Search Results (1,666)

In this study, an in situ electrochemical modulation method based on an ultramicro interdigitated array electrode (UIAE) sensor chip was developed for the detection of ammonia nitrogen (NH₃-N) in neutral aqueous solutions. One comb of the UIAE was used as the working electrode for both the modulating and sensing functions, while the other comb was used as the counter electrode. Utilizing its enhanced mass transfer and proximity effects, the feasibility of in situ modulation of the solution environment near the UIAE chip to generate an electrochemical response for NH₃-N was investigated using electrochemical methods. The proposed method enhances the concentration of hydroxide ions and active chloride in the local solution near the sensor chip. These reactive species play a key role in improving the sensor’s electrocatalytic oxidation capability toward ammonia nitrogen, facilitating the sensitive detection of ammonia nitrogen in neutral environments. A linear relationship was displayed, ranging from 0.15–2.0 mg/L (as nitrogen) with a sensitivity of 3.7936 µA·L·mg⁻¹ (0.0664 µA µM⁻¹ mm⁻²), which was 2.45 times that in strong alkaline conditions without modulation. Additionally, the relative standard deviation of the measurement remained below 2.9% over five days of repeated experiments, indicating excellent stability. Full article

Conventional active vehicle height control systems predominantly employ hydraulic or pneumatic suspension mechanisms. Although these established approaches have achieved widespread adoption in automotive applications, they remain fundamentally constrained by three critical drawbacks: (1) inadequate dynamic response characteristics, (2) high energy consumption, and (3) inherent mechanical complexity. The ongoing electrification revolution in vehicle technologies has spurred significant research interest in linear electromagnetic suspension systems. Nevertheless, their practical implementation encounters dual technical barriers: (a) complex multi-phase motor configurations requiring precise coordination, and (b) substantial thrust ripple generation under dynamic operating conditions. To address these critical limitations, our research proposes a novel motor structure, known as the flat rectangular slot structure, which offers advantages such as simple installation and high thrust with low current. Additionally, we have designed a double-vector control strategy for the motor control section, which modifies the finite-set model predictive control and enhances the accuracy of the model’s calculations. By integrating the vehicle model, we have developed a multi-layer hierarchical control strategy for the vehicle height controller. In the first layer, a PI controller is used to convert the target height into current, which is then input into the value function. In the second layer, we improve the control strategy for the linear motor by optimizing the finite-set model predictive control through the double-vector control. Through multi-step predictive calculations, we determine the optimal sector, enabling the motor to receive the corresponding control force. In the third layer, the motor thrust is input into the vehicle model to achieve closed-loop control of the vehicle body. Finally, we conduct simulation verification of the proposed control strategy. The simulation results indicate that the double-vector control significantly reduces the fluctuation in the sprung mass displacement by approximately 70% compared to single-vector control, the response speed is increased by approximately 20%, and the thrust required to achieve the target vehicle height is reduced by 5.7%. Therefore, the proposed double-vector control strategy can significantly enhance the stability of the automotive electronic control suspension, opening up new research avenues for the study of suspension stability control and energy saving in vehicles. Full article

The use of mobile phones in teaching processes, in the context of technological convergence, involves considering educational intention, pedagogical tactics, and the capacity of digital media for learning. The utilization of mobile phones in the classroom gives the students instant access to a wide range of educational resources, including educational applications, specialized websites, and multimedia material. Learning to use mobile devices responsibly and productively is essential in today’s digital age, as it prepares them for future technological interactions. The present study examines the intermediary function of a mobile education application, conceived under the problem-based learning approach, in the field of mathematics. This research was carried out with a descriptive approach. A pretest, a post-test, and a survey were created and administered for the collection of numerical data, along with an observation grid for qualitative information. The results highlight the contribution of mobile devices and problem-based learning in the development of skills for collaborative work, decision-making, and problem-solving through systems of linear equations using four techniques. The conclusions highlight the potential of mobile devices in the educational field since they are a resource that provides access to information without time or location limitations. However, it is necessary to focus on the design of pedagogical strategies to carry out a pedagogical and planned use of this resource. Full article

