Search Results (190)

Authentic Digital Interaction (ADI) refers to citizens’ direct, secure, and independent engagement with e-government services without reliance on intermediaries. This systematic literature review applies ADI as an organizing lens to synthesize recent empirical evidence on determinants shaping citizen interaction with e-government. Following PRISMA, 178 peer-reviewed studies published between January 2020, and October 2025 were identified across five databases, and 43 met the inclusion criteria. Descriptive mapping and ADI-guided narrative synthesis were used to consolidate related determinants and interpret their associations and contextual conditions. The review identifies three dominant patterns: perceived usefulness and performance expectancy are most frequently associated with intention, use, and continuance; trust and confidence shape whether perceived benefits translate into engagement; and policy and governance condition service consistency and the effects of usability and accessibility. Theoretically, the review shows that ADI provides a useful lens for interpreting e-government research beyond adoption and satisfaction by emphasizing direct, trustworthy, inclusive, and independent citizen interaction. Practically, the findings suggest that public agencies should prioritize accessible design, transparent processes, visible safeguards, and supportive governance arrangements. However, no formal risk-of-bias assessment was conducted. In addition, the evidence base remains limited by the sparse examination of participation, value co-creation, autonomy, and empowerment, and the review protocol, although prepared in advance, was not registered. Full article

Urban squares remain underrepresented in conservation-oriented literature compared with parks, street trees and green infrastructure. This scoping review uses CS-derived categories as an analytical lens to examine how the literature on urban squares frames conservation targets, direct threats, contributing factors and conservation actions. Following PRISMA-ScR, we searched Scopus and Web of Science for English-language peer-reviewed articles (2014–2024). After screening, 69 studies were included. Full texts were coded into CS-derived components and synthesised through frequency distributions, a cross-case conceptual synthesis, and an exploratory clustering of recurrent CF-DT-CT configurations. The reviewed literature is strongly centred on human-centred outcomes, particularly health, air quality and water quality, while biodiversity-related targets remain comparatively underrepresented. The most frequently investigated direct threats are pollution-related and linked to natural system management and modification, whereas other pressures are addressed less consistently. Contributing factors are dominated by meteorological conditions and vegetation coverage and composition. Reported conservation actions emphasise monitoring technologies, regulatory policy and green infrastructure, while others receive limited attention. Together, these analytical steps help make recurrent pathways and underrepresented dimensions more explicit, providing a more transparent evidence base for context-sensitive urban planning and nature-based solutions. Full article

In contemporary debates on the United Nations Sustainable Development Goals, there is growing recognition that artificial intelligence (AI) may contribute meaningfully to SDG 2 (Zero Hunger), particularly by enhancing the efficiency of food aid distribution and resource allocation. However, such optimism must be critically situated within the broader institutional and ethical contexts in which AI operates. This study argues that the effectiveness of AI in conflict-affected settings is contingent not only on technical capacity but also on governance structures, ethical safeguards, and institutional trust, dimensions closely aligned with SDG 16 (Peace, Justice, and Strong Institutions). Using the Gaza Strip as a case study, this article demonstrates that AI-driven food assistance mechanisms may inadvertently reinforce structural vulnerabilities. Specifically, algorithmic targeting of aid risks deepening dependency, exacerbating digital exclusion, and weakening already fragile governance systems. The absence of robust data accountability frameworks further complicates these dynamics, raising concerns regarding transparency, fairness, and long-term sustainability. The findings caution against privileging technical efficiency at the expense of socio-political stability. Rather, they highlight that the sustainability of AI interventions in humanitarian contexts fundamentally depends on the credibility and legitimacy of institutions. Accordingly, this study proposes a conceptual model for AI in hunger relief and digital humanitarianism that integrates technical innovation with institutional accountability and social trust. This study presents a narrative review informed by structural searching that examines the influence of AI on food security interventions in fragile contexts. This analysis applies a combined ethical governance and sustainability lens to assess current applications and risks. This research advances a broader analytical framework that moves beyond purely technical interpretations of AI, emphasizing its role as a socio-political tool, through identifying five key pillars for sustainable AI governance: data sovereignty, algorithmic accountability, inclusive system design, community-led governance, and market integrity. Full article

