Search Results (7,905)

Cryptocurrencies such as Bitcoin can be classified as commodities under the Commodity Exchange Act (CEA), giving the Commodity Futures Trading Commission (CFTC) jurisdiction over those cryptocurrencies deemed commodities, particularly in the context of futures trading. This paper presents a method for predicting both long- and short-term trends in selected cryptocurrencies based on the Fractal Market Hypothesis (FMH). The FMH applies the self-affine properties of fractal stochastic fields to model financial time series. After introducing the underlying theory and mathematical framework, a fundamental analysis of Bitcoin and Ethereum exchange rates against the U.S. dollar is conducted. The analysis focuses on changes in the polarity of the ‘Beta-to-Volatility’ and ‘Lyapunov-to-Volatility’ ratios as indicators of impending shifts in Bitcoin/Ethereum price trends. These signals are used to recommend long, short, or hold trading positions, with corresponding algorithms (implemented in Matlab R2023b) developed and back-tested. An optimisation of these algorithms identifies ideal parameter ranges that maximise both accuracy and profitability, thereby ensuring high confidence in the predictions. The resulting trading strategy provides actionable guidance for cryptocurrency investment and quantifies the likelihood of bull or bear market dominance. Under stable market conditions, machine learning (using the ‘TuringBot’ platform) is shown to produce reliable short-horizon estimates of future price movements and fluctuations. This reduces trading delays caused by data filtering and increases returns by identifying optimal positions within rapid ‘micro-trends’ that would otherwise remain undetected—yielding gains of up to approximately 10%. Empirical results confirm that Bitcoin and Ethereum exchanges behave as self-affine (fractal) stochastic fields with Lévy distributions, exhibiting a Hurst exponent of roughly 0.32, a fractal dimension of about 1.68, and a Lévy index near 1.22. These findings demonstrate that the Fractal Market Hypothesis and its associated indices provide a robust market model capable of generating investment returns that consistently outperform standard Buy-and-Hold strategies. Full article

One of the greatest quests in physics in current times is the search for a grand unified theory—to bring all the forces of nature into one coherent explanatory framework. Despite two centuries of progress, both in comprehending the individual forces and formulating mathematical constructs to explain the existence and operation of such forces, the final step to unify the localised atomic and subatomic forces with gravity has proven to be elusive. Whilst recognising that there are arguments for and against the unification of all the forces of nature, the pursuit for unity has been driving many physicists and mathematicians to explore increasingly extraordinary ideas, from string theory to various other options requiring multiple dimensions. Can process philosophy ride to the rescue? By changing our perspective, it might be possible to derive a provocative and compelling alternative way to understand basic (and advanced) physics. This process approach would see all matter objects, at whatever scale, as energetic systems (inherently dynamic). Through the use of game theory, there is a way to appreciate the combination of entropy together with all the apparent forces of nature, being gravity and the more localised forces, within a singular, metaphysically consistent, construct. The outcome, however, challenges our whole understanding of the universe and fundamentally changes our relationship with matter. Full article

Sustainable aviation fuel (SAF) is a drop-in alternative to conventional jet fuels, designed to reduce greenhouse gas (GHG) emissions while requiring minimal infrastructure changes and certification under the American Society for Testing and Materials (ASTM) D7566 standard. This study assesses recently identified high-energy-density (HED) strained polycycloalkanes as SAF candidates. Strain energy (E_se) was calculated using density functional theory (DFT), while operational properties such as boiling point (BP) and flash point (FP) were predicted using support vector regression (SVR) models. The models demonstrated strong predictive performance (R² > 0.96) with mean absolute errors of 6.92 K for BP and 9.58 K for FP, with robustness sensitivity analysis. It is found that approximately 65% of these studied polycycloalkanes fall within the Jet A fuel property boundaries. The polycycloalkanes (C₉–C₁₅) with strain energies below approximately 60 kcal/mol achieve an balance between energy density and ignition safety, aligning with the specifications of Jet A. The majority of structures were dominated by five-membered rings, with a few three- or four-membered rings enhancing favorable trade-offs among BP, FP, and HED. This early pre-screening indicates that moderately strained polycycloalkanes are safe, energy-dense candidates for next-generation sustainable jet fuels and provide a framework for designing high-performance SAFs. Full article

