Search Results (5,792)

The rapid integration of artificial intelligence (AI) into digital platforms has raised critical questions about how AI’s ethical declarations influence this sector. This study adopts a mixed-methods approach. First, a descriptive content analysis examined 54 declarations, including 45 national declarations across Africa, Asia, Europe, and the Americas, and 9 from major global actors (MGAs) such as the OECD, G7, and the EU. Ethical principle frequency was examined, and a benchmarking index was developed to compare “dominant principles” cited in over 50% of regional declarations with those cited in over 50% of MGA declarations. The analysis reveals universal adoption of societal well-being, fairness, accountability, and privacy (100%), while transparency and security show regional variation (75%). Second, a semi-systematic literature review following PRISMA guidelines identified four opportunities (e.g., global participation) and seven limitations (e.g., lack of standard frameworks, definitional ambiguities, implementation challenges, and legal enforcement difficulties). The implications of these limitations for digital platforms are then examined, leading to the identification of two dimensions for responsible platform governance: assessment mechanisms (e.g., UNESCO’s Ethical Impact Assessment) and governance implementation structures. The study further distinguishes three tiers of enforceability: declarative, procedural, and institutionalized ethics, bridging normative declarations and operational practice in platform governance. Full article

While return migrant workers (RMWs) are increasingly viewed as key to rural development, their specific impact on grain production remains ambiguous. Clarifying this role is critical to manage the dual nature of their reintegration—leveraging valuable resources and knowledge while addressing complex reintegration challenges—to ensure national food security and advance agricultural modernization. Drawing on data from the 2018 China Labor-force Dynamics Survey (CLDS), this study explicitly tests the hypothesis that migration experience significantly reduces the likelihood that RMW households engage in grain production. The empirical results from probit models support this hypothesis, and this finding is robust across multiple specifications. Further analysis shows that migration experience significantly reduces land cultivation scales—especially among larger producers—and increases land abandonment. Additionally, it inhibits technology adoption or invest in agricultural technology. These results suggest that migration experience may weaken, rather than enhance, RMWs’ commitment to grain production, challenging the policy expectation that they can lead agricultural transformation. The study calls for more nuanced policy interventions that account for the structural constraints facing RMW households and their limited contribution to large-scale, efficient grain farming. Full article

Ensuring the structural reliability of power transmission networks is a fundamental prerequisite for the stable operation of modern energy systems. To address the challenges posed by complex weather interference and the small scale of insulator defects during power line inspections, this paper proposes LID-YOLO, a lightweight insulator defect detection network. First, to mitigate image feature degradation caused by weather interference, we design the C3k2-CDGC module. By leveraging the input-adaptive characteristics of dynamic convolution and the spatial preservation properties of coordinate attention, this module enhances feature extraction capabilities and robustness in complex weather scenarios. Second, to address the detection challenges arising from the significant scale disparity between insulators and defects, we propose Detect-LSEAM, a detection head featuring an asymmetric decoupled architecture. This design facilitates multi-scale feature fusion while minimizing computational redundancy. Subsequently, we develop the NWD-MPDIoU hybrid loss function to balance the weights between distribution metrics and geometric constraints dynamically. This effectively mitigates gradient instability arising from boundary ambiguity and the minute size of insulator defects. Finally, we construct a synthetic multi-weather condition insulator defect dataset for training and validation. Compared to the baseline, LID-YOLO improves precision, recall, and mAP@0.5 by 1.7%, 3.6%, and 4.2%, respectively. With only 2.76 M parameters and 6.2 G FLOPs, it effectively maintains the lightweight advantage of the baseline, achieving an optimal balance between detection accuracy and computational efficiency for insulator inspections under complex weather conditions. This lightweight and robust framework provides a reliable algorithmic foundation for automated grid monitoring, supporting the continuous and resilient operation of modern energy systems. Full article

This paper proposes an end-effector (EE) driven whole-body trajectory tracking control algorithm for wheeled mobile manipulators based on linear and angular motion decomposition. Instead of solving a high-dimensional optimization problem across all degrees of freedom, the proposed method formulates the control objective directly in the EE space and decomposes the required motion into planar linear, vertical, and angular components. To address redundancy between the mobile base and the manipulator under non-holonomic constraints, a control authority switching strategy with a radial blending function is introduced. This approach eliminates ambiguity in control allocation while preventing abrupt switching near workspace boundaries. The kinematic controller guarantees exponential convergence of position and orientation errors without requiring a full dynamic model. Numerical simulations demonstrate stable tracking performance in three-dimensional space. Compared with a quadratic programming-based whole-body controller, the proposed method achieves comparable or faster error convergence while reducing computational burden by more than 13 times on average. These results indicate that the proposed EE-driven framework provides a computationally efficient and practically deployable solution for real-time mobile manipulator control. Full article

