Search Results (133)

Background/Objectives: Smartphone addiction (SA) and gaming addiction (GA) have become risk factors for individuals of all ages in recent years. Especially during adolescence, it has become very difficult for parents to control this situation. Physical activity and the effective use of free time are the most important factors in eliminating such addictions. This study aimed to test a new machine learning method by combining routine regression analysis with the gradient-boosting machine (GBM) and random forest (RF) methods to analyze the relationship between SA and GA with leisure time management (LTM) and the enjoyment of physical activity (EPA) among adolescents. Methods: This study presents the results obtained using our developed GBM + RF hybrid model, which incorporates LTM and EPA scores as inputs for predicting SA and GA, following the preprocessing of data collected from 1107 high school students aged 15–19 years. The results were compared with those obtained using routine regression results and the lasso, ElasticNet, RF, GBM, AdaBoost, bagging, support vector regression (SVR), K-nearest neighbors (KNN), multi-layer perceptron (MLP), and light gradient-boosting machine (LightGBM) models. In the GBM + RF model, probability scores obtained from GBM were used as input to RF to produce final predictions. The performance of the models was evaluated using the R², mean absolute error (MAE), and mean squared error (MSE) metrics. Results: Classical regression analyses revealed a significant negative relationship between SA scores and both LTM and EPA scores. Specifically, as LTM and EPA scores increased, SA scores decreased significantly. In contrast, GA scores showed a significant negative relationship only with LTM scores, whereas EPA was not a significant determinant of GA. In contrast to the relatively low explanatory power of classical regression models, ML algorithms have demonstrated significantly higher prediction accuracy. The best performance for SA prediction was achieved using the Hybrid GBM + RF model (MAE = 0.095, MSE = 0.010, R² = 0.9299), whereas the SVR model showed the weakest performance (MAE = 0.310, MSE = 0.096, R² = 0.8615). Similarly, the Hybrid GBM + RF model also showed the highest performance for GA prediction (MAE = 0.090, MSE = 0.014, R² = 0.9699). Conclusions: These findings demonstrate that classical regression analyses have limited explanatory power in capturing complex relationships between variables, whereas ML algorithms, particularly our GBM + RF hybrid model, offer more robust and accurate modeling capabilities for multifactorial cognitive and performance-related predictions. Full article

Eco-migration (EM) constitutes a specialized form of migration aimed at enhancing living environments and alleviating ecological pressure. Nevertheless, large-scale external migration has intensified habitat fragmentation (HF) in resettlement areas. This paper takes the Shule River Resettlement Project (SRRP) as a case, based on the China Land Cover Dataset (CLCD) data of the resettlement area from 1996 to 2020, using the Landscape Pattern Index (LPI) and the land use transfer matrix (LTM) to clearly define the stages of migration and the types of resettlement areas and to quantitative explore how EM affects HF. The results show that (1) EM accelerates the transformation of natural habitats (NHs) to artificial habitats (AHs) and shows the characteristics of sudden changes in the initial stage (1996–2002), with stability in the middle stage (2002–2006) and late stage (2007–2010) and dramatic changes in the post-migration stage (2011–2020). In IS, MS, LS, and PS, AH increased by 26.145 km², 21.573 km², 22.656 km², and 16.983 km², respectively, while NH changed by 73.116 km², −21.575 km², −22.655 km², −121.82 km², and −213.454 km², respectively. The more dispersed the resettlement areas are the more obvious the expansion of AH will be, indicating that the resettlement methods for migrants have a significant effect on habitat changes. (2) During the resettlement process, the total number of plaques (NP), edge density (ED), diversity (SHDI), and dominance index (SHEI) all continued to increase, while the contagion index (C) and aggregation index (AI) continued to decline, indicating that the habitat is transforming towards fragmentation, diversification, and complexity. Compared with large-scale migration bases (LMBs), both small-scale migration bases (SMBs), and scattered migration settlement points (SMSPs) exhibit a higher degree of HF, which reflects how the scale of migration influences the extent of habitat fragmentation. While NHs are experiencing increasing fragmentation, AHs tend to show a decreasing trend in fragmentation. Ecological migrants play a dual role: they contribute to the alteration and fragmentation of natural habitat patterns, while simultaneously promoting the formation and continuity of artificial habitat structures. This study offers valuable practical insights and cautionary lessons for the resettlement of ecological migrants. Full article

