Search Results (29,938)

This study aims to examine the thematic structure and temporal evolution of the literature on technology acceptance in distance education using the Structural Topic Modeling method. Peer-reviewed articles indexed in the Scopus database between 2016 and 2025 were included in the analysis, yielding 18 consistent thematic clusters. Technology Acceptance Model (TAM) Core Constructs and Structural Equation Modeling (SEM)/Partial Least Squares (PLS) Methodology emerged as the most prevalent themes, reflecting the field’s sustained reliance on quantitative model-testing approaches. Trend analyses revealed a pronounced decline in TAM Core Constructs, while Unified Theory of Acceptance and Use of Technology (UTAUT) and Extensions, Hybrid and Emergency Remote Teaching, and Mobile and AI Applications demonstrated significant upward trends. The period comparison showed that pandemic-related themes became markedly prominent in the 2021–2025 period. Topic correlation analysis indicated the presence of competing temporal niches within the literature. Taken together, the findings suggest that research on technology acceptance in distance education is undergoing a structural transition from single-framework TAM dominance toward a multi-model, context-sensitive research paradigm. These findings carry important implications for the sustainable development of technology-enhanced distance education ecosystems and offer evidence-based guidance for researchers and policymakers committed to building inclusive and adaptive educational systems. Full article

We introduce the MERI (Market Evolutionary Resilience Index), a cognitive big data framework operationalising jamming transition physics into a daily operational regime detector (low-latency inference, 8 ms per observation). Opaque models cannot be deployed in regulated environments because every automated alert must decompose into auditable feature contributions. The MERI addresses this by treating the market as a complex adaptive system whose metabolic state constitutes the primary observable. Three cognitive layers fuse heterogeneous streaming data: an EGARCH-GED econometric baseline, a Random Forest classifier on a 15-dimensional physics-derived feature space, and a TreeSHAP Gini attribution audit ensuring full prediction-level transparency. Fisher Information Gain epistemic gating restricts automated intervention to predictions exceeding 2.5 nats certainty. Evaluated on South African financial markets (2015–2025, $N=2870$ trading days, Eskom load-shedding as exogenous forcing), the MERI achieves 97.3% accuracy (AUC = 0.9973, recall = 1.000), statistically equivalent to Temporal Fusion Transformers (Model Confidence Set, 90% confidence) while delivering 85.7% high-certainty predictions versus 23.4% for deep learning. A Granger-validated 48-h early warning lead ($F=62.003$, $p<0.001$), $7.78\times$ recovery hysteresis (Cohen’s $d=2.13$), and infrastructure dominance of 78.0% (Gini) confirm that the framework is operationally feasible for daily monitoring in the South African JSE–Eskom setting. Cross-domain portability is proposed as a theoretical extension pending empirical validation. Full article

Evaluating joint predictive performance for multivariate hydroclimatic models requires metrics that simultaneously assess marginal accuracy and cross-variable dependence recovery. Existing metricsthe Energy Score, Variogram Score, and their derivativesdo not adapt to the structural complexity of the residual correlation matrix, treating a single correlated pair identically to a fully dense dependence structure. We propose two novel metric families: Metric E (E-CVWMD: Enhanced Coefficient-of-Variation Weighted Marginal-Dependence) and Metric E2 (E-CVWMD-Pairwise), which are designed for mixed-type multivariate responses combining continuous and binary outcomes within a cross-validation framework. We position Metrics E and E2 as diagnostic ranking tools for comparing competing models rather than as strictly proper scoring rules, and we provide a strictly proper Log-Loss variant (E-LL/E2-LL) for applications that require the full properness guarantee. Metric E assigns variable-level weights proportional to the coefficient of variation (CV) of each outcome on the training partition and adaptively calibrates the marginal-dependence trade-off parameter ${\alpha }^{*}$ via a global distance-correlation test. Metric E2 refines this by replacing the global test with a pairwise Spearman screening index $\widehat{\pi }$, the proportion of variable pairs with significant residual correlationwhich maps linearly to ${\alpha }^{*}\left(\widehat{\pi }\right)=1-\widehat{\pi }/2\in [0.5,1]$. Applied to the validation of a Generalized Multivariate Functional Additive Mixed Model (GMFAMM) on 62 Valle del Cauca meteorological stations ($N_{\mathrm{test}}\approx$ 31,663), the naive significance-based index saturates ($\widehat{\pi }=1.0$) at this large sample sizeevery pair, including correlations as small as $|{\widehat{\rho }}_s|\approx 0.01$, is flagged “significant”which is precisely the sample-size sensitivity we address. Under the effect-size screening ($|{\widehat{\rho }}_s|\ge 0.05$), three negligibly correlated pairs are excluded, yielding $\widehat{\pi }=0.70$ and ${\alpha }_{E2}^{*}=0.65$, a better-calibrated weight than Metric E’s ${\alpha }_E^{*}\approx 0.797$ under the same data. A large-scale simulation study with 37,440 model evaluations confirms that Metric E inverts the correct ranking at correlation levels $\rho \ge 0.40$ (CDR = 0%), while E2 maintains correct discrimination in 14 of 15 simulation conditions (M1 vs. M3). We also delimit the metrics’ scope: E2 degrades under near-saturated uniform dependencea regime in which the strictly proper Energy Score remains preferableand the pairwise index is sensitive to sample size, for which we provide an effect-size-based variant. An R package (mvmetrics v0.2.0) implementing both metrics, the Log-Loss variant, alternative weighting schemes, and the effect-size screening is publicly available. Full article

