Search Results (10,899)

This study examined how environmental attitudes, message framing, and cultural context shape conservation judgments in national parks and protected areas (NPPAs). Participants from the U.S. (N = 181) and India (N = 157) reported their environmental attitudes using the New Environmental Paradigm (NEP) scale and responded to scenarios depicting unsustainable behaviors (trampling vegetation, feeding wildlife, and littering) framed in either gain or loss terms. Regression analyses showed that stronger pro-environmental attitudes consistently predicted greater disapproval of unsustainable actions and higher willingness to donate. Indian respondents generally expressed stronger pro-conservation judgments, and the NEP × Country interaction was significant for trampling, indicating cultural moderation of attitude effects. Message framing had minimal impact, reaching significance only for littering and showing no moderation by country. NPPA pass ownership positively influenced all outcomes, while age predicted donation intentions only. These findings underscore the importance of values-aligned, context-sensitive strategies to encourage sustainable behaviors across diverse cultural settings. Full article

We study thrust production in a single-fluid magnetohydrodynamic (MHD) thruster with Hall-type coaxial geometry and show how velocity–field alignment and magnetic topology set the operating regime. Starting from the momentum equation with anisotropic conductivity, the axial Lorentz force density reduces to $f_z={\sigma }_{\theta z}{E}_z{B}_r(\chi -1)$, with the motional-field ratio $\chi \equiv \left(u{B}_r\right)/E_z$. Hence, net accelerating force ($f_z>0$) is achieved if and only if the motional electric field $E_m=u{B}_r$ exceeds the applied axial bias $E_z$ ($\chi >1$), providing a compact, testable design rule. We separate alignment diagnostics (cross-helicity $h_c=\mathbf{u}·\mathbf{B}$) from the thrust criterion ($\chi$) and generate equation-only axial profiles for $\chi \left(z\right)$, $j_{\theta }\left(z\right)$, and $f_z\left(z\right)$ for representative parameters. In a baseline case $(E_z=150\mathrm{V}{\mathrm{m}}^{-1},{\sigma }_{\theta z}=50\mathrm{S}{\mathrm{m}}^{-1},u_0=12\mathrm{km}{\mathrm{s}}^{-1},B_{r0}=0.02\mathrm{T},L=0.10\mathrm{m})$, the $\chi >1$ band spans $\approx 21.2\%$ of the channel; a lagged correlation peaks at $\Delta z^{★}\approx 8.82\mathrm{mm}$$(C_{HU}=0.979)$, and ${\int }_0^L{f}_z\mathrm{d}z$ is slightly negative—indicating that enlarging the $\chi >1$ region or raising ${\sigma }_{\theta z}$ are effective levers. We propose a reproducible validation pathway (finite-volume MHD simulations and laboratory measurements: PIV, Hall probes, and thrust stand) to map $f_z$ versus $\chi$ and verify the response length. The framework yields concrete design strategies—$B_r\left(z\right)$ shaping where u is high, conductivity control, and modest $E_z$ tuning—supporting applications from station-keeping to deep-space cruise. Full article

