Search Results (2,239)

Emotional education is fundamental in teacher training due to its impact on teacher well-being and student learning. In Chile, initial teacher training has addressed this dimension in a fragmented manner, and little is known about whether in-service teachers exhibit distinct patterns of retrospectively perceived preparation across different domains of emotional education. This study aimed to identify and characterize latent profiles of retrospectively perceived knowledge in emotional education acquired during initial teacher training among Chilean in-service teachers. Using a retrospective design, 412 practicing teachers participated (86.7% female; mean age = 39.2 years; mean teaching experience = 12.6 years). The EEITT Scale assessed perceived knowledge acquired in Mood, Emotional Regulation, Psychosocial Well-being, and Identity. Confirmatory factor analysis supported the predefined four-factor structure, and latent profile analysis was conducted in R by comparing solutions from one to eight profiles. Six profiles were retained, showing a combination of differences in overall perceived preparation and more specific configurations across dimensions. Emotional Regulation emerged as one of the most discriminating dimensions, particularly in profiles characterized by comparatively low perceived preparation in this domain, alone or together with Psychosocial Well-being. The six-profile solution showed adequate classification quality (entropy = 0.824), although classification uncertainty was greater for one intermediate profile. Overall, the findings indicate that teachers do not report a uniform pattern of perceived preparation in emotional education; instead, distinct subgroups differ in both the overall level and configuration of perceived preparation across domains. These results suggest that reliance on sample-level averages may obscure meaningful variation in teachers’ retrospective perceptions of their initial training and may inform future research on differentiated professional development strategies. Full article

We set out to fix a “contamination” problem in reconstruction-based Temporal Convolutional Network VAEs (TCN-VAEs) for cross-dataset IoT flow anomaly detection: when attack flows share an encoder window with benign flows, the shared latent code is allegedly distorted, inflating benign reconstruction error and producing false positive rates (FPRs) of 22–65% despite an ROC-AUC above 0.93. Our proposed fix, TCN-Pred, excludes the target flow from the encoder and scores it by next-flow prediction error, reducing FPR to 0.65–13%. We subjected this causal explanation to a battery of controlled ablations, holding architecture, decoder, loss, and thresholding fixed while varying one factor at a time. Each one falsified the original hypothesis: target inclusion/masking changes FPR by at most 0.001; context shuffling/reversing/zeroing changes it by at most 0.003; a context-blind constant-output predictor matches TCN-Pred’s FPR and F1 to three decimal places on all three datasets. The actual cause, confirmed on the original trained models with no retraining, is a scoring-granularity mismatch: the TCN-VAE threshold is calibrated from per-window errors averaged over 20 flows but applied to per-flow errors at evaluation (standard deviation $\approx \sqrt{20}\times$ higher, measured ratio 4.46 against a predicted 4.47). Recalibrating the identical model at matching granularity drops FPR from 22.7/47.6/64.6% to 0.65/5.0/12.5% on BoT-IoT, IoT-23 and ToN-IoT, closing 89–97% of the reported FPR gap without changing a single model weight. We report this diagnostic chain, together with an attack-prevalence sensitivity analysis, sample-disjoint calibration, normality diagnostics, and label-free and redundancy-aware (mRMR) feature-selection benchmarks, as a methodology other work should apply before attributing fixed-threshold performance to architecture. The pipeline is supervised source-domain feature selection followed by benign-only detector training, not fully unsupervised, a distinction we quantify later in the paper. Investigating dataset representativeness, we found that all three provided files reduce to only ≈6000 genuinely distinct flows via an undocumented row-duplication procedure, causing 97.8% BoT-IoT train/test near-duplicate overlap; a leakage-free re-evaluation changes FPR by only 0.23 percentage points. We also found that the TLS-metadata columns are already transformed upstream of every available artefact, so the proportion of genuinely TLS-encrypted flows cannot be recovered, and we soften the paper’s encrypted-traffic framing accordingly. Full article

