Search Results (187)

Recent journalism and media research increasingly uses embeddings, transformer-based models, and large language models, yet the methodological functions remain unevenly conceptualized. This article examines how vector-based and semantically oriented methods are incorporated into journalism and media studies and proposes a theory-informed framework of four recurrent methodological functions: semantic mapping, interpretive assistance, cross-scale articulation, and reflexive auditing. We conducted a structured review of English-language journal articles and review articles indexed in Scopus and Web of Science between 2022 and April 2026. The review combined database retrieval, conservative LLM-assisted metadata screening, full-text analytical extraction, expert manual validation, descriptive analysis, exploratory association tests, and an embedding-based map built from article-level analytical profiles. The final corpus comprised 43 articles. Empirical studies of news texts predominated, and semantic mapping was the most frequent primary methodological function. The profile-based map identified four moderately differentiated and overlapping communities centered on reception and circulation, semantic mapping of news content, affective and evaluative discourse analysis, and methodological infrastructure-building. Community membership aligned more strongly with media domain than with study type or primary methodological function, although these patterns are interpreted heuristically given the small corpus and low expected cell counts. The findings show that these methods are best understood as components of hybrid research designs combining semantic formalization, interpretation, validation, and scale-sensitive analysis. Full article

Improving the low-carbon operational performance of wastewater treatment facilities (WWTFs) is important for coordinated pollution reduction and carbon mitigation in China. The interprovincial carbon emission efficiency (CEE) of WWTFs reflects both the low-carbon performance of regional wastewater governance systems and cross-regional linkages shaped by technology diffusion, governance experience, and spatial adjacency. Using operational data from WWTFs in 30 provincial-level regions of China from 2010 to 2023, this study first measures provincial CEE with a super-efficiency slack-based measure (SBM) model incorporating undesirable outputs. It then integrates a modified gravity model, social network analysis, and the quadratic assignment procedure (QAP) to examine the evolution, structure, and formation mechanisms of the association network. The results show that the CEE of provincial WWTFs remained below the efficiency frontier overall, with periodic fluctuations and persistent structural differences. The model-inferred association network first contracted and then expanded, showing dense connections in eastern and central China and sparse connections in the northwest. Its node structure evolved from a dispersed multicore pattern to hub-centered concentration and then back toward a multicore configuration, while inter-block linkages showed a hierarchical transmission structure involving core spillovers, absorptive transmission, and peripheral reception. The QAP results indicate that interprovincial adjacency is the most stable correlate of network formation, whereas differences in technological innovation and capacity utilization show only stage-specific associations. These findings suggest that the CEE of WWTFs is not only a local performance outcome, but also a relational outcome shaped by regional linkages and structural positions. Therefore, low-carbon governance of WWTFs should move beyond single-region efficiency improvement and place greater emphasis on cross-regional collaboration, role-specific policy design, and leadership by core regions. Full article

When LiDAR detects underwater targets, the water severely attenuates the laser beams, making it impossible to extract valid echo information during 3D reconstruction of urban water bodies. This study proposes a Multi-Scale Spectral Adaptive Loss Generative Adversarial Network Based on Morphology-Spatiotemporal Decoupled Attention (MSAGAN) that effectively enhances far-field underwater echo signals for LiDAR. Its core components consist of three parts: Morphology-Aware Dynamic Receptive Field Attention (MADRA), Spatial-Temporal Decoupled Frequency-Enhanced Global Feature Fusion Block (STDFBlock), and Adaptive Dynamic Adjustment Loss Function Based on Frequency-Domain Decomposition and Gradient Response (FGADLoss). The model precisely identifies the narrow and curved local structures of the echo signals during the feature extraction process, improving the precise detection of subtle structural changes in the echo signals and enabling the extraction of valid echo signal features from a background of numerous invalid echo signals. The model reduces image fragmentation and center-of-mass drift during echo signal augmentation, improving the accuracy of water body environments’ 3D reconstruction. Through this model, the average point cloud density per square meter for lakes and ponds increased by 2.12 and 3.54, respectively, enabling effective reconstruction of urban water bodies information and offering a high-quality data basis for underwater object recognition and bathymetric surveying. Furthermore, this method effectively addresses the challenge of simultaneously obtaining degraded and ideal streak images that match the echo signals of underwater detection targets, and it also offers advantages in terms of training data requirements, making it particularly well-suited for real-world underwater detection scenarios where paired ideal-degraded data is scarce. Full article