Global modern renewable energy based on geothermal, wind, solar, and marine resources has grown rapidly over the last decades despite low energy density, intermittent supply, and other qualities inferior to those of fossil fuels. What is the explanation for this growth? The main drivers of growth are assessed using economic theories and verified with statistical data. From the neo-classic viewpoint that focuses on price substitutions, the growth can be explained by the shift from energy-intensive agriculture and industry to labour-intensive services. However, the energy resources complemented rather than substituted for each other. In the evolutionary idea, investments supported by policies enabled cost-reducing technological change. Still, policies alone are insufficient to generate the growth of modern renewable energy as they are inconsistent across countries and in time. From the behavioural perspective that is preoccupied with innovative entrepreneurs, the value addition of electrification can explain the introduction of modern renewable energy in market niches, but not its fast growth. Instead of these mono-causalities, the growth of modern renewable energy is explained by technology diffusion during the pioneering, growth, and maturation phases. Possibilities that postpone the maturation are pinpointed. Full article

Rapid climate change has exacerbated global ecosystem degradation, leading to habitat fragmentation and landscape connectivity loss. Constructing ecological networks (EN) with resilient conduction functions and conservation priorities is crucial for maintaining regional ecological security and promoting sustainable development. However, the spatiotemporal modeling and dynamic resilience assessment of EN under the combined impacts of future climate and land use/land cover (LULC) changes remain underexplored. This study focuses on the Central Yunnan Urban Agglomeration (CYUA), China, and integrates landscape ecology with complex network theory to develop a dynamic resilience assessment framework that incorporates multi-scenario LULC projections, multi-temporal EN construction, and node-link disturbance simulations. Under the Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP-RCP) scenarios, we quantified spatiotemporal variations in EN resilience and identified resilience-based conservation priority areas. The results show that: (1) Future EN patterns exhibit a westward clustering trend, with expanding habitat areas and enhanced connectivity. (2) From 2000 to 2040, EN resilience remains generally stable, but diverges significantly across scenarios—showing steady increases under SSP1-2.6 and SSP5-8.5, while slightly declining under SSP2-4.5. (3) Approximately 20% of nodes and 40% of links are identified as critical components for maintaining structural-functional resilience, and are projected to form conservation priority patterns characterized by larger habitat areas and more compact connectivity under future scenarios. The multi-scenario analysis provides differentiated strategies for EN planning and ecological conservation. This framework offers adaptive and resilient solutions for regional ecosystem management under climate change. Full article

The hospitality sector is undergoing a rapid digital change due to smart technology and artificial intelligence. This presents both possibilities and problems for the development of sustainable innovation. Yet, little is known about how leaders’ technological competencies affect employees’ capacity to engage in environmentally responsible innovation. This study addresses this gap by examining how leaders’ competencies in smart technology, artificial intelligence, robotics, and algorithms (STARA) shape employees’ green innovative behavior in hotels. Anchored in person–job fit theory and cognitive appraisal theory, we propose that when employees perceive a strong alignment between their skills and the technological demands introduced by STARA, they are more likely to appraise such technologies as opportunities (challenge appraisals) rather than threats (hindrance appraisals). These appraisals, in turn, mediate the link between leadership and green innovation. Convenience sampling was used to gather data from staff members at five-star, ecologically certified hotels in Sharm El-Sheikh, Egypt. According to structural equation modeling using SmartPLS, employees’ green innovation behaviors are improved by leaders’ STARA abilities. Crucially, staff members who viewed STARA technologies as challenges (i.e., chances for learning and development) converted leadership skills into more robust green innovation results. Conversely, employees who perceived these technologies as obstacles, such as burdens or threats, diminished this beneficial effect and decreased their desire to participate in green innovation. These findings highlight that the way employees cognitively evaluate technological change determines whether leadership efforts foster or obstruct sustainable innovation in hotels. Full article

In this study, the behavior of the spherical bearing component of the L-100 bridge part (AlfaTech LLC, Perm, Russia) is considered within the framework of a finite element model. The influence of the pattern of the coupling of the antifriction interlayer with the lower steel plate on the operation of the part is examined in terms of ideal contact, full adhesion, and frictional contact. The thickness of the antifriction interlayer varied from 4 to 12 mm. The dependencies of the contact parameters and the stress–strain state on the thickness were determined. Structurally modified polytetrafluoroethylene (PTFE) without AR-200 fillers was considered the material of the antifriction interlayer. The gradual refinement of the behavioral model of the antifriction material to account for structural and relaxation transitions was carried based on a wide range of experimental studies. The elastic–plastic and primary viscoelastic models of material behavior were constructed based on a series of homogeneous deformed-state experiments. The viscoelastic model of material behavior was refined using data from dynamic mechanical analysis over a wide temperature range [−40; +80] °C. In the first approximation, a model of the deformation theory of plasticity with linear elastic volumetric compressibility was identified. As a second approximation, a viscoelasticity model for the Maxwell body was constructed using Prony series. It was established that the viscoelastic model of the material allows for obtaining data on the behavior of the part with an error of no more than 15%. The numerical analog of the construction in an axisymmetric formulation can be used for the predictive analysis of the behavior of the bearing, including when changing the geometric configuration. Recommendations for the numerical modeling of the behavior of antifriction layer materials and the coupling pattern of the bearing elements are given in this work. A spherical bearing with an antifriction interlayer made of Arflon series material is considered for the first time. Full article