Maritime transportation is responsible for most global trade and is generally considered more environmentally efficient compared to other modes of transport, particularly for long-distance trade. With increasingly stringent emission regulations, however, accurately quantifying emissions and identifying their key determinants has become essential for effective environmental management. This study introduced a structured and comparative statistical modeling framework for ship-based emission modeling using gross tonnage (GT) as the primary predictor variable, due to its strong correlation with emission levels. Emissions for hydrocarbon (HC), carbon monoxide (CO), particulate matter with an aerodynamic diameter of less than 10 μm (PM10), carbon dioxide (CO₂), sulfur dioxide (SO₂), nitrogen oxides (NO_x), and volatile organic compounds (VOC) were estimated using a bottom-up approach based on emission factors and formulas defined by the U.S. Environmental Protection Agency (EPA), using data from 38,304 vessel movements through the Bosphorus in 2021. These EPA-estimated values served as dependent variables in the modeling process. The modeling framework followed a three-step strategy: (1) outlier detection using Rosner’s test to reduce the influence of outliers on model accuracy, (2) curve fitting with 12 regression models representing four curve types—polynomial (e.g., linear, quadratic), concave/convex (e.g., exponential, logarithmic), sigmoidal (e.g., logistic, Gompertz, Weibull), and spline-based (e.g., cubic spline, natural spline)—to capture diverse functional relationships between GT and emissions, and (3) model comparison using difference performance metrics to ensure a comprehensive assessment of predictive accuracy, consistency, and bias. The findings revealed that nonlinear models outperformed polynomial models, with spline-based models—particularly natural spline and cubic spline—providing superior accuracy for HC, PM₁₀, SO₂, and VOC, and the Weibull model showing strong predictive performance for CO and NO_x. These results underscore the necessity of using pollutant-specific and flexible modeling strategies to capture the intricacies of maritime emission dynamics. By demonstrating the advantages of flexible functional forms over standard regression techniques, this study highlights the need for tailored modeling strategies to better capture the complex relationships in maritime emission data and offers a scalable and transferable framework that can be extended to other vessel types, emission datasets, or maritime regions. Full article

Electrodermal activity (EDA) and heart rate variability (HRV) offer opportunities to grasp critical manifestations of the nervous autonomic system using low-intrusive sensing tools. A key question relies on the capacity to adequately process EDA and HRV signals to extract cognitive load markers, a multifaceted construct with intricate neural networks functioning, where emotions interfere with cognition. Here, 34 participants (20 males, 19.2 ± 1.3 years) were exposed to two-back mental tasking and watching emotionally charged images while recording EDA and HRV. HRV signals were processed using variable frequency complex demodulation (VFCDM) and wavelet packet transform (WPT) to provide high- and low-frequency (HF and LF) markers. Three methods were used to extract EDA indices: VFCDM (EDA_TVSYMP), WPT (EDA_WPT), and convex-optimization (EDA_CVX). Cognitive load and emotion epochs were distinguished by significant differences in NASA-TLX scores, mental fatigue, and stress, on the one hand; and by EDA_CVX and, remarkably, EDA_TVSYMP and HF-HRV_VFCDM on the other hand. A linear mixed-effects model and stepwise backward selection procedure showed that these two markers were main predictors of the NASA-TLX score (cognitive load). The individual perception of cognitive load was finally discriminated by k-means clustering, showing three profiles of autonomic responses relying, respectively, on EDA_TVSYMP, HF-HRV_VFCDM, or a mix of these two markers. The existence of EDA-, HRV-, and EDA/HRV-derived profiles might explain why previous attempts that have predominantly employed a single biosignal often remained unconclusive in evaluating the perceived cognitive load, thereby demonstrating the added value of the present approach to monitor mental-related workload in human operators. Full article

