Search Results (1,036)

Small-radius curved tunneling makes shield machine attitude control especially difficult. Intensified soil-machine interaction under these conditions creates a high risk of snakelike motion, which can compromise both construction safety and segment assembly quality. Accurate advance prediction of attitude parameters is therefore critical for timely course correction. We propose CNN-PatchTST, a hybrid deep learning model that integrates four complementary components. A convolutional neural network (CNN) extracts local temporal features. A PatchTST-based Transformer encoder applies global self-attention over long sequences. A direct mapping branch produces short-range inertial estimates, and a gated fusion layer performs adaptive signal integration. Operating as a unified architecture, the model simultaneously predicts all 12 key attitude parameters five steps ahead, providing operators with roughly five minutes of advance warning. Validated on construction data from the Fangbai Intercity Railway (Guangzhou Metro, minimum curve radius 350 m), CNN-PatchTST achieves a mean coefficient of determination (R²) of 0.992 across all 12 attitude parameters under three independent random seeds. Mean absolute errors (MAE) for the front and rear shield azimuths reach 0.578° and 0.430°, respectively. A pure-inertia baseline (using only historical attitude values) attains R² = 0.988, yet its azimuth MAE is 4.3 times higher than that of the full model. This result confirms that modeling control parameters is essential for accurate angular prediction. SHAP-based sensitivity analysis yields three further insights. First, historical attitude parameters account for approximately 97% of total feature importance. Second, the four most recent steps contribute over 50% of predictive power. Third, a lagged predictive association of roughly 3–4 min is observed between thrust jack pressure differentials and shield tail deviation response, suggesting a potential lagged association that warrants further causal validation. Collectively, these findings demonstrate that CNN-PatchTST delivers accurate and interpretable multi-step attitude predictions, establishing it as a practical tool for on-site guidance during small-radius shield tunneling. Full article

Background/Objectives: Recovery from schizophrenia-spectrum disorders is increasingly recognized as achievable, yet no synthesis has simultaneously examined long-term outcome rates, intervention effectiveness, cognitive predictors, social determinants, personal-recovery trajectories, and mediating mechanisms within a unified framework. This systematic review aimed to address these six domains and to propose an integrative theoretical model of functional recovery, applying meta-analysis where the evidence permitted and structured narrative synthesis elsewhere. Methods: Seven databases (PubMed/MEDLINE, PsycINFO, Embase, Cochrane CENTRAL, Web of Science, Scopus, CINAHL) were searched through December 2025 following PRISMA 2020 and MOOSE guidance. We extracted eligible records (longitudinal designs, ≥6 months, adults with schizophrenia-spectrum disorders reporting functional outcomes) into two structured databases, then consolidated and de-duplicated them, yielding 467 unique papers. After topical screening and a duplicate audit, we retained 368 studies (1980–2025) and grouped them into six thematic clusters. A pre-specified rule pooled only clusters with at least five independent primary studies. Proportions were pooled with the Freeman–Tukey transformation and associations with Fisher’s z under DerSimonian–Laird random-effects models; certainty was appraised with GRADE. Results: Three pools met the threshold. Long-term functional recovery was 35.4% (95% CI 22.6–49.4; k = 11) and symptomatic remission 39.6% (95% CI 29.3–50.5; k = 7), both with very high heterogeneity (I² = 92–98%) and low certainty. The pooled association between psychological mediators and functional outcome was weak and imprecise (r = 0.23; 95% CI −0.09–0.51; k = 7; very low certainty), reflecting facilitators (positive mental health, social support, self-efficacy, hope) and barriers (internalized stigma, substance-use comorbidity, longer duration of untreated psychosis, persistent negative symptoms) acting in opposite directions. The questions concerning specific interventions, cognitive prediction, rehabilitation, and personal recovery were synthesized narratively: structured psychosocial and combined interventions and integrated rehabilitation were associated with small-to-medium functional gains; cognition, particularly social cognition and motivation, predicted later functioning; and personal recovery followed a course partly independent of clinical status. Conclusions: Recovery and remission are attainable for a substantial minority but remain heterogeneous and modestly certain. The proposed Multi-Pathway Dynamic Recovery Model organizes the evidence into five testable principles—an ordered recovery hierarchy, social-cognitive mediation of the cognition–function link, an early-phase intervention window, social-ecological embedding, and self-reinforcing feedback loops, offered as a hypothesis-generating framework rather than a validated structure. Services should prioritize comprehensive early intervention, target social cognition and stigma, and address structural determinants to optimize long-term recovery. Full article

