Search Results (82)

Cement stabilized macadam (CSM) bases are prone to cracking and void damage under long-term traffic loading and environmental actions, which accelerates structural deterioration. Although grouting is an effective method for treating such concealed defects, laboratory-based evaluation of repair effectiveness remains limited. In this study, field-cored CSM specimens were recombined in a cylindrical mold to simulate four void conditions (1/4, 2/4, 3/4, and 4/4), and repaired using an inorganic cementitious composite grouting material based on ultra-fine cement and high-belite sulphoaluminate cement (HBSAC), and modified with ethylene-vinyl acetate (EVA) latex, wollastonite (WO) whiskers, and polyvinyl alcohol (PVA) fibers. The repair effectiveness was evaluated through ultrasonic testing, capacitance measurement, uniaxial compression with acoustic emission (AE) monitoring, and computed tomography (CT). The results show that the longitudinal wave velocity of all repaired groups increases continuously with curing time, with a maximum increase of 21.98% at 28 days. The normalized capacitance response exhibits clear time- and layer-dependent variation, with the 4/4 group showing the most pronounced spatial heterogeneity. In the uniaxial compression tests, the peak load increases from 181 kN in the control group to 201–286 kN in the repaired groups, while the tensile-related AE event proportion increases from 77.35% in the 1/4 group to 89.38% in the 4/4 group. CT analysis shows that the proportion of micropores smaller than 1 mm³ increases from 66.3% to 82.7%, whereas the proportion of pores larger than 100 mm³ decreases from 46.5% to 21.6% after repair. These results demonstrate that the composite grouting material provides effective filling, structural reconstruction, and mechanical enhancement for void-damaged CSM, and that the proposed multi-source characterization framework is suitable for evaluating grouting repair performance. Full article

Nowadays, educational software across all learning levels is increasingly enhanced with Artificial Intelligence (AI), primarily through content generation or post-session learning analytics. However, most existing systems remain weakly connected to learners’ real-time affective states and rarely exploit emotional information as a direct control signal for instructional adaptation. In this work, we propose a proof-of-concept closed-loop affect-aware educational adaptation framework that integrates real-time facial emotion recognition into a dynamic learning control system. The proposed approach is built upon a dual-model ensemble architecture, combining a transformer-based model (CAGE) and a CNN-based model (DDAMFN++) trained on large-scale in-the-wild datasets. To bridge heterogeneous emotion representations, we introduce a probabilistic fusion strategy that aligns continuous valence–arousal predictions with discrete emotion classification via a Gaussian Mixture Model (GMM), enabling unified emotion inference in real time. Based on the fused emotional state, a temporal aggregation mechanism is applied to capture sustained affective trends rather than transient expressions. These aggregated signals are then mapped to instructional decisions through an emotion-driven adaptive control policy, which adjusts activity difficulty using an Average Emotion Score (AES). This establishes a fully automated closed-loop adaptation cycle, where detected learner affect directly influences the learning environment without requiring explicit user input or post-session questionnaires. The framework is integrated into an open-source educational platform (eduActiv8) to demonstrate feasibility and system-level behavior. Results from alpha-level validation show that the system can continuously monitor learner affect, generate interpretable emotional analytics, and dynamically adjust task difficulty in real time, while reducing user interaction overhead. This study contributes a modular architecture for affect-aware educational systems by combining real-time ensemble emotion recognition, probabilistic fusion of heterogeneous outputs, and closed-loop instructional adaptation. The proposed framework provides a foundation for future research in scalable, emotion-driven intelligent tutoring and adaptive learning environments. Full article

To investigate the mechanical bearing characteristics of the “excavation-backfill” composite after the excavation of coal pillars and backfill replacement with gangue-based cemented paste backfill, mechanical bearing characteristic experiments are conducted on a series of coal samples with rectangular “excavation-backfill” roadways under uniaxial loading, covering the full deformation and failure process. The MTS universal testing machine and DS5-type acoustic emission signal acquisition system are employed, and a high-speed camera is adopted to monitor and record the full failure process. The mechanical bearing characteristics and crack evolution mechanisms of unfilled coal pillar (U-C) and backfill coal pillar (B-C) samples are explored. The results show that with the increase in “excavation-backfill” width, the uniaxial compressive strength and elastic modulus of U-C samples decrease significantly, and the samples exhibit brittle–ductile failure. When the “excavation-backfill” width is 60 mm, the backfill can distinctly improve the strength and elastic modulus of B-C samples, showing a strong strength recovery effect. The temporal characteristics of AE signals indicate that both U-C and B-C samples experience four stages subjected to uniaxial compression: quiet period, rising period, active period, and post-peak rising period. In the quiet period and rising period, the b-value fluctuates upward with energy release; in the active period, the b-value decreases significantly with large energy release; in the post-peak rising period, crack propagation and frictional slip increase, leading to an enlarged fluctuation amplitude of the b-value. Based on the location of AE sources, the three-dimensional crack chain evolution is inverted. The crack chain evolution of the U-C is mainly distributed along the dip direction (75°~90°, 255°~270°) and vertical direction (165°~180°) of the coal bedding plane, while the B-C is more uniform, indicating that the backfill evidently affects the crack distribution. This study provides new insights for predicting the crack evolution and failure mode of coal–rock composites. Full article

