Search Results (671)

This study investigates the dynamic relationship between key macroeconomic indicators, specifically inflation (CPI), the Federal Funds Rate, GDP growth, unemployment, and money supply, and U.S. stock market returns, represented by the S&P 500 index, over the period January 2015 to June 2025. The objective is to understand how inflation and monetary policy affect market performance in both the short and long run. Using an Autoregressive Distributed Lag (ARDL) modeling framework and Error Correction Model (ECM), the study examines monthly S&P 500 returns alongside macroeconomic variables, accounting for lagged effects and potential cointegration. The model captures both immediate and delayed impacts, employing the Bounds Testing approach to confirm long-run equilibrium relationships. Results show significant mean-reversion in stock returns, a delayed negative impact of inflation and interest rate increases, and a positive contemporaneous response to GDP growth. Unemployment exhibits a counterintuitive positive effect on returns, suggesting forward-looking investor expectations. The money supply also positively influences equity prices, supporting liquidity-based asset pricing theories. This paper provides updated empirical evidence on macro-finance linkages and highlights the complex interplay of monetary policy, inflation, and market expectations in shaping U.S. equity returns. Full article

Logistics and warehouse operations experience an increasing pressure to adopt sustainable practices. The logistics industry generates substantial material waste, with cardboard being the primary packaging material. Adopting Circular Economy (CE) principles to control this waste is important for enhancing sustainability. However, there is a lack of studies on transforming warehouses into more sustainable operations. This paper studies the ability to transform the linear supply chain of a distribution warehouse into a circular supply chain by applying lean manufacturing principles to eliminate cardboard waste. A structured framework is presented to outline the project’s methodology and illustrate the steps taken to apply the concept of CE. The paper also tests the capability to simulate warehouse operations with engineering software using limited available data to generate various scenarios. This study contributes by showing how discrete-event simulation combined with VSM and 6R principles can provide operational insights under data-constrained conditions. Bridging the gap between theory and practice. Multiple operational scenarios were modelled and run, including peak and off-peak demand periods, as well as a sensitivity analysis for recycling durations. A comparative evaluation is shown to demonstrate the effectiveness of each alternative and determine the most feasible solution. Results indicate that introducing recycling activities created some bottlenecks in the system and reduced its efficiency. Furthermore, suggestions for future improvements are presented, ensuring that on-site actions are grounded in a simulation that reflects reality. Full article

In order to understand how health beliefs map onto health behaviors, a national survey, administered in the wake of the COVID-19 campus closures, was conducted to explore college students’ mind-body health beliefs and their health behaviors (across dimensions of physical exercise, diet/nutrition, and socialization). To this end, the Mind-Body Health Screener (MBHS), a five-item, Likert scale, brief measure, was developed. The present study applied an online survey design administered nationally to U.S. undergraduate students during the initial lockdowns with the pandemic (n = 557). To examine the psychometric properties of the MBHS, exploratory and confirmatory factor analyses were run as well as measures of reliability. Furthermore, linear regressions and effect sizes were computed to understand the connection between mind-body health beliefs and behaviors. While initial data supported the psychometric properties of the Mind-Body Health Screener (MBHS) developed for this purpose, substantive results suggested that mind-body health beliefs did not relate to mind-body health behaviors (either before or after the campus closures), aligning with the Cognitive Dissonance Theory. Post hoc analysis did, however, suggest a significant change in health behaviors from pre-campus closures to during the closures, suggesting students engaged in more physical exercise, eating behaviors, and socializing before campus closed, observed with small to large effects. Taken together, the findings of the present study illustrate how the Cognitive Dissonance Theory is a relevant perspective to consider the relation between health beliefs and behaviors during a period of immense stress, such as the COVID-19 initial campus closures. Full article

