Search Results (442)

Background: Cognitive behavioral therapy (CBT) has shown consistent efficacy in individuals with psychosis, as supported by many trials. One classical distinction is that between affective and non-affective psychosis. Few studies have specifically examined the possible moderating role of substantial affective elements. In this systematic review and meta-regression analysis, we assess how CBT response differs across the affective spectrum in psychosis. Methods: We included studies assessing various CBT modalities, including third-wave therapies, administered in people with psychosis. The study protocol is published in the Open Science Framework. Meta-regression was conducted to assess whether the proportion of participants with affective psychosis (AP), as proxied by a documented diagnosis of schizoaffective (SZA) disorder, moderated CBT efficacy across positive, negative, and depressive symptom domains. Results: The literature search identified 4457 records, of which 39 studies were included. The median proportion of SZA disorder participants was 17%, with a total of 422 AP participants represented. Meta-regression showed a trend toward lower CBT efficacy for positive symptoms with a higher SZA disorder proportion (β = +0.10 SMD per 10% increase in AP; p = 0.12), though it was not statistically significant. No significant associations were found for negative (β = +0.05; p = 0.73) or depressive symptoms (β = −0.02; p = 0.78). Heterogeneity was substantial across all models (I² ranging from 54% to 80%), and funnel plot asymmetry was observed in negative and depressive symptoms, indicating possible publication bias. Risk of bias assessment showed the anticipated inherent difficulty of psychotherapies in blinding and possibly dropout rates affecting some studies. Conclusions: Affective symptoms may reduce the effectiveness of CBT for positive symptoms in psychotic disorders, although the findings did not reach statistical significance. Other patient-level characteristics in psychosis could indicate which patients can benefit most from CBT modalities. Full article

Understanding vehicle emissions is essential for developing effective carbon reduction strategies in the transport sector. Conventional emission models often assume homogeneity and linearity, overlooking real-world asymmetries that arise from variations in vehicle design and powertrain configurations. This study explores how machine learning and explainable AI techniques can effectively capture both symmetric and asymmetric emission patterns across different vehicle types, thereby contributing to more sustainable transport planning. Addressing a key gap in the existing literature, the study poses the following question: how do structural and behavioral factors contribute to asymmetric emission responses in internal combustion engine vehicles compared to new energy vehicles? Utilizing a large-scale Spanish vehicle registration dataset, the analysis classifies vehicles by powertrain type and applies five supervised learning algorithms to predict CO₂ emissions. SHapley Additive exPlanations (SHAPs) are employed to identify nonlinear and threshold-based relationships between emissions and vehicle characteristics such as fuel consumption, weight, and height. Among the models tested, the Random Forest algorithm achieves the highest predictive accuracy. The findings reveal critical asymmetries in emission behavior, particularly among hybrid vehicles, which challenge the assumption of uniform policy applicability. This study provides both methodological innovation and practical insights for symmetry-aware emission modeling, offering support for more targeted eco-design and policy decisions that align with long-term sustainability goals. Full article

With consumers increasing in environmental awareness, manufacturers have integrated green R&D into their strategies, aiming to grasp the green market. However, manufacturers may be too bullish on the market potential of green products and maintain an optimistic attitude toward green R&D. Despite having an optimistic attitude, manufacturers often have no demand information advantage over downstream retailers due to their position in the supply chain, away from the market. It is worth exploring what impact optimistic green R&D in a sustainable supply chain with demand information asymmetry will have. Previous studies have not managed to reveal this. In this study, a stylized model is introduced to explore this question. The main findings are as follows: (1) optimistic green R&D increases the feasibility of the retailer sharing demand information, which facilitates information communication in the sustainable supply chain; (2) in most cases, optimistic green R&D does not bring higher profits for the manufacturer, yet is likely to allow the retailer to earn more, thereby resulting in a loss–win outcome; and (3) depending on the green R&D efficiency of the manufacturer and the consumer’s environmental awareness, optimistic green R&D may not generate higher environmental benefits. Full article