Hostility toward diversity, equity, and inclusion (DEI) programs threaten to widen long-term racial economic gaps in the U.S. As the demographic makeup of the country continues to shift, a failure to address these gaps will have growing negative impacts on overall national prosperity. In this paper, three issues are discussed in order to achieve the broad goal of greater social and economic justice: (1) rebranding DEI and encouraging equity and fairness principles (EFP) for all, (2) using organizational and social science theories to illustrate and explain the ongoing sources of inequity and unfairness within Black and other minority communities, and (3) changing the focus of the argument for social justice away from a moral argument to the economic argument. This paper provides conceptual and theory-based arguments to illustrate how superior organizational performance is achieved through diversity and to make the case for the proposed EFP framework. Practical and theoretical implications are explored to reduce misunderstanding of the goals of DEI and return focus toward the continued and ongoing need to address social and economic inequality. Full article

We present a theoretical model to explain how tubulin dimers in neuronal microtubules might achieve collective quantum coherence, resulting in wavefunction collapses that manifest as avalanches within a self-organized criticality (SOC) framework. Using the Orchestrated Objective Reduction (Orch-OR) theory as inspiration, we propose that microtubule subunits (tubulins) become transiently entangled via dipole–dipole couplings, forming coherent domains susceptible to sudden self-collapse. We model a network of tubulin-like nodes with scale-free (Barabási–Albert) connectivity, each evolving via local coupling and stochastic noise. Near criticality, the system exhibits power-law avalanches—abrupt collective state changes that we identify with instantaneous quantum wavefunction collapse events. Using the Diósi–Penrose gravitational self-energy formula, we estimate objective reduction times $T_{\mathrm{OR}}=\hslash /E_g$ for these events in the 10–200 ms range, consistent with the Orch-OR conscious moment timescale. Our results demonstrate that quantum coherence at the tubulin level can be amplified by scale-free critical dynamics, providing a possible bridge between sub-neuronal quantum processes and large-scale neural activity. Full article

Climate change poses a growing threat to the mental health of children and adolescents. This narrative review synthesizes global, interdisciplinary research on the psychological impacts of climate disruption during critical developmental stages, with attention to marginalized populations. We explore three key pathways of harm: direct exposure to environmental disasters, chronic disruption of ecological and social systems, and existential distress such as eco-anxiety. Drawing on eco-social theory and developmental psychopathology, the review highlights how these impacts are shaped by age, geography, identity, and systemic inequities. It identifies both risk and protective factors, emphasizing the importance of caregiving relationships, cultural practices, education, and youth climate engagement. While activism can foster resilience and purpose, it may also incur emotional burdens that require clinical and policy attention. We argue that child and adolescent mental health must be recognized as central to climate justice and adaptation, and we offer urgent recommendations for integrated action across sectors. Full article

Existing research on live-streaming e-commerce consumption behavior is mostly limited by a single disciplinary framework, unable to systematically parse the mechanism of macro-policies and cultural values on intergenerational consumer psychology. This study takes ASEAN cross-border live-streaming e-commerce as a scenario, integrates theories of economics, political science, and sociology, and constructs an innovative three-layer analysis model of “macroeconomic system–meso-market–micro-behavior” based on multi-source data from 2020 to 2024. It empirically explores the formation mechanism of intergenerational differences in impulse buying. The results show that the behavior differences of different groups are significantly driven by income gradient, cross-border policies (tariff adjustment and consumer protection regulations), and collectivism/individualism cultural orientations. The innovative contribution of this study is reflected in three aspects: Firstly, it breaks through the limitation of a single discipline, and for the first time, it incorporates structural variables such as policy synergy effect and family structure change into the theoretical framework of impulse buying, quantifying and revealing the differentiated impact of institutional heterogeneity in ASEAN markets on intergenerational behavior. Secondly, it reconstructs the transmission path of “cultural values–family structure–intergenerational behavior” and finds that the inhibitory effect of collectivism on impulse buying tends to weaken with age. Thirdly, it proposes a “policy instrument–generational response” matching model and verifies the heterogeneous impact of the same policy (such as tariff reduction) on different generations. This study fills the gaps in related research and can provide empirical support for ASEAN enterprises to formulate stratified marketing strategies and for policymakers to optimize cross-border e-commerce regulation. which is of great significance to promote the sustainable development of the regional live-broadcast e-commerce ecology. Full article