In the context of low carbon, achieving accurate forecasting of electrical energy is critical for power management with the continuous development of power systems. For the sake of improving the performance of load forecasting, an improved MNet-Atten electric vehicle charging load forecasting based on composite decomposition and the evolutionary predator–prey and strategy model is proposed. In this light, through the data decomposition theory, each subsequence is processed using complementary ensemble empirical mode decomposition and filters out high-frequency white noise by using singular value decomposition based on matrix operation, which improves the anti-interference ability and computational efficiency of the model. In the model construction stage, the MNet-Atten prediction model is developed and constructed. The convolution module is used to mine the local dependencies of the sequences, and the long term and short-term features of the data are extracted through the loop and loop skip modules to improve the predictability of the data itself. Furthermore, the evolutionary predator and prey strategy is used to iteratively optimize the learning rate of the MNet-Atten for improving the forecasting performance and convergence speed of the model. The autoregressive module is used to enhance the ability of the neural network to identify linear features and improve the prediction performance of the model. Increasing temporal attention to give more weight to important features for global and local linkage capture. Additionally, the electric vehicle charging load data in a certain region, as an example, is verified, and the average value of 30 running times of the combined model proposed is 117.3231 s, and the correlation coefficient PCC of the CEEMD-SVD-EPPS-MNet-Atten model is closer to 1. Furthermore, the CEEMD-SVD-EPPS-MNet-Atten model has the lowest MAPE, RMSE, and PCC. The results show that the model in this paper can better extract the characteristics of the data, improve the modeling efficiency, and have a high data prediction accuracy. Full article

Interest in empirical studies of monetary policy has grown over the past decade, and particularly since the post COVID-19 pandemic period characterized by a surge in inflation rates in every corner of the globe. Against this backdrop, central banks’ traditional inflation forecast framework has been challenged, leading to renewed analysis of the monetary policy transmission mechanism. Focusing on Tunisia, an emerging small open economy subjected to external shocks, this study focuses on the role played by the monetary authority in the conduct of Tunisia’s monetary policy over the period from 2000 to 2024. This period is characterized by a deceleration of growth and an increase in inflation and unemployment. This work shows also how a VAR model with long-run restrictions justified by economic theory can be usefully applied in the analysis of monetary policy; the effects of the money market rate and other shocks; the relationship between prices and the nominal effective exchange rate; and the relationship between inflation and the output gap. Full article

Concerns about ensuring a sustainable environment are growing, attracting major attention from policy professionals worldwide. Therefore, this study investigates the nonlinear impacts of fiscal consolidation on CO₂ emissions in 17 OECD countries from 1978 to 2020. To probe the short- and long-term connections across various quantiles of CO₂ emissions, we adopted panel QARDL frameworks. The Granger non-causality test was used to investigate the variables’ association with CO₂ emission. The study’s main findings confirm the overall beneficial effect of fiscal consolidation on carbon emissions. It reduces CO₂ emissions at almost all quantiles in the short run. By contrast, in the long run, the effect is positive at lower quantiles and turns negative at upper quantiles. Furthermore, a causality analysis identified a bidirectional causal relationship between fiscal consolidation and CO₂ emissions, confirming the existence of mutual influence. While Keynesian theory links fiscal consolidation to economic recession, our findings support the non-Keynesian view, showing that such policy can foster economic growth and thereby contribute to reducing CO₂ emissions in the short run. Thus, OECD countries are orienting public spending and carbon taxation toward environmentally friendly practices while ensuring environmental protection and deficit reduction. Nonetheless, the identified mixed effect in the long run highlights the need for sustained consolidation policies by enhancing expenditure efficiency and adopting targeted taxation measures to achieve lasting emission reductions and support the transition to cleaner energy, even when emissions are relatively low. Full article

Soaring housing prices worldwide are compromising housing affordability, potentially leading to significant economic, social, and health repercussions. Understanding the price discovery process within the real estate market is therefore crucial for policymakers. While the relationship between land and housing prices in urban residential markets has been widely examined in the literature, the results are often context-specific, leaving the question of whether the land market leads the housing market or vice versa open to debate. Saudi Arabia, with its rapidly growing real estate market, evolving demographics and urbanization trends, and transformative regulatory reforms, presents a compelling context for revisiting the land–housing nexus. This study examines the long-term relationship between land and housing markets and investigates the short-term price dynamics with the ultimate goal of understanding the price formation in the housing market. The study dataset comprises quarterly time-series price indices published by the General Authority for Statistics (GASTAT) in Saudi, representing the nation-wide price movements of residential lands and villas from 2014Q1 to 2025Q1. The study employs the Johansen cointegration method and the Granger causality testing. The results of cointegration analysis confirm a significant long-run equilibrium relationship between the two markets, while the error correction model reveals that both land and housing prices adjust to restore this equilibrium. Granger causality test results show a unidirectional relationship, where land prices predict future housing prices, consistent with the neoclassical rent theory. These findings reinforce the long-term, intrinsic link between land and housing markets observed in prior studies. The dynamics in the Saudi market are likely shaped by rapid urbanization that intensified speculation in the land market, and also the prevalence of self-building enabled by government-supported financing. This study underscores the importance of striking a delicate balance between supply and demand side policies in the real estate market while monitoring the impact of these policies on housing affordability. Full article