Rationale/Aim: We aimed to identify potential latent profiles and factor structures that may reflect characterological differences in how transcendence is experienced and expressed in individuals and investigate the nature and structure of transcendence as a character virtue rooted in Awakened Awareness (as a tendency towards spiritual perception). Methods: Two studies were conducted on a multinational sample of 11,595 participants. Study 1 applied latent profile analysis (LPA) to the indicator variables Awakened Awareness, Achieving Awareness, Appreciation of Beauty and Excellence, Hope, Gratitude, Humor, Spirituality, Interbeing, and Absorption; ANOVA was then used to compare profile means for authentic living, self-alienation, spiritual quest, and speciesism. Study 2 used exploratory factor analysis (principal component analysis, principal axis factoring, and maximum likelihood) to investigate Awakened Awareness, Achieving Awareness, Appreciation of Beauty and Excellence, Hope, Gratitude, Humor, Spirituality, Interbeing, and Absorption. Results: Study 1 generated a best-fitting solution with four significant and distinct profiles, which we labeled Integrated, Embodied, Disembodied, and Low Transcendence (BLRT-p < 0.01). The Integrated Transcendence profile scored the highest on positive authenticity (M = 25.07; SD = 0.292) and spiritual quest (M = 57.46; SD = 6.51) and lowest on self-alienation (M = 9.93; SD = 6.44) and speciesism (M = 2.25; SD = 1.10). Study 2 generated a two-factor solution of Embodied Transcendence and Disembodied Transcendence factors in each exploratory factor analytic technique we utilized. Implication: These results suggest that transcendence is experienced and expressed in varied ways. It appears that individuals may either score highly in all transcendence-related strengths or low in all transcendence-related strengths, or exhibit a proclivity toward Disembodied Transcendence (i.e., relatively high Interbeing, Absorption, and Appreciation of Beauty and Excellence) or Embodied Transcendence (i.e., relatively high Gratitude, Hope, and relational Spirituality). These findings may encourage people to respect those who experience and express spiritual transcendence in different ways and cultures. Full article

While continuously promoting agricultural water-saving and efficiency improvement, Ningxia is confronted with problems such as deepening groundwater tables and growing ecological vulnerability. Exploring the trade-off relationships and their evolutionary characteristics between socioeconomic development and water resource carrying capacity, as well as between water use efficiency improvement and groundwater-ecosystem maintenance, is of great significance for coordinated water resource governance in arid irrigation districts. Based on time-series data covering 2000–2024, this paper establishes a DPSIR evaluation model and constructs a progressive quantitative analytical framework coupling the entropy-weight-Tapio decoupling, rate-scissors difference and PLS-SEM models. During the study period, the growth rate of the response (R) dimension (13.76%) was far higher than that of the state (S) dimension (3.23%) from 2011 to 2020, confirming the objective existence of dual trade-offs. The two categories of trade-offs underwent a three-stage evolution of “latent-intensified-remediation”, showing the counter-intuitive feature of “effective total-volume control alongside continuous groundwater table deepening”. Hidden transmission barriers were identified for 2008–2016 (θ₁, θ₂ dropped to 0.46–1.32°): the transfer of water-saving dividends to industry caused groundwater extraction to rise rather than fall to a certain extent. PLS-SEM analysis reveals that structural lock-in acts as the core inhibiting factor for ecological protection. The total effect of socioeconomic development on ecology reaches 0.921, whereas structural lock-in produces a chained negative mediating effect of −0.192 by suppressing water use efficiency. Improvement in water use efficiency presents dual characteristics of overall ecological gain and localized groundwater-recharge loss. It can be concluded that engineering-only water-saving measures cannot balance water-intake reduction and recharge deficits. It is necessary to simultaneously advance low-water-consumption cropping-pattern restructuring, rigid enforcement of the 2.5 m ecological groundwater table threshold, and the substitution mechanism for saved-water volume between industry and agriculture, so as to build a coordinated “water-saving-recharge-ecology” regulation system. Full article

