Search Results (14,454)

Fiber Bragg Grating (FBG) sensors facilitate compact, multiplexed, and electromagnetic interference-immune monitoring in embedded and harsh environments. The removal of the polymer jacket, a measure taken to withstand elevated temperatures or facilitate integration, exposes the fragile glass. This underscores the necessity of functional coatings, which are critical for enhancing durability, calibrating sensitivity, and improving compatibility with host materials. This review methodically compares coating materials and deposition routes for FBGs, encompassing a range of techniques including top-down physical-vapor deposition (sputtering, thermal/e-beam evaporation, cathodic arc), bottom-up chemical vapor deposition (CVD)/atomic layer deposition (ALD), wet-chemical methods (sensitization/activation, electroless plating (EL), electrodeposition (ED)), fusion-based processes (casting and melt coating), and hybrid stacks (e.g., physical vapor deposition (PVD) seed → electrodeposition; gradient interlayers). The consolidation of surface-preparation best practices and quantitative trends reveals a comprehensive understanding of the interrelationships between coating material/stack, thickness/microstructure, adhesion, and sensitivity across a range of temperatures, extending from approximately 300 K to cryogenic regimes. Practical process windows and design rules are distilled to guide method selection and reliable operation across cryogenic and high-temperature regimes. Full article

The Nonlinear Twist Extrusion (NLTE) method, a novel severe plastic deformation (SPD) technique, aims to enhance grain refinement and achieve a more uniform plastic strain distribution. Grain size and its uniform distribution strongly influence the physical properties of metals. Therefore, predicting texture evolution during processing is essential for optimizing forming parameters and improving material performance. In this study, a rate-dependent crystal plasticity formulation is implemented in an explicit framework in Abaqus finite element software, based on a finite strain approach with multiplicative decomposition of the deformation gradient. Crystal plasticity finite element (CPFEM) simulations are conducted on single-crystal copper under boundary conditions representing the NLTE process. The influence of dynamic friction coefficients on texture evolution is systematically investigated, and the results are compared with experimental observations. The study provides new insights into deformation mechanisms during NLTE and highlights the strong correlation between texture development and forming parameters. Full article

To enhance the thermo-mechanical coupling performance of heavy-duty bearings with smart sensing capability in electric shovel applications, this study proposes a multi-objective optimization methodology for sensor-embedded bearing rings incorporating smart sensor-embedded grooves. Driven by multi-physics coupling analysis and experimental validation, a coupled thermal–mechanical model integrating frictional heat generation, heat transfer, and stress response was established. Parametric finite element simulations were conducted, with varying groove depths and axial positions. A comprehensive performance index combining three metrics—maximum temperature, equivalent stress, and principal strain—was formulated to evaluate design efficacy. Experimental tests on thermal and strain responses were employed to validate the simulation model confirming its predictive ability. Among the 21 parameter combinations, the configuration featuring an 8 mm groove depth located 20 mm from the large end face exhibited relatively optimal synergy across thermal dissipation, structural strength, and strain sensitivity. The proposed framework provides a certain theoretical and practical guidance for the design and optimization of the sensor-embedded groove structure in intelligent heavy-duty bearings. Full article

Macroalgae have low nutrient bioavailability, often requiring pretreatments—physical, chemical, or biological—typically using low-solid loading hydrolysis, which produces separate liquid and solid phases. In contrast, high-solid loading hydrolysis offers a single-phase alternative, though it remains underexplored for macroalgae. This study evaluated the effectiveness of high-solid loading hydrolysis for breaking polysaccharides and increasing the availability of nutrients and antioxidant compounds in Codium tomentosum. Treatments using mixtures containing 25% dry biomass and 75% water or 0.5N and 1N NaOH, autoclaved for 30 or 60 min, were performed. Among the tested treatments, high-solid loading alkaline autoclaved treatment (1N NaOH, 60 min) was most effective in reducing neutral detergent fiber and enhancing the availability of bioactive compounds, particularly soluble proteins and phenols. Based on these results, a sequential enzymatic hydrolysis with Natugrain^® at 0.2 and 0.4% was also applied to pre-treated C. tomentosum with water or 1N NaOH. Enzymatic hydrolysis after autoclaving had no major effect on fiber, soluble protein, or ash, but increased phenol levels. In conclusion, high-solid loading alkaline treatment (1N NaOH) followed by enzymatic hydrolysis with Natugrain^® enzyme reduced fiber content and enhanced soluble protein and phenolic compounds, thereby improving the nutritional and functional potential of C. tomentosum for inclusion in animal feeds. Full article

