Search Results (3,760)

This paper presents an analytical investigation of the free vibration behavior of fiber metal laminate (FML) beams with three types of boundary conditions, considering the temperature-dependent properties and the interfacial slip. In the proposed model, the non-uniform temperature field is derived based on one-dimensional heat conduction theory using a transfer formulation. Subsequently, based on the two-dimensional elasticity theory, the governing equations are established. Compared with shear deformation theories, the present solution does not rely on a shear deformation assumption, enabling more accurate capture of interlaminar shear effects and higher-order vibration modes. The relationship of stresses and displacements is determined by the differential quadrature method, the state-space method and the transfer matrix method. Since the corresponding matrix is singular due to the absence of external loads, the natural frequencies are determined using the bisection method. The comparison study indicates that the present solutions are consistent with experimental results, and the errors of finite element simulation and the solution based on the first-order shear deformation theory reach 3.81% and 3.96%, respectively. At last, the effects of temperature, the effects of temperature degree, interface bonding and boundary conditions on the vibration performance of the FML beams are investigated in detail. The research results provide support for the design and analysis of FML beams under high-temperature and vibration environments in practical engineering. Full article

As global industry enters the digital era, automation is becoming increasingly pervasive. Due to their superior efficiency and reliability, Permanent Magnet Synchronous Motors (PMSMs) are playing an increasingly prominent role in industrial applications. Sliding Mode Control (SMC) has emerged as a modern control strategy that is widely employed not only in PMSM drive systems, but also across broader power and industrial control domains. This technique effectively mitigates key challenges associated with PMSMs, such as nonlinear behavior and susceptibility to external disturbances, thereby enhancing the precision of speed and torque regulation. This paper provides a thorough review and evaluation of recent advancements in SMC as applied to PMSM control. It outlines the fundamentals of SMC, explores various SMC-based strategies, and introduces integrated approaches that combine SMC with optimization algorithms. Furthermore, it compares these methods, identifying their respective strengths and limitations. This paper concludes by discussing current trends and potential future developments in the application of SMC for PMSM systems. Full article

Spun piles adjacent to the pile cap need sufficient confinement to ensure the formation of plastic hinges during severe earthquakes. However, the high confinement ratio required for precast piles according to ACI 318-19 results in tightly spaced spirals, which are difficult to implement. Since higher confinement is only needed at specific regions of the pile, external transverse reinforcement using steel jacketing has been proposed as an alternative solution. An experimental and numerical study was conducted to evaluate the effectiveness. The experimental results showed that the jacket enhanced both the strength and energy dissipation of the connection, but had only a minor effect on its ductility. A parametric study using finite element analysis was performed to investigate the parameters influencing connection behavior. The results indicated that variations in jacket thickness did not significantly impact the connection’s performance. A jacket height equal to 1.53 times the pile diameter was found to be the maximum effective height. It was also observed that higher axial loads led to a sudden loss of connection strength, thereby reducing ductility. Partial bonding between the jacket, grout, and pile was found to be acceptable within a certain range. The numerical analysis found that the steel jacket increases the ductility. Full article

This article presents an original experimental and numerical approach to examining the pull-out behavior of fastening systems made of steel bars simultaneously embedded in both ends of concrete samples. This double-embedded configuration simulates a connection between the existing concrete structure and a new external exoskeleton, promoting seismic strengthening. Pull-out tests were performed across six specimen configurations combining different concrete strength classes in order to compare the adhesive solution against traditional monolithic cast-in rebar embedments. The adhesive-anchored bars achieved a peak pull-out force of ~28.6 kN, which is about 18% higher than with mixed anchorage (one end adhesive, one end cast-in). All specimens failed in concrete cracking and pull-out cone formation, with no steel bar yielding, indicating that failure was governed by concrete strength. Finite element simulations in ANSYS Explicit Dynamics were validated against these experiments, confirming the observed behavior and enabling the extension of our analysis to broader concrete strength ranges. Overall, the results demonstrate that double-ended adhesive anchorage significantly increases the connection’s load-bearing capacity and ductility compared to mixed configurations. Full article