Hyperspectral image super-resolution (HSI SR) focuses on enhancing the spatial resolution of HSIs while preserving their inherent spectral information. Existing single-image HSI SR methods still suffer from blurred spatial edges and spectral distortion. Although numerous spatial–spectral enhancement networks can enhance spatial–spectral feature extraction, they often lead to a cumbersome network architecture. To address these issues, we propose a Spatial–Spectral Distillation Mamba Network, called SSDM-Net, for HSI SR, which contains a main reconstruction branch and two training-only auxiliary branches for spatial and spectral knowledge distillation. Specifically, the spatial and spectral auxiliary branches, which are utilized exclusively during training, provide edge-aware guidance and capture spectral correlations, respectively. During training, the spatial–spectral knowledge is transferred to the main branch. During inference, the auxiliary branches are removed, improving reconstruction quality without extra computational burden. In the main branch, a Mamba-based spatial–spectral global enhancement module processes spatial and latent inter-channel sequences using selective scanning whose cost is linear in the processed sequence lengths when the feature dimensions are fixed. In addition, a dynamic loss weighting strategy is developed to balance reconstruction, distillation, and auxiliary losses during optimization. Comprehensive experiments conducted on the CAVE and Houston datasets with three scale factors demonstrate that SSDM-Net produces more accurate reconstruction results than existing representative HSI SR methods. Cross-dataset experiments on the Harvard dataset further suggest that the method can maintain competitive reconstruction performance under the evaluated cross-dataset settings. Full article

Wheat flour transportation involves extended transit periods, which present considerable challenges for safety risk oversight. Therefore, it is essential to develop an efficient anomaly detection method to support risk assessment during this stage. However, existing anomaly detection methods often neglect the coupling effects across different time scales and the spatial clustering of hazard factors. To address this limitation, we propose a multi-scale spatial–temporal graph model-based unsupervised anomaly detection framework (MSTUAD), which can simultaneously capture the spatial–temporal correlations between importance and hazard factors across multiple time scales. Specifically, feature maps are first constructed for each hazard factor within a given time window to represent coupling effects. Secondly, a multi-scale spatial–temporal graph model is designed to extract spatial–temporal characteristics from these feature maps. Finally, a reconstruction model based on a variational autoencoder learns latent representations of spatial–temporal features of hazard factors, thereby capturing the intrinsic characteristics of normal wheat flour. Experimental validation on the wheat flour transportation hazard factor dataset and three public industrial datasets demonstrates that MSTUAD significantly outperforms state-of-the-art anomaly detection methods, achieving an average F1-score greater than 84.55%. This approach provides valuable decision support and technical guidance for relevant regulatory authorities. Full article

(1) Background: Whether spatial predictive patterns in non-communicable disease mortality persist after accounting for socio-demographic development and biomarkers remains understudied in East Africa. (2) Methods: This study used heterogeneous graph transformer (HGT) models and other techniques to model spatial patterns in cause- and sex/age-specific mortality (hypertensive heart disease [HHD], ischaemic heart disease [IHD], stroke, and diabetes), incorporating risk factors and socio-demographic development (SDI), using data from the Global Burden of Disease (GBD) study, 1990–2023, across Burundi, Kenya, Rwanda, Tanzania, and Uganda. (3) Results: HGT achieved higher performance than OLS spatial lag benchmarks (R² 0.948–0.970 vs. 0.194–0.376). Spatial predictive patterns were disease-specific. Stroke was the only disease with consistent positive spatial structure (SDI-only: 0.645%, 95% CI [0.380, 0.907]), with spatial structure strengthening after 2015. HHD exhibited severe and stable degradation (Risk-only: −137.892%, 95% CI [−181.908, −96.380]), driven by the interaction between metabolic risk covariates and geographic adjacency. Diabetes showed consistently severe degradation (SDI + Risk: −201.941%, 95% CI [−257.349, −150.082]). IHD patterns were weak and unstable. Sex disaggregation revealed stronger stroke spatial signals, indicating latent sex-specific patterns masked by aggregation. GBD measurement uncertainty contributed less than 0.025% of result variance, with model randomness dominating. (4) Conclusions: Spatial predictive patterns in NCD mortality in East Africa are disease-specific. Stroke shows emerging cross-border spatial structure after 2015, while HHD and diabetes reflect country-specific determinants. Sex-disaggregated graph construction reveals latent spatial heterogeneity invisible to aggregate models, supporting disease-specific, sex-stratified regional health strategies. Full article