For sustainability education to support societal transformation, it is necessary to understand how pre-service teachers conceptualize sustainable development and which Sustainable Development Goals (SDGs) they prioritize. This study examined pre-service teachers’ metaphorical perceptions of sustainable development, their 2030 SDG priorities, and convergence/divergence between the two. A qualitatively driven convergent parallel mixed-methods design was used with 93 pre-service teachers from six teacher education programs at a public university who had completed a sustainability-related course. Data were collected through a metaphor elicitation form and an SDG priority-ranking form. Content analysis produced 226 valid metaphor units grouped into six overarching themes and 17 subthemes. Metaphors centered mainly on future and intergenerational transfer, environmental protection and responsibility, and sustaining life and continuity. Zero Hunger, Good Health and Well-being, Clean Water and Sanitation, No Poverty, and Quality Education received the highest priority. Participants conceptualized sustainable development positively, holistically, and with a future orientation, yet prioritized immediate basic human needs. Environmental protection, systems integrity, and intergenerational responsibility were prominent metaphorically, whereas Climate Action, Responsible Consumption and Production, ecosystem-related goals, and Partnerships for the Goals ranked relatively low. The findings highlight a need for interdisciplinary, practice-oriented teacher education that makes the interdependence of the SDGs more explicit. Full article

Automated assessment of programming assignments remains a major challenge in higher education, particularly in large-scale courses where timely, consistent, and pedagogically meaningful feedback is difficult to provide, while Large Language Models (LLMs) have shown strong capabilities in code understanding and feedback generation, their use as standalone evaluators is fundamentally limited by inconsistency, lack of transparency, and weak alignment with instructional objectives. This paper argues that these limitations are not intrinsic to LLMs, but rather arise from their deployment as isolated components. In response, we propose a system-centric approach to AI-assisted assessment, introducing a hybrid framework that integrates LLMs within a structured, context-aware, and pedagogically aligned evaluation pipeline. The framework combines (i) explicit rubric-based decomposition of evaluation criteria, (ii) pedagogically guided prompting, and (iii) Retrieval-Augmented Generation (RAG) grounded in course-specific materials. Together, these components transform the evaluation process from a black-box prediction task into a traceable and reproducible decision process. The proposed approach is implemented in a real-world educational platform, EvaluaTeC, and evaluated on a dataset of 1287 programming submissions from 429 students. Experimental results show that the hybrid framework improves agreement with consolidated instructor reference grades ($r=0.9059$ vs. $0.7207$ baseline), reduces evaluation error (MAE = 0.5134), and exhibited lower output variability in the recorded aggregate statistics, while maintaining practical latency and cost. Beyond numerical improvements, the system approximates key statistical properties of human grading and generates structured, pedagogically aligned feedback. These findings demonstrate that reliable AI-assisted assessment emerges from the integration of LLMs within structured and context-aware systems, rather than from model capabilities alone. This work contributes a principled framework for explainable and scalable automated assessment, advancing the design of trustworthy AI systems in education. This shift reframes automated assessment as a systems problem rather than a purely model-centric task. Full article

Rapid workforce transformation driven by widespread AI adoption requires online higher education institutions to evolve curriculum design at a greater speed to support diverse learners in acquiring the competencies needed for career transitions in a dynamic job market. This study explores the integration of a flexible online Instructor-Created Content (ICC) tool into course design, using a consistent learning theory framework: Translating Research in Environmental Education (TREE). Using an action research approach, we examined the influence of embedded learning theory constructs on student retention. The pre- and post-test findings show a consistent decline in attrition rates across all courses that were redesigned using applied learning theories. These results suggest that instructional strategy alignment to the TREE framework positively impacts student persistence. The courses with the most dramatic decreases in attrition were Psychology of Learning (PSY/110) (declined from 22.6% to 18.9%) and Mathematics for Early Educators II (MTH/214) (declined from 23.7% to 10.3%). These results underscore the value of maintaining theoretical coherence and consistency in curriculum design by the instructional design teams to support student learning. A human-centered approach, combined with AI-assisted tools, enriches our curriculum development efforts while maintaining efficiency, paving the way for scalable and sustainable learning environments. Full article

