Search Results (120)

Fault diagnosis of rolling bearings is crucial for ensuring the maintenance and reliability of industrial equipment. Existing cross-domain diagnostic methods often struggle to maintain performance under evolving mechanical and environmental conditions. This limits their robustness in long-term real-world deployments. To address this, we propose a novel online learning framework that continuously adapts to distribution shifts using streaming vibration data. Specifically, the proposed framework consists of three core modules: the Feature Extraction Module that encodes raw vibration signals into low-dimensional latent representations; the Fault Sample Generation Module (comprising a generator and discriminator network) that synthesizes diverse fault samples conditioned on normal-condition data; and the Classification Module that incrementally adapts by leveraging both synthesized fault samples and streaming normal-condition signals. We also introduce a domain-shift indicator ScoreODS to dynamically control the transition between prediction and fine-tuning phases during deployment. Extensive experiments on both public and private datasets demonstrate that the proposed method outperforms the most competitive method, achieving about a 4% improvement in diagnostic accuracy and enhanced robustness for long-term fault diagnosis under distribution shifts. Full article

Urban Building Energy Modeling (UBEM) plays a critical role in supporting data-driven strategies for the energy transition of cities. However, its application is often hindered by the lack of harmonized, high-quality input data representing the building stock. This paper presents the methodology and outputs of a national research project to construct an Italian library of reference buildings suitable for UBEM applications described with scorecards. The methodological workflow included six key phases: definition of a national data classification framework, acquisition and integration of heterogeneous data sources, data harmonization, statistical analysis and clustering, archetype formalization, and dissemination. The result is a library of 380 scorecards covering residential, educational, office, commercial, and catering buildings across multiple climate zones and construction periods. Each scorecard is based on empirical data from public databases, field surveys, or technical standards, and includes detailed descriptions of geometry, envelope characteristics, HVAC systems, internal gains, and ventilation. The scorecards are shared openly on the project’s website and were built to work with different UBEM platforms. Overall, both the method and the results help bring more consistency to UBEM practice and support better, data-driven urban energy planning. Full article

Pseudo-entropy is a complex-valued generalization of entanglement entropy defined on non-Hermitian transition operators and induced by post-selection. We present a simulation-based protocol for detecting nonclassicality and coherent errors in quantum circuits using this pseudo-entropy measure $\check{S}$, focusing on its imaginary part $\Im \check{S}$ as a diagnostic tool. Our method enables resource-efficient classification of phase-coherent errors, such as those from miscalibrated CNOT gates, even under realistic noise conditions. By quantifying the transition between classical-like and quantum-like behavior through threshold analysis, we provide theoretical benchmarks for error classification that can inform hardware calibration strategies. Numerical simulations demonstrate that 55% of the parameter space remains classified as classical-like (below classification thresholds) at hardware-calibrated sensitivity levels, with statistical significance confirmed through rigorous sensitivity analysis. Robustness to noise and comparison with standard entropy-based methods are demonstrated in a simulation. While hardware validation remains necessary, this work bridges theoretical concepts of nonclassicality with practical quantum error classification frameworks, providing a foundation for experimental quantum computing applications. Full article

Nowadays, agriculture is facing significant challenges, including climate change. Precision agriculture might address these issues by optimizing resource use and promoting sustainability. In this work, a case study of tomato crop monitoring is presented, employing a large amount of gas sensor data collected over three years (2020–2022) to develop models for phenological phase classification. A k-NN classifier achieved accuracies above 99% across multiple train/test splits, with AUC, sensitivity, specificity, precision, and F1-score above 98%. Results demonstrate the feasibility of low-computational-cost systems capable of real-time detection of the transition point between plants’ developmental stages. Full article

The desire to improve the safety of navigation, especially in restricted and very busy areas like the straits, leads researchers to evaluate possible uses of Artificial Intelligence as an alternative to the traditional probabilistic methods. This is possible thanks to the large amount of available AIS data generated by ships in transit. In this work, a Machine Learning algorithm (Classification Decision Tree) was trained with eight features coming from AIS data of the Strait of Messina (Italy), with the aim of carrying out a two-class classification of the single AIS data to find anomalies in ship transits that could compromise navigation safety. Since anomalous events are relatively rare, compared to the large amount of information related to the normal navigation situations, the challenge of this work was to obtain an artificial dataset with the aim of simulating the possible anomalous navigation conditions for the Strait investigated, known the active risk mitigation means one. For this reason, the dataset containing abnormal events was obtained simulating different risk scenarios. A hyperparameters tuning with a Bayesian optimization approach and a 5-fold cross validation have been performed to improve the quality of the model and a large dataset has been tested. The accuracy of both validation and test phases is <99.5% and <95.9%, respectively. This can make it possible to identify anomalous navigation conditions in real time, in order to quickly classify possible conditions of risk. The method can be used as a Decision Support Tool by the authority in order to improve the capacity of the single operator to identify the possible risk situation inside the Strait of Messina. Full article