The growing negative consequences of climate change support the need to deepen and investigate factors that may sustain the engagement of pro-environmental behaviors. In this scenario, eco-emotions represent a key factor that can potentially shape sustainable behaviors. In keeping with this, the present study aimed at observing the potential relationships between eco-emotions and readiness to change (RTC), namely a psychological construct closely related to pro-environmental behaviors. Specifically the RTC dimensions were the following: perceived importance of the problem, motivation, self-efficacy, effectiveness of the proposed solution, social support, action, and perceived readiness. In detail, Generalized Additive Models (GAMs) were performed in order to detect both linear and non-linear associations between eco-emotions and the dimensions of RTC by assuming a complex perspective. The final sample was composed of 252 participants (mean age = 32.99, SD = 14.640). The results pointed out several significant associations (both linear and non-linear) between eco-emotions and the RTC dimensions. In detail, the perceived importance of the problem was linearly associated with anger and anxiety, while sorrow and enthusiasm showed non-linear effects. Furthermore, motivation was linearly linked to anger and guilt and non-linearly to contempt, enthusiasm, and sorrow. In terms of self-efficacy, anger, enthusiasm, and sorrow showed linear relationships, whereas isolation showed a non-linear association. Perceived effectiveness of the proposed solution was linearly related to enthusiasm and sorrow and non-linearly to anger, powerlessness, isolation, and anxiety. Similarly, social support was linearly connected with enthusiasm, isolation, and sorrow, and non-linearly with powerlessness and anxiety. Moreover, action was primarily driven by anger in a linear relationship, while enthusiasm, powerlessness, guilt, and anxiety showed non-linear associations. Finally, perceived readiness was linearly related to anxiety and non-linearly to anger, contempt, enthusiasm, powerlessness, guilt, and sorrow. These findings should be interpreted in light of the study’s limitations, including its cross-sectional nature, reliance on self-reported measures, use of snowball sampling, and sample demographic characteristics, all of which may affect the generalizability of the results. Nevertheless, the results pointed out the presence of several significant linear (e.g., anxiety and the perceived importance of the problem) and non-linear (e.g., contempt and motivation) associations between various eco-emotions and RTC factors. The findings underscore the need for a complex approach to this field of research, suggesting that further studies, policies, and environmental awareness programs should consider the multifaceted nature of these phenomena in order to develop effective and valuable interventions. Full article

Research on big data analytics (BDA) and supply chains often inventories “capabilities” but rarely explains how firms progress through adoption—or how governance over data and related resources shapes resilience outcomes. Drawing on 16 semi-structured interviews with senior managers in the manufacturing sector, we analyze organizational practices around data, analytics, and decision-making and synthesize a governed-adoption process framework. The framework specifies how five governance levers—ownership, standards, stewardship, access, lineage—operate differentially across four adoption gates (data plumbing—descriptive monitoring—predictive alerting—prescriptive decisioning). To move beyond staged descriptions, we make the underlying generative mechanisms explicit—Comparability, Explainability, Authorization, Fidelity, Executability—and link them to dynamic-capability micro foundations (sensing, seizing, reconfiguring) via decision-latency outcomes (“resilience timers”: Time-to-Detect, Time-to-Decide, Time-to-Reconfigure, Time-to-Recover). Brief deviant-case contrasts (e.g., notification without action; dashboards without owners) clarify boundary conditions under which governance enables or impedes resilient action. We also state concise, testable propositions (e.g., standards+lineage as a necessary condition for improving Time-to-Detect; ownership+access as necessary for improving Time-to-Decide) and provide gate exit-criteria to support evaluation and future comparative tests. Claims are bounded to analytic generalization from a single-country, manufacturing-sector qualitative sample; we make no assertion of statistical validation. Practically, the framework prioritizes governance work ahead of tool spend, helping organizations convert dashboards into repeatable decisions at speed. Full article

The rapid rise of bio-hybrid robots and hybrid human–AI systems has triggered an explosion of terminology that inhibits clarity and progress. To investigate how terms are defined, we conduct a narrative scoping review and concept analysis. We extract 60 verbatim definitions spanning engineering, human–computer interaction, human factors, biomimetics, philosophy, and policy. Entries are coded on three axes: agency locus (human, shared, machine), integration depth (loose, moderate, high), and normative valence (negative, neutral, positive), and then clustered. Four categories emerged from the analysis: (i) machine-led, low-integration architectures such as neuro-symbolic or “Hybrid-AI” models; (ii) shared, moderately integrated systems like mixed-initiative cobots; (iii) human-led, medium-coupling decision aids; and (iv) human-centric, low-integration frameworks that focus on user agency. Most definitions adopt a generally positive valence, suggesting a gap with risk-heavy popular narratives. We show that, for researchers investigating where living meets machine, terminological precision is more than semantics and it can shape design, accountability, and public trust. This narrative review contributes a comparative taxonomy and a shared lexicon for reporting hybrid systems. Researchers are encouraged to clarify which sense of Hybrid-AI is intended (algorithmic fusion vs. human–AI ensemble), to specify agency locus and integration depth, and to adopt measures consistent with these conceptualizations. Such practices can reduce construct confusion, enhance cross-study comparability, and align design, safety, and regulatory expectations across domains. Full article