Background/Objectives: This study systematically synthesized the evidence on the effectiveness of aquatic exercise (AE)-based interventions for improving sleep quality in patients with chronic diseases and identified key moderating factors. Methods: A meta-analysis of 11 randomized controlled trials sourced from Google Scholar, PubMed, Web of Science, Embase, Cochrane Library, and Scopus (published between 2016 and 2025) was conducted. Sleep quality was assessed using subjective tools (e.g., PSQI). Results: While AE-based interventions showed potential for enhancing nighttime sleep quality (standard mean difference = 0.825, p < 0.001), high statistical heterogeneity (I² = 93.41%) was observed. Given this variance, the analysis prioritized the clinical outcomes of specific patient populations over the pooled effect size. Preliminary evidence suggests significant improvements were confirmed in populations with post-COVID syndrome (p < 0.001), Parkinson’s disease (p = 0.002), and chronic back pain (p = 0.008). Conversely, no significant benefits were observed in fibromyalgia (p = 0.191), ankylosing spondylitis (p = 0.737), or type 2 diabetes (p = 0.836). Moderator analysis further indicated that the mode of AE might influence outcomes, with recreational aquatic therapy and deep-water running suggesting superior efficacy compared to resistance training. Conclusions: AE-based interventions were suggested as an effective intervention for improving sleep quality. The observed benefits likely stem from the synergistic effects of physical exercise and the unique physiological properties of the aquatic environment, such as buoyancy and hydrostatic pressure. However, the field relies heavily on subjective questionnaires and lacks physiological mechanism studies. These findings provide a preliminary evidence-based framework for clinicians to develop targeted AE-based interventions for chronic disease patients. Full article

This study investigates the complex relationship between fracture patterns and acoustic emission (AE) mechanisms during triaxial deformation experiments on Westerly granite under various confining pressures (5, 10, 20, and 40 MPa). Using numerical simulations with the Particle Flow Code (PFC2D, 6.0, Itasca Consulting Group Inc., Minneapolis, MN, USA), this research emphasizes the significant influence of confining pressure on crack development, AE events, spatiotemporal distribution, energy dissipation, and peak stress in the samples. AE source mechanisms, categorized into T-Type, C-Type, and S-Type, show the dominance of T-Type fractures during post-peak unstable failure and the emergence of C-Type fractures as precursors to critical damage. Additionally, increasing confining pressure is found to correlate with changes in fracture dynamics, evidenced by an increase in big events and a decrease in small events. The analysis of b-values across different pressures reveals fluctuations that indicate change in fracture features. Fractures originate in the model center and propagate towards both ends as loading progresses, ultimately leading to failure. In summary, these findings provide important insights into the fracture patterns of granite and the underlying mechanisms of AE release. Moreover, they carry practical implications for identifying failure precursors and for the potential application of early warning systems in rock engineering. Full article

