Search Results (118)

This study examined differences in self-justification and symbolic consumption tendency according to levels of impulse-buying tendency among South Korean adult members of Generation Z sports consumers. Drawing on Cognitive Dissonance Theory (CDT) and Symbolic Self-Completion Theory (SSCT), the study aimed to clarify whether impulse-buying tendency functions as a meaningful basis for segmentation in sports product consumption. Data were collected from South Korean adults aged 20 years or older who had purchased sports products within the previous 12 months. For group-based comparison, participants were classified into low (n = 128) and high (n = 106) impulse-buying tendency groups using a mean-split procedure. A 29-item questionnaire assessed impulse-buying tendency (9 items), self-justification (4 items), and symbolic consumption tendency across five subdimensions (16 items). Confirmatory factor analysis supported the adequacy of the measurement model (χ²/df = 2.285, p < 0.001, IFI = 0.918, TLI = 0.906, CFI = 0.918, SRMR = 0.044, RMSEA = 0.074), with satisfactory reliability and convergent validity. MANOVA results showed that the high impulse-buying tendency group reported significantly higher levels of self-justification, self-development and reinforcement, conformity and belonging, and communication and exchange than the low group. These findings suggest that impulse-buying tendency differentiates consumers in terms of post-purchase cognitive responses and identity-related consumption patterns among adult Generation Z sports consumers. The results highlight the heterogeneity of South Korean Gen Z sports consumers and suggest that sports product marketing may benefit from more segmented strategies based on post-purchase reassurance and symbolic value. However, causal interpretations are limited by the cross-sectional design. Full article

Background: Live-streaming e-commerce has emerged as a significant retail channel, especially in the apparel industry, characterized by high impulse-driven purchase rates and elevated product returns. Reverse logistics processes associated with these returns generate considerable environmental impacts that require systematic evaluation. Methods: This study performs a gate-to-gate Life Cycle Assessment (LCA) using SimaPro software, with a functional unit of 1 kg for one pair of returned jeans. Secondary inventory data were obtained primarily from the Ecoinvent database and supplemented with literature-based estimates for transport distances and packaging masses. Results: Key hotspots analyzed include transportation modes, packaging materials, and waste disposal pathways. Transportation mode selection was the dominant environmental hotspot, with air freight exhibiting the highest impacts across most midpoint and endpoint categories. Low-density polyethylene (LDPE) packaging and landfill disposal of textile waste were also major contributors to global warming, ozone formation, and resource depletion. Conclusions: The findings underscore the necessity of integrating Circular Supply Chain (CSC) principles into reverse logistics network design for live-streaming platforms. Optimizing transportation modes and packaging choices can effectively balance operational responsiveness with environmental sustainability. This study offers empirical evidence and practical decision-supporting insights for more sustainable return management in high-return digital retail environments. Full article

This paper investigates multiple shocks from climate policy uncertainty (CPU) and green finance (GF) to carbon prices (CAPs) using the joint impulse response function (jIRF) method. The empirical findings indicate that jIRF estimates of CPU and GF on CAPs are more accurate compared to the simple sum of generalized impulse response functions, primarily due to the consideration of cross-correlations among simultaneous shocks. This highlights GF’s potential to alleviate the effects of CPU over the entire study period. Moreover, this study focuses on the post-pandemic era and reveals a positive association between GF and CAPs, indicating the evolving role of ecological governance. A key contribution lies in the introduction of the jIRF, which captures the interdependencies among concurrent shocks and underscores the evolving role of GF both before and after the COVID-19 pandemic. This study enhances the theoretical foundation of GF by illustrating its adaptation to changing macroeconomic conditions. Consequently, this study underscores the imperative for China to sustain economic growth, ensure consistency in climate policies, and bolster market-oriented reforms in green financing and carbon markets. Full article