Artificial intelligence (AI) recommender systems operationalize personalized marketing by transforming behavioral data into individualized choice architectures, yet greater model complexity or personalization granularity need not improve every service objective. This study compares population-level item-neighborhood recommendation, behaviorally segmented neighborhood recommendation, and neural collaborative filtering (NCF) across 776,741 deduplicated 1–5 ratings from 646,576 anonymized users and 541 Coursera courses. The primary warm-start evaluation is restricted to 10,296 users (1.59% of the user population) with sufficient interaction history; the remaining sparse-history users contribute to fitting but not to the within-user confirmatory estimand. Configurations are selected only on three validation seeds and then frozen before guarded final evaluation across ten deterministic user-aware seeds. No rank-capable model dominates across objectives: KNN provides the strongest RMSE and personalized-neighborhood coverage profile, ClusteredKNN achieves the strongest observed Top-K retrieval with lower neighborhood support, and a metadata-rich NCF variant achieves the lowest MAE at substantially greater fitting cost. The rating-only UserMean baseline further shows that low numerical prediction error need not imply useful item ranking. Overall, the findings support a contingency view of AI personalization: greater segmentation granularity or model complexity does not inherently create greater value; model choice should instead reflect the service objective, available behavioral evidence, coverage tolerance, operating cadence, and governance requirements. Full article

Traditional economic geography teaching has failed to effectively help students identify the sustainable development intentions contained in industrial transformation. Sustainable Development Education (ESD) has become a key path to address this challenge, but there remains a lack of empirical exploration of how to effectively integrate ESD into economic geography courses. Therefore, this study embeds place-based teaching into the course and creates a ten-week intervention module called “Observation of Hometown Industries” at a comprehensive university in Hubei Province, China, requiring 105 second-year undergraduate geography majors to conduct field research on the sustainable transformation of local industries. Based on a mixed research design, the results indicated that the most pronounced gains occurred in students’ self-efficacy for sustainability (relative increase 20.0%), closely followed by sustainability-related knowledge and sense of place belonging (19.8%), and systems-thinking capacity (18.2%), with effect sizes ranging from 0.76 to 0.89, indicating moderate-to-strong educational relevance. Qualitative analysis further revealed that this place-based teaching approach helps students recognize the complex trade-off between economic growth, environmental conservation, and social equity in the sustainable transformation of industries. This study provides empirical evidence for cultivating key sustainable literacy in higher education and offers an operational teaching model for integrating ESD into economic geography courses. Full article

Over the past few decades, Canada has witnessed a profound shift in how individuals relate to religion—not simply in terms of declining affiliation, but in how meaning, identity, and belonging are negotiated beyond traditional religious institutions. While previous research has identified key factors that support intergenerational religious transmission, less attention has been paid to how individuals negotiate challenges to their religious identities at critical moments across the life course. Drawing on select qualitative interviews from the Transmission of Religion Across Generations project in Canada, this article examines how individuals make meaning with and from religion when their beliefs are stressed or challenged. Focusing on autobiographical narratives of participants socialized into Christian and Muslim traditions, it explores how religious identities are maintained, transformed, or abandoned at critical touchpoints and turning points. The findings suggest that participants’ trajectories varied according to social supports and interpretive resources available to them, as well as the degree of institutional flexibility within their religious traditions. By examining the processes and conditions of meaning-making, this article offers a framework for understanding how religious identities are negotiated and supported at key moments across the life course, shaping patterns of religious continuity, transformation, and decline. Full article