The exponential growth of digital data and the escalating sophistication of cyber threats have intensified the demand for secure yet computationally efficient encryption methods. Conventional algorithms (e.g., AES-based schemes) are cryptographically strong and widely deployed; however, some implementations can face performance bottlenecks in large-scale or real-time workloads. While many modern systems seed from hardware entropy sources and employ standardized cryptographic PRNGs/DRBGs, security can still be degraded in practice by weak entropy initialization, misconfiguration, or the use of non-cryptographic deterministic generators in certain environments. To address these gaps, this study introduces FileCipher. This novel file-encryption framework integrates a chaos-enhanced Cryptographically Secure Pseudorandom Number Generator (CPRNG) based on the State-Based Tent Map (SBTM). The proposed design achieves a balanced trade-off between security and efficiency through dynamic key generation, adaptive block reshaping, and structured confusion–diffusion processes. The SBTM-driven CPRNG introduces adaptive seeding and multi-key feedback, ensuring high entropy and sensitivity to initial conditions. A multi-threaded Java implementation demonstrates approximately 60% reduction in encryption time compared with AES-CBC, validating FileCipher’s scalability in parallel execution environments. Statistical evaluations using NIST SP 800-22, SP 800-90B, Dieharder, and TestU01 confirm superior randomness with over 99% pass rates, while Avalanche Effect analysis indicates bit-change ratios near 50%, proving strong diffusion characteristics. The results highlight FileCipher’s novelty in combining nonlinear chaotic dynamics with lightweight parallel architecture, offering a robust, platform-independent solution for secure data storage and transmission. Ultimately, this paper contributes a reproducible, entropy-stable, and high-performance cryptographic mechanism that redefines the efficiency–security balance in modern encryption systems. Full article

Cultural ecosystem services (CESs) are the non-material benefits people derive from ecosystems and are important for human well-being. Most research has focused on individual CES supply–demand relationships, with little systematic study of the overall CES structure, interactions, and mechanisms in metropolitan areas. This study takes the Nanjing Metropolitan Area as a case study, integrating multi-source geospatial data and employing the MaxEnt model, self-organizing maps (SOMs), Spearman correlation analysis, and the Optimal Parameters-based Geographical Detector (OPGD). It analyzes supply–demand matching, trade-offs, synergies, and drivers for four CES categories: aesthetic (AE), recreational entertainment (RE), knowledge education (KE), and cultural diversity (CD). The main findings are as follows: (1) CES supply and demand are spatially zoned: the core area has surplus supply, secondary centers are balanced, and the periphery has both weak supply and demand. (2) Three supply–demand bundles have distinct synergy and trade-off patterns: Bundle 1 primarily exhibits strong synergy between AE and CD; Bundle 2 shows a weak trade-off relationship; and Bundle 3 forms a synergy centered on AE. (3) The explanatory power of driving factors exhibits pronounced spatial heterogeneity: Bundle 1 is dominated by non-quantifiable social factors; Bundle 2 features dual synergistic drivers of population and transportation; and Bundle 3 demonstrates synergistic effects driven by facilities and economic factors. Overall, this study contributes an integrated metropolitan-scale framework that connects CES supply–demand mismatch patterns with bundle typologies, interaction structures, and bundle-specific drivers. The results provide an operational basis for targeted planning and coordinated ecological–cultural governance in the Nanjing Metropolitan Area and offer a transferable reference for other metropolitan regions. Full article

This study investigates the solar origins of short-term periodicities in the near-Earth solar wind and interplanetary magnetic field (IMF) using long-term observations (1995–2024) and Potential Field Source Surface modeling. We establish that the 27-day periodicity in solar wind speed and its harmonics (13.5-day and 9-day) are governed by the combined influence of polar and low-latitude coronal holes. Polar coronal holes serve as the fundamental stabilizers of the global coronal structure, while the rotation of the Sun in the presence of low-latitude coronal holes acts as the primary mechanism generating periodic fluctuations. The absence of low-latitude coronal holes diminishes or erases these periodicities. For IMF components forming the Parker spiral, the periodicity is controlled by the structure of the heliospheric current sheet (HCS). A stable 27-day period emerges under a two-sector IMF configuration (HCS average slope $\mathrm{SL}>0.4$, latitudinal extent beyond ±30°), while a stable four-sector structure ($\mathrm{SL}>0.6$, latitudinal extent beyond ±60°) superimposes a clear 13.5-day periodicity. However, periodicity weakens or disappears when the HCS is flat and equatorial, or when global structural changes and transient disturbances disrupt recurrence patterns. In contrast, $B_z^{\mathrm{GSE}}$ exhibits weak periodicity due to its transient nature, while $B_z^{\mathrm{GSM}}$ shows intermittent 27-day periodicity modulated by the Russell-McPherron effect. Consequently, geomagnetic indices (Kp, Dst, AE) display periodic behavior similar to $B_z^{\mathrm{GSM}}$, consistent with its crucial role in solar wind-magnetosphere coupling. These results quantitatively link solar surface morphology to heliospheric recurrence, clarifying the conditions under which periodicities emerge or are suppressed throughout the Sun-Earth system. Full article