Synthetic nanopores were recently demonstrated with memristive and nonlinear voltage-current behaviors, akin to ion channels in a cell membrane. Such ionic devices are considered a promising candidate for the development of brain-inspired neuromorphic computing techniques. In this work, we show the composite behavior of nanopore-array large memristors, formed with different membrane materials, pore sizes, electrolytes, and device arrangements. Anodic aluminum oxide (AAO) membranes with 5 nm and 20 nm diameter pores and track-etched polycarbonate (PCTE) membranes with 10 nm diameter pores are tested and shown to demonstrate memristive and nonlinear behaviors with approximately 10⁷–10¹⁰ pores in parallel when electrolyte concentration across the membranes is asymmetric. Ion diffusion through the large number of channels induces time-dependent electrolyte asymmetry that drives the system through different memristive states. The behaviors of series composite memristors with different configurations are also presented. In addition to helping understand fluidic devices and circuits for neuromorphic computing, the results also shed light on the development of field-assisted ion-selection-membrane filtration techniques as well as the investigations of large neurons and giant synapses. Further work is needed to de-embed parasitic components of the measurement setup to obtain intrinsic large memristor properties. Full article

The heart is innervated by the autonomic nervous system (ANS), which plays a role in regulating the heart rate. Cross-country mountain biking (MTBXC) is a sport with high physiological demands, where the autonomic nervous system plays a significant role. The main objective of this study was to analyze the asymmetry of heart rate in Colombian National Team mountain bikers, sub-23 category, during a 20 min cardiovascular autonomic profile test. Method: The cardiovascular autonomic profile was measured through heart rate variability during a 20 min test, divided into eight phases (supine, controlled ventilation at 10 cycles/min, controlled ventilation at 12 cycles/min, postural change, orthostasis, Ruffier test, 1 min recovery, and final recovery) in a group of n = 10 MTB cyclists from the National Sub-23 Team, including 5 males and 5 females. Results: The results for the male athletes were as follows: age: 19 ± 1 years; VO2max: 67.5 mL/kg/min; max power: 355 W; HRmax: 204 bpm. The results for the female athletes were as follows: age: 19 ± 1 years; VOmax: 58.5 mL/kg/min; max power: 265 W; HRmax: 194 bpm. Both genders showed the expected autonomic behavior in each phase. Asymmetrical propagation of heart rate was observed, with a greater deceleration pattern after postural changes and effort and a symmetrical acceleration pattern in these two phases. Discussion: Athletes exhibit increased vagal response compared to non-athletes. Mountain bikers show rapid heart rate reduction after exertion. Conclusion: This study demonstrates how mountain bikers exhibit increased heart rate deceleration following sympathetic stimuli. Full article

User requirements are the core driving force behind the iterative development of complex products. Their comprehensive collection, accurate interpretation, and effective integration directly affect design outcomes. However, current practices often depend heavily on single-source data and designer intuition, resulting in incomplete, biased, and fragile design decisions. Moreover, multi-source heterogeneous user requirements often exhibit inherent asymmetry and imbalance in both structure and contribution. To address these issues, this study proposes a symmetric and balanced optimization method for multi-source heterogeneous user requirements in complex product design. Multiple acquisition and analysis approaches are integrated to mitigate the limitations of single-source data by fusing complementary information and enabling balanced decision-making. Firstly, unstructured text data from online reviews are used to extract initial user requirements, and a topic analysis method is applied for modeling and clustering. Secondly, user interviews are analyzed using a fuzzy satisfaction analysis, while eye-tracking experiments capture physiological behavior to support correlation analysis between internal preferences and external behavior. Finally, a cooperative game-based model is introduced to optimize conflicts among data sources, ensuring fairness in decision-making. The method was validated using a case study of oxygen concentrators. The findings demonstrate improvements in both decision robustness and requirement representation. Full article