Background/Objectives: The inherent low aqueous solubility of lipophilic drugs, belonging to Class II based on Biopharmaceutical classification system, negatively impacts their oral bioavailability. However, the manufacturing of pharmaceutical dosage forms for these drugs faces challenges related to environmental impact and production complexity. Herein, the surfactant-enriched cross-linked scaffold addresses the limitations of conventional approaches, such as the use of organic solvents, energy-intensive processing, and the demand for sophisticated equipment. Methods: Scaffold former (Pluronic F68) and scaffold trigger agent (propylene glycol) were used to prepare cross-linked scaffold loaded with candesartan cilexetil as a model for lipophilic drugs. Moreover, surfactants were selected based on the measured solubility to enhance formulation loading capacity. Design-Expert was used to study the impact of Tween 80, propylene glycol, and Pluronic F68 concentrations on the measured responses. In addition, in vitro dissolution study was implemented to investigate the drug release profile. The current approach was assessed against the limitations of conventional approach in terms of environmental and manufacturing feasibility. Results: The optimized formulation (59.27% Tween 80, 30% propylene glycol, 10.73% Pluronic F68) demonstrated a superior drug loading capacity (19.3 mg/g) and exhibited a solid-to-liquid phase transition at 35.5 °C. Moreover, it exhibited a rapid duration of solid-to-liquid transition within about 3 min. In vitro dissolution study revealed a remarkable enhancement in dissolution with 92.87% dissolution efficiency compared to 1.78% for the raw drug. Conclusions: Surfactant-enriched cross-linked scaffold reduced environmental impact by eliminating organic solvents usage and reducing energy consumption. Moreover, it offers significant manufacturing advantages through simplified production processing. Full article

Recent advances in exoskeleton-assisted rehabilitation have highlighted the significance of lower limb movement intention recognition through deep learning. However, discrete motion phase classification and continuous real-time joint kinematics estimation are typically handled as independent tasks, leading to temporal misalignment or delayed assistance during dynamic movements. To address this issue, this study presents iTransformer-DTL, a dual-task learning framework with an improved Transformer designed to identify end-to-end locomotion modes and predict joint trajectories during sit-to-stand transitions. Employing a learnable query mechanism and a non-autoregressive decoding approach, the proposed iTransformer-DTL can produce the complete output sequence at once, without relying on any previously generated elements. The proposed framework has been tested with a dataset of lower limb movements involving seven healthy individuals and seven stroke patients. The experimental results indicate that the proposed framework achieves satisfactory performance in dual tasks. An average angle prediction Mean Absolute Error (MAE) of 3.84° and a classification accuracy of 99.42% were obtained in the healthy group, while 4.62° MAE and 99.01% accuracy were achieved in the stroke group. These results suggest that iTransformer-DTL could support adaptable rehabilitation exoskeleton controllers, enhancing human–robot interactions. Full article

The Hard X-ray Modulation Telescope (HXMT), China’s first X-ray astronomy satellite, has significantly contributed to the study of fast variability in black hole X-ray binaries through its broad energy coverage (1–250 keV), high timing resolution, and sensitivity to hard X-rays. This review presents a comprehensive overview of timing analysis techniques applied to black hole X-ray binaries using Insight-HXMT data. We introduce the application and comparative strengths of several time-frequency analysis methods, including traditional Fourier analysis, wavelet transform, bicoherence analysis, and Hilbert-Huang transform. These methods offer complementary insights into the non-stationary and nonlinear variability patterns observed in black hole X-ray binaries, particularly during spectral state transitions and quasi-periodic oscillations. We discuss how each technique has been employed in recent Insight-HXMT studies to characterize timing features such as low-frequency QPOs, phase lags, and power spectrum evolution across different energy bands. Moreover, we present novel phenomena revealed by Insight-HXMT observations, including the detection of high-energy QPOs, spectral parameter modulation with QPO phase, and a new classification scheme for QPO types. The integration of multiple analysis methods enables a more nuanced understanding of the accretion dynamics and the geometry of the inner accretion flow, shedding light on fundamental physical processes in relativistic environments. Full article