High renewable energy penetration in Integrated Energy Systems (IES) introduces significant challenges related to bilateral source-load uncertainty and low-carbon economic dispatch. To address these issues, this paper proposes a novel scheduling framework that synergizes data-driven scenario generation with multi-objective distributionally robust optimization (DRO). Specifically, a deep temporal feature extraction model based on Long Short-Term Memory Autoencoder (LSTM-AE) is integrated with K-Means clustering to generate four typical operation scenarios, effectively capturing complex source-load fluctuations. To further enhance system efficiency and environmental sustainability, a refined Power-to-Gas (P2G) model considering waste heat recovery is developed to realize energy cascading, coupled with a joint market mechanism that integrates Green Certificate Trading (GCT) and tiered carbon pricing. Building on this, a multi-objective DRO model based on Conditional Value at Risk (CVaR) is formulated to optimize the trade-off between operating costs and carbon emissions. Case studies based on California test data demonstrate that the proposed method reduces total operating costs by 9.0% and carbon emissions by 139.9 tons compared to traditional robust optimization (RO). Moreover, the results confirm that the system maintains operational safety even under extreme source-load fluctuation scenarios. Full article

One of the main sources of entertainment is live video streaming platforms, which allow viewers to watch video streams in real time. However, because of the increasing demand for high quality content, the vulnerability of streaming systems against cyberattacks highlights how crucial it is to implement strong security mechanisms without sacrificing performance. Therefore, the safeguard of video streams against cyberthreats such as content tampering and interception is a top priority while still maintaining robustness against network fluctuations. Two distinct scenarios are proposed to test AES-128 encryption in securing HTTP live streaming segments against content tampering and resilience to packet loss. Results show that AES-128 encryption provides confidentiality and successfully prevents meaningful manipulation of the video content, confirming its reliability as segment encryption does not significantly alter packet loss-induced playback behavior compared to unencrypted streaming under the tested conditions, Performance analysis shows that AES-128 has no significant difference in data loss for up to 4% of network packet loss compared to unencrypted segments. Full article

As port operations rapidly evolve toward intelligent and heavy-duty applications, fault diagnosis for core equipment demands higher levels of real-time performance and robustness. Deep learning, with its powerful autonomous feature learning capabilities, demonstrates significant potential in mechanical fault prediction and health management. This paper first provides a systematic review of deep learning research advances in rotating machinery fault diagnosis over the past eight years, focusing on the technical approaches and application cases of four representative models: Deep Belief Networks (DBNs), Convolutional Neural Networks (CNNs), Auto-encoders (AEs), and Recurrent Neural Networks (RNNs). These models, respectively, embody four core paradigms, unsupervised feature generation, spatial pattern extraction, data reconstruction learning, and temporal dependency modeling, forming the technological foundation of contemporary intelligent diagnostics. Building upon this foundation, this paper delves into the unique challenges encountered when transferring these methods from generic laboratory components to specialized port equipment such as shore cranes and yard cranes—including complex operating conditions, harsh environments, and system coupling. It further explores future research directions, including cross-condition transfer, multi-source information fusion, and lightweight deployment, aiming to provide theoretical references and implementation pathways for the technological advancement of intelligent operation and maintenance in port equipment. Full article

Acoustic emission (AE) is a passive non-destructive testing (NDT) method that allows for online monitoring of oil tank bottom corrosion without production shutdown. However, AE signals are susceptible to ambient noise interference, causing the AE inspection system to mistakenly identify noise as corrosion signals, which significantly reduces AE inspection performance. Therefore, it is important to distinguish between AE signals caused by corrosion and those caused by noise. To address this, an AE inspection platform for vertical atmospheric tank corrosion is established. Six common noise sources in field AE inspections, including mechanical vibration and friction, fluid and raining disturbance, external impacts, and oil leakage are simulated. The impacts of these noises on AE location events are analyzed. Variational mode decomposition (VMD) and dispersion entropy (DE) are used to extract multi-domain features of AE signals. An improved distance evaluation (IDE) algorithm is then introduced to obtain a highly correlated feature subset. A support vector machine (SVM) model optimized by the bald eagle search (BES) algorithm is proposed to identify different noise sources. Field experiments demonstrate that for mechanical friction, external impacts, and effective corrosion signals, the proposed method achieves identification accuracy of 92.95% and 94.00% in the training and test sets, respectively. This proves the reliability of the BES-SVM model, which uses multi-domain features for AE source identification in oil tank bottom corrosion inspections. Moreover, the impacts of the optimization algorithm, feature selection algorithm, and feature type on AE source identification are further investigated. Full article