Achieving sustainable urban water management is increasingly critical amid climate change, water scarcity, population growth, and intensifying pressure on freshwater resources. Life Cycle Sustainability Assessment (LCSA) has emerged as a promising integrated framework to evaluate the holistic sustainability of municipal wastewater reuse systems by combining environmental Life Cycle Assessment (LCA), Life Cycle Costing (LCC), and Social Life Cycle Assessment (S-LCA). This paper presents a critical narrative review synthesizing current evidence on LCSA applications specifically in urban municipal wastewater reuse contexts—encompassing non-potable urban, agricultural, and potable reuse—and identifying which configurations deliver genuine sustainability benefits, where major methodological shortcomings persist, and how the field can advance toward more robust, policy-relevant practice. The evidence base was assembled through structured searches in Scopus, Web of Science, and Google Scholar, screened from over 300 references to 25 peer-reviewed studies retained for in-depth analysis. Among the retained studies, positive outcomes were most often reported under low-carbon energy grids, short transport distances, and appropriate reuse-type matching: hybrid decentralized systems were associated with overall sustainability scores up to 4.8 times higher than conventional supply, while cluster-scale systems were associated with reductions in global warming potential of 15–40%. Persistent pitfalls include weak pillar integration, an underdeveloped social dimension, inconsistent system boundaries, and insufficient consideration of absolute sustainability. In response, this paper proposes the RENEW-LCSA Framework (Renewable Water-Oriented Life Cycle Sustainability Assessment Framework)—a practical six-step operational approach incorporating reuse-type-specific Social LCA indicators, explicit absolute sustainability assessment against the Water Scarcity Index (WSI) and local carbon budgets, and evidence-based integration of Nature-Based Solutions (NbSs). Three paradigmatic cases—Windhoek (Namibia), Singapore (NEWater), and Los Angeles County (USA)—illustrate the framework’s systematic application. By moving beyond optimistic narratives, this review advances the credibility of LCSA as a decision-support tool for safe, equitable, and circular urban water management, contributing to SDG 6 and the European Water Reuse Regulation (2020/741). Full article

When natural disasters strike, the destruction of terrestrial communication infrastructure creates urgent demands for emergency networks. Efficient UAV deployment in capsule airport–UAV hierarchical networks has emerged as a critical challenge due to limited aerial resources and stringent quality-of-service requirements. This paper develops a QoS-aware joint optimization model for UAV deployment, integrating air-to-ground (A2G) channel modeling with resource allocation, where upper-level position optimization is coordinated with lower-level frequency allocation and power control through a hierarchical decomposition strategy. The proposed QoS-TLK-VNS-K algorithm combines graph coloring for interference mitigation with iterative power control for SINR guarantee. Empirical evaluation using multi-scenario simulations demonstrates that the proposed approach significantly outperforms the traditional distance-based coverage method. Statistical validation over 30 independent runs demonstrates significant improvements in QoS satisfaction ($+23.8\%$, $p<0.001$), average SINR ($+104.0\%$, $p<0.001$), minimum user rate ($+194.9\%$, $p<0.001$), and Jain’s fairness index ($+16.2\%$, $p<0.001$) compared to the distance-based baseline. These results demonstrate that the framework effectively addresses the trade-off between interference suppression and network connectivity in multi-UAV emergency communication systems. Full article