In advanced semiconductor manufacturing, computational lithography, particularly sub-resolution assist features (SRAFs), is crucial for enhancing the process window. However, conventional SRAF placement methodologies are hampered by a critical trade-off between speed and pattern fidelity, and they largely fail to optimize the complex, curvilinear layouts essential for advanced nodes. This study develops a deep learning framework to replace and drastically accelerate the optical refinement of SRAF shapes. We established a large-scale dataset with coarse, binarized SRAF patterns as inputs. Ground-truth labels were generated via an Level-Set Method (LSM) optimized purely for optical performance. A U-Net convolutional neural network was then trained to learn the mapping from the coarse inputs to the optically optimized outputs. Experimental results demonstrate a dual benefit: the model provides a multi-order-of-magnitude acceleration over traditional CPU-based methods and is significantly faster than modern GPU-accelerated algorithms while achieving a final pattern fidelity highly comparable to the computationally expensive LSM. The U-Net-generated SRAFs exhibit high fidelity to the ground-truth layouts and comparable optical performance. Our findings demonstrate that a data-driven surrogate can serve as an effective alternative to traditional algorithms for SRAF optical refinement. This represents a promising approach to mitigating computational costs in mask synthesis and provides a solid foundation for future integrated optimization solutions. Full article

Tunisia’s northwestern region offers a rich and diverse civilization heritage and cultural potential. However, it has been socially and economically marginalised since the 70s. This study explores the link between urban cultural heritage and the construction of collective identity, emphasising the potential of built heritage to foster sustainable community-based development. In addition to physical attributes, the literature highlights the importance of social interactions in shaping territorial identity. Identity, in this context, is not static but a dynamic territorial construction that integrates architectural, urban, and social dimensions. The cities of El Kef and Tabarka serve as case studies of spatially and culturally marginalised areas, facing significant challenges to both tangible and intangible heritage. With a negative population growth rate (−0.36% between 2004 and 2014) and the lowest economic development indicator nationally (0.3% in 2012), these cities reflect the urgent need for an alternative approach. Through spatial diagnosis, interviews, and stakeholder engagement, the research demonstrates that a renewed territorial model—grounded in heritage valorization and local identity—can support inclusive and adaptive development. Key findings reveal a generational gap in the perception and representation of heritage between younger and older residents. This indicates that cultural identity is not a static inheritance but a dynamic process requiring active community investment. Ultimately, the study concludes that urban identity assets critically influence the capacity of a community to build a shared vision for the enhancement of its territorial identity. This reconnection between territory, memory, and planning enables a collective reappropriation of space, proposing a long-term vision for heritage-integrated urban regeneration. Full article

Plasma-facing components (PFCs) are among the most critical functional components in a nuclear fusion device. Their reliability and durability under high heat loads are directly tied to the safe operation and lifetime of the fusion device. Under cyclic high thermal loads, accumulated plastic strain can lead to material property degradation. Furthermore, severe temperature gradients generate alternating tensile and compressive stresses within the material, resulting in the initiation and propagation of microcracks, ultimately causing structural failure of the PFCs. This study focuses on the issues of thermal stress concentration and plastic strain accumulation at the tungsten (W)/copper (Cu) joint interface and proposes an optimized design scheme based on a laminated tungsten structure. Using a combined approach of finite element simulation and theoretical analysis, the effects of tungsten layer thickness and interface geometry on the thermomechanical performance of the PFC joint were systematically investigated. The results indicate that reducing the thickness of tungsten sheet can significantly decrease the interfacial stress level. As the tungsten sheet thickness is reduced from the millimeter scale to the micrometer scale, the thermal mismatch at the W/Cu interface is reduced, thereby leading to a notable reduction in normal stress along the axial direction. In particular, when the thickness falls below 10 μm, the axial normal stress approaches zero, and the equivalent stress at the interface is effectively mitigated. Further research indicates that optimizing the flat W/Cu interface into an arc-shaped interface can alter the location of stress concentration. When the ascending distance of the Cu exceeds 600 μm, the stress concentration at the interface vertex is essentially eliminated. However, an excessively ascending distance of the Cu can exacerbate plastic deformation in the copper layer. By optimizing the extended distance of the Cu, a balance between stress relief and plastic strain accumulation can be effectively achieved. Full article

