Search Results (10,065)

Cost overrun remains a pervasive issue in building construction projects, yet most predictive studies operate within a single data domain, ignoring the systemic interactions across project, schedule, resource, quality, and safety dimensions. This study quantifies the incremental predictive value of integrating these five construction data domains and identifies the cross-domain interaction patterns that explain prediction accuracy. As a simulation-based methodological study, an integrated dataset of 100,000 records was synthesised with theory-grounded causal structures derived from the construction management literature; no real project data were used. Gradient Boosting (GB), Random Forest (RF), and Linear Regression were evaluated on an 80/20 hold-out test split, with robustness verified through alternative domain orderings and hyperparameter sensitivity. SHAP analysis, including exact interaction values, was used to interpret feature importance and cross-domain synergies. The full five-domain GB model achieved R² ≈ 0.97 and MAPE ≈ 6%, a 220% relative R² improvement over the Project-domain baseline (R² rising from 0.305 to 0.975), robust across three ordering schemes. Schedule and Quality contributed the largest marginal gains (ΔR² = +0.312 and +0.255), whereas Resource integration yielded approximately one-thirty-first of Schedule’s return. Because the dataset is synthetic, the results are interpreted as a methodological demonstration rather than empirical evidence from real projects; they provide a reusable framework for prioritising data-integration investment and show that, within the simulated causal structure, cross-domain interactions—particularly Schedule × Risk and Project Type × Change Cost—carry predictive information that single-domain analyses cannot recover. Validation on real, partially integrated datasets is identified as essential future work. Full article

Background and Objectives: Low participation rates in exercise programs among older adults highlight the need for theory-driven, biopsychosocial interventions that enhance adherence, self-efficacy, and functional outcomes. Grounded in principles of motor learning and behavioral reinforcement within physiotherapy practice, this study aimed to examine the effect of periodic assessments combined with verbal feedback on functional and psychological outcomes in community-dwelling older adults. Methods: A pilot RCT was conducted involving 54 individuals aged ≥65 years (53 women and 1 man), recruited from senior community centers. Participants were randomly allocated to an intervention group (periodic assessment and verbal feedback; n = 27) or a control group (n = 27). Both groups participated in an identical 12-week structured exercise program, delivered twice weekly, focusing on balance, gait, and lower-limb functional training. An intention-to-treat approach was applied. Data were analyzed using Linear Mixed Models, with statistical significance set at p < 0.05. Results: Significant group × time interactions were observed in favor of the intervention group for key kinesiology-related functional outcomes, including the Short Physical Performance Battery (SPPB; p < 0.001), Timed Up and Go test (TUG; p = 0.011), and Activities-specific Balance Confidence Scale (ABC; p < 0.001). No statistically significant differences were identified between groups for the Behavioral Regulation in Exercise Questionnaire–2 (BREQ-2; p = 0.164) and the Self-Efficacy for Exercise Scale (ESE; p = 0.108), indicating that the primary psychological outcome (ESE) was not confirmed. However, both ESE and BREQ-2 demonstrated significant baseline differences favoring the intervention group, and, therefore, these findings should be interpreted with caution despite statistical adjustment. Conclusions: Periodic assessments followed by verbal feedback appear to selectively improve the functional effectiveness of structured exercise programs in older women, particularly physical performance, functional mobility, and balance confidence, with no significant differential effect on the primary psychological outcome (ESE; group × time interaction: p = 0.108). These findings support assessment-informed and feedback-driven physiotherapy strategies as a promising adjunct to exercise programs in older adults, with potential implications for optimizing functional outcomes within applied kinesiology and rehabilitation contexts. Full article

Body size is a key determinant of agonistic interactions in swimming crabs. This study quantified agonistic interactions during pairwise contests among different size classes. Four size classes of male juveniles, Portunus trituberculatus, were examined (extra-large: 70.16 ± 1.12 g; large: 45.07 ± 1.15 g; medium: 25.30 ± 1.19 g; small: 15.08 ± 1.73 g; n = 12 per size class). The frequency and duration of agonistic behaviors and fighting intensity were recorded by a video recording system and analyzed. Larger crabs initiated more frequent and intense aggressive interactions, whereas smaller crabs exhibited mainly avoidance and submissive responses, particularly against larger opponents. The most intense and prolonged contests occurred between opponents with relatively small size differences. Conversely, highly size-mismatched pairs exhibited shorter, less intense interactions. These agonistic interactions were strongly size-dependent, consistent with Resource Holding Potential theory and size-contest dynamics. These behavioral patterns provide insights into social dynamics and inform aquaculture management practices. Full article