Service platform enterprises have become a prominent economic form in China’s digital economy in the past two decades. The scope of complementary assets is expanding; for example, big data, precision marketing and user traffic conversion are among the emerging manifestations of complementary assets. Nevertheless, scholars have not yet explored how service platform enterprises utilize and maintain these vast complementary assets in the dynamic environment. Building on value innovation theory, this article attempts to reveal the impacts of complementary assets on the value innovation of service platform enterprises, and the conditioning roles of environmental dynamics. By contrasting findings and theoretical replication, we find that (1) complementary assets (specialized complementary assets, universal complementary assets) have promoting effects on service platform enterprises’ value innovation (customer value, partnership, business model changes); (2) environmental dynamics (market changes, technological changes) have moderating effects on the relationship between complementary assets and the value innovation of service platform enterprise. This research provides a novel and fine-grained theoretical framework to illustrate the multidimensional impacts of complementary assets and the contingent roles of a two-dimensional environmental dynamics on value innovation, thereby enriching the literature on value innovation theory and complementary assets, and providing actionable insights for service platform enterprises in leveraging complementary assets for value innovation, as well as guidance for regulatory departments in digital governance. Full article

Human–machine trust has shifted over the past three decades from trust in automation to trust in AI, while research paradigms, disciplines, and problem spaces have expanded. Centered on AI in healthcare, this narrative review offers a longitudinal synthesis that traces and compares phase-specific changes in theory and method, providing design guidance for human-AI systems at different stages of maturity. From a cross-disciplinary view, we introduce an Interdisciplinary Human-AI Trust Research (I-HATR) framework that aligns explainable AI (XAI) with human–computer interaction/human factors engineering (HCI/HFE). We distill three core categories of determinants of human-AI trust in healthcare, user characteristics, AI system attributes, and contextual factors, and summarize the main measurement families and their evolution from self-report to behavioral and psychophysiological approaches, with growing use of multimodal and dynamic evaluation. Finally, we outline key trends, opportunities, and practical challenges to support the development of human-centered, trustworthy AI in healthcare, emphasizing the need to bridge actual trustworthiness and perceived trust through shared metrics, uncertainty communication, and trust calibration. Full article

Poly(3,4-ethylenedioxythiophene): polystyrene sulfonate (PEDOT:PSS) is a conductive water-processable polymer with many important applications in organic electronics. The electrical conductivity of PEDOT:PSS layers is very diverse and can be changed by changing the processing and post-deposition conditions, e.g., by using different solvent additives, doping or modifying the physical conditions of the layer deposition. Despite many years of intensive research on the relationship between the microstructure and properties of these layers, there are still gaps in our knowledge, especially with respect to the detailed understanding of the charge carrier transport mechanism in organic semiconductor thin films. In this work, we investigate the effect of acid doping of PEDOT:PSS thin films on the intra-chain and inter-chain conductivity by developing a model that treats PEDOT:PSS as a nanocomposite material. This model is based on the effective medium theory and uses the percolation theory equation for the electrical conductivity of a mixture of two materials. Here its implementation assumes that the role of the highly conductive material is attributed to the intra-chain conductivity of PEDOT and its quantitative contribution is determined based on the optical Drude–Lorentz model. While the weaker inter-chain conductivity is assumed to originate from the weakly conductive material and is determined based on electrical measurements using the van der Pauw method and coherent nanostructure-dependent analysis. Our studies show that doping with methanesulfonic acid significantly affects both types of conductivity. The intra-chain conductivity of PEDOT increases from 260 to almost 400 Scm⁻¹. Meanwhile, the inter-chain conductivity increases by almost three orders of magnitude, reaching a critical state, i.e., exceeding the percolation threshold. The observed changes in electrical conductivity due to acid doping are attributed to the flattening of the PEDOT/PSS gel nanoparticles. In the model developed here, this flattening is accounted for by the inclusion shape factor. Full article

Instagram is a hugely popular social media site; however, it has also been cited in many times as being a source of low self-esteem, unhappiness, and body dissatisfaction. Despite this, there is potential to use Instagram as a self-care delivery system and create positive changes in users’ mental health by showing them a specific type of image. In this paper, we use Stress Reduction Theory to demonstrate that viewing images of nature on Instagram can improve well-being (H1), by increasing feelings of connectedness with nature (H2). Furthermore, we posit this same influence will elicit more altruistic behaviour from users (H3). In a laboratory experiment, participants accessed images using either the #naturephotography hashtag, or a control hashtag (#bookshelves). Analyses showed that, in line with the proposed positive effects of SRT, viewing natural images improved well-being and positive emotions, and this was at least partially mediated by increased connectedness to nature. Future studies that use a more longitudinal approach, and examine how images can be presented within a more robust psychiatric intervention are then discussed. Full article