Vietnam’s domestic gold price has persistently exceeded the world price by a wide margin, even as recent reforms have begun to relax the state’s historical monopoly over gold-bar production and imports. This paper asks why the gap persists, and whether it is confined to normal market conditions or extends into periods of extreme price movement. Using daily data spanning 2 January 2019 to 31 July 2026 (1856 trading days), covering the reform introduced by Decree No. 232/2025/ND-CP we decompose the domestic premium into a currency component and a pure physical-gold component, and use a copula-based framework to separately assess average price linkage and tail (extreme-event) co-movement between the domestic and world markets. Domestic gold bars traded at an average premium of 16.0% over import-parity world prices, of which 13.8 percentage points reflect the physical-gold component driven by constrained arbitrage, while currency factors account for only about 2 percentage points. The average linkage between the two markets is weak, indicating persistent segmentation, and this segmentation extends into the tails of the distribution for most of the sample. The premium itself carries substantial latent risk: a reversion to price parity would imply a one-off loss of about 9.6% of value, roughly eight to ten times the historical one-day 5% Value-at-Risk. Following the reform’s effective date, however, we find early evidence of emerging co-movement specifically in extreme upside price movements, even though the physical premium itself has not yet narrowed—consistent with a reform that has been enacted in law but remains at an early stage of operational implementation. The results indicate that administrative restrictions on the physical gold supply chain, rather than currency controls, are the principal source of Vietnam’s persistent gold-price gap, with direct implications for how the ongoing liberalization process should be sequenced. Full article

Major airway diseases, including chronic obstructive pulmonary disease (COPD), asthma, bronchiectasis and chronic rhinosinusitis without nasal polyps (CRSsNP), frequently coexist and share inflammatory, epithelial and remodeling features. However, whether these clinically distinct airway disorders are driven by a unified genetic liability and how this shared liability maps to disease-relevant tissues, genes and immune-regulatory programs remain incompletely understood. We integrated GWAS summary statistics for COPD, asthma, bronchiectasis and CRSsNP using linkage disequilibrium score regression, local genetic correlation analysis and Genomic structural equation modeling. A latent shared airway disease factor, termed gAirwayDisease, was constructed to capture common genetic liability across the four conditions. We then applied an integrative functional genomics framework, including gsMap spatial enrichment, PoPS gene prioritization, MAGMA gene-set enrichment, GTEx v8 lung MTWAS, OneK1K and DICE immune-cell MTWAS, scMORE regulon analysis and phenome-wide Mendelian randomization. All six airway disease pairs showed positive genetic correlations, with estimates ranging from 0.508 to 0.685. Genomic SEM supported a single shared factor, with positive standardized loadings for COPD, asthma, bronchiectasis and CRSsNP and excellent model fit. Spatial mapping localized gAirwayDisease-associated signals to airway- and epithelial-associated anatomical domains. PoPS prioritized immune and airway-relevant genes, including SMAD3, GATA3, IL1R1, RUNX3 and STAT6, while MAGMA enrichment highlighted B-cell activation, T-cell activation and transcriptional regulatory pathways. Lung MTWAS identified SLC9A2 and ORMDL3 as top genetically regulated expression signals. OneK1K immune-cell MTWAS highlighted recurrent IL18R1 associations across CD4 and CD8 T-cell subsets. scMORE further identified 36 significant regulon–cell type pairs across dendritic cells, B cells, monocytes, T cells and NK cells, including BCL11A, TCF4, KLF4, RUNX1 and STAT4 regulons. MR-PheWAS linked genetically predicted gAirwayDisease to respiratory, allergic, lung function and immune-related traits. This study defines gAirwayDisease as a genetically informed latent factor capturing shared liability across major airway diseases. Integrated functional genomic analyses highlight airway epithelial and immune regulatory programs associated with shared disease susceptibility and prioritize candidate genes and regulons for future experimental validation. Full article