In recent years, with the development of novel hardware such as memristors, integrating chaotic systems with hardware implementations has enabled efficient and controllable generation of chaotic signals, providing new avenues for both theoretical research and engineering applications. In this work, we propose a novel memristor-based chaotic system, named the three-dimensional discrete echo-memristor map (3D-DEMM). The 3D-DEMM is capable of generating complex dynamic behaviors with self-similar attractor structures; specifically, under different parameters and initial conditions, the system produces similar attractor shapes at different amplitudes, which we refer to as an echo chaotic map. By incorporating the discrete nonlinear characteristics of memristors, the 3D-DEMM is systematically designed. We first conduct a thorough dynamic analysis of the 3D-DEMM, including attractor visualization, Lyapunov exponents, and NIST tests, to verify its chaoticity and self-similarity. Subsequently, the attractors of the 3D-DEMM are captured on a DSP platform, demonstrating discrete-time hardware simulation and real-time operation. Experimental results show that the proposed system not only exhibits highly controllable chaotic behavior but also demonstrates strong robustness in maintaining amplitude-invariant attractor shapes, providing a new theoretical and practical approach for memristor-based chaotic signal generation and applications in information security. Full article

Hydroelectric power projects were an integral part of twentieth-century postcolonial modernisation in Ireland. In 1925, the Cumann na nGaedheal government began the Shannon Scheme, which created the then-largest dam in Europe at Ardnacrusha. Hydroelectric power stations have since emerged across Ireland, from Poulaphouca and Ballyshannon to Inniscarra and Carrigadrohid. Despite the importance of hydropower in shaping Irish environments, ecocritical scholars like Matthew Henry and Sharae Deckard have shown that depictions of hydropower are generally understudied in the environmental and energy humanities and in Irish studies. This article traces twentieth-century hydroelectric power projects in Ireland through three plays: Denis Johnston’s The Moon in the Yellow River (1931), Frank Harvey’s Farewell to Every White Cascade (1958), and Conor McPherson’s The Weir (1997). Depictions of hydropower in these stage and radio dramas reveal an ongoing cultural awareness of one of modernity’s more insidious pollutants, namely, noise pollution. Exploring sound elements in representations of hydropower across diverse media and genres requires grappling with the legacy of colonialism on material environments in technocratic solutions to postcolonial national development and to planetary crises like climate change. Using postcolonial ecocritical and ecomedia studies lenses, this article analyses aural environments in Johnston, Harvey, and McPherson’s plays to elucidate intersections of medium, energy extraction, and hydropower that continue to resonate across Ireland. Besides providing historical insight into changing relationships with material environments, these plays also expose environmental and multispecies injustices caused by energy extraction projects on Ireland’s rivers. The aural environments in these plays also raise questions about what kind of modernisation and infrastructure projects would support multispecies modernities for more just and decolonial futures. Ultimately, this article demonstrates how these twentieth-century literary representations of hydroelectric energy extraction imagine alternative possibilities to anthropocentric modernisation through attending to multisensory and multispecies attachments to place. Full article

Green spaces in subtropical cities are important for providing ecological services that support human well-being and serve as reservoirs for diverse microbial communities, which in turn support ecosystem functions. However, studies on the characteristics of the phyllosphere microbial community and public health risks associated with putative pathogens in various urban green spaces exposed to anthropogenic stress remain limited. To address this gap, we collected leaf samples from forests, greenbelts, parklands, and wetlands across Wuhan, China, and analyzed the bacterial and fungal communities via next-generation sequencing (NGS) techniques. For bacterial and fungal communities, alpha diversity was significantly greater in low-traffic zones than in high-traffic zones. Beta diversity analysis revealed distinct clustering of bacterial and fungal communities according to the urban green space type. Anthropogenic factors (foot traffic) influence green space type to shape microbial community structure, function, and stability, with shifts significantly associated with soil physicochemical properties via Mantel tests and redundancy analysis. The relative abundance of Enterobacter and Enterococcus was significantly greater in high-intensity parklands (HIPS) and high-intensity greenbelts (HIGS) (41.84, 38.32%), respectively. Our findings provide important information for the sustainable management of urban green spaces by regulating microbial communities, offering new insights into ecosystem health and human well-being. Full article