As one of the world’s most widely used packaging materials, paper obtains its properties from its major component: wood. Variations in the species of wood result in variations in the paper’s mechanical properties. The pulp and paper production industry is known to be a polluting industry and a consumer of a large amount of energy but remains an essential heavy industry globally. Paper production, based largely on the kraft process, is mainly intended for the food packaging sector and, thus, is associated with contamination risks. The lack of standardized regulations and the different analytical techniques used make information on the subject complex, particularly for inorganic elements where little information is available in the literature. Most research in this field is based on sample preparation using mineralization via acid digestion to obtain a liquid and homogeneous matrix, mainly with a HNO₃/H₂O₂ mixture. The most commonly used techniques are Atomic Absorption Spectrometry (AAS), Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES), and Inductively Coupled Plasma Mass Spectrometry (ICP-MS), each with its advantages and disadvantages, which complicates the use of these tech-niques for routine analyses on an industrial site. In the same field of inorganic compound analysis, Microwave Plasma Atomic Emission Spectrometry (MP-AES) has become a real alternative to techniques such as AAS or ICP-AES. This technique has been used in several studies in the food and environmental fields. This publication aims to examine, for the first time, the state of the art regarding the analysis of inorganic elements in food packaging and different matrices using MP-AES. The entire manufacturing process is studied to identify possible sources of inorganic contaminants. Various analytical techniques used in the field are also presented, as well as research conducted with MP-AES to highlight the potential benefits of this technique in the field. Full article

Background: Type 2 diabetes mellitus (T2DM) is a major global public health challenge. Aerobic exercise (AE) can be a key strategy for non-pharmacological intervention in T2DM through multi-targeted modulation of glucose and lipid metabolism, inhibition of chronic inflammation, and reduction of oxidative stress. This study aims to investigate the current status of AE intervention in T2DM research and analyze its future evolution. Methods: Using the R-based bibliometric software package and the Java-based visualization software CiteSpace and VOSviewer, we analyzed the literature and cited references related to AE intervention in T2DM research included in the Web of Science Core Collection (WOSCC) and China National Knowledge Infrastructure (CNKI) from 2014 to 2024. Results: This study included a total of 882 relevant literature sources (488 of which were indexed in WOSCC and 394 in CNKI). From the perspective of research trends, the number of literature sources on AE interventions for T2DM has shown fluctuating changes over time. In terms of research output, the United States, China, and Canada are at the forefront. It is worth noting that, although China has a relatively high number of published papers, there is still a significant gap in terms of the depth of international collaboration and the presentation of results in top-tier journals. Among researchers, Dai Xia (China) and Riddell MC (Canada) are the scholars with the highest number of published articles in this field. Keyword analysis indicates that mechanisms such as oxidative stress, insulin resistance, inflammatory responses, and glucose metabolism disorders remain core research hotspots. Time-series analysis reveals that the research paradigm in this field has evolved from single exercise methods to comprehensive exercise prescription studies, and multi-dimensional intervention studies combining exercise, diet, and pharmacological interventions are emerging as new research frontiers. Conclusions: This study uses bibliometric methods to visualize and analyze the progress of AE in T2DM intervention research from a broader perspective, providing a scientific overview and macro-level predictions for the research landscape in this field. Full article

This paper investigates point-to-point multimodal digital semantic communications in a task-oriented setup, where messages are classified at the receiver. We employ a pre-trained transformer model to extract semantic information and propose three methods for generating semantic codewords. First, we propose semantic quantization that uses quantized embeddings of source realizations as a codebook. We investigate the fixed-length coding, considering the source semantic structure and end-to-end semantic distortion. We propose a neural network-based codeword assignment mechanism incorporating codeword transition probabilities to minimize the expected semantic distortion. Second, we present semantic compression that clusters embeddings, exploiting the inherent semantic redundancies to reduce the codebook size, i.e., further compression. Third, we introduce a semantic vector-quantized autoencoder (VQ-AE) that learns a codebook through training. In all cases, we follow this semantic source code with a standard channel code to transmit over the wireless channel. In addition to classification accuracy, we assess pre-communication overhead via a novel metric we term system time efficiency. Extensive experiments demonstrate that our proposed semantic source-coding approaches provide comparable accuracy and better system time efficiency compared to their learning-based counterparts. Full article