Neonatal hypoxic–ischemic encephalopathy (HIE) remains one of the leading causes of neonatal mortality and long-term neurodevelopmental disability despite therapeutic hypothermia, which provides only partial neuroprotection. Among adjunctive therapies, magnesium sulfate (MgSO₄) has emerged as one of the most biologically plausible neuroprotective agents because of its ability to modulate glutamate-mediated excitotoxicity, intracellular calcium influx, oxidative stress, neuroinflammation, and apoptotic pathways. Nevertheless, encouraging molecular and preclinical findings have not translated into consistent clinical benefit. This narrative review critically examines the translational gap between the molecular mechanisms of magnesium sulfate and its clinical performance in neonatal HIE. Evidence from experimental models, clinical studies and recent meta-analyses was integrated to identify the biological and methodological factors potentially responsible for this discrepancy. We discuss the evolving pathophysiology of HIE across the primary, latent, secondary and tertiary phases of brain injury and analyze how the timing of intervention, lesion heterogeneity and inadequate biological stratification may influence therapeutic responsiveness. Current clinical research has largely evaluated broad neurological outcomes, particularly cerebral palsy, despite the heterogeneous neuropathological substrates underlying neonatal brain injury. We argue that this strategy may dilute genuine treatment effects by grouping together distinct lesion phenotypes with different biological mechanisms. Accordingly, we propose the “Neuroprotection per Effective Therapy (NET)” framework, a conceptual translational model integrating molecular targets, experimental evidence, MRI-defined lesion phenotypes, methodological quality, and advanced statistical approaches to improve patient stratification and outcome selection. Rather than questioning the biological efficacy of magnesium sulfate itself, this review suggests that future progress will depend on aligning molecular mechanisms with clinically meaningful phenotypes. Precision-based translational strategies may ultimately allow magnesium sulfate and other neuroprotective therapies to better reveal their therapeutic effects in biologically appropriate patient subgroups. Full article

In an era of information explosion and overload, individuals face a constant influx of mixed-quality, contradictory data. Navigating this complex landscape requires critical thinking, which enables individuals to dynamically adjust their reasoning and refine judgments. Consequently, accurately measuring this dynamic ability is essential. However, existing assessments predominantly focus on single-round argumentation and fail to capture the iterative process, potentially yielding unrepresentative evaluations of real-world performance. To address this gap, this study develops the Iterative Argumentation Task (IAT), a novel assessment tool that presents participants with a sequence of interrelated passages simulating real-world information flow. By capturing the process of evaluation, position changes, and argument revisions, the IAT evaluates three ability dimensions: single-round argument analysis, inter-argument relationship analysis, and argument integration. We recruited 355 undergraduates to validate the IAT. Confirmatory factor analysis provided evidence for the proposed measurement structure. Furthermore, correlations with established critical thinking and reasoning tests demonstrated convergent and discriminant validity, respectively. Moreover, performance differences between the undergraduates and 33 debaters established known-groups validity. Finally, latent profile analysis identified four distinct profiles across the IAT dimensions. Together, these findings establish the IAT as a theoretically sound and practically effective tool for assessing critical thinking. Full article

This study directly compares a photovoltaic (PV)-powered vapor-compression chiller with a solar-thermal-driven absorption chiller for localized cooling of the tomato crop zone in a 1536 m² smart greenhouse under a hot-arid climate. The principal contribution is a controlled system-level comparison of two solar-cooling pathways under the same greenhouse load, solar-aperture area, terminal equipment, rated cooling capacity, and crop-zone temperature-control constraints. The previously validated greenhouse model was transitioned from EnergyPlus 8.9 to Version 23.1, after which the two alternative plants were connected to the same base model. Base-case annual simulations produced nearly identical chiller cooling energy (1669.3 and 1668.9 MWh) and was only 4 and 5 h above 28 °C. The PV-powered system required 101.6 MWh of net grid electricity, whereas the absorption system used 202.3 MWh of electricity and 253.2 MWh of natural gas and achieved an 84.23% solar fraction. Static operational primary energy was 331.2 and 912.7 MWhPE, respectively; HSDH28 was 0.50 and 0.81 °C·h; and peak grid import was 113.46 and 63.61 kW. The absorption case additionally required 10,103.6 m³/yr of cooling-tower makeup water. Storage/EMS sensitivity changed the absorption solar fraction from 58.17% to 88.30% and natural-gas use from 187.7 to 674.0 MWh/yr without materially changing cooling service. Matched 50–100 W/m² daytime latent-load sensitivity increased annual cooling by 14.7–28.8%. At the 100 W/m² bound, HSDH28 increased to 49.32 °C·h for PV and 8.06 °C·h for absorption, while the principal energy–infrastructure trade-off remained: static primary energy was 712.4 versus 1354.2 MWhPE and peak grid import was 137.46 versus 63.96 kW. A bounded hourly primary-energy-factor stress test did not reverse the technology ranking, and balanced TOPSIS scores were 0.766 for PV and 0.234 for absorption. The results show that PV vapor compression minimizes operational primary energy and cooling-water use, whereas solar-thermal absorption reduces electrical peak demand and shows greater thermal-control resilience at the highest tested latent-load bound. Full article