This study presents a qualitative analysis of the fractional coupled nonlinear Schrödinger equation (FCNSE) to obtain its complete set of solutions. An appropriate wave transformation is applied to reduce the FCNSE to a fourth-order dynamical system. Due to its non-Hamiltonian nature, this system poses significant analytical challenges. To overcome this complexity, the dynamical behavior is examined within a specific phase–space subspace, where the system simplifies to a two-dimensional, single-degree-of-freedom Hamiltonian system. The qualitative theory of planar dynamical systems is then employed to characterize the corresponding phase portraits. Bifurcation analysis identifies the physical parameter conditions that give rise to super-periodic, periodic, and solitary wave solutions. These solutions are derived analytically and illustrated graphically to highlight the influence of the fractional derivative order on their spatial and temporal evolution. Furthermore, when an external generalized periodic force is introduced, the model exhibits quasi-periodic behavior followed by chaotic dynamics. Both configurations are depicted through 3D and 2D phase portraits in addition to the time-series graphs. The presence of chaos is quantitatively verified by calculating the Lyapunov exponents. Numerical simulations demonstrate that the system’s behavior is highly sensitive to variations in the frequency and amplitude of the external force. Full article

Prior work established the presence of six crosscutting patterns of clinically significant family maltreatment (FM) and psychological health (PH) problems among active-duty service members. Here, we develop a brief screener for these patterns via Classification and Regression Trees (CART) analyses using a sample of active-duty members of the United States Air Force. CART is a predictive algorithm used in machine learning. It balances prediction accuracy and model parsimony to identify an optimal set of predictors and identifies the thresholds on those predictors in relation to a discrete condition of interest (e.g., diagnosis of pathology). A 22-item screener predicted membership in five of the six classes (sensitivities and specificities > 0.96; positive and negative predictive values > 0.90). However, for service members at extremely high risk of clinically significant externalizing behavior, sensitivity and positive predictive values were much lower. The resulting 22-item brief screener can facilitate feasible, cost-effective detection of five of the six identified FM and PH problem patterns with a small number of items. The sixth pattern can be predicted far better than chance. Researchers and policymakers can use this tool to guide prevention efforts for FM and PH problems in service members. Full article

Impulsive purchasing behavior among university students has gained increased attention in the context of digital consumption settings; however, much of the existing research is product-specific and quantitative, leaving the subjective nuances of this phenomenon underexplored. This study investigates how college students perceive and explain their impulsive purchase behavior across various product categories and platforms, using qualitative data from focus groups (n = 72). By revealing the prevalence of key patterns—interest-aligned, emotional relief, hedonistic lifestyle, social influence, inquisitive reviewer, presentation appeal, and controlled purchase—this research uncovers the underlying identity-affirming practices, internal emotional negotiations, and external sociotechnical cues that shape such behavior. Ultimately, it reframes impulsive buying as a socially embedded, identity-driven act rather than an act of irrationality. These findings advance our understanding of consumer psychology by emphasizing the lived experiences and self-construction processes of young consumers navigating media-saturated, algorithmically curated purchasing environments. Full article

Vibration loads during deep drilling are one of the main causes of reduced service life of drilling tools and emergency failure of downhole motors. This work investigates the adaptive operation of an original elastic element based on an open cylindrical shell used as part of a drilling shock absorber. The vibration protection device contains an adjustable radial clearance between the load-bearing shell and the rigid housing, which provides the effect of structural nonlinearity. This allows effective combination of two operating modes of the drilling shock absorber: normal mode, when the clearance does not close and the elastic element operates with increased compliance; and emergency mode, when the clearance closes and gradual load redistribution and increase in device stiffness occur. A nonconservative problem concerning the contact interaction of an elastic filler with a coaxially installed shaft and an open shell is formulated, and as the load increases, contact between the shell and the housing, installed with a radial clearance, is taken into account. Numerical finite element modeling is performed considering dry friction in contact pairs. The distributions of radial displacements, contact stresses, and equivalent stresses are examined, and deformation diagrams are presented for two loading modes. The influence of different cycle asymmetry coefficients on the formation of hysteresis loops and energy dissipation is analyzed. It is shown that with increasing load, clearance closure begins from local sectors and gradually covers almost the entire outer surface of the shell. This results in deconcentration of contact pressure between the shell and housing and reduction of peak concentrations of equivalent stresses in the open shell. The results confirm the effectiveness of the adaptive approach to designing shell shock absorbers capable of reliably withstanding emergency overloads, which is important for deep drilling where the exact range of external impacts is difficult to predict. Full article

This paper presents the design and evaluation of a Linear Time-Varying Linear Quadratic Gaussian (LTV-LQG) controller for an energy efficient electric vehicle, using a predetermined driving strategy as the reference trajectory. The proposed approach begins with the development of a structured nonlinear vehicle model based on relevant subsystems, enabling accurate energy consumption estimation with a deviation of less than 2% from experimental measurements. This model serves as the basis for computing a near-optimal driving trajectory. The nonlinear model is linearized along the predefined trajectory to support control design. A time-varying control structure is then developed, integrating a Kalman filter that estimates unmeasured external disturbances, such as wind, and enhances feedback performance. The proposed control strategy is evaluated through simulations and compared to a rule-based switching controller that replicates human-like driving behavior. The simulation results demonstrate that the LTV-LQG controller consistently satisfies the time constraints in both headwind- and tailwind-dominant scenarios, where the switching controller tends to exceed the time limit. Moreover, in tailwind-dominant cases, the LTV-LQG controller achieves lower energy consumption (up to 15.4%). The proposed framework represents a computationally efficient and practically feasible control solution for electric vehicles operating under realistic disturbance conditions. Full article