Agricultural enterprises serve as the cornerstone of food security. However, they operate under significant resource constraints and environmental risks. Adopting a systems lens, this study examines digitalization as a critical variable reshaping the input–output logic of agribusinesses. Using a longitudinal panel dataset of Chinese listed agricultural firms from 2013 to 2022 and Ordinary Least Squares regression, the study empirically identifies the mechanisms driving ESG performance. The results demonstrate that digitalization significantly enhances overall ESG performance, functioning as a governance mechanism that improves internal resource integration and transparency. Critically, the moderation analysis reveals a dynamic substitution relationship among system elements. Traditional inputs, specifically management expenses, financial slack, and intangible assets, exert significant negative moderating effects. This confirms the logic of factor substitution, suggesting that as digitalization advances, traditional governance modes relying on high administrative costs face diminishing marginal returns. In the environmental dimension, digitalization facilitates a transition from post-event remediation to whole-process control through intelligent traceability, effectively internalizing external constraints and reducing waste emissions. Additionally, heterogeneity analysis highlights significant structural variations. The ESG-enhancing effect of digitalization is more pronounced in firms characterized by high financial leverage, low long-term debt, and low industry concentration. Spatially, the marginal improvement is stronger in Western regions compared to the East, underscoring the Hu Huanyong Line as a critical structural boundary. Ultimately, digitalization serves as a core governance element that drives the structural transformation from traditional operating paradigms to digital governance architectures, thereby providing a robust pathway for corporate sustainability. Full article

This study examines the evolution of sustainability reporting research by integrating financial market dynamics, regulatory frameworks, and digital transformation into a unified analytical lens. It explores how these forces shape the credibility, comparability, and strategic relevance of sustainability disclosure. A bibliometric analysis of 683 publications indexed in the Web of Science (2006–2025) was conducted. Performance indicators and science-mapping techniques were applied to identify the intellectual structure of the field. Four major thematic clusters were detected: (i) corporate social responsibility and disclosure performance, (ii) governance and accountability, (iii) regulatory and institutional frameworks, and (iv) financial market and digital innovation drivers. Findings reveal that Disclosure, corporate social responsibility, and performance remain the field’s core anchors, while governance, accountability, innovation, and strategy increasingly shape reporting credibility. Sustainability reporting reduces information asymmetry, lowers financing costs, and builds stakeholder trust; however, persistent fragmentation, greenwashing, and weak assurance highlight the need for global harmonization. Regulatory initiatives and market instruments are converging to institutionalize sustainability disclosure. The study advances a policy and managerial agenda advocating stronger governance oversight, harmonized disclosure frameworks, and technology-enabled assurance mechanisms to enhance transparency, accountability, and investor confidence. Full article

Land subsidence and coseismic deformation can interact in groundwater-stressed sedimentary basins, yet basin-scale identification of event-timed vertical offsets in InSAR products requires explicit control of referencing and processing effects. This study evaluates whether the 27 September 2021 Arkalochori earthquake (Mw 6.0; central Crete) produced detectable coseismic vertical offsets within the Messara Basin by applying a reproducible screening workflow to Copernicus European Ground Motion Service (EGMS) Level-3 Vertical time series, from two processing generations (EGMS 2015–2021 and EGMS 2018–2022). An event-centered step metric (stepEQ), defined as the difference between post-event and pre-event mean displacements over a fixed acquisition window, is evaluated across three fixed spatial masks (MESSARA, R15060, R8750) together with a dispersion-based precision proxy (σstep) and a cross-generation sensitivity diagnostic (ΔstepEQ). A supplementary 2 + 2 subset sensitivity analysis indicates that the adopted fixed 3 + 3 estimator is stable at the basin scale, with sensitivity concentrated mainly in threshold-adjacent cases. Results indicate that Arkalochori-related offsets are not expressed as a basin-wide step across Messara; instead, non-background responses form a spatially limited and coherent subset concentrated where the basin intersects the near-source footprint. In EGMS 2018–2022, the higher vertical offset class (C2; |stepEQ| > 40 mm) is exclusively subsidence-direction and is enriched toward the screening center (up to ~19% within the radii mask R8750 m) but remains sparse at the basin scale mask (MESSARA mask) (~1%). Step-dominated points co-locate with strongly subsiding mean vertical velocity regimes and are hosted almost entirely by post-Alpine basin deposits, indicating strong material and background-deformation conditioning of step detectability. Cross-generation comparison shows basin-scale stability of background behavior but localized near-source sensitivity, supporting use of ΔstepEQ as a Quality Control (QC) lens for threshold-adjacent interpretations. The workflow provides a transparent, transferable approach for prioritizing candidate coseismic-step locations in EGMS time series. Results are interpreted as screening-level evidence in the derived vertical signal using event timing, spatial coherence, and QC diagnostics. Full article

