Search Results (4,432)

(1) Background. Food sovereignty and local sustainability are ensured by large agro-industrial holdings and small-scale farms; this synergy forms a complementary model of the agrifood system. Maintaining this model’s balance requires the creation of a unified digital ecosystem that integrates all suppliers and consumers into production chains, thereby eliminating unnecessary intermediaries. (2) Methods. This study employs a comprehensive methodological framework, including systems analysis and mathematical modeling, to develop service algorithms. Object-oriented design and software engineering methods facilitated the development and implementation of a service-oriented architecture for the digital system. (3) Results. The study presents a multi-tier architecture featuring an integration bus based on a service-oriented approach. To implement direct supply-and-demand coupling strategies, the system integrates both internal services (microeconomic indicators) and external services (macroeconomic indicators). Additionally, a recommender system based on neural networks and mathematical models was developed to personalize consumer requests and manage product sales. (4) Conclusions. The software solution is consistent with the AgTech 4.0 concept and enables the creation of a seamless environment for interstate trade. The implementation of the system enhances the transparency of the “product footprint”, facilitates the redistribution of surpluses, and, consequently, contributes to price stabilization. Full article

Distinguishing between human-made interferences and natural background disturbances is of great significance for the safe operation of terrestrial optical cables because human-caused damage can be halted through timely intervention. To address the problem of small-sample disturbance recognition in distributed acoustic sensing (DAS) systems, this paper proposes a fused CNN–SVM classification model based on hybrid features. A convolutional neural network is employed to extract the high-level spatiotemporal features of disturbance signals, which are subsequently fused with statistical features and fed into a support vector machine for classification. Evaluated on open-source data, the proposed model achieves accuracy improvements of 9.1%, 8.7%, and 2.7% over the conventional CNN, the statistical-feature-based SVM, and the conventional CNN-SVM model, respectively. Furthermore, based on field-measured data, a dataset comprising 5664 samples was constructed, covering four typical disturbance-event types: background noise, drilling, knocking, and digging. The field classification results demonstrate that the three-layer convolutional structure of the model achieves a recognition accuracy of 98.5%. Both the ROC curves and multiple evaluation metrics indicate that the proposed three-layer fused CNN–SVM model delivers better classification performance and more balanced category recognition, offering a feasible reference for similar fiber disturbance engineering tasks. Full article

Graphite nozzles remain the dominant choice for small hybrid and solid rocket motors operating on laboratory and university budgets, owing to their low cost, ease of machining, and rapid turnaround during iterative design campaigns. These same programs, however, must contend with the fact that graphite erodes through coupled thermochemical and mechanical mechanisms when exposed to the oxidizing species generated by high-energy propellant combustion, and the resulting throat-area growth fundamentally alters the time histories of chamber pressure, thrust, and delivered specific impulse. This paper presents a nozzle-erosion reconstruction model that extracts the time-resolved throat area from coupled thrust and chamber-pressure measurements using the thrust coefficient relationship, scales the reconstructed area history against pre- and post-test throat measurements, identifies the onset and rate of erosion, and accounts for variable sensor lag between the thrust-stand and pressure-transducer signal chains. The model is exercised on two complementary sets of laboratory-scale GOX/ABS hybrid hot-fire data that together span roughly two orders of magnitude in total throat-area change and peak chamber pressures from 0.5 to 3.4 MPa: a controlled three-operating-point campaign conducted in support of the NASA Plume-Surface Interaction (PSI) program, and a set of higher-pressure firings from the laboratory development series in which the technique was matured. Reconstructed erosion-onset times, erosion rates, and total throat-diameter change are reported for each firing, the reconstruction accuracy is characterized as a function of erosion magnitude. A correlation of graphite erosion with chamber pressure is examined across the combined envelope. The results demonstrate the robustness of the reconstruction technique and provide a reusable framework for post-test reconstruction of transient nozzle geometry in rocket-engine ground testing. Full article