Background/Objectives: Implantation in in vitro fertilization (IVF) depends jointly on patient prognosis, embryo quality and endometrial receptivity, yet these are usually assessed in isolation. We developed and internally evaluated, as a proof of concept, an index combining all three. Methods: In this single-center prospective pilot study, 46 women undergoing embryo transfer were scored on the day of transfer for patient prognosis (SART model), embryo morphology and endometrial receptivity (Endo-R ultrasound score), each on a 0–10 scale and combined as (SART/10) × (Embryo + Endo-R)/2. The outcome was biochemical pregnancy (10 events). Discrimination (area under the curve, AUC), internal validation and incremental value beyond age were assessed by bootstrap resampling, cross-validation and likelihood-ratio tests. Results: The Integrated Score achieved the highest discrimination of the indices examined (AUC 0.86, 95% CI 0.71–0.98; cross-validated 0.80) but did not significantly outperform age alone (0.79; p = 0.33), and its embryo component contributed none (0.51; χ² = 0.42, p = 0.52 when added to age). Only the difference from the Embryo Score survived Bonferroni correction. The Endo-R Score added information beyond both age and the SART Score (both p = 0.003). Conclusions: The index did not add measurable discrimination beyond patient age in this sample; the endometrial component carried independent information and is the domain worth pursuing. The prognosis model was applied outside its validated endpoint and population, so it ranks rather than calibrates risk. With 10 events these estimates are exploratory and the Youden-derived cut-off (≥4.2) is optimistic. The study contributes a specified rubric and open calculator for the multicenter validation that must follow. Full article

The role of the endometrial microbiome in embryo implantation remains incompletely understood. While most studies have focused on taxonomic composition and Lactobacillus dominance, the ecological organization of microbial communities associated with implantation success has received limited attention. The aim of this study was to investigate whether implantation outcome following euploid embryo transfer is associated with differences in endometrial microbial network structure and community organization. Endometrial biopsies collected during the window of implantation from 95 women undergoing subsequent euploid embryo transfer were analyzed using 16S rRNA gene sequencing. Microbial diversity, co-occurrence networks, community structure, hub taxa, network robustness, and microbial association patterns were evaluated. The Pregnant group demonstrated a more interconnected microbial network with a greater number of significant associations, larger ecological modules, and increased network integration than the Non-pregnant group. Seventeen microbial associations were unique to the Pregnant network, whereas eleven were unique to the Non-pregnant network, indicating implantation-associated ecological rewiring. Lactobacillus-centered association patterns also differed between groups. These findings suggest that successful implantation is associated with coordinated endometrial microbial community organization rather than changes in individual taxa. Microbial network structure may represent a novel dimension of endometrial receptivity assessment. Full article