The timely and accurate monitoring of regional soil salinity is crucial for the sustainable development of land and the stability of the ecological environment in arid and semi-arid regions. However, due to the spatiotemporal heterogeneity of soil properties and environmental conditions, improving the accuracy of soil salinization monitoring remains challenging. This study aimed to explore whether partitioned modeling based on salinization degrees during both the bare soil and vegetation cover periods can enhance the accuracy of regional soil salinity prediction. Specifically, this study integrated in situ hyperspectral data and satellite multispectral data using spectral response functions. Subsequently, machine learning methods such as random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR) were employed, in combination with sensitive spectral indices, to develop a multi-source remote sensing soil salinity estimation model optimized for different salinization degrees (mild or lower salinization vs. moderate or higher salinization). The performance of this partitioned modeling approach was then compared with an overall modeling approach that does not distinguish between salinization degrees to determine the optimal modeling strategy. The results highlight the effectiveness of considering regional soil salinization degrees in enhancing the sensitivity of spectral indices to soil salinity and improving modeling accuracy. Classifying salinization degrees helps identify spectral variable combinations that are more sensitive to the construction of soil salinity content (SSC) models, positively impacting soil salinity estimation. The partitioned modeling strategy outperformed the overall modeling strategy in both accuracy and stability, with R² values reaching 0.84 and 0.80 and corresponding RMSE values of 0.1646% and 0.1710% during the bare soil and vegetation cover periods, respectively. This study proposes an optimized modeling strategy based on regional salinization degrees, providing scientific evidence and technical support for the precise assessment and effective management of soil salinization. Full article

This work aims to compare the hardness (H) and roughness (R_a) of glass/epoxy composites after being exposed to various hostile environments, which is possible because the constituents are always the same. Considering the stacking sequence [45₂, 90₂, −45₂, 0₂]_s, the hardness increases for all solutions up to a certain exposure time, from which it decreases for longer immersion times. For the same stacking sequence, roughness had its highest increase (around 44.5%) for the alkaline solution after 36 days of immersion, while the highest decrease (around 25%) occurred for all mortars after 30 days of exposure. For the stacking sequence [0₂, 90₂]_2s, the hardness varied in the opposite direction for acidic and alkaline solutions, observing a direct increase in H with immersion time. However, for samples immersed in oil, hardness decreased as a function of immersion time. In terms of roughness, there was a linear increase with immersion time for all samples, which increased linearly. Therefore, it can be concluded that the stacking sequence has a significant influence on hardness and roughness. Furthermore, knowledge of the variation in hardness and roughness is very important because it can be associated with the structural response of a composite exposed to hostile environments. Full article

Functional somatic symptoms (FSSs) in children—such as headaches, stomachaches, and muscle pain without clear medical explanations—pose a significant clinical challenge, often leading to repeated healthcare visits and impairments in daily functioning. While the role of parental psychological factors in shaping children’s FSSs has been suggested, empirical evidence remains limited and fragmented. This study addresses this gap by systematically examining the associations between parents’ reflective functioning, emotion regulation, alexithymia, and physical and mental health, and the frequency and severity of children’s FSSs. A total of 339 parents of children aged 6–12 completed surveys assessing their capacity to understand mental states, regulate emotions, and identify or describe feelings, as well as their self-reported physical and mental health. They also indicated whether their child experienced FSSs (e.g., headaches, stomachaches) more than once per week. Results revealed that parents of children with FSSs reported significantly lower levels of reflective functioning (lower certainty, higher uncertainty), higher alexithymic traits, and greater emotion regulation difficulties, alongside poorer physical and mental health indices. Logistic regression analyses demonstrated that emotion regulation difficulties and poorer mental health significantly increased the likelihood of a child exhibiting FSSs, while lower reflective functioning also emerged as a significant predictor. Furthermore, multiple linear regression indicated that emotion regulation challenges and poor mental health predicted greater severity of FSSs. These findings offer novel insights into how parents’ psychological and health characteristics can shape children’s somatic symptom expression, highlighting the need for family-focused interventions. By identifying and addressing parental emotional and cognitive difficulties, clinicians may be able to mitigate the intergenerational transmission of maladaptive stress responses, ultimately reducing the burden of FSSs in children. Full article