Recently, the live commerce market has experienced rapid growth, accompanied by increasingly intense competition. To improve business performance in this dynamic environment, it is essential to foster competent streamers and create effective commerce environments. Therefore, this study developed a research model based on the stimulus–organism–response (S-O-R) framework, focusing on streamer competencies and the commerce environment, to explore ways to effectively enhance live commerce business performance. Data for this study were collected through a questionnaire and analyzed using statistical techniques with 390 respondents. The results revealed that streamers’ competencies (expertise, demonstration skills, and interactive ability) significantly influence consumers’ internal states (perceived functional value of products and perceived trust in product recommendations), which in turn significantly influence purchase intentions. Moreover, the physical surroundings of the studio and the social surroundings, including peers’ perceptions of live commerce, were found to moderate the relationships between consumers’ internal states and their purchase intentions. This study holds academic significance in that it presents a model that effectively understands the mechanisms influencing viewers’ purchase decisions in live commerce contexts. The findings and practical implications discussed in this study are expected to provide valuable insights for developing strategies to enhance the performance of live commerce. Full article

Temperature excursions during refrigerated transport strongly affect the quality and shelf life of perishable food, yet reproducing realistic, time-varying cold-chain temperature histories in the laboratory remains challenging. In this study, we present a compact, portable climate chamber driven by Peltier modules and an identification-guided control architecture designed to reproduce real refrigerated-truck temperature histories with high fidelity. Control is implemented as a cascaded regulator: an outer two-degree-of-freedom PID for air-temperature tracking and faster inner PID loops for module-face regulation, enhanced with derivative filtering, anti-windup back-calculation, a Smith predictor, and hysteresis-based bumpless switching to manage dead time and polarity reversals. The system integrates distributed temperature and humidity sensors to provide real-time feedback for precise thermal control, enabling accurate reproduction of cold-chain conditions. Validation comprised two independent 36-day reproductions of field traces and a focused 24-h comparison against traditional control baselines. Over the long trials, the chamber achieved very low long-run errors ($\mathrm{MAE}\cong 0.19 °\mathrm{C}$, $\mathrm{MedAE}\cong 0.10 °\mathrm{C}$, $\mathrm{RMSE}\cong 0.33 °\mathrm{C}$, $R^2=0.9985$). The 24-h test demonstrated that our optimized controller tracked the reference, improving both transient and steady-state behaviour. The system tolerated realistic humidity transients without loss of closed-loop performance. This portable platform functions as a reproducible physical twin for cold-chain experiments and a reliable data source for training predictive shelf-life and digital-twin models to reduce food waste. Full article

Objectives: The present study analyzes the effects of a high-intensity interval training (HIIT) program based on the Tabata method on physiological and psychological variables in contemporary dancers (n = 10) and sedentary individuals (n = 8), who performed a 10-week protocol, with sessions of self-loading exercises structured in intervals of 20 s of effort and 10 s of rest three times a week. Methods: Parameters of body composition, muscle strength, aerobic and anaerobic capacity, heart rate variability, as well as perceptions of health, anxiety, stress, sleep quality, and levels of physical activity and sedentary lifestyle were evaluated. Results: The results showed that no significant changes occurred in most body composition variables, except for visceral fat, where group differences were observed (F = 5.66, p = 0.030, η²ₚ = 0.261). In the indicators of strength and power, the dancers improved the height and relative power of the jump (F = 5.996, p = 0.026, η²ₚ = 0.273), while the sedentary ones increased the strength of the handgrip (p = 0.023). In terms of functional performance, both groups significantly increased anaerobic endurance (F = 10.374, p = 0.005, η²ₚ = 0.393), although no changes were recorded in maximal oxygen consumption or heart rate variability (p > 0.05). On a psychological level, improvements in healthy lifestyle habits and a decrease in the trait anxiety variable were evidenced in dancers (p = 0.023), while in sedentary participants no relevant effects were found. Conclusions: In conclusion, the Tabata protocol may represent an efficient and complementary strategy to enhance strength, anaerobic power, and psychological well-being, particularly among dancers. The observed improvements suggest potential benefits related to movement quality, injury prevention, and general physical conditioning. Full article