Background: Despite the cardiometabolic benefit of semaglutide, its impact on quality of life and whether patients’ characteristics influence clinical outcomes and health-related quality of life (HRQoL) remain ambiguous. Method: A retrospective review of patient charts was conducted after semaglutide initiation to assess the clinical impact of semaglutide, followed by a prospective analysis to evaluate HRQoL using the 36-Item Short Form Health Survey (SF-36). Descriptive and correlative analyses were conducted using SPSS software version 29 (IBM Corp., Armonk, NY, USA). Results: From a total of 715 patients, 255 (average age 59.1 years; 58.1% male participants) were subjected to clinical outcome analysis. The use of semaglutide was associated with significant reductions in HbA1c, total bilirubin, and TG and elevations in T4, TSH, Scr, and HDL. When each fifth value of each clinical parameter was compared with the baseline, gender revealed a significant impact, as females showed increased rates of elevated HDL (73.2% vs. 55.7%), reduced weight (69.8% vs. 55.7%), and reduced BMI (72.5% vs. 53.8%) compared to those in males. Despite the number of comorbidities significantly influencing BMI (p = 0.015), it had no impact on HbA1c post semaglutide use (p = 0.062). The same number of patients (n = 255), albeit having slightly different demographic and clinical characteristics, was included in the HRQoL analysis cohort. Females represented 54.5% of the cohort, and 71.0% were aged between 40 and <65 years. The average scores for all domains within the physical component summary (PCS) and mental component summary (MCS) were below 50, indicating a lack of perceived improvement in the overall quality of participants’ lives considering the pre-treatment period as the basis of comparison. In particular, younger age [OR 0.975, CI95% 0.953–0.998, p = 0.033] and being female [OR 0.273, CI95% 0.162–0.459, p < 0.001] led to reduced odds of scoring ≥ 50 in PCS, indicating a poor physical health state. On the contrary, older age [OR 1.036, CI95% 1.011–1.06, p = 0.004] increased the odds of scoring ≥ 50 in MCS, indicating a better mental health state in elderly vs. young semaglutide users. Although education level had significant influence on PCS, this did not extend to MCS. Upon investigating if type of change in a clinical parameter correlates with PCS and MCS scoring, only the decline in T4 reduced the odds of scoring MCS ≥ 50 [OR 0.5, CI95% 0.274–0.913, p = 0.024], while no significant influence was found either with other parameters or between clinical parameters and PCS. Conclusions: A lack of perceived improvement in HRQoL is noted with semaglutide use. Age, gender, and education play significant roles in HRQoL post semaglutide initiation. Overall, before prescribing semaglutide, patient counseling on its positive and negative effects is crucial to promote long-term adherence and optimize clinical outcomes. Full article

Carbon capture and storage technologies are widely adopted, primarily in conventional power plants. Maritime transport must align with the 2050 targets and sharply reduce its environmental footprint. Onboard Carbon Capture and Storage (OCCS) appear to be an immediately feasible solution until alternative fuels are adopted and fully implemented. This study presents a regulatory compliance assessment and a techno-economic analysis of the implementation of OCCS. An LNG tanker was selected as a case study due to the inherent compatibility between LNG storage systems and CO₂ storage on board. The examined regulation includes the calculation of the corresponding penalties arising from the enforcement of the EU ETS, FuelEU Maritime, and the IMO NZF framework. The cost of installing the OCCS is also considered when evaluating the proposal’s sustainability. The results demonstrate that OCCS shows real promise in the fight against maritime transport emissions, but at present, it is not economically viable. Its viability depends mainly on clear regulatory guidelines and effective incentives that encourage its adoption, while offsetting investment and operating costs. Finally, the current study also seeks to resolve an ambiguity in the existing legislation that renders the OCCS a viable option. Full article

Despite substantial research conducted over the past decades, the transition to a circular construction industry remains in its infancy. The deconstruction of buildings to recover materials for reuse is recognized as a promising strategy for advancing circularity. However, terminological ambiguity and a lack of conceptual consensus continue to lead to misinterpretation and may impede theoretical and practical progress. Based on a systematic literature review of 51 academic and non-academic sources, this paper analyzes the use of core terminology related to deconstruction processes. Ten central terms and expressions are identified, among which ‘demolition’ and ‘deconstruction’ are the most consistently applied, whereas ‘selective demolition’ is used with varying interpretations. To further document the current state of terminology in the field, a glossary of general terms commonly employed is also presented. Clear communication and the explicit definition of applied terms are essential to ensure efficient on-site construction processes and the relevance and value of future studies in this field. To this end, this study aims to enhance transparency and contribute to coherence within the terminological landscape of deconstruction research. Full article