This study presents a topology optimization-based design methodology for a fail-safe stub axle of a lightweight 4 × 4 electric scooter with independent suspension, with the objective of developing a structural design roadmap. Topology optimization was performed under five critical load conditions: vertical, longitudinal, and lateral impacts, as well as braking and cornering under braking, representing standard driving scenarios defined in the literature. The final geometry was built by combining the topology optimization results from each load case, and it was evaluated using finite-element analysis, showing that it is safe under all critical loading conditions with respect to yield strength. The fatigue life assessment was performed using the Goodman–Haigh approach, based on load-condition pairs recommended in the literature, and it was found that the stresses in critical regions remain within the infinite fatigue life region. In addition, based on literature data, the proposed lightweight design approach indicates potential benefits in terms of both energy consumption and manufacturing cost. Overall, the findings suggest that the presented methodology can serve as a fail-safe design roadmap for the development of electric vehicle components. Full article

Small-scale vertical-axis wind turbines (VAWTs) are increasingly essential for the “blue economy,” providing autonomous power to remote coastal communities, offshore platforms, and marine industries. However, the design of efficient Darrieus-type rotors is complicated by complex unsteady aerodynamics, particularly the phenomenon of dynamic stall. This study aims to establish and validate a cost-effective yet accurate mathematical modeling approach for simulating unsteady turbulent flow around a Darrieus H-rotor to support practical engineering applications. The research methodology integrates computational fluid dynamics (CFD) with physical experiments in a hydrodynamic channel. The numerical model utilizes the unsteady Reynolds-averaged Navier–Stokes (URANS) equations closed with the Strain-Adaptive Linear Spalart–Allmaras (SALSA) turbulence model, chosen for its efficiency in capturing flow separation. The system of initial equations was being devised relatively to an arbitrary curvilinear coordinate system. The pressure and velocity fields have been coordinated using the artificial compressibility method adapted to calculate non-stationary problems. Experimental verification was conducted in the GT-400 hydrodynamic tube using a three-bladed H-rotor model, where flow structures were visualized via the colored jet method at tip speed ratios λ ranging from 2 to 5 and Reynolds number 1470. The findings reveal that dynamic stall occurs over a significant portion of the blade trajectory, characterized by vortex generation at the leading edge and subsequent advection along the chord. Qualitative comparison demonstrates a high degree of correlation between the calculated vortex dynamics and physical flow spectra. These results confirm that the URANS-SALSA approach provides a rational compromise between computational cost and physical accuracy. Full article

The Probability Hypothesis Density (PHD) filter based on the Track-Before-Detect (TBD) approach is a key technique for detecting weak targets whose numbers are unknown and time-varying. To overcome the limitations of existing algorithms, such as high computational cost, poor real-time performance, and low tracking efficiency in dense clutter, this paper proposes a fast non-sequential particle PHD filter for TBD. Specifically, an adaptive particle generation method based on differential localization is introduced in the prediction stage, allowing newly generated particles to quickly concentrate around potential target locations. In the update stage, particles are divided into three groups to simplify weight calculation and improve efficiency. Furthermore, a parallel resampling strategy is adopted to further enhance real-time performance. Numerical experiments demonstrate that the proposed method maintains tracking accuracy with only a small number of particles, thereby significantly reducing computational complexity and improving real-time capability. This work offers a practical reference for the engineering deployment of TBD algorithms. Full article

Intrusion detection in industrial cyber-physical systems is constrained by small labelled-attack corpora and by the subtler signal of physical-process attacks compared with classical IT-network intrusions, motivating renewed interest in foundation-model-based detectors; classical detectors are typically trained per dataset and degrade under the distribution shift that is common in operational technology, where attack repertoires evolve faster than retraining cycles. Two foundation-model families are now plausible candidates: open-source Large Language Models (LLMs) and recent tabular foundation models (TabPFN, TabICL) pre-trained for in-context tabular inference. We compare the two families head-to-head, alongside Random Forest and XGBoost classical anchors, across three established industrial security benchmarks (SWaT, HAI, WUSTL-IIoT-2021) under a controlled multi-seed full-holdout protocol with paired McNemar and cross-seed Mann–Whitney tests. The empirical picture is dataset-dependent rather than universal: tabular foundation models establish a strong, previously unreported baseline that is competitive with or superior to classical anchors on every dataset evaluated, while LLMs are complementary detectors with a specific advantage on schemas that carry process-engineering semantics (such as SWaT’s named sensor channels). A per-class analysis on the WUSTL five-class attack taxonomy shows that the two families have structurally different strengths: tabular methods dominate traffic-rich attacks (Denial-of-Service, Reconnaissance), whereas LLMs are competitive on rare attack types (Backdoor, Command Injection). A confidence-gated cascade that escalates only low-confidence tabular decisions to an LLM exceeds either detector alone at a small query budget, and a leave-one-attack-type-out analysis shows that foundation-model detectors generalise to unseen attack families substantially better than the classical anchors. The appropriate detector choice in industrial cyber-physical security is therefore informed by the dataset’s feature schema, the attack-type mix, and the operational cost envelope, rather than by a specific performance metric. Full article