Background/Objectives: Psoriasis is a chronic immune-mediated dermatosis with an important psychodermatologic burden, in which psychological stress, perceived disease impact, stigma, and attitudes toward mental health care may influence patient support needs. Although psychological and psychiatric interventions are increasingly discussed in dermatology, patient openness to different forms of supportive care, including music therapy, remains insufficiently characterized. This study aimed to characterize psychodermatologic burden and support-related attitudes among patients with psoriasis, with particular emphasis on openness to psychological counseling, psychiatric care, and music therapy-based support, and to explore the factors associated with these attitudes. Methods: In this single-center cross-sectional study, 102 adults with cutaneous or nail psoriasis completed an original self-administered questionnaire between July 2023 and January 2025. Results: Most participants perceived stress as related to psoriasis onset or worsening (86.3%) and considered dermatologist guidance toward psychological or psychiatric support beneficial (80.4%). Psychological counseling was the most accepted supportive intervention (75.5%), followed by music therapy (54.9%) and psychiatric consultation (48.0%), while previous psychological or psychiatric care was reported by 22.5%. Willingness to seek psychological support was independently associated with perceived psoriasis impact, whereas willingness to seek psychiatric support was associated with perceived stress impact. Awareness of music therapy was associated with higher educational level. Willingness to try music therapy was associated with high perceived stress impact, higher educational level, music listening, and awareness of music therapy. Conclusions: Patients with psoriasis frequently perceive stress as relevant to their disease and show variable openness to supportive care. These findings support an individualized psychodermatologic approach in which dermatologists may help identify patients receptive to psychological, psychiatric, or music therapy-based interventions. Full article

Large language models (LLMs) exhibit strong general conversational capabilities, yet their deployment in domain-specific service environments such as hospitality remains limited by the absence of emotionally grounded datasets and validated end-to-end system architectures. This paper presents HOSPIT-LLM, an EU-funded euROBIN Technology Exchange Program pilot, as a complete, integrated pilot pipeline for human-centered conversational AI in hotel reception scenarios. We deploy a multimodal hotel-terminal assistant in a real hotel reception, capturing synchronized dual-camera video and audio to collect authentic guest–staff interactions. Speech is transcribed using Whisper, and emotion is extracted from the corresponding video segments via DeepFace, producing 582 real Greek guest–receptionist exchange examples. The resulting data are classified into eight Standard Operating Procedure (SOP) categories. To address data scarcity, we augment the corpus with 1269 synthetic dialogues generated by eight diverse LLMs through the OpenRouter API, yielding a total of 1851 dialogue records with explicit emotion-token annotation. We fine-tune Qwen3.5-35B-A3B using Low-Rank Adaptation (LoRA) through a two-stage process: supervised fine-tuning (SFT) on an 888-example conversation pool and Simple Preference Optimization (SimPO) on a 1899-pair preference pool, each split 80/10/10 into training, validation, and test. The resulting model is integrated into an interactive hotel-terminal system combining YOLO-based person detection, face-recognition-driven guest personalization, Kokoro neural text-to-speech (TTS), and a multi-service orchestration layer connected to the hotel Property Management System (PMS). Evaluation combines standard text metrics, emotion-aware scoring, and a large-model judge. The results indicate targeted improvements in the rule-based contextual emotion-policy match and staff-emotion policy compliance compared to the base model, while general response-quality gains remain more modest. In particular, the rule-based contextual policy-match score improves from 0.614 to 0.901, while forbidden staff-emotion outputs decrease from 0.142 to 0.018. The deployed pilot demonstrates the practical integration of a personalized, emotion-aware LLM assistant in an interactive hotel-terminal setting; end-to-end latency and fully hotel-side edge deployment were not evaluated and are left for future work. HOSPIT-LLM provides a reproducible framework for multimodal dataset creation, preference-based fine-tuning, and deployment of human-centered AI systems. A mobile robotic embodiment is planned as future work. Full article