Tomato (Solanum lycopersicum L.) is one of the major vegetable crops worldwide. Research on the Janus kinase–signal transducer and activator of transcription (JAK–STAT) signaling pathway in tomatoes and other plant systems is extremely limited. In this study, the roles of STAT, a crucial element of the JAK–STAT signaling pathway in tomato seed germination and low-temperature stress responses are examined, employing gene family analysis and genetic transformation. The results indicate that the S. lycopersicum genome contains only one member of the STAT gene family, SlSTAT. Subcellular localization experiments reveal that SlSTAT is found in both the cytoplasm and nucleus, suggesting its potential involvement in biological functions within these cellular compartments. Among the 26 different tomato tissue/organs tested, SlSTAT exhibited higher expression levels in hypocotyl (8 days past germination; 8 DPG), and low expression of SlSTAT significantly reduced the germination rate and impacted biomass at 8 DPG. In addition, the SlSTAT gene was significantly downregulated during low-temperature treatment. Compared with the wild-type (WT) tomatoes, the SlSTAT-overexpressing plants showed more resistance to low-temperature conditions, whereas the downexpressing tomatoes exhibited increased sensitivity. The expressions of low-temperature marker genes (SlCBF1-3) and N⁶-methyladenosine (m⁶A)-modification-related genes (m⁶A writer, reader, and eraser genes) were detected to explore possible molecular mechanisms by which SlSTAT causes changes in tomato low-temperature stress resistance. The expression changes of SlCBF1-3 in transgenic plants do not merely follow a straightforward linear relationship with the changes in SlSTAT expression, suggesting a more complex molecular mechanism and a non-direct interaction between SlSTAT and the promoters of SlCBFs. On the other hand, SlSTAT also changes the expression levels of RNA m⁶A-modification-related genes, especially SlFIP37 (writer gene), SlYTP8/9 (reader genes), and SlALKBH8 (eraser gene), ultimately leading to changes in the levels of m⁶A modification. These research findings lay the groundwork for exploring functions of JAK–STAT pathway in tomato development and stress responses, expanding the scope of JAK–STAT signaling studies in plant systems. Full article

This article reviews the properties and formation process of interface traps in MOS and linear bipolar devices. Transistors are the core components of modern electronic devices, and their performance and reliability directly affect the performance of the entire system. In radiation environments, the emergence and evolution of interface traps severely impacts the functionality of transistors, being a significant factor in device failure. However, our understanding of the properties and formation processes of interface traps is still limited. Therefore, research on interface traps is of great theoretical and practical significance. This paper focuses on studying the radiation response patterns of transistor interface traps. By reviewing relevant literature and research findings from both domestic and international sources, this review provides a detailed overview of the current state of research on the transformation of interface traps and the annealing processes that occur during the irradiation of microelectronic devices. Finally, based on this foundation, this paper discusses the current state of simulation research methods for interface traps. Through an in-depth exploration of the formation mechanisms of interface traps and their role in transistor performance, this study aims to provide guidance for device design, radiation hardening, and reliability assessment, and ensure the reliability and stability of devices in radiation environments. Full article

This research investigates the impact of second-order slip conditions, Stefan flow, and convective boundary constraints on the stagnation-point flow of couple stress nanofluids over a solid sphere. The nanofluid density is expressed as a nonlinear function of temperature, while the diffusion-thermo effect, chemical reaction, and thermal radiation are incorporated through linear models. The governing equations are transformed using appropriate non-similar transformations and solved numerically via the finite difference method (FDM). Key physical parameters, including the heat transfer rate, are analyzed in relation to the Dufour number, velocity, and slip parameters using an artificial neural network (ANN) framework. Furthermore, response surface methodology (RSM) is employed to optimize skin friction, heat transfer, and mass transfer by considering the influence of radiation, thermal slip, and chemical reaction rate. Results indicate that velocity slip enhances flow behavior while reducing temperature and concentration distributions. Additionally, an increase in the Dufour number leads to higher temperature profiles, ultimately lowering the overall heat transfer rate. The ANN-based predictive model exhibits high accuracy with minimal errors, offering a robust tool for analyzing and optimizing the thermal and transport characteristics of couple stress nanofluids. Full article

Past literature has advocated the integration of channels through the offline-to-online and online-to-offline models. However, little research has investigated the interrelationships effects between the two channels. Drawing on the literature from self-service technology (SST) and expectation–confirmation theory, this study aims to investigate key attributes of SST and assess their impact on consumer evaluations across offline-to-online and online-to-offline channels. A questionnaire survey was administered at convenience stores and 360 user responses were collected through a physical self-service kiosk. Two-stage structural equation modeling with linear structural relations (LISREL version 8.54) software was used for data analysis. The empirical results verified the attributes of physical SST (physical system’s service quality and perceived convenience) and online SST (virtual system’s service quality and perceived ubiquity) as critical antecedents of satisfaction and attitude and the subsequent behavioral intentions toward each channel. However, some transitional effects from offline (physical kiosk use) to online (kiosk app adoption) intention were not as significant as hypothesized. The offline attributes of perceived convenience and satisfaction had no significant impact on online SST significantly (kiosk app), except for the physical system’s service quality. Discussions and implications are provided, including strategies for concise functional design and essential SST services. Full article