The model explicitly incorporates boundary conditions that account for the complex interplay between sections experiencing varying degrees of reduction. This interaction significantly influences the overall deformation behavior and force loading. The control effect is associated with boundary conditions determined by the unevenness of the compression, which have certain quantitative and qualitative characteristics. These include additional loading, which is less than the main load, which implements the process of plastic deformation, and the ratio of control loads from the entrance and exit of the deformation site. According to this criterion, it follows from experimental data that the controlling effect on the plastic deformation site occurs with a ratio of additional and main loading in the range of 0.2–0.8. The next criterion is the coefficient of support, which determines the area of asymmetry of the force load and is in the range of 2.00–4.155. Furthermore, the criterion of the regulating force ratio at the boundaries of the deformation center forming a longitudinal plastic shear is within the limits of 2.2–2.5 forces and 1.3–1.4 moments of these forces. In this state, stresses and deformations of the plastic medium are able to realize the effects of plastic shaping. The force effect reduces with an increase in the unevenness of the deformation. This is due to a change in height of the longitudinal interaction of the disparate sections of the strip. There is an appearance of a new quality of loading—longitudinal plastic shear along the deformation site. The unbalanced additional force action at the entrance of the deformation source is balanced by the force source of deformation, determined by the appearance of a functional shift in the model of the stress state of the metal. The developed theory, using the generalized method of an argument of functions of a complex variable, allows us to characterize the functional shift in the deformation site using invariant Cauchy–Riemann relations and Laplace differential equations. Furthermore, the model allows for the investigation of material properties such as the yield strength and strain hardening, influencing the size and characteristics of the identified limit state zone. Future research will focus on extending the model to incorporate more complex material behaviors, including viscoelastic effects, and to account for dynamic loading conditions, more accurately reflecting real-world milling processes. The detailed understanding gained from this model offers significant potential for optimizing mill roll designs and processes for enhanced efficiency and reduced energy consumption. Full article

This paper develops a novel modeling framework that integrates time-varying quantile-based spillover effects into a regime-switching realized volatility model. A dynamic spillover factor is constructed by identifying the most influential contributors to Bitcoin’s realized volatility across different quantile levels. This quantile-layered structure enables the model to capture heterogeneous spillover paths under varying market conditions at a macro level while also enhancing the sensitivity of volatility regime identification via its incorporation into a time-varying transition probability (TVTP) Markov-switching mechanism at a micro level. Empirical results based on the cryptocurrency market demonstrate the superior forecasting performance of the proposed TVTP-MS-HAR model relative to standard benchmark models. The model exhibits strong capability in identifying state-dependent spillovers and capturing nonlinear market dynamics. The findings further reveal an asymmetric dual-tail amplification and time-varying interconnectedness in the spillover effects, along with a pronounced asymmetry between market capitalization and systemic importance. Compared to decomposition-based approaches, the X-RV type of models—especially when combined with the proposed quantile-driven factor—offers improved robustness and predictive accuracy in the presence of extreme market behavior. This paper offers a coherent approach that bridges phenomenon identification, source localization, and predictive mechanism construction, contributing to both the academic understanding and practical risk assessment of cryptocurrency markets. Full article

Gas–water two-phase flow in horizontal and inclined wells is significantly influenced by gravitational forces and spatial asymmetry around the wellbore, resulting in complex and variable flow patterns. Accurate measurement of water holdup is essential for analyzing phase distribution and understanding multiphase flow behavior. Water holdup imaging provides a valuable means for visualizing the spatial distribution and proportion of gas and water phases within the wellbore. In this study, air and tap water were used to simulate downhole gas and formation water, respectively. An array capacitance arraay tool (CAT) was employed to measure water holdup under varying total flow rates and water cuts in a horizontal well experimental setup. A total of 228 datasets were collected, and the measurements were processed in MATLAB (2020 version) using three interpolation algorithms: simple linear interpolation, inverse distance interpolation, and Lagrangian nonlinear interpolation. Water holdup across the wellbore cross-section was also calculated using arithmetic averaging and integration methods. The results obtained from the three imaging algorithms were compared with these reference values to evaluate accuracy and visualize imaging performance. The CAT demonstrated reliable measurement capabilities under low- to medium-flow conditions, accurately capturing fluid distribution. For stratified flow regimes, the linear interpolation algorithm provided the clearest depiction of the gas–water interface. Under low- to medium-flow rates with high water content, both inverse distance and Lagrangian methods produced more refined images of phase distribution. In dispersed flow conditions, the Lagrangian nonlinear interpolation algorithm delivered the highest accuracy, effectively capturing subtle variations within the complex flow field. Full article