Background: Multiple System Atrophy (MSA) is a rapidly progressing neurodegenerative movement disorder characterized by autonomic failure, parkinsonism, and cerebellar ataxia. While its non-motor symptoms are well-documented, personality features in MSA remain underexplored. This study characterizes the personality traits of non-demented patients with MSA and explores their association with clinical variables. Methods: Twenty-six patients with MSA were assessed using the Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF). Dementia was excluded by Montreal Cognitive Assessment. Descriptive statistics and non-parametric analyses were conducted to examine clinical, demographic, and MMPI-2-RF variables. Results: Patients commonly showed elevated scores in somatic domains: Somatic Complaints (39%), Malaise (58%), and Neurological Complaints (85%), as well as in internalizing emotional traits: Low Positive Emotions (39%), Introversion (46%), Suicidal Ideation (46%), and Hopelessness (54%). Externalizing behavioral traits were absent, with only 4–8% of patients showing elevations in aggression or behavioral dysfunction. Strong correlations were found between somatic and emotional traits (r = 0.656, p < 0.001), and between Neurological Complaints and disease duration (r = 0.662, p < 0.001). Conclusions: This exploratory study reveals a distinct personality pattern in MSA, characterized by marked suicidal ideation, emotional vulnerability with internalizing coping, and absence of externalizing behaviors. These features highlight the need for suicide risk screening, interventions to alleviate psychological suffering, and tailored multidisciplinary care. Larger, longitudinal studies are warranted to confirm these preliminary results and clarify whether these traits reflect premorbid personality, early disease manifestations, or secondary responses, as well as their prognostic and clinical relevance. Full article

Over the past decade, two-dimensional materials have become a research hotspot in chemistry, physics, materials science, and electrical and optical engineering due to their excellent properties. Graphene is one of the earliest discovered 2D materials. The absence of a bandgap in pure graphene limits its application in digital electronics where switching behavior is essential. In the present study, researchers have proposed a variety of methods for tuning the graphene bandgap. Machine learning methodologies have demonstrated the capability to enhance the efficiency of materials research by automating the recording of characteristic parameters from the discovery and preparation of 2D materials, property calculations, and simulations, as well as by facilitating the extraction and summarization of governing principles. In this work, we use first principle calculations to build a dataset of graphene band gaps under various conditions, including the application of a perpendicular external electric field, nitrogen doping, and hydrogen atom adsorption. Support Vector Machine (SVM), Random Forest (RF), and Multi-Layer Perceptron (MLP) Regression were utilized to successfully predict the graphene bandgap, and the accuracy of the models was verified using first principles. Finally, the advantages and limitations of the three models were compared. Full article

Background: Chronic disease significantly contributes to global healthcare demands and costs. Despite these chronic illnesses, good health is achievable through public health strategies that enhance control over health determinants. This systematic review investigates how control over health determinants affects the health status of individuals with chronic diseases. Objective: To assess the impact of limited control over health determinants on health status in people with chronic diseases and identify potential clinical applications. Methods: A systematic review was conducted following PRISMA 2020 and COSMOS-E guidelines. Searches across five databases (PubMed, Google Scholar, ScienceDirect, CINAHL, PsycARTICLES) between February and April 2023 identified cohort studies published in the last 10 years. Studies involving individuals aged 16 years and older with at least one chronic disease were included. The Newcastle–Ottawa scale was used to assess study quality. Results: Four cohort studies (n = 576) were included, involving participants with chronic diseases such as COPD, diabetes, and Parkinson’s disease. The methodological quality averaged 6/9. Significant correlations were observed between control over four health determinant domains—social, behavioral, biological, and healthcare system—and declining health outcomes. Common biases included detection and comparability bias. Discussion: The studies had acceptable methodological quality and low external bias risks. However, the meta-analysis was compromised due to the heterogeneity observed in the exposure variables of the included articles. The review emphasizes the importance of integrating control over health determinants into patient care, with healthcare professionals positioned to enhance patient control and improve outcomes. Conclusions: Lack of control over health determinants, particularly in social and behavioral domains, correlates with poorer health outcomes in patients with chronic conditions. Assessing and improve healthcare control could identify high-risk patients and improve their quality of life. Full article