RNA viruses pose a significant global public health burden due to their high mutation rates, zoonotic potential, and ability to evade immune responses. A common aspect of their replication is the generation of defective interfering particles (DIPs), which contain truncated defective viral genomes (DVGs) that depend on full-length standard (STD) virus for replication. DVGs have gained recognition as they are increasingly detected in clinical samples from natural infections. While their role in modulating type I interferon (IFN-I) responses is well established, their impact on the complement (C′) system is not understood. In this study, we examined how DVGs influence C′-mediated lysis during parainfluenza virus 5 (PIV5) infection using real-time in vitro cell viability assays. Our results demonstrated that C′ effectively killed human lung epithelial cells infected with STD PIV5, whereas co-infection with DIP-enriched stocks significantly suppressed C′-mediated killing through mechanisms that were dependent on DVG replication but independent of IFN-I production. The titration of DI units in co-infection with STD PIV5 showed a strong linear relationship between DIP-mediated decreases in surface viral glycoprotein expression and the inhibition of C′-mediated lysis. Our findings reveal a previously unrecognized function of DVGs in modulating C′ pathways, shedding light on their potential role in viral persistence and immune evasion. Full article

Deformation is a key physical quantity that reflects the safety status of dams. Dam deformation is influenced by multiple factors and has seasonal and periodic patterns. Due to the challenges in accurately predicting dam deformation with traditional linear models, deep learning methods have been increasingly applied in recent years. In response to the problems such as an excessively long training time, too-high model complexity, and the limited generalization ability of a large number of complex hybrid models in the current research field, we propose an improved multi-teacher distillation network for regression tasks to improve the performance of the model. The multi-teacher network is constructed using a Transformer that considers global dependencies, while the student network is constructed using Temporal Convolutional Network (TCN). To improve distillation efficiency, we draw on the concept of clustering integration to reduce the number of teacher networks and propose a loss function for regression tasks. We incorporate an adaptive weight module into the loss function and assign more weight to the teachers with more accurate prediction results. Finally, knowledge information is formed based on the differences between the teacher networks and the student network. The model is applied to a concrete-faced rockfill dam located in Guizhou province, China, and the results demonstrate that, compared to other knowledge distillation methods, this approach exhibits higher accuracy and practicality. Full article

The identification of the most pertinent site parameters to classify soils in terms of their amplification of seismic ground motions is still of prime interest to earthquake engineering and codes. This study investigates many options for improving soil classifications in order to reduce the deviation between “exact” predictions using wave propagation and the method used in seismic codes based on amplification (site) factors. To this end, an exhaustive parametric study is carried out to obtain nonlinear responses of sets of 324 clay and sand columns and to constitute the database for neuronal network methods used to predict the regression equations of the amplification factors in terms of seismic and site parameters. A wide variety of parameters and their combinations are considered in the study, namely, soil depth, shear wave velocity, the stiffness of the underlaying bedrock, and the intensity and frequency content of the seismic excitation. A database of AFs for 324 nonlinear soil profiles of sand and clay under multiple records with different intensities and frequency contents is obtained by wave propagation, where soil nonlinearity is accounted for through the equivalent linear model and an iterative procedure. Then, a Generalized Regression Neural Network (GRNN) is used on the obtained database to determine the most significant parameters affecting the AFs. A second neural network, the Radial Basis Function (RBF) network, is used to develop simple and practical prediction equations. Both the whole period range and specific short-, mid-, and long-period ranges associated with the AFs, F_a, F_v, and F_l, respectively, are considered. The results indicate that the amplification factor of an arbitrary soil profile can be satisfactorily approximated with a limited number of sites and the seismic record parameters (two to six). The best parameter pair is (PGA; resonance frequency, f₀), which leads to a standard deviation reduction of at least 65%. For improved performance, we propose the practical triplet $\left({PGA;V}_{s30};f_0\right)$ with V_s30 being the average shear wave velocity within the upper 30 m of soil below the foundation. Most other relevant results include the fact that the AFs for long periods (F_l) can be significantly higher than those for short or mid periods for soft soils. Finally, it is recommended to further refine this study by including additional soil parameters such as spatial configuration and by adopting more refined soil models. Full article