Currently, the development of civil engineering industry is gradually slowing down, with the focus gradually shifting toward the reinforcement and renovation of existing buildings. Among these existing structures, reinforced concrete (RC) structure is a kind of structure with high proportion. Therefore, this paper conducts research on the seismic properties of RC buildings after adding new beams to existing columns. This paper first introduces the design situation of the specimen, followed by an experimental investigation of its mechanical properties using pseudo-static tests. Based on the failure patterns and hysteresis curves, the differences between the new-type specimen and RC specimen are analyzed. The findings indicate that, while ensuring load-bearing capacity, the new-type joints exhibit better seismic performance: the bearing capacity and maximum displacement are increased by at most 9.2% and 14.9% respectively, and the fuller hysteresis curve shows that the new-type specimen has better energy dissipation capacity. Finally, this paper extends the analysis of the design parameters of the specimens using finite element components. The modeling results reveal that the bearing capacity varies by less than 1% with different parameters such as connector thickness, concrete strength grade, and bolts quantity and strength, indicating that these parameters have a relatively small impact on the bearing capacity. While for the specimen dimensions and thickness and strength of wrapped steel of beam, the maximum increase in bearing capacity is 32.3% and 6.0%, respectively. Indicating that their impact is quite significant. The findings of this paper provide a reference for structural design and contribute to advancing the work of reinforcement and renovation of existing concrete structures. Full article

With the deep integration of artificial intelligence and big data, intelligent optimization algorithms have become key tools for solving many complex problems. However, as problem scale and complexity grow rapidly, the performance of traditional algorithms often faces significant challenges. The Teaching Learning Based Optimization algorithm has attracted widespread attention for its simple structure, few parameters, and high solution efficiency, and has been successfully applied across various engineering and scientific fields. Nevertheless, when dealing with high-dimensional, multimodal global optimization problems and real-world applications, the standard Teaching Learning Based Optimization still exhibits certain limitations, such as reduced accuracy of the optimal solution due to insufficient initial population diversity, and difficulty in escaping local optima caused by premature convergence. To address these issues, this paper proposes an Improved Teaching Learning Based Optimization algorithm. The improved ITLBO upgrades original TLBO from three perspectives: first, a population interaction strategy combining chaotic disturbance and Gaussian mutation is designed to enrich initial population diversity; second, bipolar cooperative search utilizing dynamic weighting of optimal and worst individuals balances global exploration and local exploitation to avoid premature convergence; third, oscillatory random mapping learning with sinusoidal oscillation factor periodically perturbs individuals to continuously replenish population diversity in iterations. Numerical results show that the proposed method exhibits superior convergence performance and stability on classical global optimization benchmarks. Furthermore, the algorithm is applied to practical cloud resource scheduling problems, and experimental outcomes verify that ITLBO improves solution accuracy by approximately one order of magnitude over original TLBO and reduces small-scale cloud scheduling cost by 12% while achieving preferable robustness. Full article

Digital transformation in construction small and medium-sized enterprises (SMEs) depends on employees’ willingness to use information systems in their daily work. This study examines the role of transformational leadership (TL) and perceived organizational support (POS) in employees’ behavioral intention to use information systems in Chinese engineering construction SMEs. It also considers the mediating role of perceived usefulness (PU) and perceived ease of use (PEOU) and the moderating role of organizational culture. A total of 361 valid responses were collected from employees in Chinese engineering construction SMEs. The results show that TL and POS both act as organizational drivers of employees’ adoption intention. TL influences BI by improving employees’ cognitive evaluation of information systems through PU and PEOU. POS provides resource-based support to help employees feel more confident using these systems. OC further conditions how employees respond to leadership and support signals during digital transformation. These findings suggest that technology acceptance in engineering construction SMEs is shaped by both individual technology beliefs and organizational conditions. This study extends technology acceptance research by making the Theory of Planned Behavior more concrete through managerial and support mechanisms. It also provides practical guidance for SME managers seeking to support digitalization through clear leadership communication, targeted resource support, and a learning-oriented culture. Full article