Endogenous growth theory explains the asymmetric dynamic relationship between economic and social development through human capital accumulation and innovation, institutional quality, and demand channels. The objective of this paper is to assess the dynamic relationship between social and economic development in developing countries, where institutional imperfections and development instability create the most pronounced asymmetries. A composite social development index, obtained using the entropy method, operationalizes social development as the expansion of human capabilities in three dimensions: health, education, and material security. A panel vector error correction model (PVECM), estimated using the generalized method of moments (GMM) on panel data from 18 countries in Central Asia, the Middle East, and North Africa for the period 2001–2023, revealed asymmetric dynamic relationships: improved social indicators are associated with a short-term slowdown in economic indicators and more favorable economic dynamics in the medium term. In contrast, economic growth is accompanied by a positive lagged response in social development, although the short-term response may reflect the costs of social adjustment. The influence of control variables confirms the positive role of agglomeration for economic development, revealing the social costs of rapid urbanization and demographic pressure on social development. Estimates of the error correction coefficients indicate a slow adaptation of the system to long-term equilibrium, high inertia, and institutional rigidity of macrosocial processes. Impulse response functions confirm the dynamic and delayed nature of the interaction between economic and social development and positive shocks in the medium term. The obtained empirical results substantiate the need for institutional regulation of policy decisions on human capital accumulation and innovation, as well as social reforms. Full article

(1) Background: Attention-deficit/hyperactivity disorder (ADHD) is characterized by persistent difficulties related to inattention, hyperactivity, and impulsivity, which significantly impair daily functioning. The primary objective of this study is to examine the utility of intra-individual metrics as indicators of dynamic cognitive regulation during the intervention with a serious video game (The Secret Trail of Moon, MOON). (2) Methods: Performance data were collected from participants with ADHD enrolled in a randomized clinical trial. Within the MOON group, intra-individual metrics were derived from repeated gameplay sessions of a continuous performance task. For each participant, simple linear regression models were used to estimate the slope of performance across repeated exposures to the task. Slopes were interpreted as indicators of intra-individual change over time. The within-subject standard deviation was also calculated to observe how much a person’s performance fluctuates between sessions. (3) Results: A total of 76 patients with ADHD participated in the clinical trial and were randomized in a 1:1 ratio (MOON: n = 38, 50% and control: n = 38, 50%). The mean performance index of the MOON group (M = 0.88, SD = 0.09) indicates a generally high level of response accuracy, with moderate inter-individual variability across participants. Notably, moderate intra-individual variability (e.g., RT variability, lapse-related indices) was observed, suggesting fluctuations in attentional control despite stable average performance. The absence of linear improvement should not be interpreted as a lack of intervention effect, but rather as evidence of rapid task familiarization and ceiling effects. (4) Conclusions: Intra-individual variability may be a key metric for understanding attentional control in ecological, game-based environments. In this context, performance variability and attentional stability emerge as more sensitive indicators of cognitive regulation than mean-level changes. Full article

This study examines dynamic interdependencies and risk transmission among major cryptocurrencies and traditional financial assets, including Bitcoin, Ethereum, U.S. equities, and gold, over the period 2017–2024. Particular attention is given to the structural shift associated with the 2024 U.S. spot Bitcoin exchange-traded fund (ETF) approval, which marked a significant milestone in the institutionalization of cryptocurrency markets. Using daily data, the analysis distinguishes volatility-driven co-movement from structural spillover effects across markets. Dependence structures are modeled using tail-sensitive Student-t copulas applied to GARCH-filtered returns to capture nonlinear and extreme co-movements, while a vector autoregressive framework combined with generalized impulse response functions and Diebold–Yilmaz connectedness measures is employed to evaluate order-invariant shock transmission dynamics across pre- and post-ETF regimes. The results reveal three main findings. First, cryptocurrencies display strong internal dependence and short-horizon contagion, with Bitcoin consistently acting as the dominant transmitter of shocks to Ethereum over an approximately three-day transmission window. Second, linkages between cryptocurrencies and equity markets remain moderate and largely regime-dependent rather than indicative of persistent structural spillovers. Third, gold remains weakly connected throughout the sample, maintaining its role as a diversification asset. Portfolio analysis further indicates that including Bitcoin can reduce portfolio variance by 4–7% and Value-at-Risk by up to 5%, although economic gains are sensitive to transaction costs. Overall, the findings suggest that cryptocurrencies function as a partially segmented asset class, offering conditional diversification benefits despite increasing institutional adoption. Full article