Background: Pleomorphic adenoma is the most common benign salivary-gland neoplasm and frequently affects the minor salivary glands of the palate. Although surgical excision is generally curative, incomplete encapsulation, pseudopod-like extensions, and microscopic tumor deposits may contribute to recurrence, which can occur many years after initial treatment. Recurrent palatal pleomorphic adenoma is uncommon and may present diagnostic and reconstructive challenges. Case Presentation: A 64-year-old man was referred with a painless, slowly enlarging recurrent mass of the right posterior hard palate, six years after excision of a pleomorphic adenoma at another institution. Magnetic resonance imaging and contrast-enhanced computed tomography demonstrated a well-defined enhancing palatal lesion measuring approximately 2.1 × 1.9 × 1.9 cm on MRI and 1.8 × 2.2 × 1.9 on CT, without adjacent maxillary bone destruction or cervical lymphadenopathy. An incisional biopsy was consistent with pleomorphic adenoma. Wide local excision was performed, including the tumor, surrounding palatal soft tissue, and underlying periosteum. The resulting defect was reconstructed using a facial artery musculomucosal flap. The postoperative course was uneventful, with satisfactory flap healing and preservation of speech and swallowing. At six months of follow-up, there was no clinical evidence of recurrence or functional impairment. Conclusions: Recurrent pleomorphic adenoma of the palate may develop several years after primary treatment. Complete surgical excision with tumor-free margins and appropriate reconstruction is essential. Long-term clinical follow-up is recommended because late recurrence and, rarely, malignant transformation may occur. Full article

In the digital education system, the increasing demand for measuring lexical and semantic text similarity has led to the need for developing scalable and intelligent automated grading systems. Traditional assessment methods used for short-answer grading are labor-intensive, time-consuming and prone to human-evaluator bias. Furthermore, computer science curricula have different evaluation challenges as programming-centric courses require rigorous syntactical and structural validation, whereas theoretical courses require deep conceptual and semantic comprehension. To address these dual challenges, we introduce the Virtual University Automatic Short-Answer Grading (VUASAG) framework, a model addressing both statistical-based lexical analysis and Transformer-based semantic architectures. Utilizing an expanded Mohler dataset spanning multiple computer science domains (including Data Structures, Introduction to Programming, Object-Oriented Programming, and Software Engineering), we evaluated the efficiency of traditional string-matching metrics alongside state-of-the-art Transformer-based models (T5, BERT, XLNet, and SBERT). The empirical results demonstrate that token-based statistical models perform robustly on keyword-dependent syntax validation while Transformer-based models excel by capturing deep conceptual context. Specifically, Sentence-BERT (SBERT) achieved superior predictive accuracy across courses, yielding a minimum Root Mean Square Error (RMSE) of 0.9511 for the Introduction to Programming course, paired with moderate positive Pearson and Spearman correlation coefficients. Confidence intervals are estimated using Fisher’s z-transformation with a 95 percentile confidence level. Question-level five-fold and two-fold cross-validation is conducted for all four courses, and SBERT is found to perform better. Finally, to demonstrate the practical utility, we deployed a web-based portal for students’ evaluation leveraging a LLaMA-based pre-trained model to deliver real-time, personalized pedagogical feedback measuring code functionality, quality, documentation, and error handling. This reduces the educator workload while preserving grading integrity. Full article

Automated sentiment analysis of online-education reviews is useful for understanding learner feedback. Classification-based methods usually capture only document-level polarity. They may miss aspect-level signals and may collapse to the majority class under the heavy imbalance typical of course reviews. When the task is reformulated as generation, document-level and aspect-level outputs can be unified. However, out-of-vocabulary aspect labels, parsing failures, and weakly grounded links between granularities may also be introduced. Multi-perspective and Holistic Evaluation T5 (MHE-T5), a model built on the Text-to-Text Transfer Transformer (T5), is proposed as a constrained multi-granularity generative model. It emits aspect-level and document-level sentiment in one schema. The model combines grammar/finite-state machine (FSM)-constrained decoding, a document–aspect consistency coupling with a proved alignment property, and a cross-granularity contrastive objective. The decoding guarantee is limited to schema parse-validity and closed-vocabulary conformity; it does not guarantee semantic correctness of the selected aspect or polarity. Across four datasets, including a large rating-derived Coursera corpus, two human-annotated education aspect-based sentiment analysis (ABSA) datasets, and the standard Multi-Aspect Multi-Sentiment (MAMS) benchmark, generative models improve macro-averaged F1-score (Macro-F1) over Bidirectional Encoder Representations from Transformers (BERT) by 0.36 to 0.61 on the three datasets that carry discriminative baselines. MHE-T5 attains the highest document-level Macro-F1 among the evaluated benchmarks while providing formal schema-level guarantees on the closed-vocabulary settings. A controlled comparison with DeepSeek-V3 on identical examples, used as a large language model (LLM) baseline, shows that the fine-tuned 220M model is a competitive schema-constrained fine-grained aspect extractor under the fixed protocol. Full article