In the highly competitive restaurant industry, prioritising customer satisfaction is crucial for establishments pursuing differentiation and repeat business. Within this context, creating unique and memorable experiences has evolved from a marketing trend into a strategic imperative, compelling restaurants to deliver encounters that transcend mere functional service and quality. However, prior research has primarily examined quality factors and satisfaction in isolation, overlooking the mediating role of experiential realms in this relationship. This study offers a novel contribution by integrating service quality and experiential marketing within a single empirical model, addressing a gap in the hospitality literature. Specifically, few studies have empirically examined how tangible and intangible quality cues translate into the four experiential realms of the Experience Economy—aesthetic, escapist, entertainment, and educational—and how these, in turn, influence satisfaction and revisit intentions. Drawing on the Experience Economy framework, this study develops and tests a conceptual model linking quality antecedents—physical environment, food quality, and customer service—to the four experiential realms (aesthetic, escapist, entertainment, and educational) and subsequent satisfaction and revisit intentions. Using data collected from 312 restaurant customers, the hypotheses were tested through Structural Equation Modelling (SEM). The findings reveal that quality antecedents significantly influence experiential realms, which in turn enhance satisfaction and revisit intentions—offering a more nuanced mechanism than previously theorised. By being among the first to empirically test these relationships in the sit-down restaurant context, this study adds theoretical and practical insight into experience-based brand differentiation. Moreover, it provides actionable insights for restaurant managers seeking to transform quality delivery into memorable, loyalty-building experiences. Full article

Background: Obesity is a complex health issue influenced by various factors, including behavioral patterns that can be assessed more deeply in real time using Ecological Momentary Assessment (EMA), which can capture the moment in which a person experiences a situation or an emotion that could trigger an eating behavior. Methods: This systematic review synthesizes findings from 89 studies employing EMA to investigate obesity and overweight-related behaviors. The studies were identified through comprehensive searches across multiple databases and included peer-reviewed articles. The primary aim was to analyze how EMA contributes to understanding the temporal dynamics of eating behaviors, physical activity, and psychological factors associated with overweight and obesity. Results: Key findings indicate that EMA provides a nuanced understanding of real-time contexts influencing behaviors contributing to overweight and obesity. Studies consistently report that EMA captures fluctuations in eating habits, exercise routines, stress levels, and emotional states, elucidating the interplay between these factors and weight status. Methodological variations across studies included differences in EMA implementation (e.g., smartphone apps, electronic diaries), assessment frequency, and duration. These variances highlight the flexibility and adaptability of EMA in capturing diverse behavioral aspects relevant to obesity and overweight research. Moreover, the review discusses methodological challenges such as participant compliance, data integration, and real-time data interpretation in longitudinal analyses. Conclusions: In conclusion, EMA emerges as a powerful tool for exploring the complex, dynamic nature of overweight and obesity-related behaviors. Future research should focus on refining EMA methodologies, enhancing data analysis techniques, and integrating findings into personalized interventions aimed at reducing obesity effectively. Full article