Low-light image enhancement (LLIE) remains challenging due to severe degradation of high-frequency structures and semantic ambiguity under extreme darkness. Although existing methods achieve satisfactory brightness recovery, they often suffer from structural inconsistency and semantic drift, as diverse scenes are typically processed with uniform enhancement strategies or static text prompts. To address these issues, we propose a Multi-Modal Structural and Semantic-Adaptive Network (MSSA-Net) under a structure-anchored paradigm. First, we design a Multi-Scale Self-Refinement Block (MSRB) to enhance degraded visible representations through multi-scale feature extraction and progressive refinement. Meanwhile, a pseudo-infrared structural prior derived from the input image is introduced to provide noise-insensitive geometric cues. These cues are extracted via a Structure-Guided Cross-Attention (SGCA) module to produce structure-dominant features. The refined visible features and structural features are then adaptively integrated through an adaptive residual fusion (ARF) module to achieve balanced restoration. Furthermore, we develop a Large Multi-modal Model (LMM)-Driven Scene-Adaptive Attention mechanism that generates instance-aware scene tags from a coarse preview and injects semantic embeddings into visual features. Extensive experiments demonstrate that MSSA-Net improves structural fidelity, brightness recovery, and semantic naturalness across multiple benchmarks. Full article

Five-axis Computerized Numerical Control (CNC) machine tools play a pivotal role in the precision manufacturing of aeroengine turbine blades, where ultra-high reliability and accuracy are essential. Failure Mode, Effects and Criticality Analysis (FMECA) has been widely applied in the reliability assessment of such advanced machining systems due to its systematic evaluation of potential failure modes. However, traditional FMECA approaches often overlook the ambiguity of human cognition and the interdependence among expert evaluations, limiting their effectiveness in complex aerospace manufacturing environments. To address these issues, this paper proposes a novel FMECA framework based on generalized intuitionistic linguistic theory. A new Generalized Intuitionistic Linguistic Weighted Geometric Average (GILWGA) operator is introduced to couple multi-source expert information and quantify the fuzziness inherent in subjective assessments. Additionally, an intuitionistic linguistic entropy-based weighting scheme is developed to dynamically evaluate key risk factors, including severity, occurrence, detectability, and controllability. The proposed framework is applied to a case study involving the spindle system of a five-axis CNC machine tool used in aeroengine blade production. The results demonstrate that the proposed method offers more robust and consistent failure mode prioritization, providing effective decision support for reliability-centered maintenance in aerospace equipment manufacturing. Full article

Accurate remote sensing scene classification is essential for large-scale Earth observation but remains challenging due to significant inter-class similarity and complex spatial layouts in medium- and low-resolution imagery. Conventional convolutional neural networks (CNNs) effectively capture local structural patterns but struggle to model long-range semantic dependencies, whereas Vision Transformers excel at global context modeling yet often show reduced sensitivity to fine-grained spatial structures. To address these limitations, we propose CrtNet, a structure-aware Cross-Model Residual Transformer Network that establishes a dual-stream collaborative architecture integrating convolutional structural representations with Transformer-based semantic modeling through gated residual cross-model interactions. In this framework, a convolutional branch first extracts stable local structural features with strong spatial inductive biases. These features are continuously injected into the Transformer encoding process via residual cross-model connections, enabling persistent structural guidance during global attention modeling. In addition, a sample-adaptive dynamic gating mechanism is introduced to flexibly balance structural and semantic features during prediction. Extensive experiments conducted on two public remote sensing benchmarks, EuroSAT and UCM, demonstrate that CrtNet consistently outperforms representative CNN-based, Transformer-based, and hybrid state-of-the-art models, particularly in visually ambiguous scene categories. Full article

In this article I analyse the period of social and political upheaval faced by mixed Greek Catholic and Roman Catholic families living in the Subcarpathian countryside in the 1930s and 1940s. Focusing on a vernacular perspective often overlooked in nation-centric historiographies, I describe the nature of neighbourly relations and collective identity both before and after World War II. I pay particular attention to the ambiguous connections between religious and ethnic identities before the war, highlighting phenomena such as bi-ritualism and diglossia. I then juxtapose this with the specific circumstances of 1944–1945, when villagers were frequently forced to choose their ethnic identity under the threat of Polish and Ukrainian nationalist guerrillas, especially active during that time. Building on a rich body of ethnographic material, I argue that choices of ethnic identity during a “state of exception” were often unstable and shaped primarily by the imperative of survival and other pragmatic considerations. However, I also present tragic stories of mixed families, where the ethnic choices made by some individuals were rooted in their deeply held convictions. Additionally, I reference scholars who are re-evaluating and complicating the relationship between nationalism and religious identity in rural European communities living in border areas, including Norman Davies, Kate Brown, Max Bergholz, and Jarosław Syrnyk. Full article