The interaction of electromagnetic waves with atmospheric aerosols plays a significant role in communication systems and multimedia information transmission. Understanding the interaction of vortex light beams with an aerosol-laden atmosphere is indispensable for establishing a framework of the environmental channel. During the interaction, different optical effects such as absorption and scattering will result in energy attenuation, and this yields the deterioration of the transmission feature of the vortex beam signal. In this study, we present a theoretical analysis of Gaussian vortex beams (GVBs) scattering by diverse aerosol (unformed carbon, dust, sulphate, silicate, soot, and nitrate) particles in the atmosphere on the basis of the well-established generalized Lorenz–Mie theory (GLMT). Combined with the lognormal distribution model for aerosol particles, the attenuation and transmission characteristics of GVBs for different aerosol particles are analyzed. The extinction efficiency $\left(Q_{ext}\right)$ factor of GVB, caused by the absorption and scattering of various aerosols, becomes smaller compared to that of a basic Gaussian beam (GB). Increasing the OAM mode index, the energy attenuation and transmission caused by aerosol absorption and scattering further decrease. Moreover, this research provides a basis to analyze the optical characteristics of the twisted beams in different atmospheric channels, such as wireless communication networks over aerosol-laden systems and material interactions. Full article

Non-attributed graph sequence offers a powerful formalism for modeling the structural dynamics of complex systems—such as social networks, urban infrastructures, and document transmission pathways—where vertex interactions evolve over time without explicit attribute information. Mining association rules from such sequences to uncover recurring topological patterns have attracted growing interest. Yet two fundamental challenges remain: (1) how to effectively encode edge-level temporal dynamics in non-attributed settings, and (2) how to perform efficient and semantically meaningful temporal association rule mining under structural uncertainty. To address these within a systems-oriented framework, we propose two novel algorithms: the weighted temporal association rule mining algorithm and the fuzzy weighted temporal association rule mining algorithm. The first algorithm introduces time-dependent numerical weights to quantify the strength and persistence of vertex connectivity, integrating them into support and confidence measures to capture both the intensity and evolution of interactions. The second algorithm extends this by incorporating fuzzy set theory, modeling ambiguous or context-sensitive relationships (e.g., indistinct links or weakly correlated vertices) and generating fuzzy-weighted rules that enhance interpretability for real-world system analysis. Evaluated through five comprehensive experiments across diverse datasets and scales using standard metrics (support, confidence, rule count, running time), our methods produce more selective rule sets and achieve lower computational times compared to the classical Apriori algorithm. The proposed approaches thus establish a robust, data-driven foundation for analyzing temporal evolution and structural uncertainty in dynamic complex systems—providing a generalizable methodology applicable beyond domain-specific constraints. Full article

This paper critically examines disability-inclusive design theory and practice through the lens of new materialism, tracing a shift from designing for users to designing with and ultimately from disability. It reveals a key paradox: while participatory and disability-led approaches foreground lived experience and plural voices, design outcomes must still function across shared, pluriversal contexts. Individual accounts of disability generate situated, partial knowledge that cannot be straightforwardly generalised, creating persistent tensions between singular experiences and collective design needs. By introducing a relational ontology, the paper reframes design knowledge as emergent from dynamic interactions between people, materials and contexts, destabilising binaries such as designer/user and disabled/non-disabled. The proposed praxeology advances disability leadership, positionality and embedded participation as core to design practice. These insights prompt new research questions around how plural, situated knowledges can inform scalable design decisions, how conflicting lived experiences can be ethically negotiated, and how relational, material perspectives can reshape methodologies for inclusive and socially just design. Full article

In online retail, consumers cannot experience product quality before purchase. With the adoption of artificial intelligence (AI), platforms can certify product quality information. However, stronger platform certification may reduce sellers’ incentives to disclose and limit personalized information such as product fit. This study examines the conditions under which a platform should adopt AI-assisted platform certification (AIPC). We develop a game-theoretic model with one platform and two competing sellers. We compare the case of not adopting AIPC with adopting AIPC, and examine how AIPC affects seller disclosure, pricing, and profits. Sellers decide whether to disclose product information and set prices. Consumers update their quality beliefs based on seller disclosure and platform labels. Our results show that AIPC is not always the preferred strategy. When product-fit information spillovers between competing sellers are strong, the platform may be better off not adopting AIPC. When information spillovers are weak, AIPC adoption depends on consumers’ prior belief regarding product quality. Specifically, when consumers have a low prior belief that an uncertified or undisclosed product is of high quality, AIPC benefits the platform and sellers but reduces consumer surplus. When this prior belief is sufficiently high, AIPC creates a win–win–win outcome for the platform, sellers, and consumers. Full article