Large depth of field (DOF) is a core pursuit in integral imaging (InIm). In this paper, we propose a DOF-enhanced InIm display system comprising a transmissive mirror device (TMD), a semi-transparent mirror (STM), two 2D displays, and a micro-lens array (MLA). The two 2D displays pre-render two sets of elemental image arrays (EIAs), each corresponding to a distinct depth plane. The STM spatially coaxializes the two EIAs emitted by the two 2D displays. At the same time, the TMD collaborates with the STM to adjust the effective projection distances of the coaxialized EIAs onto the MLA to different values. The MLA couples with the two EIAs projected at different effective distances, enabling the reconstruction of three-dimensional (3D) images at two separate central depth planes (CDPs). This system solves the narrow DOF issue in conventional InIm displays by reconstructing 3D images at two separate CDPs, thus enhancing the DOF. Notably, the proposed system achieves an approximate two-fold increase in DOF compared to a conventional one. A prototype of the DOF-enhanced InIm display system is constructed, and experimental results verify its feasibility. Full article

A four-quadrant low-coherence envelope detection method was proposed for measuring the surface spacing of optical components, eliminating the requirement for precise control of the delay line scanning step to generate a π/2 phase shift. The method employs an orthogonal polarization Mach–Zehnder (MZ) fiber interferometer, illuminated by a broadband superluminescent diode (SLD), and a four-quadrant polarization-resolved detector to simultaneously acquire spatially phase-shifted interference signals carrying surface spacing information. The interference envelope is directly demodulated to extract surface spacing, thereby decoupling measurement accuracy from mechanical stepping constraints. To enable real-time, high-precision calibration of the delay line, two complementary schemes were implemented: wavelength division multiplexing (WDM)-based calibration and dual optical path calibration. Experimental results confirm that the dual-path scheme exhibits weak dependence on scanning velocity and remains stable across a wide speed range. Repeat measurements of the surface spacing of a 1 mm thick sapphire plate yielded a standard deviation (STD) of 1.3 μm. By relaxing the strict π/2 phase shift condition traditionally imposed on scanning step size, this method improves operational efficiency while maintaining measurement reliability—providing a robust and broadly applicable solution for metrology, including lens surface spacing and transparent plate thickness characterization. Full article

In Canada, Indigenous communities have been disproportionately flooded. Specifically, Fort Albany First Nation (FN) located on a flood plain near the mouth of the Albany River in subarctic Ontario, Canada, has been evacuated frequently due to flooding or the threat of flooding―even though dikes were constructed in the late 1990s to safeguard the community. Thus, a fundamental question needs to be asked: Why is Fort Albany FN located on a flood plain in the first place? We answer the question through an Indigenous environmental justice lens using document and archival research in the context of the treaty making process between Fort Albany FN and the British Crown, and the establishment of reserves. In brief, procedural issues were noted, as there was no transparency in reserve choice at the time of signing the treaty, and during the actual surveying of the reserve boundaries with certain types of land being excluded from reserve locations, unbeknownst to the FNs peoples. The Cree were also misled into believing that they would retain access to their whole traditional homeland―and not be confined to reserve land―the Cree believed that they only agreed to share the land. Historically, the Cree harmonized with the seasons and would not be residing in the Albany River floodplain during river freeze-up and during river break-up―adaptive behaviour to avoid flooding. Harmonizing with the environment had allowed the mobile Cree to live successfully with the annual flooding of the Albany River for millennia, until being forced to live permanently on reserve land by the colonial government. Nonetheless, the Cree still sustain their cultural worldview acknowledging the Cree cycle of life. The way forward for Fort Albany First Nation will be either relocation to high ground or trying to tame nature by reinforcing the existing dikes—or some novel combination of both based on two worldviews. Full article