Visual saliency modeling has achieved high predictive performance in natural image domains, yet its generalization to abstract art remains limited by the lack of explicit semantic structure and the scarcity of eye-tracking data. In such semantically ambiguous contexts, understanding the underlying drivers of attention is as critical as predictive accuracy. This paper presents an interpretable, ’white-box’ saliency framework tailored to abstract art, which constructs predictions through a weighted combination of 35 modular heuristics grounded in perceptual psychology and art theory, including contrast, grouping, isolation and symmetry. Heuristic weights are optimized via a genetic algorithm and refined by a context-aware modulation mechanism that adapts to image-level visual features. Evaluation against eye-tracking data from 40 abstract paintings demonstrates that the model with the expanded activation variant produces stable, meaningful predictions while achieving a competitive KL-divergence score (1.11 ± 0.55), which is comparable to the SalGAN baseline (1.11 ± 0.53). Analysis of the optimized weights reveals strong contributions from contrast, texture, and grouping mechanisms, while nearly half of the heuristics, including most horizontal symmetry heuristics are systematically pruned by the model. Moreover, context-aware modulation reveals that these weights are not static but shift dynamically based on image-level features such as edge density and intensity variation. By prioritizing transparency over raw predictive performance, this study demonstrates that explainable saliency models can function as robust investigative tools for decoding the principles of human visual perception in data-scarce domains. Full article

This research explores the multifaceted dynamics of water (in)security in Pakistan-Occupied Jammu and Kashmir (POJK), examining the region as a vector of broader transboundary hydro-politics between India and Pakistan. The study begins by outlining the current state of water infrastructure and governance within POJK, highlighting key issues such as water scarcity, environmental degradation, and socio-political marginalization in access to water. It then transitions into a critical analysis of transboundary water management between India and Pakistan under the auspices of the Indus Waters Treaty (IWT), emphasizing how the unique geopolitical ambiguity of POJK complicates cooperative water governance. The paper contends that POJK’s water scarcity is both a humanitarian concern and a flashpoint for regional instability, warranting more inclusive, transparent, and robust green political governance frameworks. By bridging localized realities with transboundary water cooperation, this study offers a structured interpretive analysis of how water (in)security in POJK reverberates beyond its borders, informing the need for a deep ecological approach. Full article

Software defect prediction is becoming key for software quality assurance. Traditional software defect prediction approaches have predominantly focused on analyzing code-level metrics, often overlooking valuable information available during the requirements phase. However, when a requirement change request (RCR) is issued, usually during the maintenance and evolution phase, predicting software defects provides an important preventative measure. Work in requirement-based software defect prediction methods typically focus on identifying requirement flaws, such as ambiguity or incompleteness, and fail to adequately predict defects that may manifest later in the operational software system. This paper proposes a context-driven representation model, named FORESIGHT, that predicts software defect types from requirements change requests using machine learning methods. The proposed model uses binary indicators to represent contextual metrics derived from change-request characteristics and supports multi-class prediction from both primary defect types and defect manifestation types. To build its representation model, three datasets were created from real-world industrial projects in different software domains (Web, Mobile, and ASRS). FORESIGHT was evaluated using Random Forest, XGBoost, and Gradient Boosting classifiers. Results show certain software defect types can be reliability predicted with Random Forest achieving the highest macro-F1 (0.815–0.873 for primary defect type prediction; 0.683–0.833 for defect manifestation prediction) across all three datasets, outperforming XGBoost and Gradient Boosting on every dataset–task combination. Findings show that contextual metrics from requirements change requests, structured within the FORESIGHT representation model, enable reliable pre-implementation prediction of specific defect types in deployed software systems. Full article

Building on Gutman’s (1987) argument that architectural practice should reflect the nature of the problem, this article explores four eras of architectural practice: the Patronage Model, the Clientage Model, the Transitional Models, and Future Models. Each era is examined in relation to six “Questions of Praxis”: (1) What is the nature of the problem?, (2) What is the nature of the intervention?, (3) What knowledge is valued?, (4) What is the stance toward the problem?, (5) What is the continuity in the relationship?, and (6) What is the prioritization of professional obligations? Through a comparative analysis of questions 2–5—the analytic core of action-taking—alongside four drivers of change in today’s volatile, uncertain, complex, ambiguous world, yields 16 possible futures for architects. Further synthesis identifies five primary roles for architects of the future: systems-thinking designer (embracing complexity), steward (building trust amid volatility), facilitator (reducing ambiguity through shared meaning), curator (making sense of uncertainty), and strategic forecaster (transforming volatility into preparedness). These roles embody a care-based approach—prioritizing ongoing relationships over episodic interventions, collective capacity-building over expert prescriptions, and adaptive readiness over static solutions. This reflects the positioning of architecture as a public good, focused on strengthening social, ecological, and systemic foundations so communities not only withstand disruption but also adapt, learn, and thrive through it. Full article