This paper presents a novel asymmetric U-shaped refractive index sensor, which is based on a MIM waveguide and coupled with a U-shaped resonator, which integrates a ring, a circular cavity, and two rectangular cavities (URRCTR), in addition to an inverted rectangular nanostructure. The efficiency of the proposed sensor was investigated and optimized through the FEM. Simulation results indicate that the interaction between the broadband mode supported by the inverted square-shaped structure on the primary waveguide and the confined narrowband mode of the URRCTR resonator generates a distinct asymmetric feature in the transmission profile, a characteristic indicative of Fano resonance. The geometric parameters of the structure are crucial for tuning the Fano resonance features. Through systematic optimization, the sensor achieves a sensitivity of 3480 nm/RIU and a figure of merit (FOM) of 55.23. Due to its high sensitivity, compact footprint, and favorable temperature-dependent properties, the presented sensor reveals considerable promise for various applications in integrated photonic sensing. Full article

We propose a point cloud-based framework for open-vocabulary object pose estimation, called Pov9D. Existing approaches are predominantly RGB-based and often rely on texture or appearance cues, making them susceptible to pose ambiguities when objects are textureless or lack distinctive visual features. In contrast, Pov9D takes 3D point clouds as input, enabling direct access to geometric structures that are essential for accurate and robust pose estimation, especially in open-vocabulary settings. To bridge the gap between geometric observations and semantic understanding, Pov9D integrates category-level textual descriptions to guide the estimation process. To this end, we introduce a text-conditioned shape prior generator that predicts a normalized object shape from both the observed point cloud and the textual category description. This shape prior provides a consistent geometric reference, facilitating precise prediction of object translation, rotation, and size, even for unseen categories. Extensive experiments on the OO3D-9D benchmark demonstrate that Pov9D achieves state-of-the-art performance, improving Abs IoU@50 by 7.2% and Rel 10° 10 cm by 27.2% over OV9D. Full article

Numerical simulations, unlike experimental studies, eliminate material and setup costs while significantly reducing testing time. In this study, a random distribution program for steel-recycled polyethylene terephthalate hybrid fiber recycled concrete (SRPRAC) was developed in Python (3.11), enabling direct generation in Abaqus. Mesoscopic simulation parameters were calibrated through debugging and sensitivity analysis. The simulations examined the compressive failure mode of SRPRAC and the influence of different factors. Results indicate that larger recycled coarse aggregate particle sizes intensify tensile and compressive damage in the interfacial transition zone between the coarse aggregate and mortar. Loading rate strongly affects outcomes, while smaller mesh sizes yield more stable results. Stronger boundary constraints at the top and bottom surfaces lead to higher peak stress, peak strain, and residual stress. Failure was mainly distributed within the specimen, forming a distinct X-shaped damage zone. Increasing fiber content reduced the equivalent plastic strain area above the compressive failure threshold, though the effect diminished beyond 1% total fiber volume. During initial loading, steel fibers carried higher tensile stresses, whereas recycled polyethylene terephthalate fibers (rPETF) contributed less. After peak load, tensile stress in rPETF increased significantly, complementing the gradual stress increase in steel fibers. The mesoscopic model effectively captured the stress–strain damage behavior of SRPRAC under compression. Full article

Based on 350 visualizations, this paper examines the depiction of museum curators by the popular generative artificial intelligence (AI) model, ChatGPT4o. While the AI-generated representations do not reiterate popular stereotypes of curators as nerdy, conservative in dress, and stuck in time, rummaging through collections, they contrast sharply with real-world demographics. AI-generated imagery severely under-represents women (3.5% vs. 49–72% in reality) and disregards ethnic communities outside of Caucasian communities (0% vs. 18–36%). It not only over-represents young curators (79% vs. approx. 27%) but also renders curators to resemble yuppie professionals or people featured in fashion advertising. Stereotypical attributes are prevalent, with curators widely depicted as having beards and holding clipboards or digital tablets. The findings highlight biases in the generative AI image creation data sets, which are poised to shape an inaccurate portrayal of museum professionals if the images were to be taken uncritically at ‘face value’. Full article