Tight gas reservoirs present unique forecasting challenges due to steep decline rates, nonlinear production dynamics, and sensitivity to operational conditions. Conventional decline-curve methods and reservoir simulations are limited either by oversimplifying assumptions or by the need for extensive input data, although univariate deep learning models fail to fully capture external influences on well performance. To address these limitations, this study develops a transfer learning–enhanced N-BEATSx (Neural Basis Expansion Analysis Time Series with exogenous variables) framework for multivariate forecasting of tight gas well production. The model integrates exogenous variables, particularly casing pressure, with production histories to jointly represent reservoir behavior and operational effects. A pretraining dataset, comprising more than 100,000-day records from Block S of the Sulige Gas Field, was used to initialize the model, which was subsequently applied in a zero-shot setting to wells A1 and A2. Comparative analysis with the transfer learning-enhanced N-BEATS model demonstrates that N-BEATSx achieves consistently higher accuracy, with RMSE reductions of 23.9%, 39.1%, and 33.1% for Well A1 in short-, medium-, and long-term forecasts, respectively. These advances establish N-BEATSx as a robust tool for multivariate production forecasting, with direct industrial value in optimizing resource allocation, guiding development strategies, and enhancing operational decision-making in unconventional gas fields. Full article

Caregiver self-regulation may be a critical component of caregivers’ effective delivery of caregiver-mediated interventions (CMIs). CMIs are a highly evidence-based group of interventions that target a broad range of challenges, including social communication, emotion regulation, and externalizing behaviors, for autistic and neurotypical children. CMIs teach caregivers to be “coaches” to help their children learn and practice skills in daily life. However, being a good “coach” likely requires caregivers to optimally self-regulate their emotions, thoughts, and behaviors when working with their children in moments that are often emotionally heightened. Caregiver self-regulation is a set of skills that promote parenting autonomy and confidence: self-sufficiency, self-efficacy, self-management, personal agency, and problem solving. This conceptual paper will briefly discuss the literature on the role of caregiver self-regulation in CMIs and argue that future implementation research on CMIs should measure caregiver self-regulation because, in line with recent expansion of the theory of planned behavior, caregiver self-regulation may predict more effective implementation of CMIs. We also argue, in line with CFIR 2.0, that supporting caregiver self-regulation could ultimately improve the implementation of CMIs with regard to each implementation outcome in the Implementation Outcomes Framework. For example, enhancing caregiver self-regulation may improve CMI appropriateness (by increasing alignment with each caregiver’s values and culture), adoption (by increasing engagement to finish the full CMI protocol), and even CMI sustainability (by increasing caregivers’ ability to problem-solve and generalize to new child challenges independently, freeing up provider time to work with new caregivers and allowing the agency to provide the CMI for a reduced relative cost). Should future research demonstrate that caregiver self-regulation is an implementation determinant, future implementation strategies may need to include support for caregiver self-regulation, because it may explain or enhance the implementation of CMIs across early intervention and community mental health systems. Full article

Background: In an era of unprecedented technology adoption in healthcare, it is imperative to understand and predict factors influencing users’ perspective. This study employs a risk-integrated technology acceptance model aiming to identify the determinants of the intention to use mobile health applications among patients with chronic diseases in Romania. Methods: A face-to-face survey method was used to collect research data from 207 subjects, and the partial least squares structural equation modeling approach was employed for data analysis. Results: The behavioral intention to use mobile health applications (INT) was influenced positively by the perceived ease of use (PEOU, f² = 0.358, β = 0.500, p < 0.001) and perceived usefulness (PU, f² = 0.271, β = 0.678, p < 0.001). Another core predictor, with a negative effect on the intention to use, was the user’s perceived risk of using the technology (RISK, f² = 0.239, β = −0.321, p < 0.001), in turn influenced by the perceived degree of cyber-insecurity (CYBER, f² = 0.492, β = 0.639, p < 0.001). Digital self-efficacy (DSE) was identified as an external determinant with strong positive influence on PEOU (f² = 0.486, β = 0.610, p < 0.001). The model shows strong performance, reflected in a high Tenenhaus goodness-of-fit index (0.770) and solid explanatory power for the outcome variable (adjusted R² = 0.718). Conclusions: This study validates an extended risk-integrated technology acceptance model, offering robust insights into the determinants of mobile health application adoption among chronic patients in Romania. The findings provide actionable guidance for designing targeted interventions and healthcare policies to enhance technology adoption in this population. Full article