This study investigates whether internationally diversified firms substitute between accrual-based and real earnings management and examines how institutional supervision moderates this relationship. Drawing on a sample of Taiwanese firms listed on the Taiwan Stock Exchange from 2003 to 2016, we conduct regression analyses to test our hypothesis. We find that internationally diversified firms actively shift between accrual and real earnings management strategies depending on the constraints they face. Specifically, firms tend to rely more on accrual-based manipulation when information asymmetry is high and switch to real earnings management when accruals are more easily detected. We also show that stronger institutional supervision—measured by information transparency and investor protection—significantly curbs accrual-based earnings management. These findings reflect the higher volatility and agency problems associated with international operations, such as exposure to foreign risks and the distance between parent and subsidiary firms. By highlighting the conditions under which firms manage earnings and the supervisory mechanisms that constrain such behavior, this study offers practical insights for managers seeking to smooth earnings, investors aiming to evaluate firm transparency, and policymakers designing regulations to deter opportunistic financial reporting. Full article

This study presents a microfluidic chip platform designed using a multiscale 3D printing strategy for fabricating microfluidic chips with integrated, high-resolution, and customizable membrane structures. By combining two-photon polymerization (2PP) for submicron membrane fabrication with liquid crystal display printing for rapid production of larger components, this approach addresses key challenges in membrane integration, including sealing reliability and the use of transparent materials. Compared to fully 2PP-based fabrication, the multiscale method achieved a 56-fold reduction in production time, reducing total fabrication time to approximately 7.2 h per chip and offering a highly efficient solution for integrating complex structures into fluidic chips. The fabricated chips demonstrated excellent mechanical integrity. Burst pressure testing showed that all samples withstood internal pressures averaging 1.27 ± 0.099 MPa, with some reaching up to 1.4 MPa. Flow testing from ~35 $\mathsf{\mu }$L/min to ~345 $\mathsf{\mu }$L/min confirmed stable operation in 75 $\mathsf{\mu }$m square channels, with no leakage and minimal flow resistance up to ~175 $\mathsf{\mu }$L/min without deviation from the predicted behavior in the 75 $\mathsf{\mu }$m. Membrane-integrated chips exhibited outlet flow asymmetries greater than 10%, indicating active fluid transfer across the membrane and highlighting flow-dependent permeability. Overall, this multiscale 3D printing approach offers a scalable and versatile solution for microfluidic device manufacturing. The method’s ability to integrate precise membrane structures enable advanced functionalities such as diffusion-driven particle sorting and molecular filtration, supporting a wide range of biomedical, environmental, and industrial lab-on-a-chip applications. Full article

Background: Current cosmological fits typically assume a direct relation between cosmic time (t) and the scale factor ($a\left(t\right)$), yet this ansatz remains largely untested across diverse observations. Objectives: We (i) test whether a single power-law scaling ($a\left(t\right)\propto t^{\alpha }$) can reproduce late- and early-time cosmological data and (ii) explore whether a dynamically evolving ($\alpha \left(t\right)$), modeled as a scalar–tensor field, naturally induces directional asymmetry in cosmic evolution. Methods: We fit a constant-$\alpha$ model to four independent datasets: 1701 Pantheon+SH0ES supernovae, 162 gamma-ray bursts, 32 cosmic chronometers, and the Planck 2018 TT spectrum (2507 points). The CMB angular spectrum is mapped onto a logarithmic distance-like scale ($\mu ={log}_{10}{D}_{\ell }$), allowing for unified likelihood analysis. Each dataset yields slightly different preferred values for $H_0$ and $\alpha$; therefore, we also perform a global combined fit. For scalar–tensor dynamics, we integrate $\alpha \left(t\right)$ under three potentials—quadratic, cosine, and parity breaking (${\alpha }^{3}sin\alpha$)—and quantify directionality via forward/backward evolution and Lyapunov exponents. Results: (1) The constant-$\alpha$ model achieves good fits across all datasets. In combined analysis, it yields $H_0\simeq 70\mathrm{km}{\mathrm{s}}^{-1}{\mathrm{Mpc}}^{-1}$ and $\alpha \simeq 1.06$, outperforming $\Lambda$CDM globally ($\Delta \mathrm{AIC}\simeq 401254$), though $\Lambda$CDM remains favored for some low-redshift chronometer data. High-redshift GRB and CMB data drive the improved fit. Numerical likelihood evaluations are approximately three times faster than for $\Lambda$CDM. (2) Dynamical $\alpha \left(t\right)$ models exhibit time-directional behavior: under asymmetric potentials, forward evolution displays finite Lyapunov exponents (${\lambda }_L\sim {10}^{-3}$), while backward trajectories remain confined (${\lambda }_L<0$), realizing classical arrow-of-time emergence without entropy or quantum input. Limitations: This study addresses only homogeneous background evolution; perturbations and physical derivations of potentials remain open questions. Conclusions: The time-scaling approach offers a computationally efficient control scenario in cosmological model testing. Scalar–tensor extensions naturally introduce classical time asymmetry that is numerically accessible and observationally testable within current datasets. Code and full data are available. Full article