Generative artificial intelligence (GAI) enables students to offload cognitive tasks to an external system, yet the consequences of such cognitive offloading for the development of critical thinking—a core dimension of human intelligence—remain underexplored. Drawing upon cognitive offloading theory and distributed cognition theory, this study investigates the heterogeneity of critical thinking outcomes among Chinese university students who use GAI, focusing on how different patterns of human–AI collaboration relate to cognitive autonomy relinquishment. A questionnaire survey was administered to 353 university students across multiple provinces in China. Cluster analysis and regression analysis were employed to identify distinct user profiles and to examine predictors of critical thinking gains and cognitive autonomy. Four distinct user profiles emerged, ranging from “simple Q&A users” (25.2%) to “critical co-thinkers” (15.6%). Learning motivation was the strongest predictor of both critical thinking gains (β = 0.42) and lower cognitive autonomy relinquishment (β = −0.35). Notably, offloading depth positively predicted cognitive autonomy relinquishment (β = 0.25), revealing a paradoxical pattern: sophisticated GAI use was associated with greater dependence. A “high depth–high dependence” subgroup (25.8%) was identified, disproportionately composed of female students and Information and Communication Technology (ICT) majors. The findings challenge the assumption that deeper GAI engagement automatically yields cognitive benefits. Because all constructs were measured through self-report, the findings are interpreted as reflecting students’ perceptions of their cognitive behaviors and abilities; the methodological implications of this design are discussed in detail. Educational interventions should prioritize metacognitive training over technical skill development to ensure that cognitive offloading enhances rather than undermines critical thinking. Full article

Existing studies have mainly focused on either intangible cultural heritage (ICH) or traditional villages separately, while limited attention has been paid to their coupled spatial relationship and influencing mechanisms at the provincial scale. To address this gap, this study investigates the spatial characteristics and influencing factors of 869 national and provincial intangible cultural heritage (ICH) items and 777 traditional villages in Yunnan Province using Geographic Information Systems (GISs) and geographic detector methods. The results indicate significant differences in their spatial distribution patterns: ICH exhibits a “multi-core clustering” structure, whereas traditional villages present a “dual-core clustering with multiple dispersed patches” pattern. The study further reveals a spatial mismatch as well as a significant positive spatial correlation between ICH and traditional villages. Natural environmental conditions and historical-cultural factors jointly shape their spatial differentiation, while socio-economic factors such as urbanization exert a stronger influence on ICH distribution, and demographic and economic conditions more strongly affect traditional villages. This study contributes to the literature by integrating cultural landscape theory with GIS-based spatial analysis to reveal the spatial interaction mechanisms between ICH and traditional villages in Yunnan Province. The findings provide theoretical support and practical implications for cultural heritage conservation, rural revitalization, and territorial spatial planning in ethnically diverse border regions. Full article

Farmers’ investment decisions can shape their capacity to implement practices consistent with sustainable development objectives. The article identifies the declarative structure of investment decisions on Polish dairy farms based on a survey and diverse theoretical frameworks (resource-based view, institutional approach, real options theory, behavioural theory, and the theory of planned behaviour). The purpose is to identify the determinants of the extent and structure of declared agricultural investments. The authors determined the relationships between declared investments and groups of variables and identified investment axes and interdependencies. Investment decision predictions are founded on logistic regression, an SEM model for relationship structuring, and residual correlation analysis for mapping relationships and evaluating the correlation demasking effect, according to which raw correlations between investment axes may hide underlying residual associations between them. We found that declared farmland investments were associated with milk production volume and appeared to be linked to long-term farm development objectives. The respondents became less keen on investing in livestock production as they aged, whereas older farmers showed a greater propensity to undertake energy-related investments. These results suggest that farmers’ declared investment intentions may be consistent with conditions conducive to achieving sustainable development objectives through their potential association with farm viability, resource-use efficiency, and rural economic development. Our findings may have potential policy relevance by informing the design of public measures aimed at strengthening farms’ adaptive capacity in the context of sustainability transitions. Full article

This study examines how two forms of financial literacy—objective financial literacy (OFL; demonstrated knowledge of interest rates, inflation, and diversification) and perceived financial literacy (PFL; self-assessed confidence in financial matters)—relate to financial wellbeing through distinct capability pathways, and whether self-regulation conditions these links. We use three nationally representative cross-sections from Portugal (2015, 2020, 2023; N = 3648), a European setting marked by declining objective literacy and constrained market participation. Guided by capability theory, we propose a dual-lane model in which OFL operates through behavioural capability (BC; enacted saving, investing, and planning behaviours) to shape objective financial wellbeing (OFW; resilience, assets, and saving), while PFL operates through perceived capability (PC; financial self-efficacy and perceived control) to shape subjective financial wellbeing (SFW; perceived security, satisfaction, and freedom from financial stress). We also test whether non-impulsive, future-oriented behaviour (NIB) strengthens the associations along the objective lane. Structural equation models provide partial support for the dual-lane model, revealing three asymmetries with implications for European policy: (1) the link between behavioural capability and objective financial wellbeing weakens in 2023, suggesting that macroeconomic conditions can undercut even prudent financial behaviour; (2) perceived financial literacy directly predicts subjective financial wellbeing, but perceived capability does not mediate this association, indicating that financial confidence shapes wellbeing independently of self-efficacy; and (3) non-impulsive, future-oriented behaviour amplifies the association between objective literacy and objective wellbeing in 2015 and 2023 but not in 2020, showing that the benefits of self-regulation are context-dependent. The findings inform financial education and policy across Europe by distinguishing intervention levers for objective versus subjective outcomes and identifying conditions under which behavioural interventions are most effective. Full article