Driver models, which provide mathematical or computational representations of human driving behavior, are crucial for intelligent driving systems by enabling stable and repeatable operations. However, existing models typically employ fixed weighting parameters to simulate preview delay, failing to reflect individual driver differences and real-time dynamic behaviors. This paper proposes a Brain-Memory Driver Model (BMDM) that emulates human brain memory mechanisms to dynamically adjust preview weights by integrating global path curvature, real-time vehicle speed, and steering torque. This emulation involves a three-stage process: capturing data in an Instantaneous Memory (IM) region, filtering data via a forgetting mechanism in a Short-Time Memory (STM) region to reduce scale, and retaining data based on correlation strength in a Long-Time Memory (LTM) region for persistent mining. By deploying a trained behavioral memory database, the model dynamically calibrates preview weights based on the driver’s state and real-time curvature variations under different road conditions. This enables the model to more accurately simulate authentic preview characteristics and improves its adaptability. Simulation results from an automated steering case study demonstrate that the improved model exhibits control performance closer to the real driving process, reproducing authentic steering behavior within the human–vehicle–road closed-loop system from an intelligent biomimetic perspective. Full article

Adaptive digital back-propagation (A-DBP) is a promising candidate for mitigating Kerr nonlinearity due to its ability to estimate the optimal nonlinear scaling factor adaptively. However, the adaptive process relying on the gradient-dependent algorithm is prone to fluctuation, leading to extra iterations or even divergence and resulting in huge computational efforts in A-DBP. In this paper, an improved A-DBP algorithm with long-term memory (LTM) is proposed, employing root mean square propagation (RMSProp) to achieve low-complexity and high-robustness compensation performances. The A-DBP-LTM algorithm based on RMSProp was numerically validated through the simulated transmission of 69 Gbaud DP-16QAM over 2000 km and further verified through an experiment involving 26-λ 63 Gbaud DP-16QAM transmission over 1200 km. Compared with conventional digital back-propagation and A-DBP based on a gradient-descent algorithm, our proposed method allows substantial complexity reductions of 31.35% and 58.47%, respectively. Furthermore, high robustness in only a few iterations and a 0.33 dB improvement in the optical signal–noise ratio penalty were also experimentally demonstrated. Full article

We report the fabrication and characterization of Al-doped ZnO (AZO) optoelectronic synaptic devices based on sol–gel-derived thin films with varying Al concentrations (0~4.0 wt%). Structural and optical analyses reveal that moderate Al doping modulates the crystal orientation, optical bandgap, and defect levels of ZnO films. Notably, 2.0 wt% Al doping yields the widest bandgap (3.31 eV), stable PL emission, and uniform deep-level absorption without inducing significant lattice disorder. Synaptic performance, including learning–forgetting dynamics and persistent photoconductivity (PPC), is strongly dependent on Al concentration. The 2.0 wt% AZO device exhibits the lowest forgetting rate and longest memory retention due to optimized trap formation, particularly Al–oxygen vacancy complexes that enhance carrier lifetime. Visual memory simulations using a 3 × 3 pixel array under patterned UV illumination further confirm superior long-term memory (LTM) behavior at 2.0 wt%, with stronger excitatory postsynaptic current (EPSC) retention during repeated stimulation. These results demonstrate that precise doping control via the sol–gel method enables defect engineering in oxide-based neuromorphic devices. Our findings provide an effective strategy for designing low-cost, scalable optoelectronic synapses with tunable memory characteristics suitable for future in-sensor computing and neuromorphic vision systems. Full article

In this work, we prove that the initial value problem for the Schrödinger–Korteweg–de Vries (SKdV) system is locally well posed in Gevrey spaces for $s>-\frac{3}{4}$ and $k\ge 0$. This advancement extends recent findings regarding the well posedness of this model within Sobolev spaces and investigates the regularity properties of its solutions. Full article

Methodologies to measure motion perception are vital for deepening our understanding of the vision system and the factors that influence it. While existing work has primarily focused on the fastest perceivable velocities, less attention has been paid to the lower threshold of motion (LTM; slowest perceivable velocities). In this study, we designed an optical system to measure LTM in a sample of healthy young adults and to assess the influence of retinal location (central vs. peripheral retina) and stimulus composition (broadband vs. mid-wave) on LTM. The system was based on a xenon light source and a fiber-optic cable that created a bright light stimulus that could be moved along a computer-controlled precision translation slide. The stimulus, exposed for one-second intervals at both a central (fovea) and a peripheral (33 deg) location, was moved at varying speeds to determine the slowest detectable speed. In all, 37 healthy young participants (M = 19.32 ± 1.97 years) were tested. We found substantial between-subject variability in LTM and an interaction between stimulus wavelength and retinal location. The measurement of LTM using this novel apparatus and methodology provides insights into the relationship between slow-moving, ecologically valid stimuli and perceptual detection at the slowest speeds. Full article