Information asymmetry leads to farmers’ opportunistic behavior of disobeying pest control regulations in certified vegetable areas, but the improvement of farmers’ digital literacy has become an important means to break through the constrained dilemma of pest control information and change farmers’ pest control behaviors. Based on survey data from certified vegetable areas of Shaanxi, Gansu, and Ningxia provinces in China, this study used Heckman two-stage model to analyze the impact of the improvement of farmers’ digital literacy on opportunistic behavior in pest control. The results are as follows. Firstly, the improvement of farmers’ digital literacy can restrain their opportunistic behavior in pest control. Secondly, the improvement of farmers’ digital literacy restrain their opportunistic behavior through three paths, namely, enhancing the awareness of obeying pest control regulations for certified vegetables, reducing the cost and risk of pest control in obeying the certification standards. Thirdly, the traceable certification label plays a positive moderating role in the process of improving digital literacy to restrain farmers’ opportunistic behavior. Accordingly, this study suggests strengthening the training of farmers’ digital literacy, promoting the digitalized traceability system for certified vegetables, establishing examination mechanisms for online pesticide purchases and logistics distribution, and imposing severe penalties for opportunistic behaviors. Full article

This study investigates the solution structure of the nonlinear Rayleigh oscillator equation through two widely used semi-analytical techniques: the Laplace–Adomian Decomposition Method (LADM) and the Homotopy Perturbation Method (HPM). The Rayleigh oscillator exhibits inherent asymmetry in its nonlinear damping term, which disrupts the time-reversal symmetry present in linear oscillatory systems. Applying the LADM and HPM, we derive approximate solutions for the Rayleigh oscillator. Due to the absence of exact analytical solutions in the literature, these approximations are benchmarked against high-precision numerical results obtained using Mathematica’s NDSolve function. We perform a detailed error analysis across different damping parameter values ε and time intervals. Our results reveal how the asymmetric damping influences the accuracy and convergence behavior of each method. This study highlights the role of nonlinear asymmetry in shaping the solution dynamics and provides insight into the suitability of the LADM and HPM under varying conditions. Full article

In this paper, the low-cycle fatigue deformation behavior of polycrystalline γ-TiAl alloys at different temperatures was investigated by molecular dynamics simulations. The results showed that the fatigue process comprises an initial cyclic softening stage followed by saturation, and the stress–strain response of the material shows significant asymmetry. With an increase in temperature, the asymmetry between tensile and compressive stresses gradually decreases, and the amplitude of saturated stress decreases significantly. The decrease in dislocation density leads to the cyclic softening of the alloy, and the evolution of dislocation density is temperature-dependent. The dislocation density first decreases and then tends to be stable, while at 900 °C and 1000 °C, it shows an abnormal trend of decreasing first and then increasing. In addition, microscopic mechanism analysis shows that grain coarsening, dislocation annihilation, and phase instability lead to the cyclic softening of the alloys. The fatigue plastic accumulation at low temperatures is mainly dominated by dislocation slip, while at high temperatures, grain boundary slip gradually replaces dislocation slip and becomes the main deformation mechanism. This work reveals new insights into the mechanical behavior of polycrystalline γ-TiAl alloys under cyclic plasticity and temperature-dependent deformation mechanisms. Full article