Background/Objectives: Lymph-node metastasis remains central to staging, prognosis, surveillance, and treatment planning in malignant melanoma. At present, most statistical models assessing nodal metastatic risk in malignant melanoma consider pathological descriptors, inflammatory markers, metabolic alterations, clinical data, and related variables independently of each other. Therefore, we developed a transparent artificial intelligence (AI)-based approach to assess whether the propensity for nodal metastasis is determined by a single layer of local histopathological aggressiveness or by the integration of different biological levels, including local histopathological aggressiveness, systemic inflammatory–metabolic dysregulation, biological heterogeneity, or a clinicobiological pattern. Methods: In this retrospective study, we assessed 73 adult patients undergoing surgical removal of malignant melanoma. The primary endpoint was histopathologically confirmed lymph-node metastasis. Routinely collected patient-related data, including clinical, anatomical, operative, histopathological, nodal, comorbidity, biological, clinical course, and available staging data, were structured into interpretable constructs. These included the Histopathologic Aggressiveness Index (HAI), the Inflammatory–Metabolic Dysregulation Index (IMDI), the Biological–Histological Discordance Score (BHDS), model-estimated nodal metastatic probability, integrated clinicobiological risk, and explanation stability. The AI-based framework was evaluated by applying bias-reduced and penalized logistic regression, machine learning benchmarking, leave-one-out cross-validation, bootstrap estimation, permutation testing, decision curve analysis, rule extraction, feature stability evaluation, network analysis, similarity-based retrieval, conformal uncertainty estimation, and unsupervised phenomapping. Results: For 72 out of 73 patients, nodal histopathology results were available. Among these patients, 22 had positive nodal status. Positive nodal status was associated with a higher Breslow thickness, an increased mitotic rate, ulceration, lymphovascular invasion, a nodular subtype, and palpable adenopathy. The HAI demonstrated the strongest discriminative signal between node-positive and node-negative patients (median values of 67.8 vs. 45.3; p < 0.001) and retained an independent association with nodal metastasis within the bias-reduced logistic model (odds ratio [OR] per 10-point increase: 2.74; 95% confidence interval [CI]: 1.58–4.75; p < 0.001). The IMDI showed a weak exploratory relationship and did not retain an independent association after adjustment. Similarly, the BHDS did not show significant differences in separating the two endpoint groups. The penalized logistic model including only the HAI showed good performance under leave-one-out cross-validation, with ROC AUC = 0.889, PR-AUC = 0.706, and Brier score = 0.137. Through rule extraction, we found a cohort-specific HAI threshold value > 58.6, above which all node-positive cases were located. With respect to explainability, feature stability, network analysis, similarity retrieval, conformal prediction, and phenomapping, there was convergence toward a dominant high-risk phenotype defined primarily by histopathological criteria. Conclusions: Routine melanoma registries may be transformed into internally evaluated melanoma intelligence frameworks. Histopathologically confirmed lymph-node metastasis among patients with malignant melanoma was organized primarily along an axis of local histopathological aggressiveness, while systemic inflammatory–metabolic dysregulation provided subordinate contextual biological information. Full article