In this work, we prove that the initial value problem for a system of two Kadomtsev–Petviashvili II (KP II) equations coupled via both dispersive and nonlinear terms is locally well-posed in anisotropic Gevrey spaces $G_{s_1,s_2}^{{\delta }_1,{\delta }_2,\varrho }\left({\mathbb{R}}^2\right)\times G_{s_1,s_2}^{{\delta }_1,{\delta }_2,\varrho }\left({\mathbb{R}}^2\right)$ with $-1/3<s_1<0$ and $s_2\ge 0$. This advancement extends recent findings regarding the well-posedness of this model within anisotropic Sobolev spaces $H^{s_1,s_2}\left({\mathbb{R}}^2\right)\times H^{s_1,s_2}\left({\mathbb{R}}^2\right)$. The current strategy is based on both linear and nonlinear estimates. Additionally, to further explore the system’s temporal behavior, we establish that Gevrey regularity of order $3\rho$ (or simply Gevrey—3$\rho$ regularity in time) exists. Full article

The pollution caused by organic dyes in water bodies has become a major environmental issue, and removing such pernicious dyes presents an immense challenge for the scientific community and governments. In this study, a sorbent based on nickel ferrite (NiFe₂O₄) fibers was fabricated by the solution blow spinning (SBS) method for the adsorptive removal of anionic Cong red (CR) dye. The cubic–spinel structure and the magnetic and porous nature of NiFe₂O₄ were confirmed by XRD, magnetometry, BET, and SEM analyses. The saturation magnetization confirmed the magnetic nature of the fibers, which favorably respond to an external magnetic field, facilitating separation from a treated solution. The sorption kinetics of CR on NiFe₂O₄ were best described by the pseudo-second-order model, while sorption equilibrium agreed best with the Freundlich, Langmuir, Sips, and Temkin isotherm models, suggesting a complex mechanism involving chemisorption, monolayer coverage, and heterogeneous adsorption. The NiFe₂O₄ fibers annealed at 500 °C showed a high CR removal efficiency of ~97% after only 30 min. The sorbent’s porous structure and high specific surface area were responsible for the improved removal efficiency. Finally, the results indicated the potential of the NiFe₂O₄ fibers in the remediation of water contaminated with Congo red dye. Full article

Vehicle-to-vehicle dynamic wireless charging (V2V-DWC) represents a modern advancement in electrified transportation, where a specialized charging vehicle delivers power to another vehicle on the move. The rising popularity of this technology can be attributed to the gradual advancements in energy storage technologies and the scarcity of plug-in charging infrastructure. V2V wireless power transfer provides a solution for electric vehicles (EVs) to recharge their batteries while in transit. The existing literature confirms the empirical validation of this concept through analytical and experimental studies, yet the challenge of misalignment remains insufficiently explored. Achieving optimal power transfer in V2V systems necessitates precise alignment of the inductive coils. Lateral misalignment (LTM) occurs due to the deviation of the coils from the proper alignment, leading to significant energy losses. Additionally, the development of effective controllers to address the V2V misalignment problem remains inadequate. This study proposes the development of a neural network-based adaptive fuzzy logic controller (ANFIS) to alleviate the misalignment issues in V2V-DWC systems. A comparative analysis is conducted between the proposed ANFIS controller and the conventional fuzzy logic controller (FLC) to evaluate their performance across various degrees of LTM. The performance of the proposed ANFIS controller is evaluated through simulations in MATLAB/Simulink, supplemented by experimental testing. The results indicate that the proposed ANFIS controller surpasses the FLC in both simulation and experimental contexts in addressing the V2V misalignment challenge. Full article

To improve the application of carbon-fiber-reinforced polymers (CFRPs) in civil engineering, the long-term durability of CFRP anchorage systems has become a critical issue. Temperature fluctuations can significantly impact the bond performance between CFRPs and the load transfer medium (LTM), making it essential to understand the effects of temperature on the durability of CFRP anchorages. Therefore, this study investigates the influence of temperature on the durability of CFRP anchorages through aging tests on 30 epoxy-filled CFRP-bonded anchorage specimens, followed by pull-out tests. The long-term degradation of CFRP cable anchorage performances in representative regions of the globe was predicted using Arrhenius theory. The experimental results show that after long-term temperature exposure, the maximum bond strength of the CFRP-LTM interface in the anchoring zone degrades after 30 days but continues to increase after 150 days. In contrast, the residual bond strength of the CFRP-LTM interface in the anchorage zone continuously decreases over time, with the degradation rates gradually decreasing over time. Higher temperatures lead to more severe degradation of anchoring performance. Based on the experimental results, it is predicted that the anchoring performance of a CFRP cable anchorage system will reach degradation rates of 63.72%, 83.36%, and 94.73% after 50 years in regions with average annual temperatures of 0 °C, 10 °C, and 20 °C, respectively. Therefore, the temperature has a significant long-term impact on the anchoring performance of CFRP cable bonding systems, necessitating a more conservative design in higher-temperature areas. Full article