Bio-based and biodegradable polymers such as short-chain-length (scl) poly(3-hydroxybutyrate) (PHB) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) are widely adopted in diverse areas such as healthcare, manufacturing, and packaging. However, high production costs and the complexity of tailoring their thermal properties, such as glass transition temperature (Tg), melting temperature (Tm), and crystallization temperature (Tc), hinder further adoption. The current study reported on the development of a raw dataset of PHB and PHBV materials compiled from 572 instances collected from the literature (558 instances) and in-house experiments (14 instances). The dataset encompassed compositional physicochemical parameters, molecular features, and corresponding thermal characteristics. After assessing data quality and filtering for completeness and available features, curated datasets were created for machine learning (ML) analysis. Two ML models, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), were utilized to predict values of Tg, Tc, and Tm using feature engineering methods that integrated chemistry-based descriptors with polymer-specific and experimental variables. The predictive performance of the models was systematically investigated using different combinations of input features to identify the most informative descriptor sets for each target property. The best-performing models were obtained using 118 data points for Tg and Tm and 201 data points for Tc, achieving R² values of 0.77, 0.76, and 0.82 for Tg, Tc, and Tm, respectively. Despite the reliable prediction of the thermal properties of scl-PHAs, the main limitations of the study were the relatively small dataset size for certain targets and incomplete or missing reporting of experimental conditions in the literature sources, which may introduce variability in the compiled data. The findings implied that curated polymer datasets and interpretable ML models can support the rational design of sustainable polymers with tailored properties for specific applications. Full article

Plot combine harvesters are specialized machines used in breeding trials, germplasm evaluation, and small-batch seed harvesting. Compared with conventional field combine harvesters, they have higher requirements for sample independence, grain integrity, seed purity, low residual grain, rapid plot switching, and plot-level data reliability. However, existing studies remain relatively fragmented, and many studies mainly focus on individual components, whereas analyses of whole-machine coordination, residual-grain control, crop adaptability, and data integration remain insufficient. This paper presents a structured review of the research progress in plot combine harvesters from an agricultural-engineering perspective, covering representative international and domestic models, headers, threshing and separation systems, cleaning systems, residual-seed removal devices, simulation methods, intelligent monitoring, and seed-quality sensing. Existing evidence indicates that plot combine harvesters are developing toward whole-machine low-residue design, coordinated threshing–cleaning–conveying optimization, standardized evaluation methods, sample identification, data traceability, and long-term field validation under continuous multi-plot harvesting conditions. Key challenges include coordinating small-batch intermittent material flow, controlling residual grain during frequent plot switching, balancing threshing completeness with seed protection, improving adaptability to different crops and breeding materials, and validating intelligent sensing technologies under field conditions. This paper provides an engineering reference for improving the mechanization, precision, and intelligence of breeding-trial harvesting equipment. Full article

Modern connected vehicles rely on the controller area network (CAN) to disseminate safety-critical in-vehicle information, including sensor-related and vehicle-state signals such as engine revolutions per minute (RPM) and gear state, among electronic control units (ECUs). Because CANs lack built-in authentication and encryption, malicious message injection and spoofing can compromise the integrity and availability of vehicular sensing and control functions. Existing deep-learning-based intrusion-detection systems (IDSs) show a clear trade-off: supervised methods perform well on known attacks but rely on costly labels, whereas unsupervised methods can identify unseen attacks but often suffer from high false-positive rates. To address these limitations, this paper proposes a semi-supervised generative adversarial network (SGAN) framework for CAN bus intrusion detection that combines image-based CAN representation with adversarial learning. Consecutive CAN messages are converted into $64\times 9$ grayscale images, and the proposed framework is trained in three phases. First, the discriminator establishes an initial decision boundary using a small labeled subset. It then refines this boundary through distribution-level likelihood objectives and generated samples. Finally, the generator is trained to produce realistic samples capable of deceiving the discriminator. The proposed method was evaluated on the Hacking and Countermeasure Research Lab (HCRL) car-hacking dataset using leave-one-class-out experiments to simulate unknown attacks and achieved an average accuracy of 99.73% and an average F1-score of 99.63% on unknown attacks. Moreover, with only 0.21 M parameters and 3.25 M floating-point operations (FLOPs), the model is well suited for resource-constrained in-vehicle platforms. These results indicate that the proposed framework can serve as a practical cybersecurity component for protecting CAN-carried data in vehicular sensing applications. Full article