The general zeroth-order Randić index $R_{\alpha }^0\left(G\right)={\sum }_{v\in V}d{\left(v\right)}^{\alpha }$ ($\alpha \ne 0,1$) unifies several classical indices, including the first Zagreb ($\alpha =2$), forgotten ($\alpha =3$), and inverse degree ($\alpha =-1$) indices. We characterize the connected graphs that attain extremal $R_{\alpha }^0$ values for most parameter regimes. Nested stars maximize for $\alpha \ge 2$, quasi-stars for $\alpha <0$, and quasi-regular graphs for $0<\alpha <1$. For $1<\alpha <2$, we show that the maximizer is a threshold graph, but the exact structure depends on n, m, and $\alpha$; a counterexample demonstrates that the previously claimed optimality of the quasi-star in this regime is not always valid, and a complete characterization remains open. Minimizers follow the opposite pattern. Our majorization-based framework, combined with the sign of the third derivative of $x^{\alpha }$, yields explicit closed-form formulas for the extremal values. We also extend the analysis to disconnected graphs under $\delta \left(G\right)\ge 1$, identifying star-forests and clique-forests as the extremal maximizers (for $\alpha \ge 2$ and $\alpha <0$, respectively), while the regimes $0<\alpha <1$ and $1<\alpha <2$ are addressed with quasi-regular graphs and left as open problems, respectively. Full article

Seed vigor directly affects emergence rate, early emergence capacity, and seedling uniformity in ginseng. This study integrated pre-sowing laser treatment with hyperspectral prediction for rapid evaluation of ginseng seed vigor. Cracked-stage Mountain Cultivated Ginseng Seeds and American Ginseng Seeds were treated with four semiconductor laser conditions, including red light, blue light, red–blue 3:1, and red–blue 6:1, for 5, 15, and 30 min. Emergence rate, emergence energy, and emergence index were evaluated, and 350–2500 nm hyperspectral reflectance data were collected. Different preprocessing, feature selection, and machine learning methods were compared to construct an optimized support vector regression model for emergence rate prediction. The results showed that laser effects were seed-type-specific and parameter-dependent. Mountain Cultivated Ginseng Seeds responded strongly, with 5 min blue light treatment producing the best performance, achieving a 100.0% emergence rate, 40.4 percentage points higher than the control, with significantly higher emergence energy and emergence index. In American Ginseng Seeds, no treatment differed significantly from the control, although the 30 min red light treatment showed the highest emergence rate trend. Savitzky–Golay smoothing combined with the second derivative was the optimal preprocessing method. The Caterpillar Fungus Optimizer-based support vector regression (CFO-SVR) joint optimization model achieved the best prediction performance, with ${\mathrm{R}}_{\mathrm{p}}^2$ of 0.9517, ${\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}}_{\mathrm{P}}$ of 2.4766, and ${\mathrm{M}\mathrm{A}\mathrm{E}}_{\mathrm{P}}$ of 1.0730. These results provide a feasible technical reference for pre-sowing treatment of ginseng seeds and rapid, non-destructive evaluation of emergence vigor. Full article

Brazilian campos rupestres are a global center of plant endemism, but they are increasingly threatened by climate change and human activities. In this context, Sincoraea, an endemic genus of the Espinhaço Mountain Range, provides an important model for understanding how reproductive traits influence species persistence. This study investigated the floral biology, reproductive systems, nectar dynamics, and floral visitors’ interactions of several Sincoraea species through fieldwork conducted from 2023 to 2025. Floral biology and pollination were analyzed in four species, while pollen and stigma traits were examined in eight species using light and scanning electron microscopy. The flowers had white tubular corollas, diurnal anthesis, and high pollen viability during anthesis. Nectar production peaked in the morning. All recorded pollinators were hummingbirds and all species were self-incompatible, except S. burle-marxii, which was partially self-compatible. The reproductive effectiveness index was 1.00 for all species, suggesting that natural pollination provided effective conspecific pollen transfer without evidence of pollen limitation. These findings highlight the strong dependence of Sincoraea on floral visitors and emphasize the importance of conserving plant–floral visitor networks in campos rupestres. These interactions are essential for maintaining genetic diversity and the long-term stability of ecosystems. Full article