Neuromorphic computing, inspired by the brain, holds significant promise for advancing artificial intelligence. Artificial optoelectronic synapses, which can convert optical signals into electrical signals, play a crucial role in neuromorphic computing. In this study, we successfully fabricated a flexible artificial optoelectronic synapse device based on the ZnO/PDMS structure by utilizing the magnetron sputtering technique to deposit the ZnO film on a flexible substrate. Under UV light illumination, the device exhibits excellent synaptic plasticity, including excitatory postsynaptic current (EPSC), short-term potentiation (STP), and paired-pulse facilitation (PPF). By growing ZnO nanowires, we improved the fabrication processes and further enhanced the synaptic properties of the device, demonstrating long-term potentiation (LTP) and the transition from short-term memory (STM) to long-term memory (LTM). Additionally, the device exhibits outstanding flexibility, maintaining stable synaptic plasticity under bending conditions. This device shows broad application potential in mimicking visual systems and is expected to contribute significantly to the development of neuromorphic computing. Full article

Atmospheric weighted mean temperature (Tm) is a crucial parameter for retrieving atmospheric precipitation using the Global Navigation Satellite System (GNSS). It plays a significant role in GNSS meteorology research. Although existing empirical models can quickly obtain Tm values for the Yunnan–Guizhou Plateau, their accuracy is generally low due to the region’s complex environmental and climatic conditions. To address this issue, this study proposes an enhanced empirical Tm model tailored for the Yunnan–Guizhou Plateau. This new model incorporates surface temperature (Ts) data and employs the least squares method to determine model coefficients, thereby improving the accuracy of the Tm empirical model. The research utilizes observational data from 16 radiosonde stations in the Yunnan–Guizhou Plateau from 2010 to 2018. By integrating Ts into the Hourly Global Pressure and Temperature (HGPT2) model, we establish the enhanced empirical Tm model, referred to as YGTm. We evaluate the accuracy of the YGTm model using Tm values obtained from the 2019 radiosonde station measurements as a reference. A comparative analysis is conducted against the Bevis model, the HGPT2 model, and the regional linear model LTm. The results indicate that at the modeling stations, the proposed enhanced model improves Tm prediction accuracy by 24.9%, 16.1%, and 22.4% compared to the Bevis, HGPT2, and LTm models, respectively. At non-modeling stations, the accuracy improvements are 26.2%, 17.1% and 24.4%, respectively. Furthermore, the theoretical root mean square error and relative error from using the YGTm model for GNSS water vapor retrieval are 0.27 mm and 0.93%, respectively, both of which outperform the comparative models. Full article

The use of industrial residues in civil construction is an exciting alternative to mitigate environmental impacts and promote the circular economy. This work developed new compositions of geopolymer mortars activated by NaOH from fine kaolin residue (RCF), coarse kaolin residue (RCG) and granite (RG). All residues were benefited and characterized by chemical analysis (X-ray fluorescence), mineralogical phases (X-ray diffraction) and granulometry (laser granulometry). Additionally, the RCF was calcined at 650 °C for 2 h (RCFC) to produce metakaolin, which is the starting point for the geopolymer reaction. A mixture of experimental designs was accomplished to evaluate the water/binder factor (W_exp (%)) necessary for new geopolymer mortar compositions to reach the consistency index (260 mm, ASTM C1437-15) and the effect of different curing conditions on the simple compressive strength (SCS). The geopolymeric compositions with RCFCs, pre-cured at room temperature, exhibited the highest W_exp% values (>40%) and significant SCS, with curing conditions A and B reaching 6 MPa and 7 MPa, respectively. Such behavior can be explained by the fact that the pre-curing step at room temperature keeps the system humidity relatively high, favoring the dissolution of Si⁴⁺ and Al³⁺ ions and, therefore, increasing the Si/Al ratio, which positively influences the geopolymerization kinetics reaction. Full article