This paper presents a detailed analysis of the thermal and flammability properties of polylactide- (PLA) and poly(ethylene terephthalate)- (PET) based polymer blends with biofillers, such as calcium lignosulfonate (CLS), lignosulfonamide (SA) and lignosulfonate modified with tannic acid (BMT) and gallic acid (BMG). Calorimetric studies revealed the presence of two glass transitions, one cold crystallization temperature, and two melting points, confirming the partial immiscibility of the PLA and PET phases. The additives had different effects on the temperatures and ranges of phase transformations—BMT restricted PLA chain mobility, while CLS acted as a nucleating agent that promoted crystallization. Thermogravimetric analyses (TGA) analyses showed that the additives significantly affected the thermal stability under oxidizing conditions, some (e.g., BMG) lowered the onset degradation temperature, while the others (BMT, SA) increased the residual char content. The additives also altered combustion behavior; particularly BMG that most effectively reduced flammability, promoted char formation, and extended combustion time. CLS reduced PET flammability more effectively than PLA, especially at higher PET content (e.g., 65% reduction in PET for 2:1/CLS). SA inhibited only PLA combustion, with strong effects at higher PLA content (up to 76% reduction for 2:1/SA). BMT mainly reduced PET flammability (48% reduction in 1:1/BMT), while BMG inhibited PET more strongly at lower PET content (76% reduction for 2:1/BMG). The effect of each additive also depended on the PLA:PET ratio in the blend. FTIR analysis of the char residues revealed functional groups associated with decomposition products of carboxylic acids and aromatic esters. Ultimately, only blends containing BMT and BMG met the requirements for flammability class FV-1, while SA met FV-2 classification. BMG was the most effective additive, offering enhanced thermal stability, ignition delay, and durable char formation, making it a promising bio- based flame retardant for sustainable polyester materials. Full article

Extracellular matrix (ECM) bioscaffolds have demonstrated therapeutic potential across a variety of clinical and preclinical applications for tissue repair and regeneration. In parallel, these scaffolds and their components have shown the capacity to modulate the immune response. Unlike synthetic implants, which are often associated with chronic inflammation or fibrotic encapsulation, ECM bioscaffolds interact dynamically with host cells, promoting constructive tissue remodeling. This effect is largely attributed to the preservation of structural and biochemical cues—such as degradation products and matrix-bound nanovesicles (MBV). These cues influence immune cell behavior and support the transition from inflammation to resolution and functional tissue regeneration. However, the immunomodulatory properties of ECM bioscaffolds are dependent on the source tissue and, critically, on the methods used for decellularization. Inadequate removal of cellular components or the presence of residual chemicals can shift the host response towards a pro-inflammatory, non-constructive phenotype, ultimately compromising therapeutic outcomes. This review synthesizes current basic concepts on the innate immune response to ECM bioscaffolds, with particular attention to the inflammatory, proliferative, and remodeling phases following implantation. We explore how specific ECM features shape these responses and distinguish between pro-remodeling and pro-inflammatory outcomes. Additionally, we examine the impact of manufacturing practices and quality control on the preservation of ECM bioactivity. These insights challenge the conventional classification of ECM bioscaffolds as medical devices and support their recognition as biologically active materials with distinct immunoregulatory potential. A deeper understanding of these properties is critical for optimizing clinical applications and guiding the development of updated regulatory frameworks in regenerative medicine. Full article

The present-day issues related to the cotton-growing industry, namely yield estimation, pest effect, and growth phase diagnostics, call for integrated, scalable monitoring solutions. This write-up reveals Cotton Multitask Learning (CMTL), a transformer-driven multitask framework that launches three major agronomic tasks from UAV pictures at one go: boll detection, pest damage segmentation, and phenological stage classification. CMTL does not change separate pipelines, but rather merges these goals using a Cross-Level Multi-Granular Encoder (CLMGE) and a Multitask Self-Distilled Attention Fusion (MSDAF) module that both allow mutual learning across tasks and still keep their specific features. The biologically guided Stage Consistency Loss is the part of the architecture of the network that enables the system to carry out growth stage transitions that occur in reality. We executed CMTL on a tri-source UAV dataset that fused over 2100 labeled images from public and private collections, representing a variety of crop stages and conditions. The model showed its virtues state-of-the-art baselines in all the tasks: setting 0.913 mAP for boll detection, 0.832 IoU for pest segmentation, and 0.936 accuracy for growth stage classification. Additionally, it runs at the fastest speed of performance on edge devices such as NVIDIA Jetson Xavier NX (Manufactured in Shanghai, China), which makes it ideal for deployment. These outcomes evoke CMTL’s promise as a single and productive instrument of aerial crop intelligence in precision cotton agriculture. Full article