Immersive teleoperation of quadruped robots requires the operator to interpret a remote environment, make decisions, and maintain control over a dynamic platform through mediated visual feedback and networked command transmission. This study presents and validates a reproducible augmented reality (AR)–ROS 2 architecture designed to analyze the relationship between system-level technical conditions and operator experience during immersive teleoperation of a physical quadruped robot. The system integrates Meta Quest 3, Unity, UDP communication, ROS 2 middleware, and Xiaomi CyberDog within a modular workflow that jointly supports immersive control, live RGB/depth perception, physical robot execution, and user-centered HRI evaluation. The architecture decouples command transmission and visual feedback into two UDP-based channels: the control channel maps virtual joysticks and discrete HUD buttons to ROS 2 locomotion and action commands, while the perception channel transmits compressed RGB and depth frames for display in the AR interface. Under nominal conditions, the control channel achieved a mean end-to-end latency of 9.37 ms, a P95 of 18.45 ms, an effective update frequency close to 18 Hz, and 100% packet reception. Compared with ROS-TCP-Endpoint, the proposed UDP bridge showed similar latency but higher flow integrity, avoiding duplicate, parsing, and dropped-command events. The visual channel achieved mean end-to-end latencies of 21.09 ms for RGB and 26.99 ms for depth, with frame reception rates of 91.78% and 91.10%, respectively, under a shared 18.67 Mbps mobile network. A complementary network degradation analysis showed that the control channel remained operational under bandwidth reduction, packet loss, added latency, combined degradation, and physical separation of the robot. The user evaluation with 25 participants yielded a corrected Raw NASA-TLX score of $7.13\pm 4.10$, a positive perceived performance score of $95.20\pm 5.10$, and a System Usability Scale score of $90.30\pm 6.63$, indicating low perceived workload and high usability. These results show that low end-to-end latency, high flow integrity, and stable visual feedback were accompanied by low perceived workload and high usability, indicating that the technical temporal response, stability, and robustness of the communication channels are consistent with an effective and low-demand operator experience during AR-based teleoperation of a physical quadruped robot. Full article

This study examines whether the spatial configurations observed in Edo middle–lower-class samurai residences can be interpreted as a recurrent spatial genotype. Previous studies have repeatedly identified the structural centrality of the Ura (裏・ウチ: family-living domain), but have not examined whether this tendency co-occurs with the consistently high control role of the entry court as a recurrent boundary-control anchor. A space syntax analysis of 77 Edo middle–lower-class samurai residences yields a mean Base Difference Factor (BDF) of 0.86, indicating a relatively dispersed integration distribution rather than a concentration around a single dominant space. Accordingly, a conventional single-maximum integration value classification produces 16 apparent single-maximum types without a dominant pattern. By combining the top 10% upper-integration-band criterion, a justified graph distribution analysis, and a control value analysis, this study identifies the entry court as a recurrent boundary-control anchor coexisting with Ura family-living integration centrality. This recurrent relationship is interpreted as a Boundary-Regulating Genotype. The genotype is expressed in three configurational states. In the Double state (24 cases), the boundary/access zone and the Ura family-living domain are both involved in the upper integration distribution. In the Deep state (42 cases), the upper integration field is oriented toward the Ura domain, while the entry court remains a boundary-control anchor. In the Shallow state (11 cases), integration shifts toward boundary/access spaces. This relational genotype provides a basis for reconsidering Sekkyaku-honi as a boundary-centered access-regulation logic linking the public road, entry court, Omote (表: formal reception) domain, and Ura family-living domain. Full article

In Optical Camera Communication (OCC), precise localization of LED arrays under complex tilt conditions is a core challenge for reliable decoding. This paper proposes an OCC reception scheme for RGB-LED arrays that integrates YOLO-OBB rotated object detection with two-stage geometric correction. The system first employs a YOLOv8n-OBB model to extract a quadrilateral region of interest that tightly encloses the LED array boundary. This effectively suppresses background interference caused by superimposed perspective tilt and in-plane rotation. A coarse-to-fine two-stage correction framework is then applied. The first stage rapidly eliminates the dominant perspective distortion based on the detected bounding-box corners. The second stage performs a refined correction using the actual LED center positions. Two homography matrices are cascaded into a combined transformation, achieving two-stage correction accuracy through a single coordinate mapping. In the corrected image, K-Means clustering constructs a 16 × 16 LED topological grid. A locking strategy is adopted so that subsequent frames skip repeated LED detection and clustering. The steady-state per-frame processing time is reduced to approximately 78.9 ms. Experiments covered 16 cross-combinations of vertical tilt from 0° to 45° (0°, 15°, 30°, 45°) and in-plane rotation from 0° to 40° (0°, 15°, 30°, 40°). The uncorrected scheme and the horizontal-box scheme experienced severe bit errors or complete failure under complicated distortion. The proposed scheme maintained error-free transmission under all 16 tested conditions. The ratios of opposite sides of the corrected LED grid remained stable between 0.997 and 1.004. The system simultaneously achieves high reliability and low-latency real-time processing under complex geometric distortions. Full article