Total organic carbon (TOC) is a key parameter for geological sweet spot optimization and resource evaluation. However, accurate TOC prediction remains challenging under small-sample conditions because conventional physical and machine learning methods are commonly limited by insufficient labeled data. To address this problem, this study proposes a hybrid deep learning framework that integrates a convolutional autoencoder with a BP neural network (CAE-BPNN). In this framework, the CAE is first used to learn representative feature expressions from abundant unlabeled logging data, and the learned encoder is then transferred to the supervised prediction stage, where labeled samples are used for TOC prediction through the BPNN. The dataset includes 177 measured TOC samples and 1388 unlabeled logging samples from the Fengcheng Formation shale in Well MY1, in the Mahu Sag, and data from Well MY2 are used for independent validation. Model performance is evaluated using five-fold cross-validation with the coefficient of determination (R²) and root mean square error (RMSE) as metrics. For Well MY1, CAE-BPNN achieves the best performance, with R² = 0.89 and RMSE = 0.061, outperforming CNN (R² = 0.85, RMSE = 0.075), GBDT (R² = 0.83, RMSE = 0.076), RF (R² = 0.81, RMSE = 0.082), and BPNN (R² = 0.77, RMSE = 0.085). In the independent validation using Well MY2, CAE-BPNN also shows superior predictive performance, with R² = 0.81 and RMSE = 0.367. These results indicate that unlabeled logging data can effectively enhance feature representation and improve TOC prediction accuracy under limited labeled-sample conditions. The proposed method provides an effective solution for small-sample TOC prediction and offers a reliable basis for movable oil evaluation using the oil saturation index (OSI), as well as a reference for predicting other geological parameters such as S1, S2, and porosity. Full article

The May–September 2021 cryptocurrency-mining prohibition in the People’s Republic of China removed an industry that had become heavily concentrated in Chinese jurisdictions over the years preceding the ban. Using the full population of 97,005 Bitcoin blocks mined between May 2020 and February 2022, retrieved from the mempool.space block API, with pool-operator corporate domicile and China-linkage coded from publicly documented corporate-parent records, we estimate the post-prohibition shift in pool-attributed block share. On the top-13 panel covering approximately ninety-five per cent of attributed blocks, a two-way fixed-effects projection records a differential China-linked share movement of $-5.45$ percentage points per pool after the 24 September 2021 NDRC and PBOC enforcement event (cluster-robust SE 2.55; CRV1 p = 0.054, not significant at five per cent; wild cluster bootstrap p = 0.014 and exact-permutation p = 0.023 reported as small-sample diagnostics, not relied on for significance; under a permutation conditioned on baseline share the latter rises to p = 0.100). This paper’s primary magnitudes are the aggregate compositional movements, which do not depend on the cluster-level test. Aggregate China-linked share falls from 78.47 to 60.01 per cent between the placebo (May–October 2020) and persistence (November 2021–February 2022) windows; non-China-linked share rises from 3.50 to 27.02 per cent, with Foundry USA absorbing approximately 15.7 percentage points (approximately 67 per cent of the non-China-linked share gain). The within-non-China-subset normalised Herfindahl–Hirschman Index falls from 10,000 to 4191, consistent with rising within-subset dispersion among non-Chinese-domiciled operators. Among the identified non-China-linked operators absorbing material share in the analytical panel, the displacement is concentrated at named legal persons, chiefly with documented United States and European corporate domicile; the residual excluded/unknown category is not used to support that claim. Full article

The field of competitive strategy has attracted growing academic interest in recent years; however, the intellectual framework and thematic evolution of this research area remain fragmented. This study aims to systematically map the evolution of competitive strategy research using an unsupervised machine learning framework. Drawing on a dataset of approximately 3900 journal articles indexed in the Scopus database between 2015 and 2025, the study employs probabilistic topic modeling, specifically Latent Dirichlet Allocation (LDA), together with keyword co-occurrence network analysis, thematic mapping, and community detection techniques to identify the latent thematic structure of the field. The findings reveal a modular and interconnected conceptual landscape in which capability-based strategic perspectives, particularly dynamic capabilities and the resource-based view, continue to occupy central positions in the literature. At the same time, themes related to digital transformation, artificial intelligence, supply chain resilience, environmental, social, and governance (ESG)-oriented management, and sustainability-focused strategic capabilities demonstrate substantial growth and emerging prominence. Temporal analyses further indicate a gradual reconfiguration toward digitally integrated, sustainability-oriented, and capability-driven strategic frameworks. By integrating topic modeling with network-based bibliometric analysis, the study provides a comprehensive and data-driven mapping of the field’s intellectual evolution. The study contributes to competitive strategy research by synthesizing the latent thematic structure of the field and by showing how complementary computational and bibliometric techniques can support large-scale literature mapping in a rapidly evolving research domain. Full article