Fractional calculus and distribution theory share a common conceptual origin in the symbolic interpretation of differentiation and integration. Despite this connection, most developments in fractional calculus have traditionally been formulated within the framework of ordinary functions, while the systematic use of distributions remains limited. In this work, a novel distributional framework is developed by constructing a fractional Taylor representation of the product of Euler gamma and Riemann zeta functions in terms of fractional derivatives of the Dirac delta distribution. The proposed formulation enables the derivation of new fractional identities via Laplace transformation and facilitates the analytical solution of fractional differential equations containing such functions. Closed-form solutions are obtained in both classical and generalized distributional senses, allowing the extension of solutions from the positive real axis to the entire real line. Furthermore, the framework is applied to fractional operators of Erdélyi–Kober type, yielding new integral and derivative transforms. Fractional differential and integral equations with singular terms arise naturally in several engineering models involving memory effects, impulsive responses, and anomalous transport phenomena. However, the presence of nonremovable singularities—such as those associated with Euler gamma and Riemann zeta functions—significantly restricts the applicability of classical analytical methods. Overall, the proposed distributional framework bridges the gap between abstract fractional calculus and practical engineering models by enabling analytical solutions of fractional systems with singular memory kernels that were previously inaccessible using classical methods. Full article

Mechanistic tumor growth models are widely used to describe disease progression and treatment response, but their utility depends on accurate estimation of parameters governing the underlying biological processes. In this study, we employ a Physics-Informed Neural Network (PINN) to estimate the parameters of a tumor growth model that captures both tumor dynamics and drug effects. We introduce a piecewise PINN that splits the time domain at dosing events to handle non-smooth dose-driven dynamics, and we incorporate drug injection by representing the pharmacokinetic subsystem analytically via an impulse-response function. The approach is evaluated on synthetic tumor-volume trajectories generated from known parameter sets and dosing schedules from an experimental cohort of 54 mice. Across the cohort, the PINN accurately reconstructs total tumor volume and robustly estimates the tumor proliferation rate a, with inferred values closely aligned with the true values ($R^2=0.841$). The framework was also able to estimate the drug killing effect parameter b. This consistency is further supported by forward ODE simulations using the PINN-estimated parameters. Within the evaluated setting, performance depended on the model structure, parameter identifiability, and training configuration, underscoring the need for careful loss weighting and further validation. Overall, the results demonstrate the feasibility of piecewise PINNs for parameter inference in tumor growth models and support their further study in realistic therapeutic settings. Full article

Indonesia’s financial system is bank-centric, with banks managing approximately 78% of the nation’s financial assets; therefore, the effectiveness of monetary policy transmission depends on banks’ responsiveness to the central bank’s interest rate policy (the BI Rate). However, a policy-relevant anomaly persists: deposit rate pricing is more strongly anchored to the Deposit Insurance benchmark (IDIC Rate) than to the BI Rate. This study argues that this research is significant because it identifies a “Dual Benchmark System” that traditional single-anchor models fail to address, representing a critical friction in emerging market transmission. This study examines this dual-benchmark paradigm and the associated asymmetric risks using a panel VAR with a Generalized Impulse Response Function (GIRF) on quarterly data for 63 commercial banks from 2010 to 2024. The results indicate that IDIC Rate shocks have a larger and more persistent effect on deposit rates than BI Rate shocks, generating asymmetric transmission risks. This dominance creates a structural “price ceiling” that keeps funding costs high, ultimately raising lending rates for borrowers and distorting deposit growth rates. Furthermore, this analysis reveals that external policy signals are far more influential than internal financial performance. This suggests that under the Basel III framework and prevailing financial regulations, banks prioritize liquidity compliance and safety net protection over internal operational efficiency. Macroeconomic shocks remain weaker than policy shocks and dissipate more quickly. This finding reveals a potential systemic coordination risk, implying an urgent need for tighter policy coordination between the Central Bank and the IDIC to reduce structural frictions, maintain transmission effectiveness, and protect long-term financial stability. Full article