This study integrates the SOR framework with signalling theory, centring on information disclosure transparency as the core construct, to systematically examine its direct and indirect effects on consumers’ purchase hesitation. It specifically investigates the mediating roles of seller uncertainty and product uncertainty, whilst also testing the moderating effects of product price, type, and attributes. The research employs PLS-SEM in conjunction with the PROCESS Macro for empirical validation, drawing on 814 valid responses collected from online consumers in Taiwan. The principal findings indicate the following: (1) information disclosure transparency exerts a significant negative direct effect on purchase hesitation (B = −0.582, p < 0.001); (2) both seller uncertainty (indirect effect = −0.061) and product uncertainty (indirect effect = −0.060) exhibit partial mediation; (3) the model demonstrates strong predictive relevance for purchase hesitation (Q² = 0.486), underscoring its robust explanatory power in consumer decision-making processes; and (4) product price, type, and attributes significantly moderate the relationships between information disclosure transparency and the two uncertainty constructs. By extending signalling theory—originally developed in traditional markets—to the digital consumption context, this study provides empirical support for the signalling efficacy of information disclosure. It thereby offers an alternative theoretical lens for analysing consumer behaviour in online environments. Full article

To solve common defects such as warpage deformation, interface debonding, and uneven filling during the two-color injection molding of medical goggles while meeting their multi-performance requirements, including high light transmittance, impact resistance, chemical corrosion resistance, and structural stability, this study conducts research on the process optimization of two-color injection molding. Firstly, based on the principle of material compatibility and Moldflow simulation, a suitable material combination was selected: the first-shot frame adopts Apec 1745 PC material, and the second-shot lens uses Makrolon 2858 PC material, which effectively avoids the risk of interface non-fusion. Subsequently, a high-precision 3D simulation model was established using Moldflow software, and the injection sequence of “frame first, lens second” was optimized and determined. A gating system with double-gate (for the frame) and single-gate side feeding (for the lens), as well as a cooling system with an 8 mm diameter, was designed, and all key indicators of mesh quality meet the simulation requirements. Taking the mold and melt temperatures, holding pressures, and holding times of the two shots as design variables and warpage deformation as the optimization objective, sample data were obtained through an L₃₂ (7⁴) orthogonal test. A BP neural network was constructed to describe the nonlinear relationship between parameters and quality, and the Sparrow Search Algorithm (SSA) was combined to optimize the weights and thresholds of the network, forming a BP-SSA intelligent optimization model. The results show that the mean absolute percentage error (MAPE) of the proposed model is only 2.28%, which is significantly better than that of the single BP neural network (14.36%). The optimal process parameters obtained by optimization are a mold temperature of 130 °C, first-shot melt temperature of 311 °C, second-shot melt temperature of 310 °C, first-shot holding pressure of 83 MPa, second-shot holding pressure of 70 MPa, first-shot holding time of 14 s, and second-shot holding time of 8 s. Simulation and mold test verification indicate that after optimization, the warpage deformation of the goggles is reduced to 0.8956 mm (simulation) and 0.944 mm (measured), with a relative error of only 5.4%, which is 67.9% lower than the initial simulation result. The integrated method of “material selection—CAE simulation—orthogonal test—BP-SSA intelligent optimization” proposed in this study provides technical support for the high-precision manufacturing of thin-walled transparent multi-material medical products. Full article

Generative Artificial Intelligence (AI) has transitioned from a research milestone to a general-purpose technology with wide-ranging implications for organizations, labor markets, and information systems. Thanks to improvements in deep learning, generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, transformer-based language models, and reinforcement learning from human feedback (RLHF), generative AI can now create high-quality text, images, audio, code, and other types of content. This review synthesizes the core technical foundations and best practices for training, evaluation, and governance, with an emphasis on scalability and human oversight. The paper examines applications across customer service, marketing, software development, healthcare, finance, law, logistics, and the creative industries, and assesses the labor implications of generative AI using a sociotechnical lens. This study also develops a disruption index that integrates task exposure, adoption rates, time savings, and skill complementarity. The paper concludes with actionable recommendations for policymakers, organizations, and workers, emphasizing the importance of reskilling, algorithmic transparency, and inclusive innovation. Taken together, these contributions situate generative AI within broader debates about automation, augmentation, and the future of work. Full article