Background/Objectives: Many HIV-positive (HIV+) males receiving highly active antiretroviral therapy (HAART) experience metabolic complications, including non-alcoholic fatty liver disease (NAFLD); lipodystrophy; and intestinal dysbiosis, often characterized by a Prevotella-rich enterotype. Gut microbial fermentation produces short-chain fatty acids (SCFAs), which play important roles in host metabolism. This study investigated the relationships among HAART, anthropometrics, diet, intestinal permeability, gut microbiota composition, and lipodystrophy in HIV+ males. Methods: Forty males aged 23–60 years were enrolled, including 19 HIV+ participants recruited from the East Tennessee State University (ETSU) Health Infectious Diseases Specialty Clinic and 20 HIV-negative (HIV−) controls recruited through standard methods. Participants provided a stool sample for 16S rRNA gene sequencing, SCFA analysis by gas chromatography, and proximate analysis, and completed a food frequency questionnaire. Lipodystrophy-related measures included body mass index (BMI), hip-to-waist ratio (H:W), and liver health assessment using FibroScan. Blood samples were collected by venipuncture. Serum markers of intestinal permeability, including Claudin-21, flagellin, and intestinal fatty acid-binding protein (IFABP), were quantified by enzyme-linked immunosorbent assay (ELISA). Results: HIV+ males exhibited significantly higher H:W ratios (p = 0.001) and hepatic steatosis (p = 0.0047) than HIV− controls (Welsh’s t-test). Concentrations of isobutyrate (p = 0.0024), isovalerate (p = 0.0008), and valerate (p = 0.0329) were elevated in HIV+ participants, whereas butyrate (p = 0.0014) and total acetate/propionate/butyrate (APB) (p = 0.0046) were higher in HIV− males (Welsh’s t-test). HIV+ participants also showed greater abundances of Prevotella and Lachnospiraceae (Analysis of Compositions of Microbiomes; ANCOM). Retrospective analysis revealed that all HIV+ participants were men who have sex with men (MSM). Conclusions: HIV+ males demonstrated distinct gut microbiome profiles, altered SCFA production, and markers of disrupted lipid metabolism. These findings provide a foundation for future investigations of microbiome-metabolism interactions in HIV+ MSM. Full article

Background/Objectives: Postural control in elite athletes may reflect sport- and side-related balance demands. Conventional Centre of Pressure (CoP) path length alone offers only limited information about directional and frequency-domain sway characteristics. This cross-sectional study described CoP path length, mediolateral/anteroposterior (ML/AP) directionality, and power spectral density (PSD)-derived frequency-domain descriptors in elite athletes from sports with distinct movement demands. Methods: A total of 116 asymptomatic elite athletes from volleyball, football, short track, ice hockey, and field hockey were assessed during single-leg stance. CoP path length, the ML/AP index, and PSD outcomes were analysed. PSD was calculated in LabVIEW using a fast Fourier transform (FFT) routine from the complete 60 s trial acquired at 1 kHz after removal of the DC component. Spectra were not normalised and are reported as absolute spectral-density values in mm²/Hz. PSD outcomes were summarised in low-frequency (0.02–0.6 Hz) and higher-frequency (1–5 Hz) windows, and the PSD quotient was defined as PSD 0.02–0.6 Hz/PSD 1–5 Hz. Results: Observed sport–sex groups differed in subject-averaged CoP path length (F(5,110) = 22.26, p < 0.001, eta_p² = 0.503), ML/AP index (F(5,110) = 4.07, p = 0.002, eta_p² = 0.156), PSD 0.02–0.6 Hz (F(5,110) = 38.67, p < 0.001, eta_p² = 0.637), PSD 1–5 Hz (F(5,110) = 4.83, p < 0.001, eta_p² = 0.180), and the exploratory PSD quotient (F(5,110) = 3.33, p = 0.008, eta_p² = 0.132). Paired-side comparisons showed greater right-side CoP path length, greater right-side PSD 0.02–0.6 Hz, and a higher right-side ML/AP index, whereas PSD 1–5 Hz and the PSD quotient did not differ significantly between sides. Conclusions: The combined analysis of CoP path length, ML/AP directionality, and PSD-derived descriptors characterised sport-, sex-, and side-specific postural-control profiles in this cohort. Mechanistic interpretations of segmental neuromuscular control remain tentative because the study was cross-sectional and did not include electromyography, kinematics, or prospective injury data. Full article