Ultra-Wideband (UWB) technology enables centimeter-level indoor positioning, but it remains highly sensitive to channel dynamics, multipath and Non-Line-of-Sight (NLoS) propagation. Recent studies increasingly apply Machine Learning (ML) methods to address these issues by modeling nonlinear channel behavior and mitigating ranging bias. This paper presents a comprehensive review and provides a critical synthesis of 169 research works published between 2020 and 2024, offering an integrated overview of how ML techniques are incorporated into UWB-based Indoor Positioning Systems (IPSs). The studies are grouped according to their functional objective, learning algorithm, network architecture, evaluation metrics, dataset, and experimental setting. The results indicate that most approaches apply ML to channel classification and ranging error mitigation, with Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and hybrid CNN–Long Short-Term Memory (LSTM) architectures being among the most common choices due to their ability to capture spatial and temporal patterns in the Channel Impulse Response (CIR). Despite the reported accuracy improvements, scalability and cross-environment generalization remain open challenges, largely due to the scarcity of public datasets and the lack of standardized evaluation protocols. Emerging research trends highlight growing interest in transfer learning, domain adaptation, and federated learning, along with lightweight and explainable models suitable for embedded and multi-sensor systems. Overall, this review summarizes the progress made in ML-driven UWB localization, identifies current gaps, and outlines promising directions toward more robust and generalizable indoor positioning frameworks. Full article

This study examines the short- and medium-run linkages within Mexico’s energy–economy system under conditions of limited information. The analysis is motivated by the structural relevance of hydrocarbons for fiscal stability and by the growing need to understand how energy shocks propagate through economic and environmental subsystems. Using a vector autoregression (VAR) framework, nine interdependent macroeconomic and energy variables are jointly evaluated after harmonizing mixed-frequency data, standardizing series, and ensuring stationarity through ADF and KPSS tests. Dynamic responses are assessed through impulse response functions (IRFs), generalized IRFs (GIRFs), and forecast error variance decomposition (FEVD), complemented by Granger causality tests. Results show that oil rents exert a persistent and positive influence on GDP and public expenditure, while shocks to coal-fired generation and oil prices consistently reduce economic activity and increase emissions. Renewable capacity expands pro-cyclically but displays limited autonomous effects. Overall, the evidence reveals a fiscally and environmentally constrained system dominated by hydrocarbons, underscoring the importance of improving PEMEX’s operational efficiency, accelerating fiscal diversification, and strengthening institutional conditions for renewable investment. Full article

Oligodendrocytes (OLs) constitute the main glial population in the central nervous system and are indispensable for the stability and performance of neural networks. Although best known for generating and maintaining myelin to speed impulse conduction, their influence extends further. By modulating myelin in response to activity, supplying metabolic substrates, and engaging in neuroimmune communication, OLs help preserve the structural integrity and plasticity of neuronal circuits. Growing evidence now positions defective OLs as central players in Alzheimer’s disease (AD). Experimental work suggests that OL injury can act as an early trigger, fostering amyloid-β (Aβ) deposition and Tau hyperphosphorylation. Conversely, toxic Aβ aggregates and pathological Tau proteins damage OLs, causing myelin breakdown and progressive neurodegeneration that fuels a self-perpetuating cycle. Here, we synthesize current knowledge of OL physiology and its multifaceted contributions to AD pathogenesis, with particular attention to the bidirectional interplay between OL dysfunction and the disease’s core features—Aβ and tau. On this basis, we outline prospective therapeutic avenues to protect or restore oligodendrocyte function in AD. Full article

