINAW = 0.0378, reducing PINAW by 75.16% relative to conventional KDE while maintaining comparable coverage. Full article

Russian-language resources for medical natural language processing (NLP) are expanding rapidly; however, their fragmentation, uneven curation, and limited clinical reliability hinder the development of safe machine learning systems for prognosis, prevention, and precision medicine. We provide the first systematic survey of Russian medical NLP datasets and analyze their suitability for clinically meaningful tasks as defined by the MedHELM taxonomy. We additionally perform expert clinical validation of three representative public corpora—RuMedPrimeData (real outpatient notes), MedSyn (synthetic clinical notes), and RuMedNLI (translated natural language inference)—assessing clinical plausibility, diagnosis accuracy, and logical consistency. Experts identified substantial reliability issues: across randomly sampled subsets of each corpus, only approximately 20% of RuMedPrimeData records, fewer than 15% of MedSyn records, and approximately 55% of RuMedNLI pairs met essential quality criteria, which can hinder downstream ML systems built on these data. To support robust applications—ranging from medical chatbots and triage assistants to predictive and preventive models—we outline practical requirements for high-quality datasets: coordinated, expert-validated, machine-readable corpora aligned with clinical guidelines and insurance logic, standardized de-identification, and transparent provenance. Strengthening these data foundations will enable the development of reliable, reproducible, and clinically relevant AI systems suitable for real-world healthcare applications. Full article

This paper presents a design research study that develops a comprehensive conceptual framework for an integrated digital twin system for architectural heritage. The framework aims to explore mechanisms for real-time monitoring and the coupled regulation of structural health, microclimatic conditions, and energy performance. In the context of the ongoing global warming emergency, this framework supports climate adaptation strategies for heritage sites. It enables a fully coordinated operational process encompassing real-time sensing, predictive analysis, coupled control, and decision support. In the structural dimension, the framework is designed to utilise sensors to monitor and warn against cracks, settlement, and deformation, whilst integrating models to analyse stress conditions. In the microclimate dimension, the study envisages predicting and adjusting HVAC and lighting systems based on environmental parameters and footfall monitoring data via algorithms, with the aim of balancing occupant comfort with humidity control and mould prevention. Regarding energy, the framework optimises equipment operation through smart metering and algorithms and we propose a modelling tool for the quantitative assessment of energy-saving retrofit effects. Furthermore, the framework incorporates the establishment of an open-access dataset covering structural, microclimate, and energy use data, providing data standards and a foundation for subsequent empirical research. Full article

To address global carbon reduction demands, oxy-fuel combustion in circulating fluidized beds (oxy-CFB) has emerged as a highly promising carbon capture technology, offering extensive fuel flexibility and facilitating bioenergy with carbon capture and storage (BECCS). However, its commercialization is hindered by significant energy penalties and complex scale-up challenges. This review comprehensively analyzes the fundamental multiphase mechanisms, heat transfer behaviors, and multi-pollutant emission characteristics of oxy-CFB systems, drawing upon multiscale modeling advancements and operational data from pilot to 30 MW_th industrial demonstrations. Replacing air with an O₂/CO₂/H₂O mixture fundamentally alters gas–solid hydrodynamics and char conversion pathways, necessitating active fluidization state re-specification. Despite shifting optimal desulfurization temperatures and introducing recarbonation risks, the technology demonstrates inherent advantages in synergistic pollutant control, including the complete elimination of thermal NO_x. While atmospheric oxy-CFB is technically viable, transitioning to pressurized operation is critical to minimizing system efficiency penalties. Furthermore, integrating oxygen carrier-aided combustion (OCAC) and developing advanced predictive control strategies are essential to managing multi-module thermal inertia and enabling rapid dynamic responsiveness for modern power grids. Full article

According to the World Health Organization, antibiotic research remains insufficient, emphasizing the urgent need for new active molecules, particularly against resistant bacteria. Based on known antibacterial scaffolds, new fluoroquinolone derivatives have been synthesized by our research group, including compound 7a, a difluoroboranyl-fluoroquinolone that previously demonstrated activity against sensitive strains. Methods: The minimum inhibitory (MIC) and bactericidal (MBC) concentrations of compound 7a were determined against Staphylococcus aureus, Klebsiella pneumoniae, and Escherichia coli. The selective development of ciprofloxacin-resistant S. aureus was induced by reseeding the isolate on seven consecutive days with an antibiotic concentration that was not capable of inhibiting its development. Pharmacokinetic and toxicological properties were predicted using SwissADME, Way2Drug, and molecular docking (AutoDock Vina). In vivo toxicity was evaluated in BALB/c mice through histopathological liver and kidney analysis and serum biochemical markers. The antibacterial efficacy of 7a (80 mg/kg/day) was assessed in a murine pneumonia model induced by ciprofloxacin-resistant S. aureus. DNA gyrase inhibition was confirmed through plasmid electrophoresis assays in E. coli DH5-α cells. Results: Compound 7a exhibited both MIC and MBC values of 0.25 μg/mL, while ciprofloxacin-resistant S. aureus strains did not exhibit a detectable MIC within the concentration range tested (up to 1024 μg/mL). In silico predictions revealed favorable ADME profiles, low toxicity, and strong interaction with DNA gyrase. In vivo, 7a showed no signs of hepatotoxicity or nephrotoxicity and effectively reduced pneumonic tissue to 1.99% in infected mice. Electrophoretic assays confirmed DNA gyrase inhibition consistent with the mechanism of fluoroquinolones. Conclusions: Compound 7a evidenced activity against ciprofloxacin-resistant S. aureus in vitro and reduced infection progression in vivo. It also displays favorable drug-like properties, low predicted toxicity, and DNA gyrase inhibition. Full article