Refugees are exposed to cumulative pre-migration, migration, and post-migration stressors that increase vulnerability to depressive disorders and impaired quality of life. This study assessed the prevalence and severity of depressive symptoms among adult refugees in Serbia and examined associations with sociodemographic characteristics, traumatic experiences, social support, and Health Related Quality of Life (HQoL). The study included 324 refugees residing in four reception centers in Serbia. Data were collected between April 2023 and November 2024 using self-report questionnaires. Depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9), while HQoL was evaluated using the SF-36 Health Survey. The mean PHQ-9 score indicated mild-to-moderate depressive symptomatology. Clinically significant depressive symptoms were present in 41.4% of participants, while more than 70% reported at least mild symptoms. Depressive symptom severity was negatively associated with energy/fatigue, emotional well-being, social functioning, general health, and pain. The Energy/Fatigue domain emerged as the most prominent independent correlate of depressive symptom severity. Depressive symptoms were highly prevalent and were associated with impaired quality of life and psychosocial stressors. These findings highlight the importance of systematic mental health screening and psychosocial support among refugees. Full article

Objective: This pilot study aimed to explore how a receptive music intervention, based on musical genres reflecting maternal listening preferences during pregnancy, affects distress levels in Italian preterm infants. Specifically, it investigated the effects of soft pop/rock music, compared with classical music, on infants’ LF/HF ratio (derived from heart rate variability [HRV]) and peripheral oxygen saturation (SpO₂), which were used as physiological markers of distress. Method: This retrospective observational pilot study analyzed clinical data routinely collected between May 2014 and January 2015 from 27 preterm infants (gestational age 23–32 weeks; birth weight < 1500 g) who received receptive music therapy as part of standard family-centered care in the NICU. Maternal listening preferences during pregnancy were assessed in 30 mothers via an ad hoc questionnaire; a content analysis identified, at the group level, the three most frequently reported artists (i.e., Jovanotti, Vasco Rossi, and W. A. Mozart), which were used to create three standardized playlists. According to the internal clinical procedure, each infant underwent four sessions on consecutive days: a no-music condition on Day 1, followed by the three music conditions on Days 2–4 in randomized order. The LF/HF ratio and SpO₂ were measured at five time points per session (one pre-test, three intra-session time points, and one post-test). Wilcoxon signed-rank tests were used to compare conditions and time points, with effect sizes and a Benjamini–Hochberg (FDR) correction for multiple comparisons. Results: The LF/HF ratio did not differ significantly across music conditions or relative to the no-music condition. SpO₂ was higher during the Mozart condition than during the no-music condition at three of the five time points; this association remained significant after FDR correction, with medium-to-large effect sizes. No effect was observed for the soft pop/rock conditions on physiological indexes. Conclusions: Receptive music therapy based on maternal listening during pregnancy was not associated with changes in the LF/HF ratio. The Mozart condition was associated with higher SpO₂ than the no-music condition. Given the small sample, the single-center setting, and the retrospective observational design, these findings are preliminary and require confirmation in larger, adequately powered prospective trials. Future studies should also examine the specific musical features (e.g., tempo, harmonic structure, voice timbre) that may drive these physiological responses. Full article