Currently, there remains a lack of systematic quantitative analysis of the acoustic impact mechanism of ancestral temple theatres in relation to their core function of opera performance. This paper takes the Zhaomutang—a typical ancestral temple theatre in northeast Jiangxi—as an example, and comprehensively uses on-site mapping, impulse response testing, and ODEON three-dimensional sound field simulation to conduct acoustic sensitivity analysis on five key spatial elements of the theatre. The results show that the theatre has a hierarchical sound field pattern along its depth, characterized by “high in the front, low in the rear, stronger on the sides and weaker in the middle”. The front patio and the Xiangtang support the clarity of Gan opera dialogue and the fullness of singing through early lateral reflections and moderate reverberation (EDT of 0.8–1.1 s, C₈₀ of 3.2–6.1 dB). However, the rear patio and the Qintang show apparent loudness deficiency (G of −1.5–3.2 dB) and lack of spatial immersion (LF₈₀ below 0.23). The most effective optimization comes from the reconstruction of the geometric relationship between performers and audience: moving the performers forward and appropriately raising the stage and audience area floor can significantly shorten the rear area EDT and increase C₈₀ and G; in contrast, the improvement in sound quality brought about by adding a patio cover and raising the gables is minimal, and the changes in various parameters are generally less than 1 JND. Based on this, the “schedule priority—reversible intervention” acoustic maintenance strategy for living heritage is proposed, and it is suggested that reversible reflective components be set in the side corridor to specifically enhance the sense of immersion in the rear area sound field. The study constructs a quantitative correlation framework of space, materials, and sound field, providing methodological support and parameter basis for the acoustic assessment and protective utilization of ancestral temple theatres. Full article

Background/Objective: The Suppression Head Impulse Paradigm (SHIMP) is a specialized variant of the Head Impulse Test (HIT), designed to evaluate the suppression of the angular Vestibulo-Ocular Reflex (aVOR) by central mechanisms. These mechanisms are primarily mediated by brainstem structures, including the vestibular nuclei, their projections to ocular motor nuclei, and modulatory inputs from the cerebellum. Damage to these areas can impair the generation of anti-compensatory saccades (ACs), even when the peripheral vestibular apparatus remains intact. The present study explores this phenomenon in a cohort of patients with neurological disorders known to potentially involve the brainstem, including multiple sclerosis, severe traumatic brain injury, stroke, and Parkinson’s disease. Methods: This cross-sectional study included 119 patients with multiple sclerosis (PwMS), severe traumatic brain injury (PwTBI), stroke (PwS), and Parkinson’s disease (PwPD). The video Head Impulse Test was performed to assess the aVOR gain across all semicircular canals using both the HIMP and SHIMP. The presence, absence, or delay of ACs was systematically recorded. Results: Among the 119 patients evaluated (238 semicircular canals), 24 (20%) demonstrated normal aVOR gain but failed to generate ACs during SHIMP. The absence of ACs was observed in seven PwMS, five with PwTBI, six with PwS, and six with PwPD. Conclusions: The absence of ACs despite normal aVOR gain suggests a potential impairment in the central pathways controlling saccadic responses, independently of peripheral vestibular function. These findings underscore the clinical relevance of integrating the SHIMP into vestibular assessments to improve the identification of central vestibular dysfunction in neurological disorders. Full article

The numerous uncertainties in the process of water resource development and utilization bring multiple risks to water resource management. To enhance socio-economic benefits while considering ecological benefits, it is urgent to deeply explore risks. In this paper, Nuozhadu, Jinghong, and Ganlanba hydropower stations on the lower reaches of the Lancang River are taken as the objects. To balance the socio-economic and ecological benefits, a multi-objective optimization operation model was constructed. To describe the risk transmission, a VAR model was constructed, and the dynamic transmission among risks was explored. The results show that the ratio of ecological change is 10.38%, and the cascade power generation is 33,243 GWh (2% higher than the designed). The impacts of the perturbation for each risk on itself and others are quantitatively analyzed by the impulse response function. It is concluded that the transmission direction is generally positive, but the increase in ecological risk has negative impacts on risks of output and abandoned water, and risks of power generation and output also negatively affect abandoned water risk. Finally, the risk transmission is quantitatively estimated by the variance decomposition method. It is concluded that the power generation risk contributes most to the output and ecology risks, the ecological risk only contributes significantly to the abandoned water risk (the contribution rate is 6.30%), and the abandoned water risk contributes a lot to the others. Full article