Background: Depression is among the most prevalent psychiatric conditions and markedly disrupts everyday functioning. Its origins are multifactorial, with biological, psychological, and contextual influences jointly shaping the course of recovery and the response to treatment. Personality has been proposed as a relatively stable factor that may reflect developmental influences on emotional and cognitive functioning and may be associated with long-term clinical outcomes in depression. The present study aimed to examine the association between implicit personality characteristics in women hospitalized for depressive disorder and their long-term psychosocial functioning, using a performance-based measure of personality (the Rorschach Inkblot Method, RIM). Subjects and Methods: At baseline (T1), 58 women hospitalized for depressive disorder completed the Beck Depression Inventory (BDI-II) and the Rorschach Inkblot Method (RIM); Rorschach protocols were scored using the Ego Impairment Index—second revision (EII-2), a behaviorally derived index spanning perceptual accuracy, executive integrity, and social cognition. Demographic and clinical information was abstracted from medical records, and an independent rating of functioning was obtained with the Global Assessment of Functioning scale (GAF). Four years later (T2), patients were re-administered the BDI-II and the GAF, and major life events occurring during the follow-up interval were quantified with the Social Readjustment Rating Scale (SRRS). Results: Baseline implicit personality organization showed a significant association with psychosocial functioning four years after the index hospitalization. Among the variables examined, personality structure at admission outperformed both initial depressive symptom severity and the burden of intervening life events in predicting later functional status. In particular, EII-2 accounted for an additional 10.3% of the variance (ΔR² = 0.103, p < 0.05) over and above age, chronicity, stress, and depressive symptom severity when predicting four-year GAF-rated functioning. Among the predictors examined, age was the most influential variable in the final model (β = 0.442), indicating that demographic factors carry substantial weight alongside personality functioning in shaping long-term outcomes. Conclusions: Our findings are consistent with the view that a patient’s personality may influence the course of recovery and suggest that personality-level factors deserve attention when planning care for this clinically complex disorder. The present results indicate that implicit cognitive–perceptual features—assessed through performance-based methods that bypass conscious self-report—may be associated with long-term psychosocial functioning in women hospitalized for depression. These findings suggest that performance-based personality assessment deserves further study as a potential prognostic aid, although replication in larger and more diverse samples is needed before clinical application. Full article

Nowadays, modern manufacturing industries still rely on the expertise and manual dexterity of highly skilled operators. In this context, Mixed Reality (MR) technologies are emerging as promising solutions for contextualized task guidance to support operators during complex activities. However, their effective adoption in production environments is still limited by the lack of ergonomic evidence regarding their impact on operators’ well-being, usability, and interaction sustainability. This study investigates whether different interaction modalities with holographic instructional content influence the ergonomic suitability of a MR-based task guidance system for wire harnessing, developed in collaboration with Leonardo S.p.A. A between-subjects experimental design was adopted, involving 16 industrial workers randomly assigned to two groups: gaze and gesture interaction (Group A, n = 8), and gaze and voice interaction (Group B, n = 8). Ergonomic evaluation included both physical and cognitive aspects, assessed respectively through the Simulator Sickness Questionnaire (SSQ) and a composite usability index (UI) based on ISO 9241-11, integrating efficiency, effectiveness, and satisfaction. Results suggest comparable usability levels between the two interaction modalities (UI_A = 0.683; UI_B = 0.667). Gesture interaction was perceived as slightly more supportive of operational efficiency, whereas voice interaction was associated with lower cybersickness severity (mean TS_A = 331.5; TS_B = 196.1). Overall, the findings suggest that no single interaction modality universally outperforms the other, but rather that ergonomic suitability depends on the balance between physical workload, cognitive demands, and task characteristics. These results highlight the importance of human-centered ergonomic evaluations in the design of sustainable MR assistance systems for industrial environments. Full article