Engineering literacy research has been dominated by static, competence-based frameworks that fail to explain how attributes evolve dynamically and systematically underrepresent two critical dimensions: engineering consciousness as a cognitive precursor to decision-making, and engineering culture as a value-laden, inherited dimension sustaining professional identity across generations. These gaps are particularly consequential for STEM teacher preparation, where cultivating comprehensive engineering literacy in future educators is hypothesized to create a multiplier effect on societal STEM engagement. This study develops a theoretical model through a systematized literature retrieval and critical synthesis of engineering literacy literature (2005–2025; N = 113 publications), combined with theoretical deduction grounded in system theory and synergy theory. Two research questions guide the study: (1) How is the connotation of engineering literacy systematically reconstructed under multiple transformations? (2) What is the synergistic evolution logic among its constituent elements, and how can this logic inform curriculum design for STEM teacher preparation? The design utility of the model is illustrated through a curriculum design case. The analysis yields a “multi-driver, five-dimension synergy” dynamic model comprising five interconnected elements (knowledge, competence, consciousness, ethics, and culture) that co-evolve through a “consciousness–action–culture” spiral cycle, in which culture functions as the slow variable governing the long-term evolution of the entire system. The curriculum design case (a 64 h course for pre-service teachers centered on the Hong Kong–Zhuhai–Macao Bridge) illustrates how the model guided curricular decisions in four areas: consciousness activation before skill training, distributed cultural integration, multidimensional assessment, and operationalization of the multiplier effect through a teaching transformation module. The study advances engineering literacy theory by upgrading it from a static competence inventory to a culturally embedded, consciousness-driven adaptive ecosystem and provides a potentially transferable curriculum design framework for STEM teacher preparation. As a design case, the curriculum blueprint awaits implementation and empirical validation; claims about learning outcomes, multiplier effects, and cross-cultural applicability are theoretical hypotheses rather than empirically validated findings. Full article

Predicting academic performance in face-to-face computing and software engineering courses is hindered by the limited availability of fine-grained process data. This study proposes STEAP, a camera-based static–temporal fusion framework that integrates longitudinal classroom behavior sensing with conventional educational records. Classroom videos from 375 undergraduates enrolled in four computing-related courses were collected over nine teaching weeks. Camera-derived observable behaviors were organized into student-level weekly sequences and transformed into outcome-independent longitudinal representations. Multiple machine-learning classifiers were subsequently applied to predict students’ academic performance. Checkpoint-specific predictions were conducted at Weeks 3, 6, and 9, with each prediction using only the classroom behavioral information available up to the corresponding time point. Using the complete nine-week Temporal representation together with the pre-course Background variables, XGBoost achieved the strongest classification performance among the evaluated models, with an Accuracy of 0.867, a Macro F1 of 0.862, and an At-risk Recall of 0.924. The checkpoint analyses further indicated that classroom behavioral information collected during the early course stage already provided useful predictive information without incorporating behavioral observations from subsequent weeks. After further integrating pre-course background variables and regular assessment information, the final fusion model achieved an Accuracy of 0.896 and a Macro F1 of 0.895. Overall, longitudinal camera-derived classroom behavior provides complementary predictive information beyond conventional educational information and supports the feasibility of earlier academic-risk identification at different course checkpoints. Full article

Background: Spermatocytic tumor (ST) is a rare non-GCNIS-derived testicular germ cell neoplasm that predominantly affects older men and generally follows an indolent course. Its occurrence in a cryptorchid testis is exceptionally uncommon. Methods: A narrative literature review was conducted using the PubMed database, including studies published up to August 2026. The search used combinations of the terms “spermatocytic tumor”, “spermatocytic seminoma”, “cryptorchidism”, “undescended testis”, “immunohistochemistry”, “molecular features”, “magnetic resonance imaging”, and “treatment”. Results: A 56-year-old man presented with a painless left inguinal mass corresponding to a cryptorchid testis. Serum tumor markers were normal. MRI demonstrated a well-circumscribed heterogeneous lesion with pseudocystic areas and enhancement of the solid component. Radical inguinal orchiectomy was performed. Histopathological examination revealed the characteristic triphasic cellular population without GCNIS, lymphovascular invasion, or sarcomatous transformation. Immunohistochemistry showed SALL4 and CD117 positivity and absence of OCT3/4 and D2-40 expression, supporting the diagnosis of ST and its distinction from classical seminoma. No recurrence or metastatic disease was detected during 12 months of follow-up. Conclusions: ST arising in a cryptorchid testis represents an exceptionally uncommon presentation. Integration of clinical, radiological, morphological, and immunohistochemical findings is essential for accurate diagnosis and distinction from classical seminoma, thereby avoiding unnecessary additional treatment. Full article