Background: Three-dimensional (3D) printed scaffolds have emerged as promising tools for bone regeneration, but the optimal structural design and pore size remain unclear. Polylactic acid (PLA) reinforced with graphene oxide (GO) offers enhanced mechanical and biological performance, yet systematic evaluation of architecture and pore size is limited. Methods: Two scaffold architectures (lattice-type and dode-type) with multiple pore sizes were fabricated using UV-curable PLA/GO resin. Physical accuracy, porosity, and mechanical properties were assessed through compression and fatigue testing. Based on in vitro screening, four pore sizes (930 μm, 690 μm, 558 μm, 562 μm) within the dode-type structure were analyzed. The 558 μm and 562 μm scaffolds, showing distinct fracture thresholds, were further evaluated in rat and rabbit calvarial defect models for inflammation and bone regeneration. Results: In vitro testing revealed that while 930 μm and 690 μm scaffolds exhibited superior compressive strength, the 562 μm scaffold showed a unique critical fracture behavior, and the 558 μm scaffold offered comparable stability with higher resistance to premature failure. In vivo studies confirmed excellent biocompatibility in both groups, with early bone formation favored in the 558 μm scaffold and more continuous and mature bone observed in the 562 μm scaffold at later stages. Conclusions: This stepwise strategy—from structural design to pore size screening and preclinical validation—demonstrates that threshold-level mechanical properties can influence osteogenesis. PLA/GO scaffolds optimized at 558 μm and 562 μm provide a translationally relevant balance between mechanical stability and biological performance for bone tissue engineering. Full article

In high-temperature testing scenarios that rely on contact, fine-wire thermocouples demonstrate commendable dynamic performance. Nonetheless, their thermal inertia leads to notable dynamic nonlinear inaccuracies, including response delays and amplitude reduction. To mitigate these challenges, a novel dynamic error correction approach is introduced, which combines a Continuous Restricted Boltzmann Machine, Deep Belief Network, and Physics-Informed Neural Network (CDBN-PINN). The unique heat transfer properties of the thermocouple’s bimetallic structure are represented through an Inverse Heat Conduction Equation (IHCP). An analysis is conducted to explore the connection between the analytical solution’s ill-posed nature and the thermocouple’s dynamic errors. The transient temperature response’s nonlinear characteristics are captured using CRBM-DBN. To maintain physical validity and minimize noise amplification, filtered kernel regularization is applied as a constraint within the PINN framework. This approach was tested and confirmed through laser pulse calibration on thermocouples with butt-welded and ball-welded configurations of 0.25 mm and 0.38 mm. Findings reveal that the proposed method achieved a peak relative error of merely 0.83%, superior to Tikhonov regularization by −2.2%, Wiener deconvolution by 20.40%, FBPINNs by 1.40%, and the ablation technique by 2.05%. In detonation tests, the corrected temperature peak reached 1045.7 °C, with the relative error decreasing from 77.7% to 5.1%. Additionally, this method improves response times, with the rise time in laser calibration enhanced by up to 31 ms and in explosion testing by 26 ms. By merging physical constraints with data-driven methodologies, this technique successfully corrected dynamic errors even with limited sample sizes. Full article

The lower Mahanadi basin in eastern India is experiencing significant land and soil transformations that directly influence agricultural sustainability and ecosystem resilience. In this study, we used geospatial techniques to analyze the spatial-temporal variability of soil quality and land cover between 2011 and 2020 in the lower Mahanadi basin. The results revealed that the cropland decreased from 39,493.2 to 37,495.9 km², while forest cover increased from 12,401.2 to 13,822.2 km², enhancing soil organic carbon (>290 g/kg) and improving fertility. Grassland recovered from 4826.3 to 5432.1 km², wastelands declined from 133.3 to 93.2 km², and water bodies expanded from 184.3 to 191.4 km², reflecting positive land–soil interactions. Soil quality was evaluated using the Simple Additive Soil Quality Index (SQI), with core indicators bulk density, organic carbon, and nitrogen, selected to represent physical, chemical, and biological components of soil. These indicators were chosen as they represent the essential physical, chemical, and biological components influencing soil functionality and fertility. The SQI revealed spatial variability in texture, organic carbon, nitrogen, and bulk density at different depths. SQI values indicated high soil quality (SQI > 0.65) in northern and northwestern zones, supported by neutral to slightly alkaline pH (6.2–7.4), nitrogen exceeding 5.29 g/kg, and higher organic carbon stocks (>48.8 t/ha). In contrast, central and southwestern regions recorded low SQI (0.15–0.35) due to compaction (bulk density up to 1.79 g/cm³) and fertility loss. Clay-rich soils (>490 g/kg) enhanced nutrient retention, whereas sandy soils (>320 g/kg) in the south increased leaching risks. Integration of LULC with soil quality confirms forest expansion as a driver of resilience, while agricultural intensification contributed to localized degradation. These findings emphasize the need for depth-specific soil management and integrated land-use planning to ensure food security and ecological sustainability. Full article