This study introduces an efficient and accurate two-stage explicit computational scheme for solving partial differential equations (PDEs) containing first-order time derivatives. The suggested method is a modification of the classical Runge–Kutta scheme that introduces a new first-stage formulation. This minimizes numerical error with moderate step sizes while preserving the stability region of the classical method. Spatial discretization is performed using a sixth-order compact finite-difference scheme to obtain high-resolution solutions. The analysis of stability and convergence is strictly determined for both scalar and system forms of convection–diffusion-type equations. To illustrate the suitability of the method, a dimensionless mathematical model of the unsteady, incompressible, laminar flow of a Prandtl-type non-Newtonian nanofluid over a Riga plate is considered, accounting for viscous dissipation, thermophoresis, Brownian motion, and a magnetic field. Here, the Prandtl ternary nanofluid is defined as a non-Newtonian nanofluid that follows the Prandtl rheological model, and it exhibits three critical transport phenomena: heat conduction, viscous dissipation, and nanoparticle diffusion. Representative values of the Prandtl number P_r = 3 and Reynolds number R_e = 5 are used to perform the simulation, and other parameters, including but not limited to the Hartmann number H_a, Williamson number W_e, thermophoresis N_t and Brownian motion N_b, are varied to evaluate the flow behavior. Moreover, an artificial neural network (ANN)-developed surrogate model is used to calculate the skin friction coefficient and the local Sherwood number, using five input parameters: the Reynolds number, Prandtl number, Schmidt number, Brownian motion parameter, and thermophoresis parameter. The governing partial differential equations yield high-fidelity numerical data used to train the surrogate model. The data is split into 80% for training, 10% for validation, and 10% for testing. The ANN is tested using regression analysis and error histograms, which demonstrate high accuracy and generalization capacity. Numerical simulation combined with AI-based prediction is a cost-efficient method for real-time estimation of complex non-Newtonian nanofluid systems. Full article

Unmanned Aerial Vehicle (UAV) fertilization is important for precision agriculture. However, multi-rotor UAVs show a lot of inconsistencies in homogeneity and unclear deposition patterns when they spread granular fertilizer in different operational situations. This study utilized the DJI T40 UAV to measure discharge rates and create a correlation model. An orthogonal design combined DEM simulation with field experiments to look at how flight height and disc speed affected spreading uniformity and effective swath for single and overlapping flight paths. The discharge rate has a strong linear relationship with control parameters (R² > 0.94), which means that it is very easy to predict for all particle sizes. Single-pass deposition shows an “M-shaped” bimodal profile with particles of different sizes arranged in a radial pattern. The best values for H and n were found to be 7 m and 1200 rpm, respectively, and gave a 10 m effective swath width and a coefficient of variation (CV) of 13.79%. Deposition patterns change nonlinearly with flight height and disc speed. Particle size consistency is critical for distribution stability, with flight height being the key quality determinant and particle size variation the primary source of instability. Full article

This study investigates the hydrodynamic responses and energy harvesting performance of a hemispherical point-absorber wave energy converter (WEC) in uniform current. A frequency-domain Rankine source method (RSM) is developed to rigorously account for current-modified free-surface conditions, and an approximate free-surface Green-function method (AFSGM) is implemented to assess practical applicability under weak-current assumptions. The numerical settings for body, free-surface, and radiation-boundary discretizations are determined through convergence tests. Model validation is performed by comparing motion responses against published benchmark results under both zero-current and current conditions. The effects of current and motion constraints are examined for surge–heave free and heave-only cases. Results show that current can amplify the heave response and that surge freedom enhances heave motion through coupling effects, leading to increasing discrepancies between RSM and AFSGM as current strengthens. For heave-only motion, AFSGM provides practically acceptable predictions within |Fr| ≤ 0.045, while noticeable differences appear near resonance beyond this range, for which RSM is recommended. Energy harvesting is evaluated using a linear PTO damping model, revealing that current alters the capture width ratio (CWR) and shifts the optimal PTO damping and frequency, indicating the necessity of considering current in performance assessment and PTO design. Full article