A common misconception is that behavioral economics rejects microeconomics. This entry explains how behavioral economics, despite challenging core assumptions of rationality, remains fundamentally aligned with the structure of microeconomics. Anchored in the insight that rational market outcomes can emerge even when individual behavior is non-rational, it revisits the explanatory role of constraints in economic theory. Rather than displacing microeconomics, behavioral economics extends it by incorporating bounded rationality while preserving key structural principles. Central to this integration is Say’s law, the macro-level notion that production generates income and thus the capacity for demand. This connection makes microeconomic constraints reflect deeper macroeconomic principles. Even when market behavior is distorted by correlated cognitive biases and their associated positive feedback dynamics—such as herding or bubbles—the fundamental identity that supply generates the income necessary for demand remains intact, provided that adjustments occur over the long run. The analysis also considers how behavioral deviations affect aggregate outcomes. Ultimately, this entry shows that behavioral economics is not a departure from microeconomics but its natural extension: by embedding bounded rationality within the framework of economic constraints, it preserves the structural coherence of microeconomics while adding psychological depth. Full article

Humor is a pragmatic and interdisciplinary phenomenon whose sociocultural relevance has been increasingly recognized by the Academia. Surprisingly, although the anthropo-philosophical theory of homo risu emerged in the 7th century, linguists became interested in the study of the linguistic mechanisms of humor only a few years ago. One of those mechanisms is the use of Anglicisms, because of their pragmatic potential to provide some added value, a halo of prestige and modernity, which creates playful effects of complicity. This paper examines the way Anglicisms crucially contribute to the humorous discourse of the satirical news show El Intermedio, the longest-running program on a Spanish private TV channel. Monitoring of 300 episodes broadcast between April 2022 and December 2024 proves how, in addition to puns and irony, scriptwriters tend to resort to a number of strategies involving the creative use of Anglicisms, which perform different pragmatic functions, while showing sociolinguistic awareness. They also offer an up-to-date sample of the great vitality of Anglicisms in contemporary Spain. Full article

To address the persistent challenges in diagnosing bearing faults, this study proposes an intelligent diagnostic framework based on the principle that mechanical faults manifest as symmetry-breaking phenomena in a system’s vibration signals. In a healthy motor, vibration signals exhibit a high degree of symmetry, whereas faults introduce identifiable and distinct asymmetries. This study constructs a high-fidelity digital twin model based on the five-dimensional model theory to simulate both the symmetrical (healthy) state and various asymmetrical faulty states of motor bearings—specifically, inner race, outer race, and rolling element faults—thereby effectively addressing the critical issue of data scarcity. Building upon this framework, fault features characterizing these asymmetries are accurately extracted using an optimized variational mode decomposition (VMD) algorithm and subsequently classified with a convolutional neural network–bidirectional long short-term memory (CNN-BiLSTM) model. The results validate the model’s ability to accurately replicate bearing-fault data. The proposed diagnostic method achieves a stable and high average accuracy of 98.44 ± 0.41% over multiple runs on the simulation data. Furthermore, its effectiveness was validated on a public real-world bearing dataset, where it achieved an accuracy of over 95%, demonstrating its robustness and potential for industrial applications by effectively identifying fault-induced asymmetries. Full article

Mainstream experimental economics is characterized by its focus on theory testing and “treatment effects” on aggregate outcomes. The “agentic” alternative is concerned with the econometric specification of individual behavior. In this essay, first, a literature review of agentic experimental economics is provided, and a stylized workflow is proposed to produce and validate econometric models of individual behavior based on experimental data: (i) create a baseline (“optimal”) behavioral benchmark (by analytical means or reinforcement learning) for the considered multi-agent game, (ii) conduct experiments with human subjects, (iii) use the experimental results to characterize the structure of the deviations from the baseline behavior, and (iv) re-run the experiment with artificial agents calibrated in the previous step, and compare the outcomes with those of the human experiment. Two papers have been selected to illustrate the successful use of the proposed workflow. Finally, the relations between agent-based and experimental economics are discussed after deep learning has “tamed” the curse of dimensionality. Full article