Maintaining social adaptation in later life has become a key challenge amid China’s rapidly aging population. Using nationally representative data from the China Longitudinal Aging Social Survey (CLASS 2023), this study examined the relationship between physical exercise and social adaptation among 8913 older adults. Ordinary least squares regression and the Karlson–Holm–Breen decomposition method were applied to test both direct and mediating effects. The results showed that physical exercise significantly improved social adaptation (β = 0.452, p < 0.001), while aging identity played a partial mediating role, accounting for approximately 11.0% of the total effect. The association was stronger among those aged 80 and above, with lower education and income, without chronic diseases, and covered by social security. These findings suggest that physical exercise enhances social adaptation not only through physical benefits but also by strengthening psychological resilience and fostering a positive sense of aging, providing valuable evidence for developing inclusive aging policies and targeted exercise interventions. Full article

Hyperspectral unmixing aims to extract pure spectral signatures (endmembers) and estimate their corresponding abundance fractions from mixed pixels, enabling quantitative analysis of surface material composition. However, in geological mineral exploration, existing unmixing methods often fail to explicitly identify informative spectral bands, lack inter-layer information transfer mechanisms, and overlook the physical constraints intrinsic to the unmixing process. These issues result in limited directionality, sparsity, and interpretability. To address these limitations, this paper proposes a novel model, CResDAE, based on a deep autoencoder architecture. The encoder integrates a channel attention mechanism and deep residual modules to enhance its ability to assign adaptive weights to spectral bands in geological hyperspectral unmixing tasks. The model is evaluated by comparing its performance with traditional and deep learning-based unmixing methods on synthetic datasets, and through a comparative analysis with a nonlinear autoencoder on the Urban hyperspectral scene. Experimental results show that CResDAE consistently outperforms both conventional and deep learning counterparts. Finally, CResDAE is applied to GF-5 hyperspectral imagery from Yunnan Province, China, where it effectively distinguishes surface materials such as Forest, Grassland, Silicate, Carbonate, and Sulfate, offering reliable data support for geological surveys and mineral exploration in covered regions. Full article

Aerial robots are a well-established solution for exploration, monitoring, and inspection, thanks to their superior maneuverability and agility. However, in many environments, they risk crashing and sustaining damage after collisions. Traditional methods focus on avoiding obstacles entirely, but these approaches can be limiting, particularly in cluttered spaces or on weight- and computationally constrained platforms such as drones. This paper presents a novel approach to enhance drone robustness and autonomy by developing a path recovery and adjustment method for a high-speed collision-resilient aerial robot equipped with lightweight, distributed tactile sensors. The proposed system explicitly models collisions using pre-collision velocities, rates and tactile feedback to predict post-collision dynamics, improving state estimation accuracy. Additionally, we introduce a computationally efficient vector-field-based path representation that guarantees convergence to a user-specified path, while naturally avoiding known obstacles. Post-collision, contact point locations are incorporated into the vector field as a repulsive potential, enabling the drone to avoid obstacles while naturally returning to its path. The effectiveness of this method is validated through Monte Carlo simulations and demonstrated on a physical prototype, showing successful path following, collision recovery, and adjustment at speeds up to $3.7$$\mathrm{m}$/$\mathrm{s}$. Full article