Background/Objectives: Survival prediction in non-small cell lung cancer (NSCLC) remains challenging, particularly in limited-sample settings where end-to-end deep learning models may suffer from limited generalization. This study aimed to develop a data-efficient, multimodal, and explainable framework integrating computed tomography (CT)-derived imaging information with clinical variables for NSCLC survival prediction. Methods: CT images, tumor segmentations, and clinical data from the publicly available NSCLC Radiomics (LUNG1) dataset (377 patients) were used. Tumor-focused regions were extracted using segmentation masks, and pretrained RadImageNet-InceptionV3 embeddings were obtained from the largest tumor-containing slice and neighboring-slice summaries. Deep imaging embeddings, engineered imaging features, and clinical variables were fused into a unified tabular representation. To improve robustness under limited-sample conditions, feature blocks were compressed using principal component analysis. CatBoost, XGBoost, and LightGBM models were trained on a development set and evaluated on a strictly held-out final validation set. Results: In three-class survival stratification, assigning censored/non-event patients to the upper survival group produced the strongest ordinal prognostic performance. Under the EX_PLUS_NON_EX_TOP setting, CatBoost achieved the best holdout score-based class C-index of 0.655. In continuous survival regression, LightGBM achieved the best holdout event-patient C-index of 0.576. Clinical variables provided the dominant prognostic signal, while compact deep image embeddings contributed complementary information, particularly in separating short- and long-survival groups. SHAP analysis confirmed contributions from both clinical and image-derived features. Conclusions: The proposed framework provides a proof-of-concept demonstration of a data-efficient and explainable image-to-tabular approach for NSCLC survival prediction under strict internal holdout validation. The results suggest that pretrained CT embeddings, clinical variables, gradient-boosted trees, and SHAP-based interpretation can be combined in a feasible, limited-sample survival modeling pipeline, while external validation remains necessary before clinical translation. Full article

In contemporary engineering applications, deficiencies in dynamic mechanical properties, particularly impact toughness, are the leading cause of fracture incidents. Consequently, inadequate dynamic mechanical properties have emerged as the primary constraint limiting the further commercial application of precipitation-strengthened high-strength aluminum (Al) alloys, exemplified by the 2014 aluminum alloy. Since the dynamic mechanical properties of the 2014 wrought aluminum alloy are fundamentally governed by the decohesion and cracking of coarse second-phase constituent particles, it is necessary to quantify the correlation between microstructure and mechanical properties. Meanwhile, the size and volume fraction of constituent particles are largely dictated by the concentration of main and impurity alloying elements. Experimental results revealed that the volume fraction of coarse constituents increased with increasing Cu, Si, and Fe content, and that tensile ductility and impact toughness decreased following an inverse exponential relationship with the volume fraction of constituents. The aim of this study is to establish a quantitative relation to correlate the characteristics of coarse constituents with the tensile ductility and impact toughness of the 2014 aluminum alloy. A mathematical model was developed by regarding the coarse constituents as ellipsoidal inclusions. Their volume fraction and aspect ratio were considered in the model. Model predictions show broad agreement with experimental data. These properties are more sensitive to the volume fraction when it is low. Conversely, a larger aspect ratio leads to higher ductility and toughness. The sensitivity is also greater at a small aspect ratio. The model further indicates that reducing the volume fraction when it is high yields limited improvement, whereas further reduction at a low volume fraction leads to significant enhancement of ductility and toughness. This study correlates coarse constituent characteristics with tensile ductility and impact toughness quantitatively, and provides a theoretical framework for predicting and optimizing the mechanical properties of 2014 aluminum alloy. Full article