This study employed quarterly data spanning from 2005 to 2024 to investigate the factors affecting China’s tea exports to Malaysia using demand theory. The Autoregressive Distributed Lag (ARDL) approach and Granger causality test were applied to examine the long-run and short-run impacts of key variables, including the prices of China’s tea and coffee imported by Malaysia, Malaysia’s GDP, Malaysia’s tea production, and the international oil price. The ARDL bounds testing confirmed the existence of a long-run equilibrium among these variables. The empirical findings revealed that an increase in the price of China’s tea significantly reduced export volumes, whereas Malaysia’s GDP exerted a strong positive influence. The price of coffee exhibited a significantly negative effect, suggesting an unconventional substitution relationship with tea. Both Malaysia’s domestic tea production and the international oil price imposed downward pressures on China’s tea exports. Furthermore, the Granger causality analysis indicated that the price of China’s tea, the price of coffee, and Malaysia’s GDP all exerted short-run effects on China’s tea exports to Malaysia. These findings contribute to the export demand literature and offer implications for policies aiming to enhance bilateral tea trade between China and Malaysia. Full article

This essay proposes a transdisciplinary framework that positions cooperation as a foundational principle for ecosystem stewardship in the Anthropocene. Drawing from microbial ecology, evolutionary theory, and sustainability science, we argue that cooperation, rather than competition, is a robust and scalable strategy for resilience across biological and institutional systems. In particular, microbial behaviors such as biofilm formation, quorum sensing, and horizontal gene transfer are especially pronounced in extreme environments, where cooperation becomes essential for survival. These strategies serve as functional analogues that illuminate the structural logics of resilience: interdependence, redundancy, distributed coordination, and adaptation. As the Anthropocene progresses toward increasingly extreme conditions, including potential “Hothouse Earth” scenarios driven by climate disruption, such ecological heuristics offer concrete insights into how human institutions can adapt to stress and uncertainty. Rather than reiterating familiar calls for hybrid governance, we use microbial cooperation as a heuristic to reveal the functional architecture already present in many resilient governance practices. These microbial strategies emerging from life in extreme environments demonstrate how interdependence, redundancy, and distributed coordination can create system resilience and sustainability in the long run. By translating microbial survival strategies into institutional design principles, this framework reframes ecosystem stewardship not as a normative ideal, but as an ecological imperative grounded in the evolutionary logic of cooperation. Full article

Background/Objectives: There is increasing interest within cognitive neuro-surgery in preserving domains not traditionally assessed during awake surgery. The study aims at proposing a specific protocol to assist surgical resection in right temporal areas. Patients were not evaluated during direct cortical stimulation; instead, assessments occurred during the resection itself. The real-time neuropsychological testing (RTNT) protocol employed tasks evaluating visuospatial and social cognition, administered repeatedly throughout the resection using varied items. Methods: A consecutive series of 24 patients (median age 44) performed RTNT. The aim of RTNT is to maintain high accuracy through resection. Lesions in the right temporal cortex and the subcortical white matter beneath can cause deficits; accordingly, not all of our patients had pre-surgery performance within the normal range. In this case, the aim of RTNT is to maintain the not perfect pre-surgery level. Results: We found a statistically significant between-tasks difference in the patients’ median values (across RTNT runs), in their minimum score reached during resection, and in the delta between performance at the last vs. the first RTNT run. The tasks that varied belonged to visual–spatial attention (landmark task), face processing (recognition of famous faces), and social cognition (theory of mind). The outcome was measured by pre- vs. post-surgery neuropsychological score comparison. The number of patients scoring below the normal range did not significantly differ between post- vs. pre-intervention. Conclusions: Results demonstrated the feasibility of implementing a continuous monitoring protocol during the resection phase, and the potential of the selected tasks to assess visuospatial and social functions associated with the non-dominant (right) hemisphere. Full article

Global interest in the rising rates of loneliness runs parallel to increased concern about weakening social cohesion. Both phenomena are described as complexly entwined with trust and agency and related to wellbeing at both an individual and societal level. Whilst opinions are numerous and divided on how to alleviate loneliness and build social cohesion, there is some important coalescence around claims of the contribution of art making to both warding off loneliness and building social cohesion. This paper draws on the work of Hannah Arendt and contemporary readings of her theory of loneliness to suggest how art making can be efficacious and why it should play a central role in community building rather than be relegated to a peripheral desideratum. Drawing on data from two studies into community art making, the paper explores how the inter- and intrapersonal processes of being seen and showing can alleviate loneliness, begin to restore agency, and help build social cohesion. Full article