This study investigates the factors influencing customer engagement in AI-powered fitness applications and the subsequent impact on behavioral outcomes such as word of mouth and sustainable consumption. A quantitative research design was employed, with data collected through a structured survey from users of AI fitness applications, and analyzed using Structural Equation Modeling (SEM). Drawing on the Information Systems Success Model and engagement theory, the research examines the roles of service quality, system quality, information quality, health consciousness, anthropomorphism, and personal innovativeness. Findings reveal that higher perceived quality across service, system, and information dimensions, coupled with health consciousness and human-like features, significantly enhances user engagement. Engagement, in turn, drives both advocacy and sustainable behaviors, while personal innovativeness selectively amplifies the effect of system quality. The study advances theoretical understanding by adapting and extending established models to the context of AI-driven health technologies, while also providing practical insights for the development of intelligent, user-centric fitness applications that promote sustained engagement and responsible use. Full article

To address the problems of communication delay and waste of communication resources in the formation process of UAVs, a dynamic event-triggered formation control method for second-order multi-leader UAV systems with communication delay is studied. On the basis of considering the communication delay, a dynamic triggering mechanism is designed. By adjusting the triggering time in real time, the system can be more effectively controlled based on its current state. According to the control method, the mathematical models for the extended state observer, controller, and dynamic event-triggering function of the system have been established. Its stability is demonstrated by Lyapunov stability theory and linear matrix inequality theory, and Zeno behavior is excluded. The simulation results show that compared with the existing methods, the proposed method can avoid the dependence on the global information of the network topology, reduce the communication frequency, and effectively save communication resources. Full article

According to the “Technical Specification for Air Quality Testing in Archives Repositories,” air pollutants in archives can be categorized into exogenous and endogenous pollutants. Common exogenous pollutants include sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃), and hydrogen sulfide (H₂S), while endogenous pollutants mainly consist of formaldehyde (HCHO) and acetic acid (CH₃COOH). This study combines external electric field technology with density functional theory (DFT) and the B3LYP method to theoretically analyze the spectral characteristics and degradation mechanisms of these six pollutant gases. Molecular models of the six gases were constructed using Gaussian software. The configurations of five pollutant gas molecules (SO₂, NO₂, O₃, H₂S, and HCHO) were optimized using the B3LYP/6-31G(d) basis set, while the configuration of acetic acid was optimized using the B3LYP/3-21G basis set, yielding their stable structures and spectral information. The study found that characteristic peaks in the spectra shifted under the influence of an electric field. Additionally, by scanning the potential energy surfaces of selected molecular bonds under varying electric field strengths along specific directions, the required external electric field strengths for the degradation of the six common pollutant gases in archives were determined as follows: 0.1050 a.u. for SO₂, 0.0975 a.u. for NO₂, 0.0925 a.u. for O₃, 0.1000 a.u. for H₂S, 0.1500 a.u. for HCHO, and 0.0705 a.u. for CH₃COOH. The results clarify the degradation thresholds of these six pollutant gases under an external electric field. The findings indicate that acetic acid (0.0705 a.u.) and ozone (0.0925 a.u.) are highly sensitive to electric fields, while formaldehyde requires the strongest electric field (0.1500 a.u.) for degradation. These results provide a reference and theoretical foundation for electric field-assisted degradation technology targeting pollutant gases in archives. Full article

Words occurring in similar contexts have been observed to have similar meanings. A natural and established method within computational linguistics implements this observation by representing words as vectors with dimensions determined by words that are witnessed in fixed positions in relation to the target word. We generalize this context vector approach to part-of-speech (POS) sequences appropriate to word sequences. As with words, the context of a POS tag (considering the POS tags occurring before and after any target tag) reflects its syntactic constraints and may approximate the "meaning'' of the target tag, from a distributional perspective. We use the 111-million-word British National Corpus (BNC) and the sequence of POS labels lifted from those texts to calculate POS context vectors. We observed significant agreement between the clusters of POS context vectors and the supercategories of corresponding POS tags, and examined potential categorization of the POS categories that emerged from the vector clusters. We also found that though vector measures partially align with the predictions of generativist linguistic theories, the approach suggests a more complex relation between syntactic categories. We conclude that a mutual-information-based approach better approximates the distributional "meaning'' of syntactic categories than the conditional probability distribution of POS symbols. Full article