Data-driven urban governance increasingly seeks to incorporate the needs of different population groups, yet child-sensitive evidence for public street-space renewal remains limited in older urban districts. Most studies still evaluate environmental conditions through population averages, with insufficient attention to heterogeneous child groups that may require differentiated planning responses. Based on an analytic sample of 314 children retained from 343 usable questionnaire responses collected from children aged 6–12 in the older urban districts of Xi’an, China, this study integrates street-activity characteristics and age- and sex-standardized body mass index (zBMI) using an established person-centered analytical approach. Latent Class Analysis (LCA) was used to identify children’s activity–health profiles, and multinomial logistic regression was used to examine associations between individual, family, and perceived street-environment factors and profile membership. Three profiles were identified: high-activity–healthy, high-intensity active, and low-activity–high-risk. The model-estimated low-activity–high-risk profile represented 35.7% of the analytic sample, and children assigned to this profile reported the lowest perceived safety and convenience. The findings suggest that profile-based analysis may inform child-sensitive street-renewal prioritization. Perceived safety and convenience showed the strongest and most consistent associations with membership in either the high-activity–healthy or high-intensity active profile relative to the low-activity–high-risk profile. These findings are associative and do not establish the effects of street interventions. The study therefore represents a context-specific extension and planning application of established analytical methods. Full article

Patent document–keyword count data are typically high-dimensional, sparse, and dominated by zero entries, which makes it difficult to simultaneously reconstruct keyword frequencies and identify meaningful technological structures. This study proposes a machine learning approach to latent structure learning for zero-inflated patent keyword count data. The proposed zero-gated latent factor model (ZG-LFM) combines nonnegative matrix factorization (NMF) with keyword-specific logistic occurrence models. NMF is used to extract interpretable document–factor and factor–keyword representations, while the occurrence gate estimates the probability that each keyword appears in a given patent document. The method was evaluated in an initial domain-specific case study using a document–keyword matrix constructed from 9434 quantum computing patent documents and 175 keywords, of which 87.60% of the entries were zero. Predictive performance was assessed using root mean squared error, mean absolute error, and the area under the receiver operating characteristic curve across different numbers of latent factors. The experimental results showed that NMF provided more accurate keyword count reconstruction, whereas the proposed model consistently achieved better discrimination between zero and nonzero keyword entries. These findings indicate that latent count reconstruction and keyword occurrence modeling provide complementary information for analyzing sparse patent data. The learned latent factors further revealed coherent quantum computing subdomains, including hybrid quantum–classical execution, quantum machine learning, quantum state measurement and error analysis, quantum cryptography, superconducting chips, quantum circuits, optical control, qubit devices, and optimization algorithms. The proposed framework therefore provides interpretable latent technology structures while improving the identification of keyword occurrence patterns in zero-inflated patent data. These findings demonstrate the feasibility of the framework within the analyzed quantum computing corpus; its generalizability across other technological domains remains to be evaluated. Full article

The adoption of agroecological practices has recently gained increasing attention. However, significant disparities persist between farmers’ awareness and actual implementation. This study addresses this gap by developing an integrated analytical framework to identify farmer typologies and explain the heterogeneity of agroecological adoption among Romanian tomato growers, considered as a case study within the broader context of Romanian vegetable production. Based on a survey of 196 active cultivators, the analysis combines principal component analysis (PCA) and cluster analysis to identify latent behavioral dimensions and classify farmers into distinct typologies. In addition, an Agroecological Adoption Index (AAI) is constructed to assess and compare adoption levels across groups. The results reveal four statistically distinct farmer typologies: informed farmers with low adoption, practice-oriented low-constraint farmers, high-adoption agroecological farmers, and pragmatic adopters. The findings demonstrate that adoption is not solely determined by knowledge levels but is strongly shaped by perceived barriers, particularly limited access to information, financial constraints, and uncertainty. By moving beyond single-factor explanations, this study highlights the multidimensional nature of agroecological adoption and provides empirical evidence of the interaction between cognitive, economic, and structural determinants. The results offer important policy implications, suggesting that effective interventions should combine targeted training, advisory services, and financial support tailored to specific farmer profiles within horticultural production systems. Full article