The transition to efficient production requires innovative water control techniques to maximize irrigation efficiency and minimize waste. Analyzing and optimizing irrigation practices is essential to improve water use and reduce environmental impact. The aim of the research was to identify a discrimination method to analyze the different hydration levels in baby-leaf products. The species being researched was spinach, harvested at the baby leaf stage. Utilizing a large dataset of 261 wavelengths from the hyperspectral imaging system, the feature selection minimum redundancy maximum relevance (FS-MRMR) algorithm was applied, leading to the development of a neural network-based prediction model. Finally, a mathematical classification model K-NN (k-nearest neighbors type) was developed in order to identify a transfer function capable of discriminating the hyperspectral data based on a threshold value of absolute leaf humidity. Five significant wavelengths were identified for estimating the moisture content of baby leaves. The resulting model demonstrated a high generalization capability and excellent correlation between predicted and measured data, further confirmed by the successful training, validation, and testing of a K-NN-based statistical classifier. The construction phase of the statistical classifier involved the use of the experimental dataset and the critical humidity threshold value of 0.83 (83% of leaf humidity) was considered, below which the baby-leaf crop requires the irrigation intervention. High percentages of correct classification were achieved for data within two humidity classes. Specifically, the statistical classifier demonstrated excellent performance, with 81.3% correct classification for samples below the threshold and 99.4% for those above it. The application of advanced spectral analysis and artificial intelligence methods has led to significant progress in leaf moisture analysis and prediction, yielding substantial implications for both agriculture and biological research. Full article

This review provides a comprehensive analysis of recent advancements in lower limb exoskeleton systems, focusing on applications, control strategies, hardware architecture, sensing modalities, human-robot interaction, evaluation methods, and technical innovations. The study spans systems developed for gait rehabilitation, mobility assistance, terrain adaptation, pediatric use, and industrial support. Applications range from sit-to-stand transitions and post-stroke therapy to balance support and real-world navigation. Control approaches vary from traditional impedance and fuzzy logic models to advanced data-driven frameworks, including reinforcement learning, recurrent neural networks, and digital twin-based optimization. These controllers support personalized and adaptive interaction, enabling real-time intent recognition, torque modulation, and gait phase synchronization across different users and tasks. Hardware platforms include powered multi-degree-of-freedom exoskeletons, passive assistive devices, compliant joint systems, and pediatric-specific configurations. Innovations in actuator design, modular architecture, and lightweight materials support increased usability and energy efficiency. Sensor systems integrate EMG, EEG, IMU, vision, and force feedback, supporting multimodal perception for motion prediction, terrain classification, and user monitoring. Human–robot interaction strategies emphasize safe, intuitive, and cooperative engagement. Controllers are increasingly user-specific, leveraging biosignals and gait metrics to tailor assistance. Evaluation methodologies include simulation, phantom testing, and human–subject trials across clinical and real-world environments, with performance measured through joint tracking accuracy, stability indices, and functional mobility scores. Overall, the review highlights the field’s evolution toward intelligent, adaptable, and user-centered systems, offering promising solutions for rehabilitation, mobility enhancement, and assistive autonomy in diverse populations. Following a detailed review of current developments, strategic recommendations are made to enhance and evolve existing exoskeleton technologies. Full article

To address the unclear coupling mechanism between bubble detachment behavior and acoustic characteristics in gas–liquid two-phase flow, this paper systematically studied bubble behavior and acoustic characteristics under different conditions by building a high-precision synchronous measurement system, combining acoustic signal analysis and bubble dynamics observation. The influence mechanism of liquid surface tension, dynamic viscosity, and orifice diameter on the critical gas flow velocity of bubble flow transition was analyzed, and a flow pattern classification criterion system was established. The experimental results showed that the bubble flow state could be divided into three states according to the characteristics of the acoustic signals: discrete bubble flow, single-chain bubble flow, and dual-stage chain bubble flow. The liquid surface tension and dynamic viscosity had no significant effect on the critical gas flow velocity of the transition from discrete bubble flow to single-chain bubble flow, but significantly increased the critical gas flow velocity of the transition from single-chain bubble flow to dual-stage chain bubble flow. The increase in the orifice diameter reduced the critical gas flow velocity of the two types of flow transition. In addition, the Weber number (We) and Galileo number (Ga) were introduced to construct a quantitative classification system of flow pattern, which provided theoretical support for the optimization of industrial gas–liquid two-phase flow. Full article

Traffic congestion poses a persistent challenge for modern cities, yet the complex behavior of urban road networks—particularly multistability in traffic flow—remains poorly understood. To address this gap, we analyzed a high-resolution traffic dataset from four Chinese cities over 20 working days (5-min intervals), applying percolation theory to characterize system performance via congestion rate (f) and the size of the largest functional cluster (G). Our analysis revealed clear bimodal and multimodal distributions of G versus f across different periods, ruling out random failure models and confirming the presence of multistability. Leveraging data-driven clustering and classification techniques, we demonstrated that road segments with high betweenness centrality are disproportionately likely to become congested, and that the top 1% most topologically important roads accurately predict both stable state types and the joint behavior of G and f. These findings offer the first large-scale empirical evidence of multistability in urban traffic, laying a quantitative foundation for forecasting phase transitions in congestion and informing more effective traffic management strategies. Full article