As the iconic concept of the I Ching, the “image” (xiang 象) is the linchpin for understanding its philosophical ideas. In its reception by scholars in Europe and North America, many have explored the “image” from religious, psychological, historical, philological, and other perspectives. These studies, however, have largely centered on epistemological and consciousness-based analyses of the “image”, whereas the discourse on the “image” in the Great Commentaries on the I Ching (Yizhuan 易傳) carries inherent ontological implications. Deploying appropriate philosophical language to elaborate the ontological foundation of the “image” is therefore crucial for communicating the I Ching’s philosophical ideas to the English-speaking world. By clarifying the connotations of “visible image” (xingxiang 形象), “analogical image” (nixiang 擬象), and “manifest image” (xianxiang 見象) as articulated in the Great Commentaries on the I Ching, this study demonstrates that the Yi (易) exhibits absolute ontological identity with Spinoza’s Substance, while the “image” corresponds to Spinoza’s concept of “expression”. Rombach’s structural phenomenology reinterprets the ontology of “Substance” as the genesis of “the One”, enabling an elaboration of the emergent character of the Yi as “the Ultimate One” (Taiyi 太一). This further reveals that the ”image” is not only an expression of the existence of the “Yi/Substance/One” but also a marker of the Yi’s—or Substance’s—transformation. Drawing on Spinoza’s concept of “Substance” and Rombach’s structural phenomenology thus aids in clarifying the ontological foundation of the “image” and promotes the cross-cultural dissemination of I Ching thought in Europe and North America. Full article

Accurate instance segmentation is essential for precision livestock farming, as it supports individual tracking, posture analysis, body-shape measurement, and other downstream visual monitoring tasks. However, dairy goat segmentation in dense barn scenes remains challenging because frequent mutual occlusion, instance adhesion, partial visibility, and non-rigid posture variation often lead to incomplete masks and ambiguous instance boundaries. To address these challenges, this study develops RF-GoatDet, a real-time instance segmentation framework for dairy goats built upon RT-DETR. The main component of the proposed framework is an Adaptive Receptive Field Encoder (ARFE), which enhances feature encoding by adapting the effective receptive field to irregular goat contours, scale variation, and partially visible body regions. In addition, Coordinate Attention is introduced to strengthen direction-aware spatial representation, while a Query-Conditioned Dynamic Mask Head is used to generate instance-specific masks and improve the separation of adjacent goats. A dairy goat instance segmentation dataset containing 2288 annotated images was constructed, and a two-stage cleaning procedure was applied to reduce redundancy and visual anomalies. Experimental results show that RF-GoatDet achieves 46.5% mask AP and 62 FPS on this dataset, improving the RT-DETR baseline by 4.2 percentage points in mask AP while maintaining real-time inference. These results demonstrate that the proposed ARFE-centered framework effectively improves mask quality and instance discrimination in dense dairy goat scenes, providing a robust and efficient solution for real-time visual monitoring in precision livestock farming. Full article

Although generative artificial intelligence (GenAI) feedback shows promise for educational applications, its actual impact on learning outcomes and the factors influencing its effectiveness remain unclear. This study conducted a systematic review and meta-analysis to evaluate the effectiveness of GenAI feedback and identify key moderating factors. Following the PRISMA 2020 guidelines, we reviewed 36 experimental and quasi-experimental studies published between 2023 and 2025, yielding 72 effect sizes. The results revealed that GenAI feedback had a moderate positive effect on academic achievement (g = 0.61), with significant moderation by contextual factors. Subgroup analysis revealed that teaching methods significantly moderated the effectiveness of GenAI feedback, with stronger effects observed in learner-centered environments promoting active construction than in teacher-centered, receptive instruction; whereas educational level, disciplines, intervention duration, and GenAI role showed no significant moderation. A three-level random-effects model was employed to account for effect size dependencies, correcting for the underestimation of standard errors typical of conventional two-level models. Outcome dimension analysis showed that GenAI feedback had the strongest impact on cognitive outcomes, with promising but less established benefits for metacognitive development, and modest effects on non-cognitive outcomes. Future research should further clarify the roles of metacognitive and non-cognitive outcomes in GenAI feedback. In practice, GenAI feedback should serve as complementary scaffolding within constructivist pedagogies to support metacognitive development, while teacher emotional support should be preserved to foster students’ non-cognitive development. Full article