Integrative Ethical Water Governance (IEWG) offers a structured pathway to enhance the resilience and sustainability of food systems under intensifying water scarcity, climate change, and rising demand. This article develops and applies a scoring-based comparative framework to evaluate how four governance contexts—Türkiye’s Southeastern Anatolia Project (GAP), China’s Yangtze River Basin management, India’s watershed development programs, and California’s groundwater sustainability initiatives—perform across dual IEWG dimensions: ethical principles (rights-based approaches, justice, intergenerational equity, ecological integrity) and governance frameworks (stakeholder participation, environmental focus, equity approach, institutional integration, economic mechanisms). The analysis assigns explicit scores to each dimension, revealing distinct patterns of ethical integration, strengths and gaps in governance design, and context-specific trade-offs between agricultural production and ecosystem protection. Results show that higher aggregate IEWG scores are associated with more robust participatory structures, clearer allocation of responsibilities across scales, and better alignment of economic instruments with stewardship objectives. The study’s scoring-based comparative method provides a transparent, replicable tool for diagnosing governance performance and identifying priority areas for institutional innovation, offering a novel evaluative lens for future research and policy on ethical water–food governance. Full article

Background: This study examined how patients and caregivers perceive and experience AI-based care technologies through text mining analysis. The goal was to identify major themes, sentiments, and value-oriented interpretations embedded in their narratives and to understand how these perceptions align with key dimensions of patient-centered care. Methods: A corpus of publicly available narratives describing experiences with AI-based care was compiled from online communities. Natural language processing techniques were applied, including descriptive term analysis, topic modeling using Latent Dirichlet Allocation, and sentiment profiling based on a Korean lexicon. Emergent topics and emotional patterns were mapped onto domains of patient-centered care such as information quality, emotional support, autonomy, and continuity. Results: The analysis revealed a three-phase evolution of care values over time. In the early phase of AI-mediated care, patient narratives emphasized disruption of relational care, with negative themes such as reduced human connection, privacy concerns, safety uncertainties, and usability challenges, accompanied by emotions of fear and frustration. During the transitional phase, positive themes including convenience, improved access, and reassurance from diagnostic accuracy emerged alongside persistent emotional ambivalence, reflecting uncertainty regarding responsibility and control. In the final phase, care values were restored and strengthened, with sentiment patterns shifting toward trust and relief as AI functions became supportive of clinical care, while concerns related to depersonalization and surveillance diminished. Conclusions: Patients and caregivers experience AI-based care as both beneficial and unsettling. Perceptions improve when AI enhances efficiency and information flow without compromising relational aspects of care. Ensuring transparency, explainability, opportunities for human contact, and strong data protections is essential for aligning AI with principles of patient-centered care. Based on a small-scale qualitative dataset of patient narratives, this study offers an exploratory, value-oriented interpretation of how relational care evolves in AI-mediated healthcare contexts. In this study, care-ethics values are used as an analytical lens to operationalize key principles of patient-centered care within AI-mediated healthcare contexts. Full article

Power-system planning is being reshaped by rapid decarbonisation, electrification, and digitalisation, which collectively amplify uncertainty in demand, generation, technology adoption, and policy pathways. This review critically synthesises three principal optimisation paradigms used to plan under uncertainty in power systems: scenario-based stochastic optimisation, set-based robust optimisation (including adaptive and distributionally robust variants), and minimax-regret decision models. The review is positioned to address a recurrent limitation of many uncertainty-planning surveys, namely the separation between “method reviews” and “technology reviews”, and the consequent lack of decision-operational guidance for planners and system operators. The central contribution is a decision-centric framework that operationalises method selection through two explicit dimensions. The first is an information posture, which formalises what uncertainty information is credible and usable in practice (probabilistic, set-based, or probability-free scenario representations). The second is a flexibility posture, which formalises the availability, controllability, and timing of operational recourse enabled by smart-grid technologies. These postures are connected to modelling templates, data requirements, tractability implications, and validation/stress-testing needs. Smart-grid technologies are integrated not as an appended catalogue but as explicit sources of recourse that change the economics of uncertainty and, in turn, shift the relative attractiveness of stochastic, robust, and regret-based planning. Soft Open Points, Coordinated Voltage Control, and Vehicle-to-Grid/Vehicle-to-Building are treated uniformly under this recourse lens, highlighting how device capabilities, control timescales, and implementation constraints map into each paradigm. The paper also increases methodological transparency by describing literature-search, screening, and inclusion principles consistent with a structured narrative review. Practical guidance is provided on modelling choices, uncertainty governance, computational scalability, and institutional adoption constraints, alongside revised comparative tables that embed data credibility, regulatory interpretability, and implementation maturity. Full article